Team Maverick, an aero design engineering team, is dedicated to designing, innovating, fabricating, and testing fixed-wing UAVs. The team is currently engaged in two prominent competitions scheduled for the 2024 season. Initially, they will participate in the SAE Aero Design Challenge (ADC) International taking place in California. This globally renowned competition draws in approximately 75 teams from around the world, offering a platform to showcase aerodynamic innovations and skills on an international platform. The challenge to design UAVs embodies a vision for the future, where engineering prowess meets technological advancement. It is an opportunity for students to leave an indelible mark on the world, shaping the trajectory of UAVs and unlocking their limitless potential.

Additionally, the team is preparing for the SAE Design and Development Challenge (DDC) India in Chennai. This national competition unites around 87 teams from across India, providing a common ground for colleges to test and demonstrate their aircraft’s aerodynamic capabilities. Both competitions present significant opportunities for the team to excel on both global and national levels.

“Efficiency redefined: SimScale minimises computing demands and maximises productivity.”

– Team Maverick

A Look Into Unmanned Aerial Vehicles (UAVs)

Before diving into Team Maverick’s journey, it’s crucial to understand the pivotal role that Unmanned Aerial Vehicles (UAVs) play in modern aviation. Fixed-wing UAVs, often recognized for their likeness to conventional airplanes, rely on wings to create lift while in motion through the air. This design, a common variant among UAVs, has revolutionised industries by offering extended flight ranges and remarkable endurance. These aircraft offer extended flight times and faster speeds compared to rotor-based models.

Available in various sizes and configurations, from compact drones to large reconnaissance units, they cater to diverse sectors like logistics, agriculture, and surveillance. Technological advancements, including AI-driven autonomy and improved battery efficiency, signal an even more integral role for UAVs in everyday operations. As regulations evolve to integrate them into airspace seamlessly, the future of UAVs promises increased efficiency, safety, and expanded applications across industries.

Team Maverick: Aeronautics & Beyond

Team Maverick describes its core objective as providing students with a transformative aerospace experience. Beyond aeronautics, the team focuses on developing project and resource management skills, fostering collaboration, and ensuring industry rules and regulations compliance. With a commitment to contributing to the expansion of the field, the team is devoted to building cutting-edge aircraft for future applications and societal impact.

“We aim to produce technologically skilled, socially responsible, and aesthetically conscious engineers.”

– Rifa Ansari, Team Maverick

Every component of the aircraft they designed underwent extensive study and analysis, considering various aerodynamic parameters like wing lift and drag, empennage characteristics, and the overall aircraft performance. Determining downwash and vortex production by simulating wing behavior was a crucial aspect of their work. Additionally, they employed structural analysis methods to evaluate the strength and integrity of each individual component.

Analyzing Aerodynamics and Structural Integrity with SimScale

The team conducted simulations on various iterations of the wing, empennage, fuselage, and the entire aircraft, assessing different parameters such as takeoff and cruising conditions. To understand the aerodynamic performance of each section of the aircraft and evaluate the airflow around the entire plane, a steady-state laminar incompressible flow simulation was performed. Static structural analysis was carried out to better understand the structural integrity of components and to identify potential failure sites in the aircraft. The online tutorials provided by SimScale were instrumental in establishing the fundamental workflow for their project.

How SimScale Helped Address Challenges

The team encountered several challenges throughout the project, including difficulties with report generation, failure to generate lift and drag graphs, lower result accuracy, and issues with contact detection among multiple components.

“SimScale revolutionizes simulation with its cloud-based platform, eliminating the necessity for costly hardware. Its automated meshing tool generates top-tier computational meshes, while seamless integration with leading design applications, simplifying simulation setup. The SimScale Workbench serves as the hub for creating and overseeing simulations, offering an intuitive interface for defining setups with ease.”

– Rifa Ansari, Team Maverick

To tackle these obstacles, they sought assistance from SimScale support through online meetings, effectively addressing most of the challenges. Additionally, the team leveraged the SimScale forum, where they posted queries regarding the encountered issues, receiving valuable responses that contributed to resolving their simulation challenges.

The simulation results were analyzed and validated with manual calculations and wind tunnel testing. The analysis generated results that were close enough to the practical wind tunnel test. The simulations, employing 32 cores, typically took an average of 120-150 minutes to complete from start to finish. However, for particularly complex geometry simulations, the process required additional time. Lift and drag values were majorly obtained along with the coefficient of pitching moment for control surfaces obtained to determine the hinge moment coefficient. The designed bodies’ total deformation and overall structural strength were evaluated.

The team found the platform to offer remarkable convenience and simplicity. According to them, SimScale’s standout feature lay in its ability to utilize multiple cores, surpassing hardware limitations and significantly reducing time constraints. Team Maverick was particularly impressed by the meshing component, which seamlessly aligned with their desired mesh quality, presenting numerous parameters. Furthermore, the platform’s visual interface for analyzing solutions was not only comprehensive but also visually appealing.

“Through analysis across multiple iterations, SimScale has played a pivotal role in enhancing our project’s overall efficiency. Conducting studies swiftly and seamlessly has minimized both the cost and time associated with building numerous prototypes. In essence, SimScale has been instrumental in streamlining development timelines, cutting costs, minimizing prototype iterations, and amplifying overall efficiency”

– Rifa Ansari, Team Maverick

Next Steps for Team Maverick

On incorporating simulation results into further product development, the team strategises to execute this process in stages. They will soon finalise the entire design by analysing and evaluating various iterations. Specifically, for function-specific requirements, they are investigating the aircraft’s shape and iterating internal structures to guide its form or enhance structural integrity using CFD analysis and structural analysis. Additionally, the team aims to conduct Fluid-Structure Interaction (FSI) and crash analysis to gain insights into the product’s real-world performance.

We’re confident that SimScale’s diverse simulation capabilities will greatly benefit the Team Maverick Student team in upcoming endeavors, and we’re eager for future collaborations. If your team seeks academic sponsorship for optimizing your aircraft’s performance, whether for the SAE Aero Design Challenge or any other competition – make sure to check out our Academic Plan for students who are joining design competitions.

The post Team Maverick: Student Success Story appeared first on SimScale.

Imagine a scenario where seconds can be the difference between life and death. Picture remote villages, disaster-stricken areas, and underserved communities where access to critical medical supplies is a challenge. In such settings, medical delivery drones are not just a technological marvel; they are lifelines. These drones have the power to transcend geographical barriers, overcome logistical hurdles, and bring much-needed medical relief to those in desperate need.

In this article, I want to highlight an inspiring drone design project by Frank Lucci, a high school student out of Texas, who used SimScale’s CFD simulation tool to design a medical delivery drone from scratch. The possibilities that cloud-native simulation like SimScale can provide students and designers with are endless, enabling them to design faster, iterate more, and accelerate their innovation in ways that are otherwise beyond reach.

The Promise of Medical Delivery Drones

The key factor that has accelerated the adoption of drones for medical delivery has been the recent surge in the need for such delivery of medications and vaccines on a global scale, characterized by the impact of the COVID-19 pandemic, especially in areas facing geographical obstacles and a lack of reliable refrigerated transport.

One initiative by The World Economic Forum called Medicine from the Sky has been implemented in India, which is known for its diverse and hard-to-reach landscapes. The need for healthcare access in rural Indian regions has been a clear incentive for public and private organizations to invest in and push drone solutions. The initiative’s initial phase successfully conducted over 300 drone-enabled vaccine deliveries in Telangana, India, making it a pioneering initiative in Asia. The project then shifted its focus to the more complex terrain of Arunachal Pradesh, a Himalayan state characterized by challenging mountainous landscapes. In this phase, the initiative carried out over 650 drone flights and delivered over 8,000 medical products to 200+ patients across challenging terrains.

The challenges of medical delivery are multi-faceted. Time is often of the essence, and access to healthcare can be a matter of life and death. Traditional ground-based transportation systems may be slow and inefficient, especially in remote or disaster-stricken areas. This is where medical delivery drones step in. They offer the promise of faster response times, reduced costs, and improved access to healthcare, particularly in emergencies and underserved regions.

MediWing: A Medical Delivery Drone Design Project

Frank Lucci is a student at the BASIS San Antonio (Shavano) High School in Texas, an ardent learner of fluid dynamics and aerospace, and a member of the SimScale community. In his effort to participate in a science fair competition, Frank took the COVID-19 pandemic as a motive to design, build, and fly a drone that delivers medical payloads. Thus, his project MediWing was born.

After considering the design requirements of a medical delivery drone, including range, speed, payload weight, and modes of flight, he used some rough estimates to come up with an initial base design. The design involved a drone with airfoil-shaped wings. So, he created an initial CAD model and used SimScale to simulate and reiterate the design numerous times until he reached the optimal design. He, then, developed a detailed CAD design for a half-scale model and constructed a physical prototype using CNC and 3D printing technologies. He went on to test and tune the prototype until the drone was able to fly autonomously.

Here’s what Frank had to say about his prototype:

“The package mechanism and everything else seemed to work except for the range, which was half of the predicted and desired range. Eventually, I came to understand that all the building defects, circular flying patterns, and high wind speeds cause a huge efficiency decrease. I vowed to construct the next model way more aerodynamically efficient and overestimate the drag predicted from the simulations. After one year, hundreds of hours of work, thousands of errors and failures, and one Top-300 middle-school science fair project in the nation, Version 1 was done.”

However, Frank was not done there. He went on to design a second version of the MediWing, seeking a VTOL (vertical take-off and landing) aircraft design. Frank is currently working on optimizing his design to accommodate and fix the plane’s VTOL transition and is even considering building a full-scale version.

Frank’s use of SimScale CFD to analyze and visualize the airflow around the drone has helped him fine-tune his design effectively and facilitated his ability to innovate faster. This is exactly what SimScale offers: Unparalleled accessibility and seamless integration. With more and more students finding significant value in SimScale, SimScale continues to support academics, engineers, and designers to build the future and innovate faster and better.

Read more about Frank’s story in his entry in the SimScale Forum.

CFD Simulation for Medical Delivery Drones

Computational Fluid Dynamics (CFD) plays a critical role in shaping the design and functionality of medical delivery drones. By leveraging CFD simulations, engineers can predict and analyze how a drone will perform in various conditions, including different wind speeds, temperatures, and altitudes. This enables them to optimize the drone’s aerodynamics, stability, and payload-carrying capacity before a physical prototype is even built.

CFD simulations provide crucial insights into the airflow around the drone’s body, rotor design, and other components, allowing for fine-tuning of the design to maximize efficiency and safety. The result is a well-engineered drone that can reliably transport life-saving medical supplies to those in need.

Drone design projects like Frank’s MediWing rely on CFD simulations to ensure the drone meets stringent design requirements, complies with safety regulations, and performs optimally in challenging real-world scenarios.

The real-world implications of such innovative projects can be profound. With initiatives like that from the World Economic Forum, we do not need to imagine anymore. We are getting closer to a world where medical supplies, vaccines, and even organs can be swiftly delivered to remote areas, disaster-stricken regions, or areas with limited infrastructure. As such, lives can be saved, critical treatments can be administered on time, and healthcare access can be extended to the farthest corners of the world. The successful deployment of medical delivery drones not only addresses the challenges of today but also paves the way for a more equitable and efficient global healthcare system.

The post Wings of Hope: CFD-Enabled Design for a Medical Delivery Drone appeared first on SimScale.

Their purpose? To explain how structural systems behave when they are subjected to dynamic loadings.

Imagine you’re standing on the edge of a freeway, watching cars whizz by, or perhaps looking at a towering skyscraper standing firm against a turbulent wind. The forces and movements you observe are dynamic, constantly changing, imposing loads that challenge these structures.

This article delves deep into understanding the dynamic response, dynamic shock analysis, and their nuances. We will explore the implementation of these analyses in SimScale and how a cloud-native platform enables such FEA simulations. This article also sheds light on the intricate processes of these simulations and the subsequent interpretation of results for optimal system design.

What Is Dynamic Response Analysis?

Dynamic response analysis involves analyzing the behavior of structures under dynamic loading conditions (loads that can change in magnitude, direction, or frequency over time).

Picture a structure under dynamic loads: The load magnitude fluctuates, the direction alternates, and even the frequency evolves with time. Static studies tend to perceive these loads as constant, overlooking essential factors like damping and inertial forces.
However, reality often defies these assumptions. Loads are dynamic, varying with time and frequency.

Dynamic response analysis is designed to address this deficiency by providing a methodology to handle non-constant load conditions. It is typically employed when the frequency of a load exceeds one-third of the basic frequency.

To get a sense of the distinction between static analysis and dynamic analysis, consider the equations used in finite element models:

$$ [K] \vec{u} = \vec{F} \tag{1}$$

$$ [M] \ddot{\vec{u}} + [C] \dot{\vec{u}} + [K] \vec{u} = \vec{F} \tag{2}$$

Where \(\vec{F}\) the load vector, \([K]\) is the global stiffness matrix, \([M]\) is the global mass matrix, \([C]\) is the global damping matrix, \(\vec{u}\) is the displacement vector, \(\dot{\vec{u}}\) is the velocity vector, and \(\ddot{\vec{u}}\) is the acceleration vector.

\([M] \ddot{\vec{u}}\) is the inertial force (i.e., mass times acceleration) and \([C] \dot{\vec{u}}\) represents the damping force (i.e., damping coefficient times velocity). These terms represent the dynamic forces that distinguish dynamic simulations from static simulations.

The computation of this analysis is typically conducted via simulation software, which determines the simulation’s characteristic response by integrating each mode’s contribution to the load.

The value of using dynamic response analysis depends on various aspects of loading:

• How often it changes (load frequency)
• How big it is (load magnitude)
• Which way it’s going (load direction)
• How long it lasts (load duration)
• Where it’s applied (load location)

Dynamic response analysis can be further subdivided into several types of analysis, namely modal analysis, harmonic response analysis, and transient dynamic analysis.

Modal Analysis

Modal analysis is an analysis type that identifies the inherent dynamic properties of a system in order to create a mathematical model, called the modal model, that describes its dynamic behavior using modal data. It helps define the system’s natural characteristics, such as its natural frequency, damping, and mode shapes (mode shapes represent the characteristic displacement pattern of the system).

By studying the frequency and position of a structure, modal analysis enables us to specify when the system would experience resonance, which is the point at which the applied excitation is equal to the system’s natural frequency. This helps make informed design decisions so that phenomena like resonance are avoided.

Harmonic Analysis

Harmonic analysis is a type of dynamic response analysis that simulates the steady-state behavior of solid structures subjected to periodic loads, providing frequency-dependent results. In other words, it studies the response of linear structures under a load varying sinusoidally with time.

Harmonic analysis is particularly useful for evaluating the effects of vibrating forces or linear displacements over a range of frequencies.

Transient Dynamic Analysis

Transient dynamic analysis is a method used to assess the behavior of deformable bodies under conditions where inertial effects play a significant role. It provides time-dependent results, making it particularly useful for evaluating the effects of rapidly applied loads.

What Is Dynamic Shock Analysis?

Dynamic shock analysis specifically focuses on the response of a structure or system to sudden, high-intensity loads or impulses. It aims to assess the behavior and integrity of the structure under extreme loading conditions, such as impact, collision, or explosive forces.
Imagine an extreme scenario – an automotive crash structure colliding, an aircraft experiencing a hard landing, or an electronic device enduring a drop impact.

This is where dynamic shock analysis takes the stage, specializing in understanding how your design would respond to sudden, high-intensity loads.

While dynamic response analysis is a generalist, shock analysis is a specialist, addressing the extraordinary events where high-intensity, rapid-loading events are involved. By doing so, it helps optimize designs for maximum energy absorption and minimum deformation, predicts potential failures for safety enhancement, and even aids in meeting regulatory requirements.

What Is Dynamic Shock Analysis Used for?

Design Optimization

It helps optimize the design of automotive crash structures, ensuring they can absorb maximum impact energy while minimizing deformation and reducing the risk of occupant injury.

Safety and Failure Prediction

It enables the assessment of structures subjected to sudden loads, such as aircraft components during a hard landing, to predict potential failures and improve safety measures accordingly.

Regulatory Compliance

Dynamic shock analysis assists in meeting regulatory requirements, such as testing electronic devices to ensure they can withstand drop impacts within specified limits.

Research and Development

It aids in developing resilient and durable materials for applications like protective gear, where the analysis evaluates their ability to absorb and dissipate impact energy effectively.

FEA for Dynamic Response and Shock Analysis

Imagine being able to simulate the dynamic and shock conditions your design would endure and predict its response – without physical trials. That’s the power of finite element analysis (FEA).

By creating computerized models of structures and applying suitable loads and boundary conditions, you can foresee how these structures would react to dynamic loads and shocks.

The methodology of FEA involves breaking down the structure’s model into thousands of small, interconnected ‘finite elements.’

These elements closely represent the intricate features of the structure, thus enabling accurate calculations of stress, strain, and displacement under dynamic and shock loadings.

To learn more, check out this step-by-step guide to dynamic analysis.

Now, let’s go one step further and introduce SimScale into the equation. This is where your journey toward efficient and accurate solutions begins. SimScale’s Structural Mechanics software is a powerful tool that allows engineers to virtually test and predict the behavior of their designs under dynamic and shock conditions.

Maximize Efficiency with SimScale Simulation

SimScale’s cloud-native platform enables engineers and designers to simulate early in the design process without the hassle of software installation and expensive hardware. It empowers design teams and simulation experts alike to test their designs under various conditions by running multiple simulations simultaneously using the power of the cloud. This minimizes the testing time significantly and enables quicker design optimizations, thus enabling faster innovation. Experience the power of collaboration, innovation, and optimization with SimScale’s cloud simulation, accessible anytime, anywhere. Simply sign up, import your 3D design, and start simulating immediately in your web browser.

Nevertheless, the benefits of SimScale don’t stop at accessibility. It also brings your projects into the collaborative sphere, allowing you to share them with your colleagues and teams. This facilitates rapid design improvement and significantly shortens your workflow.

Take, for instance, TechSAT, a prominent company in the aerospace industry. They use SimScale’s simulation capabilities to optimize and validate the performance of their products. SimScale has significantly reduced TechSAT’s time to develop new products. Here is what other customers have said about SimScale.

If you’re an engineer or a product developer eager to make your design process more efficient and speed up your innovation process, it’s time to take advantage of cloud computing and take the next step towards efficient and accurate engineering solutions with SimScale’s cloud-native platform. Sign up below or request a SimScale demo today.

The post Dynamic Response and Dynamic Shock Analysis in FEA With SimScale appeared first on SimScale.

But that’s not all. In this article, we will explore how simulation can not only help study mechanical phenomena but also enable better-informed decision-making early in the design process. In other words, we will see how engineers can benefit from one particular aspect of simulation that provides them with more accessibility, collaboration opportunities, and efficiency in both time and money.

What is Solid Mechanics?

Solid mechanics is a branch of physical science that focuses on studying the movement and deformation of solid materials under external loads such as forces, displacements, and accelerations. These loads can cause different effects on the materials, such as inertial forces, changes in temperature, chemical reactions, and electromagnetic forces. This field plays a critical role in various engineering disciplines, including aerospace, automotive, civil, mechanical, and materials engineering.

Solid mechanics focuses on understanding the mechanical properties of solid materials and their response to different types of loading. These materials include metals, alloys, composites, polymers, and others. By studying how materials behave under different conditions and in different environments, engineers can gain insights into designing and optimizing structures, components, and systems to ensure their safety, reliability, and performance.

In solid mechanics, there are two fundamental elements:

• The object’s internal resistance that acts to balance the external forces, represented by stress
• The object’s deformation and change in shape as a response to external forces, represented by strain

The relationship between stress and strain is described by Young’s Modulus, which states that strain occurring in a body is proportional to the applied stress as long as the deformation is relatively small – i.e., within the elastic limit of the solid body. This can be visualized in the stress-strain curve shown below.

What is Solid Mechanics Used for?

The importance of solid mechanics lies in its practical applications and contributions to engineering and the industry. The key reasons why solid mechanics is not only practical but crucial for engineers can be categorized as follows:

• Design analysis
• Failure analysis and prevention
• Material selection and optimization
• Structural safety and load-bearing capacity
• Performance optimization and efficiency

Design Analysis

Solid mechanics provides the foundation for designing and analyzing structures and components. By applying principles of solid mechanics, engineers can assess the structural integrity and performance of systems and ensure they meet design requirements and safety standards.

It enables them to predict and understand factors such as stresses, strains, and deformations, which are vital in designing structures that can withstand expected loading conditions and environmental factors.

Failure Analysis and Prevention

Solid mechanics helps engineers investigate and analyze failures in structures or components. By understanding the causes of failure, such as excessive stress, material fatigue, or deformation, engineers can improve design practices, materials selection, and manufacturing processes to prevent failures and enhance the reliability and durability of products.

Material Selection and Optimization

Solid mechanics plays a significant role in material selection and optimization. Engineers need to evaluate the mechanical properties of different materials and assess their suitability for specific applications.

By considering factors such as strength, stiffness, toughness, and fatigue resistance, solid mechanics helps engineers choose the most appropriate materials to meet performance requirements while considering factors such as weight, cost, and manufacturability.

Structural Safety and Load-bearing Capacity

Solid mechanics allows engineers to assess the safety and load-bearing capacity of structures and objects. Through analysis and simulations, engineers can determine the structural stability, response to external forces, and ability to withstand static and dynamic loads.

This knowledge is essential in ensuring the integrity of critical structures, such as bridges, buildings, and aircraft, where failure could have severe consequences.

Performance Optimization and Efficiency

Solid mechanics helps engineers optimize designs to improve performance and efficiency. By analyzing stress distributions, material usage, and structural behavior, engineers can identify areas for improvement, reduce unnecessary material and weight, and optimize designs for enhanced strength, rigidity, or energy efficiency. This optimization process leads to cost savings, improved product performance, and reduced environmental impact.

Using Simulation in Solid Mechanics

Understanding how solid materials behave under different conditions is crucial for a wide range of engineering and design applications. By simulating the behavior of solid materials, engineers and designers can optimize their designs and reduce the need for costly physical prototyping.

Using simulation software, engineers and designers can create virtual models of their designs and analyze their performance under various conditions. They can simulate stresses, strains, and deformations in solid materials.

The example below is a structural analysis of a wheel loader arm. This simulation project enabled the design engineer to study the relative movement between the components and assess the stress performance simultaneously. This assessment was done by calculating the Von Mises stress distribution within the arm. Such an approach almost eliminates the need for physical prototyping in the early stages of the design process.

Finite Element Modeling in Solid Mechanics

Knowing that most engineering cases of solid mechanics are nonlinear by nature, analyzing them with analytical solutions may not be feasible. That’s where numerical modeling comes into play.

To simulate solid mechanics cases and assess the material behavior, engineers use finite element modeling (FEM), a numerical method upon which a simulation technique called Finite Element Analysis (FEA) is based.

FEA involves dividing a complex solid model into a finite number of smaller, interconnected elements to approximate the behavior of the structure. By applying appropriate boundary conditions and material properties, FEA can simulate the response of the structure to different loads, allowing engineers to assess stress, strain, displacement, and deformation patterns.

To further understand the details of FEA, check out our dedicated guide to Finite Element Analysis (FEA).

The FEA software in SimScale, for instance, helps engineers and designers virtually test and predict the behavior of solid bodies. This enables them to solve complex structural engineering problems under static or dynamic loading conditions.

Yet, with all this, you might still be wondering what exactly the single aspect of simulation benefiting engineers today is. Well, it goes beyond the mathematical side of simulation and capitalizes on the integration of another technology: the cloud.

Simulating Faster with SimScale

SimScale combines the capabilities of simulation with the benefits of cloud computing to enable engineers to analyze accurately, collaborate better, and innovate faster.

Using SimScale’s cloud simulation, you can access your simulation projects anytime, anywhere. All you need is a web browser. You simply sign up to SimScale, import your 3D design, and start simulating.

Furthermore, not only are your projects accessible to you, but you can also very easily share them with your colleagues and teams to collaborate on them, improve your designs quickly, and shorten your workflow significantly.

For example, the global engineering and manufacturing company Bühler uses SimScale to enable the collaboration between 15% of its mechanical and process engineers spread across 25 departments in ten business units on four continents.

The post Solid Mechanics Simulation and Analysis with SimScale appeared first on SimScale.

SimScale has multiple features useful to engineers designing and developing drones. Salient features include the ability to evaluate:

• Flow
  • Aerodynamic loads under varying flight conditions
  • Airframe-rotor-surrounding interference & impact on the stability
  • Configurational effects on payload, endurance, and efficiency
  • Aerodynamically optimized landing and take-off angles
• Structural
  • Drone drop test 
  • Blade stress analysis
  • Rotor shaft interference fit
  • Vibration analysis
  • Battery pack crush test
• Thermal Management
  • Drone electronics cooling
  • Early-stage battery pack design
  • Battery pack cooling validation

Let us see how some of the above capabilities and features were applied to evaluate the performance of a quadcopter.

Simulating a Quadcopter 

Starting with the airflow and aerodynamic performance, we can set up a CFD case to evaluate the quadcopter in hover mode. This is especially important to investigate as it thoroughly consumes the most power requirements. Drones in hover mode will also be in close proximity to the ground/roof or other objects and might be in confined spaces. Many applications will include drones entering or close to construction sites, mines, and homes for delivering packages and in urban areas where turbulence is more significant. Aerodynamic stability in hover mode is thus critical for the safety and overall performance of the drone and its surroundings. 

We have taken a Parasolid CAD file of a standard quadcopter and made minor adjustments by removing the landing legs and camera mounts. This was done to simplify the geometry. Using the CAD editing feature in SimScale called CADmode, we can create the external fluid domain and further edit the geometry. In the first instance, we created rotating zones for the rotors with a rotational speed of 10,000 rpm. We have selected the Subsonic analysis type in SimScale, which lends itself to rotating machinery applications The Subsonic analysis type includes a robust meshing strategy that produces an automated and robust hexahedral cell mesh, using the Cartesian meshing technique that significantly reduces mesh generation times by order of magnitude. It uses a finite volume-based solver optimized to handle a wide range of flow types. We have applied a globally coarse mesh with refinements around the rotating zones. Engineers and designers who evaluate the drone in hover mode will want to know its thrust performance, rotor-rotor, and rotor-airframe interactional effects. Selecting the analysis type automatically creates the simulation tree, a guided workflow for completing the simulation setup and post-processing the results. A user can now go through and set the material properties and boundary conditions and add rotational speeds to the rotating zones. The rotors are made of a polymer material (PVC); the fluid is air by default. Further time savings can be achieved by exploiting the symmetrical nature of the drone geometry and reducing the model size.

Using a transient simulation to capture time-dependent effects, we simulated for 200 minutes. Users can see velocity around the quadcopter and ascertain pressure to see forces in regions of interest, such as the rotors and joints.

We can run multiple scenarios of differing ground effect heights using further simulations. The ground effect in a multirotor is a change in the thrust generated by the rotors when flying close to the ground due to the interaction of the rotor airflow with the ground surface. It is critical for engineers to address the ground effect for safety and performance correctly. Steady-state simulations of the quadcopter take 20 minutes to run. Using the cloud’s computational power, several simulations can be set up and run in parallel, meaning the total time taken to complete dozens of simulation runs, for example, is still 20 minutes. When the quadcopter is closer to the ground, there is much more pressure on the drone’s underside, and the amount of thrust from the rotors is affected. It must have enough aerodynamic stability and structural integrity to still perform under varying forces, and this feedback is used intricately in the flight control system. Interestingly, we can see some wake air re-entering the rotors using velocity streamlines. This can cause safety problems in hover mode when close to the ground; for example, dust/dirt and other debris might enter the rotors/motors, causing damage.

Drone power requirements change depending on rotor configuration. The number of rotors can impact thrust, drag, and overall performance. Engineers can investigate if a four or six-rotor aircraft might be better and the number of blades on each propeller. In this example, we can generate a force plot from the results and see a force 544N produced. For this study, we have compared the thrust outputs using SimScale to a published academic study showing experimental results and those from a competing CFD tool. The experimental results are shown in red, and the difference is attributed to geometry variations. SimScale results match closely with the competing CFD tool. The advantage of SimScale, however, is that the rate of change of operational thrust points on the curve can be simulated in parallel for each ground effect ratio, saving much time and cost.

Flight Integrity 

The structural forces on a drone can be considerable and change instantly. There are short-term structural effects like when in hover mode or landing; and longer-term material/joint fatigue and stress due to prolonged usage. Vibration analysis is critical to understanding fundamental structural integrity influencing flight stability and safety. Control systems must account for natural frequency and induced vibrational effects. With the right inputs, a flight control system can optimize the stability of a drone by avoiding specific rotor rotational speeds that might excite vibrations and cause adverse effects on the drone. Engineers must correctly apply vibration analysis to design stable drones over various rotational speeds. We have analyzed a propeller to evaluate its natural frequencies at 10,000 rpm and calculate the corresponding eigenfrequencies we wish to avoid. We can then run a harmonic analysis that accounts for these natural frequencies and the material’s damping properties to assess the propellers’ stress and displacement at those eigenfrequencies. For example, we can see high stresses at the rotor base that will cause fatigue over time.

Engineers can also take the entire drone geometry and look at the global eigenfrequencies for the drone that might cause destabilizing movement. In this example, we calculated that 1630 and 6600 rpm would cause harmful effects due to adverse excitation of the drone. A Campbell diagram was used to find these values and represents a system’s response spectrum as a function of its oscillation regime. We have analytically calculated the eigenfrequencies as a function of shaft rotational speed. Essentially we plot the rotational speeds against the eigenfrequencies and read off where rotational speeds intersect the horizontal eigenmodes, which might cause excitation, and have found the intersection points occur at 1630 and 6600 rpm. For example, the rotor materials (PVC) and configuration can be changed to design around these problem points.

Drone Flight Simulation

CFD has been helpful in simulating complex aerodynamics, evaluating drone performance & stability and visualizing airflow behavior. Structural analysis has allowed us to generate a Campbell diagram for vibration analysis, evaluate blade stress and durability and assess drone structural integrity. All these capabilities and many more are available to engineers through a web browser, and existing template projects make it easier to start.

“We realized that some of the simulations of our drone design could be run much quicker and better with SimScale. Also, we used to not have a good process established, but now with SimScale, we have a proper design methodology and a validation tool for all the design modifications and improvements.”

— Marta Marimon, Aeronautical & Flight Mechanics Engineer at VTOL Technologies

This on-demand webinar helps teams involved with drone/UAV research and development learn how to test their products for desired aerodynamic performance and structural integrity with cloud-native CFD simulation:

The post Drone Flight Simulation appeared first on SimScale.

The project was primarily completed academically, led by a core team of 9 masters students and 5 undergraduate students. Brunel Unmanned Aerial Systems was further developed as a University society with a keen membership of multidisciplinary students to promote and engage interest and understanding of unmanned aerial systems.

The IMechE’s UAS Challenge 2020

IMechE‘s (Institution of Mechanical Engineers) Unmanned Aerial Systems Challenge 2020 is an annual and industrially recognized competition to promote the engineering sector as a whole, and the professional development of engineering students.

IMechE’s UAS Challenge Requirements 

The challenge requirements necessitated design, fabrication, testing, and demonstration of a novel unmanned aerial system (UAS) conforming to strict competition regulations and national airworthiness requirements (CAP-722). Despite primarily focusing on the development of an aircraft for participation in an academic competition, project completion sought to demonstrate the commercial viability of UAS application for humanitarian aid mission specifications. Aircraft development specifically focused on the maximization of payload capacity (for autonomous aid delivery) and search and rescue functionality, while ensuring ease of deployment, maintenance, and operation alongside low development cost.

The Task: Optimizing Aerodynamic Performance of a UAS

Brunel Unmanned Aerial Systems utilized SimScale to investigate and optimize the aerodynamic performance of the wing/fuselage structure, while simultaneously assessing the stabilizing performance of an inverted v-tail empennage. Competition requirements for increased payload capacity and conformance to maximum take-off mass criteria (6.9Kg) necessitated the maximization of aerodynamic performance to permit minimization of structural mass.

The selection of an environmentally-friendly electric propulsion system required further minimization of aircraft drag to ensure the aircraft designed provided appropriate range capabilities. The design and optimization of the empennage structure-maintained requirements for mass and drag minimization. It also introduced demand for appropriate longitudinal and lateral responses, characterizing static and dynamic stability while facilitating effective control authority. Requirements for solely autonomous aircraft function further enhanced requirements for aircraft stability.

Brunel Unmanned Aerial Systems Incorporates CAE: Challenges & Benefits

Incorporation of computer-aided engineering (CAE) within the early design phases permitted a rapid, low-cost assessment of conceptual designs, while facilitating optimization tasks through the removal of time-consuming model manufacturing processes. In previous years, CAE significantly shortened the lead time for the aerodynamic development of the aircraft, which was particularly beneficial given the short time frame of the competition.

Brunel Unmanned Aerial Systems: Challenges Faced Prior to CAE 

Limited access to appropriately scaled wind tunnel facilities on campus initially restricted the aerodynamic development of the aircraft, while the manufacture of appropriate models for testing introduced significant financial costs and time penalties. Optimization tasks were further restricted through the limited capacity for aerodynamic assessment with primary analysis techniques centered around derived data from force balances. Alternative simulation software was available on campus with limited mesh sizes (academic licenses) and access limited to college opening hours.

Brunel Unmanned Aerial Systems: Expected Benefits of CAE 

The anticipated benefits of CAE included enhanced understanding of the aircraft aerodynamic performance throughout a representative flight envelope, including yaw conditions (which proved challenging to replicate effectively in the wind tunnel). Further expected benefits from a rapid assessment of the aircraft performance included simultaneous development and continuous assessment of the propulsion system, as well as the continuous design of the flight control system (where various performance inputs were required).

 The primary benefits of CAE incorporation involved significantly improving confidence in results, especially when validated against a wind tunnel model. Enhanced confidence in results would permit the selection of appropriate timings to progress to manufacturing stages, potentially reducing the risk of failure and overall project costs.

Further non-technical advantages included opportunities for students to interact with professional-level CAE software from SimScale, facilitating the development as well as gaining an understanding of computational analysis techniques. 

Brunel Unmanned Aerial Systems’ Expected Results from SimScale 

The team anticipated that SimScale would provide a platform for the comprehensive aerodynamic development of the unmanned aerial vehicle while providing adequate output for further structural and stability analysis. Data from CFD was to be further applied in simulation and development of the flight control systems with results presenting opportunities for confidence development before progression to prototyping and flight testing (reducing overall project cost). 

The team was particularly interested in the online accessibility functionality, facilitating continued project work outside of University opening hours. Several members of the team had significant commutes, with SimScale allowing work from home and easy sharing of simulation and results. Throughout the early stages of the Covid-19 pandemic, SimScale proved to be invaluable for the team, allowing continued and uninterrupted work on the project. 

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Without access to SimScale, the team would have lost approximately 4 weeks of development, at a critical project phase, as alternative arrangements and remote access to various hardware and facilities were arranged. SimScale further mitigated significant downstream delays on other team members work with dependency on CFD results. The team expected a challenge when working with the SimScale platform, having limited nonacademic exposure to professional engineering software; however, SimScale support and the online community ensured the team was able to learn and progress quickly to support the ambitiously short project timeline.

When Did Brunel Unmanned Aerial Systems Begin To Use SimScale? 

SimScale had previously been incorporated by the Brunel Unmanned Aerial Systems team for completion of the 2019 iteration of the IMechE Unmanned Aerial Systems competition. Initial validation exercises undertaken by the previous team (2018/19) demonstrated SimScale’s accuracy and validity against a range of commercially available alternatives. SimScale was proven superior due its online functionality, permitting remote and out of college hours access to high power computing capabilities. SimScale’s comprehensive online community forum presented further benefits for learning about the CAE platform and enhancing understanding of its capabilities.

Initial CAE Challenges & How The Team Overcame Them 

The development of initial meshes for simple geometries utilizing hex-dominant parametric methods prompted the application of this method for further mesh generation. Increased complexity geometries introduced challenges for the generation of a resolved boundary layer with acceptable aspect ratios. Large investments in computational resources were made to ensure the generation of effective meshes (especially throughout mesh refinement activities). 

The problem was overcome through a significant investigation of the community-led forum and kind support from SimScale. The forum presented opportunities to witness solutions found by other users with similar problems; however, variations to applications meant some solutions were not always effective or applicable. Support from SimScale was comprehensive and wherever possible, time was invested to educate and progress our understanding of the problem on top of assisting with resolving the issue in hand. Combined assistance from both streams proved invaluable in the effective completion of this project.

Simulation Setup 

Initial simulations were completed in tandem with physical wind tunnel experiments, and panel method analysis techniques to ensure numerical model validity. Results were further compared with previous research undertaken by the team to ensure viability for further analysis. Inlet-outlet conditions were applied to permit universal domain application for investigation of aerodynamic response throughout pitch and sideslip conditions, facilitating comprehensive stability analysis, while symmetry was utilized where possible to minimize simulation cost. Aerodynamic performance result controls were implemented for rapid assessment; however, probe points were applied to permit direct comparison against any pressure tapped wind tunnel experiments.

Mitigation of mesh discretization errors dictated completion of mesh sensitivity studies, considering factors including leading/trailing edge, wake, and aerofoil surface refinement. Boundary layer refinement was applied to permit full resolution of the boundary layer as opposed to alternative wall functions, presenting significantly enhanced approximations of aerodynamic response.

Meshing 

Three-dimensional unstructured meshes were generated utilizing SimScale’s Hex-Dominant Parametric function, facilitating the rapid generation of high-quality hex-dominant meshes with effective boundary layer refinement using OpenFoam’s “SnappyHexMesh” tool.

Where possible, some three-dimensional meshes were extracted and extruded utilizing OpenFoam through Ubuntu. Effective extrusion to two-dimensional meshes permitted a significant reduction in computational costs, while presenting significantly accurate approximations of two-dimensional finite wing aerodynamic performance. Most meshes for three-dimensional investigation were generated utilizing the Hex-Dominant method.

Simulation Type 

Simulations utilized the incompressible k-ω Shear Stress Transport (SST) throughout, facilitating the effective cost-efficient resolution of the turbulent flow properties. The selection of the shear stress transport model reduced result sensitivity to inlet freestream turbulence conditions. K-ω models were found to produce accurate results within adverse pressure gradients and flow separation compared to the alternative models investigated. Turbulence intensity was approximated from known values for the wind tunnel utilized in initial validation experiments.

Aerodynamic performance result controls were implemented for rapid assessment; however, probe points were applied to permit direct comparison against any pressure tapped wind tunnel experiments.

Simulation Execution, Performance & Results 

The team ran approximately 300 simulations using their 2 student accounts. Each simulation was approximately 2.5-3 hours, varying between 16 and 32 cores. 

Initial numerical model development yielded results closely aligned with wind tunnel experimentation and panel method analysis. Simulations undertaken performed effectively, converging appropriately, and presenting acceptable results for downstream analysis. Unfortunately, downstream wind tunnel testing was not available (due to unforeseen global mitigating circumstances) to validate final results; however, panel method analysis and simplified investigation of overall aircraft stability performance demonstrated alignment of anticipated results.

Results obtained primarily focused on the investigation and optimization of aerodynamic performance throughout the flight envelope to match initial predictions obtained throughout conceptual design procedures. Results obtained effectively aligned with requirements for the aircraft outlined by conceptual design software. Particular aerodynamic efficiency gains were obtained through optimization of the inverted v-tail, with resulting reductions in size, alteration of dihedral angle, and introduction of Hoerner style wingtips, resulting in zero-lift drag reductions of up to 28% relative to the initial configuration investigated.

“SimScale presented a challenging and exciting platform to learn and apply for the development of our unmanned aerial system. The platform proved user-friendly and the tutorials available assisted with learning it quickly and effectively. The team at SimScale and the community forum were extremely friendly and supportive and always keen to assist where possible to not only ensure the success of the project but further develop the team’s understanding of CAE. Throughout our relatively short time working with SimScale, we were informed of several new features being implemented and a community board to suggest new functionality. We have really enjoyed the opportunity to engage with SimScale.” 

Thomas Hulatt

Brunel Unmanned Aerial Systems ROI, Results & Next Steps 

Isolated analysis of the wing/fuselage and empennage structures utilizing CAE demonstrated successful attainment of aerodynamic targets outlined through conceptual design procedures, while minimizing overall aircraft drag. Aerodynamic performance facilitated the generation of a viable and stable aircraft platform with minimized power requirements. Blank paper development of a novel aircraft combined with prohibitive global circumstances ensured successful aircraft development could be primarily attributed to computational work undertaken using SimScale.

The team’s application of SimScale has facilitated optimization activities previously found challenging when merely utilizing experimental methods. Less technical benefits of SimScale have included the ability to operate the platform from any computer with an Internet connection, allowing the team to work remotely and present findings in meetings away from the university’s computational facilities. This has further permitted completion of simulations overnight and in hours where the university facilities would be closed. The unforeseen closure of the university would have otherwise prohibited completion of the aerodynamic investigation; however, SimScale’s online platform permitted ongoing development of the aircraft from remote locations, with results effectively shared digitally.

Moving forward, the team hopes to finally manufacture and test the aircraft designed over the coming year to assess its validity. For the next steps, Brunel University hopes to compete in the 2021 edition of the IMechE UAS Challenge. Significant regulatory reform dictates significant variation to mission specification, weight, and dimensions necessitating blank paper design. The team plans to share lessons learned throughout the previous design cycle to permit the rapid development of the new Brunel entry.

If you enjoyed this SimScale student success story, check out more recent success stories here: 

• SimScale Formula Student Success Story: Dynamis PRC
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• SimScale’s Student Ambassador Holds Biomedical Engineering Workshop

As well as our customer success stories here. 

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But what is a kit aircraft, and how is this possible? In this article, we will explain everything from what an amateur-built aircraft is all the way to how to test aircraft components like wings for the angle of attack, stall behavior, and lift.

What Is a Kit Plane? 

If you are not familiar with it, a ‘kit plane or aircraft’ is a homemade airplane. These amateur-built planes are not for professional or commercial use, and are certified as ‘Experimental’ under the Federal Aviation Administration (FAA) or other local regulations in the United States.

This concept might sound a bit daunting if you, like me, are not an aerospace engineer. Well, actually, amateur plane-building has been around for quite a while. In fact, amateur-built aircraft initially gained popularity and momentum in 1924 with the start of the National Air Races, held in Ohio, USA. 

Kit planes typically can hold 1-4 persons in-flight, and historically employed the simplest forms of construction available including wooden frames and fabric. Today, designers like DarkAero are increasingly adopting more sophisticated construction methods, using carbon fiber, fiberglass, other composite materials, as well as full aluminum techniques. 

A New Kind of Kit Plane: How Did the DarkAero 1 Come to Fruition?

The Karl brothers grew their interest in kit planes as they frequently attended the EAA AirVenture event in Oshkosh, WI. After years of event re-attendance and a growing desire to find an aircraft kit that could fly faster, go further, and be more efficient than ever before possible, it became clear that the Karl brothers could no longer simply be enthusiasts; the DarkAero 1 concept was born. 

Together, the Karls have backgrounds in mechanical, computer, electrical, and aerospace engineering, which has supported the development and creation of their first aircraft design, the DarkAero 1. 

For the past three years, DarkAero has been working hard to turn their dream into a reality. Last month alone, the team made exciting progress on the remaining sections of the DarkAero 1 prototype. From mounting the engine to the airframe to manufacturing landing gear components, you can read about the latest and greatest DarkAero 1 feats from them here. Yet, since embarking on this aerodynamic adventure, one question always seems to surface for DarkAero: how can they assure potential customers that their kit plane will fly? 

Determining DarkAero 1 Can Fly

For starters, the DarkAero 1 was created with a very specific purpose; to be the best kit plane, and utilize the available technology more efficiently than ever before possible. The aircraft can hold two passengers, has a cruising speed of 275 mph, a 1700 mile range, and even boasts retractable landing gear. 

In order to design, test, and evaluate the DarkAero 1’s flight performance and behavior, the team used a range of engineering tools. These tools include engineering equations, wind tunnel testing with 3D printed models in the University of Wisconsin – Madison wind tunnel, and online CFD simulation software (SimScale). In the remainder of this article, we will focus on how CFD simulation results have benefited DarkAero by determining flight behavior attributes, such as lift conditions and wing stall characteristics. 

How Can We Evaluate Wing Stall? 

For aircrafts of all shapes and sizes, there is always a maximum lift coefficient or condition that needs to be met for takeoff and landing, as well as for periods during flight with reduced speed. This lift is generated by the aircraft’s wing, and is related to its angle of attack, or the angle that it is flying into the oncoming airflow.

At a shallow angle of attack, the wing produces minimal lift and the airflow around the wing follows the contour of the wing surface. There exists a critical angle of attack where the wing produces the maximum lift before stall occurs. Typically, the angle of attack for maximum lift is around 15 degrees, but can fluctuate significantly depending on the fluid type (in this case, air), airfoil shape, and Reynolds number.

A stall occurs beyond the point of where the wing achieves maximum lift and it is characterized by a reduction in lift as well as airflow separating from the surface of the wing. In terms of fluid dynamics, a stall is the result of a reduction in the lift coefficient generated by an airfoil as the angle of attack increases. In fixed-wing flights, stalls can be experienced as the pilot increases the wing’s angle of attack, while exceeding its critical angle of attack (which may be due to slowing down below stall speed in level flight). 

Despite how it sounds, a ‘stall’ does not, in fact, mean that the engine has malfunctioned or that the plane has stopped moving—this effect is palpable even in an unpowered glider aircraft. 

In general, aircraft engineers want to design a wing so that it stalls in a gentle, predictable manner. Ideally, the stall should start at the root of the wing and progress outward towards the wingtips. The ailerons, which provide roll control for the aircraft, are positioned outboard on the wings, so it is best to have the wing tips stall last so that roll control is maintained. 

This gradual, progressive stall provides an indication to the pilot that a stall is approaching. If the wing is not designed to provide this indication, a sudden loss of lift could happen without warning. A loss of lift without warning is dangerous especially at low altitude during takeoff and landing because there is little time for recovery. In order to assess and confirm correct stall behavior for DarkAero 1, the team turned to SimScale.

Evaluating Angle of Attack and Wing Stall Behavior with SimScale

The team at DarkAero used CFD through SimScale to virtually “fly” the wing of the DarkAero 1 at a range of different angles of attack to determine the angle of maximum lift and where stall occurs. Once the angle of maximum lift was determined, the Karls further refined their simulation in the region a few degrees before and after stall, in order to then predict how the stall would develop on the wing. 

From the early design stage, the wing of the DarkAero 1 was designed to have a progressive stall that initiates at the root of the wing, progressing outboard towards the wingtips. Through analyzing simulation results from SimScale, they were able to design the DarkAero 1 wing so that it stalled effectively as planned. In the video below, Ryley Karl explains how SimScale was used to evaluate the lift coefficient and stall behavior for DarkAero 1. 

The video, existing on DarkAero’s YouTube channel, gives greater insight into how they used cloud-based engineering and CFD simulation tools, from Onshape to SimScale, to help design the wing for DarkAero 1. Below, we’ve transcribed the highlights for a quick summary of what the results entailed. 

The Angle of Attack at 0°: CFD Analysis

First the team looked at the wing flying at a shallow angle of attack,  as one would see in high speed cruise flight. To keep things simple, the above image and following images concentrate on just the right wing,  with the rest of the air frame removed. The airflow streamlines over the wing show a clean and uniform pattern over the wing, following its contour. This is referred to as the airflow being attached to the wing. 

The Angle of Attack at 10°: CFD Analysis

At 10°, the airflow is still attached to the wing surface and the wing is generating more lift. 

The Angle of Attack at Initial Stall: CFD Analysis 

At an even higher angle of attack, the first warning signs of a stall can be clearly seen. A small pocket of separated airflow starts to appear at the root of the wing. This is what the team wanted to see, as they designed the wing to stall in this manner at the root first.

The Angle of Attack at Flight Stall and Beyond: CFD Analysis

As they continue to increase the angle of attack even further in different simulation runs done in parallel on SimScale, they see the stall progress outboard along the wing towards the wingtips. The inboard portion of the wing stalls first, as you can see by the separated airflow near the root of the wing.

In one simulation run, the team crept up a bit higher in the angle of attack, and the majority of the airflow separates from the wing and the total lift generated by the wing is seen to be dropping. The wingtips try to hang on to the very end of the stall, which is, again, as desired. With these results from SimScale, DarkAero can simulate stall and start to build a predicted model of the stall behavior.

Next Steps for DarkAero

As DarkAero continues down the road to production for DarkAero 1, continued collaboration is planned between the team and SimScale to evaluate other aspects of the design. Stay tuned for more details, coming soon.

Interested in Learning More About CFD and Aerodynamics from SimScale?

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Propeller Design Propeller Design Efficiency

Propeller design efficiency is judged by the useful power output it produces. For example, the useful power output for a fan is how quickly the fan can accelerate the surrounding airflow. Due to their dynamic nature, propellers’ efficiency is instead measured by thrust developed on the blades and how this powers the respective system, whether it be a boat, plane, or other application. To work out the true efficiency, the following equation is used:

Where:

• Thrust in N
• Axial Speed in m/s
• Resistance torque in Nm
• Rotational Speed in rev/s

Propeller Design How to Design a Propeller

Design parameters can impact the performance of the propellers or fans. These variables can include the number of blades needed, the size of the outer diameter, the pitch-affecting angle of attack, as well as the leading and trailing edge blade angle along with many others.

Propeller Design Efficiency Factor The Number of Blades

Increasing the number of blades will actually reduce the efficiency of the propeller but with a higher number of blades there is a better distribution of thrust helping to keep the propeller balanced, therefore a trade off must be established.

Propeller Design Efficiency Factor The Diameter

The diameter of the propeller has a significant impact on its efficiency. Larger propellers have the capacity to create more power and thrust on a larger fluid volume. Yet, most designs face limitations when it comes to diameter, so optimization must occur elsewhere.

Propeller Design Efficiency Factor Lift and Drag Distributions

Instead of the standard lift and drag coefficients, ensuring propeller design efficiency requires specific airfoils with prescribed angles of attack at each radius. The distribution of Cl (lift coefficient) and Cd (drag coefficient) along the radius can be examined by performing analysis for the design point. For maximum efficiency, the airfoils must operate at maximum L/D. If the propeller should also work fairly well under poor conditions, it is usually necessary to use a lower angle of attack for the design.

Propeller Design Efficiency Factor Velocity of Flow

The presumed velocity of the fluid flow, whether it be air or water, is another important variable to consider. This force, along with the velocity of rotation (RPM) determines the pitch distribution of the system. Large propeller designs can become less effective operating at the axial velocity. The most efficient designs are those which maintain a pitch to diameter ratio of 1:1.

Propeller Design Efficiency Factor Fluid Flow Density

While the actual density of the fluid has no effect on the efficiency of the system, it does play a role in defining the shape and size in the early phase of the design process. For example, an air propeller used for planes and drones will have a bigger face than its aquatic counterparts, as the fluid density is less.

Blades and shroudings can be optimized to maximize the power output of a device whilst minimizing losses due to flow inefficiencies. CFD from online tools such as SimScale provides a great solution for carrying out fast iterations in order to converge on an optimum design without the need for excessive physical prototyping. The following simulation project explores these concepts and overall propeller efficiency.

Increasing Propeller Design Efficiency Case Study: Propeller Design Simulation

This project simulates a propeller design at multiple RPMs. Multiple factors including the airflow over the blades, the resulting turbulence, and performance indicators such as torque, axial thrust, and velocity were evaluated.

The simulation measured the useful power, or thrust, resulting from the input power, or torque, acting upon the propeller.

Extract thrust and torque values for each operational condition using the “forces and moments” result control (Source: SimScale)

At specific and fixed free stream velocities, 5 different RPMs were tested to evaluate their respective efficiency. As exhibited below, the simulation found that the propeller design was the least efficient at the highest RPM, and should operate around 4000 RPM for best results.

Propeller Design Conclusion

Using online simulation, it’s easier than ever to be able to use CAE technology to preemptively test design iterations before prototyping. For fan or propeller design, this is especially true as the testing of different RPM speeds is crucial to ensuring a design’s overall efficiency.

Want to learn more about optimizing your propeller design? Watch our webinar to see the project simulated in real time and become a CFD master now:

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If only this was considered three decades ago, it could have saved the Space Shuttle Challenger from disaster. There are numerous articles addressing the Challenger disaster in the media, from Wikipedia pages to coverage on the National Geographic. This page on NASA website lists some of the major theories and reports on the tragedy.

One of the cruelest aspects of the catastrophe was the deadly fate of the crew who were on-board the Space Shuttle Challenger. It was originally believed that the shuttle exploded, and the crew died instantly. Later, it was found that the astronauts were alive, trapped in their seats and even conscious until the crew cabin hit the Atlantic Ocean at 321 kilometers per hour…

Could the Challenger disaster have been prevented if the engineering was executed better?

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What Was the Challenger Disaster?

The Space Shuttle Challenger, with a seven-member crew was launched on the morning of 28^th January 1986 from Cape Canaveral, Florida (USA). The original launch for the 27^th was postponed. The temperature on that day was about -7 °C. This was the 10^th flight of the Space Shuttle Challenger.

73 seconds into the flight, it was believed that the solid boosters exploded killing all the crew on-board and plunging the shuttle into the Atlantic Ocean. Initial investigations reported that the O-ring between the solid boosters failed due to the low temperatures on that day, eventually leading to the breakage of the shuttle.

However, over the last 30 years, this has been a major case study for engineers and academics alike who have questioned the theories rigorously. Today, our understanding of the matter has greatly developed and matured as technological advancements revealed the true causation of the Challenger disaster.

What Actually Happened to the Space Shuttle Challenger?

As I discussed in a previous article on hyperelasticity, rubber materials demonstrate a glass transition behavior. This means that at temperatures that surpass this glass transition temperature, materials are extremely rubbery. In contrast, temperatures below this cause materials to behave in a glassy and brittle manner.

These O-rings were installed between the solid fuel segments as shown in Fig. 01. Their purpose was to prevent hot combustion gases and particles from escaping the inside of the booster. For redundancy, two O-rings were installed. On the internal layer, a heat-resistant putty was added to further isolate the rings from the hot gases.

Three Possible Issues that Caused the Challenger Disaster

• Consider the cross-sectional view in Fig. 02. The booster ignition caused the heat-resistant putty to displace and increase air pressure between the putty and O-ring. This caused the gap between the ring (A) and insulation (E) to increase.
• Due to exposure to hot gases, the O-rings underwent erosion.
• On the shuttle Flight 51-C on 24^th January 1985, blow-by was observed. This meant that the hot gases had penetrated both O-rings completely. This launch was done at the lowest ambient temperature.

Further tests in March 1985 demonstrated that the O-ring resiliency had issues when used below 10°C. On the 31^st July 1985, a memo circulated that discussed a definite fear of losing a flight due to these conditions.

On the days before the launch, engineers continuously raised the issue about launching in cold conditions. Unfortunately, the issue eventually died down despite many engineers feeling that their concerns were not addressed. Further on, as commonly believed, there was no pressure to launch despite the delays. At this juncture, it is interesting to understand the flow of decision making. The two most notable decision makers included:

1. Engineers: Morton-Thiokol Inc. (MTI, company manufacturing O-ring), Roger Boisjoly (O-ring specialist), Arnold Thompson (Engineer), Allen McDonald (Project supervisor of solid fuel rocket), Jerry Mason (Senior VP & GM), Joe Kilminster (VP of Space Booster), Robert Lund (VP, Engineering)
2. NASA: Larry Mulloy (Manager of solid rocket booster), George Hardy (NASA Deputy Director)

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The Eve of the Launch

On the eve of the initial launch date, the MTI engineers and management recommended to delay the launch because the temperature was too low (less than 10°C). It later came out that Kilminster in particular was opposed to the launch, while Mulloy wanted to press on. As per NASA’s regulations, it was the responsibility of the contractor to demonstrate launch readiness of the components. Any inconclusive data automatically resulted in a no-go. However, Larry Mulloy put the burden on MTI to prove that the system was not ready.

Amid all the politics, Robert Lund—who was initially reluctant—agreed to the launch along with Jerry Mason. While Allen McDonald argued against the launch, Joe Kilminster declared to NASA that the data was inconclusive & hence a launch is not recommended. At this point, NASA managers Larry Mulloy & George Hardy, inform MTI that they can only make recommendations and they wanted to go ahead with the launch. They informed the higher-ups about going ahead, ignored the concerns raised, and unfortunately made history. We all know what happened next.

The Space Shuttle Challenger Launch

Going ahead, the shuttle was launched. The temperature, as predicted, was low enough for the rings to be extremely stiff and not provide sufficient sealing. As shown in Fig. 03, plumes of smoke were immediately visible.

Due to the pressure of the launch, the cylindrical containers/casing bent away from each other creating an opening. The O-rings were expected to shift to seal the gap. However, due to low temperatures below the glass transition temperature, the O-rings behaved in a glassy and brittle manner. Above these temperatures, they demonstrated extreme flexibility and elasticity. Thus, it took much longer for these O-rings to shift out of place and create a seal. Both O-rings were vaporized across a 70° arc allowing gases to leak through a growing hole. That day, the shuttle experienced more than normal wind shears, leading to rapidly increasing hole.

At 73 seconds past launch, the disintegrating external tank caused the shuttle to veer from its altitude. This increased the aerodynamic forces to more than 20g (beyond its design limit of 5g) resulting in the breakup of the shuttle. The SRB’s continues to propel in an uncontrolled fashion. The intact cabin then flew into the Atlantic ocean with the crew likely conscious almost until impact.

Conclusion

The Challenger disaster was broadcasted worldwide and played time and again. To this day, people feel that they personally witnessed the disaster, and were somehow connected. A major factor was failure to effectively test polymeric material behavior across a range of temperatures. This, along with several other contributing engineering faults, eventually led to the tragedy. The Challenger disaster is an infamous example of how even the simplest engineering concepts must be respected, tried, and tested or misfortune can strike- and sometimes be fatal.

The post The Challenger Disaster: Deadly Engineering Mistakes appeared first on SimScale.

NASA’s Lost Spacecraft The Metric System and NASA's Mars Climate Orbiter

The Mars Climate Orbiter, built at a cost of $125 million, was a 638-kilogram robotic space probe launched by NASA on December 11, 1998, to study the Martian climate, Martian atmosphere, and surface changes. In addition, its function was to act as the communications relay in the Mars Surveyor ’98 program for the Mars Polar Lander. The navigation team at the Jet Propulsion Laboratory (JPL) used the metric system of millimeters and meters in its calculations, while Lockheed Martin Astronautics in Denver, Colorado, which designed and built the spacecraft, provided crucial acceleration data in the English system of inches, feet, and pounds. JPL engineers did not take into consideration that the units had been converted, i.e., the acceleration readings measured in English units of pound-seconds^2 for a metric measure of force called newton-seconds^2. In a sense, the spacecraft was lost in translation.

Before venturing further into what happened on that dreaded day, let’s try to understand the different units of measurement and how they came into use in various regions across the globe. In the past, various regions of the world followed the measurement systems and units that were most convenient for them. For example, in one part of the world, the cycle of the sun was assumed to be a measure of time, whereas elsewhere, it was the lunar cycles that were used to define time. Additionally, the lack of communication tools prevented scholars from communicating, discussing, and comparing ideas with scholars across the globe. Thus, over the course of centuries, different units and measuring standards have evolved independently.

As the world has grown closer, the need for a single unified system of units has emerged. Credit to several developments in the metric system can be dated back to the French revolution when it was first envisioned. Subsequently, two platinum standards were created representing the meter and the kilogram in the Archives de la République in Paris. This can be considered the first step the development of the present International System of Units.

Following the French revolution, Johann Carl Fredrich Gauss, a German mathematician, strongly promoted the use of this metric system. Alongside meters and kilograms, he added the “seconds” defined in astronomy, as a coherent system of units for the physical sciences. James Clerk Maxwell and Sir Joseph John Thomson, through the British Association for the Advancement of Science (BAAS), carried forward Gauss’ initiative to formulate the requirement for a coherent system of units with base units and derived units. The CGS system, a three-dimensional coherent unit system based on the three units—centimeter, gram and second—using prefixes ranging from micro to mega to express decimal sub-multiples and multiples, emerged because of their efforts. In 1889, the first General Conference on Weights and Measures (CGPM) sanctioned the international prototypes for the meter and the kilogram. Together with the astronomical second as the unit of time, these units constituted a three-dimensional mechanical unit system, just like the CGS system, but with the base units as meter, kilogram and second.

It was Giovanni Giorgi, an Italian physicist and electrical engineer, who proved that it is possible to combine the mechanical units of this meter–kilogram–second system with the practical electric units to form a single coherent four-dimensional system by adding to the three base units, a fourth base unit of an electrical nature, such as the ampere or the ohm, and rewriting the equations occurring in electromagnetism in the so-called rationalized form. Following these developments, in 1939, the four-dimensional system based on the meter, kilogram, second and ampere was recommended to the Consultative Committee for Electricity and Magnetism (CCEM) and was approved by the International Committee for Weights and Measures (abbreviated CIPM from the French Comité international des poids et mesures) in 1946. Following suit, Ampere, Kelvin, and Candela were added as base units in 1954, and Mole was added as the 7th base unit in 1971. Today, there are seven base units: Meter (Distance), Kilogram (Weight), Seconds (Time), Ampere (Electric current), Kelvin (Temperature) and Candela (Luminosity).

USA & NASA SI in the United States

If one travels to the US, one will notice these changes immediately; there are miles instead of kilometers, pounds instead of kilograms, and so on. For almost 22 years of my life, I had used kilograms and when I went to live in the US, the “pound” was totally new to me. While I could predict how much I would get if I bought a kilogram of an item, I had no sense of what one pound meant. The US remains one of only seven countries where SI units are not adopted.

The American system of measuring distance in inches, feet, and yards is based upon the units from England, which is where the first settlers came to the US on the Mayflower. While much of the rest of the world uses the metric system of centimeters, meters, and kilometers, the US has continued to use the English units. One foot is the same as 12 inches, and a yard is 36 inches—and the confusion continues. In metric, 1 meter is 100 centimeters, and a kilometer is 1000 meters. However, it is undeniable today that a large number of multinationals and international businesses work with and/or in the United States. This makes it even more important to be able to use common units of measurement.

Comprehending the overwhelming advantages of the metric system, the US Congress adopted SI units as the preferred measurement system in 1975 through the “Metric Conversion Act” which was signed by US President Gerald Ford. However, the act also allowed the use of US customary units. Further on, in the 1980s, the federal government tried to introduce metric in the United States. Speedometers on the cars from that time showed both miles per hour and kilometers per hour. However, these attempts at changing to metric were not successful.

Even though the US Congress has adopted SI as the preferred measurement system for the United States, the vast majority of businesses continued to use US customary units. This reservation against metric, however, changed almost instantaneously, at least at the best space agency in the world in 1999. This change occurred after a disaster investigation board reported that NASA’s Mars Climate Orbiter burned up in the Martian atmosphere.

NASA’s Lost Spacecraft NASA's Mars Climate Orbiter Disaster

A NASA review board found that the problem was in the software controlling the orbiter’s thrusters. The software calculated the force that the thrusters needed to exert in pounds of force. A second piece of code that read this data assumed it was in the metric unit—“newtons per square meter”.

During the design phase, the propulsion engineers at Lockheed Martin in Colorado expressed force in pounds. However, it was standard practice to convert to metric units for space missions. Engineers at NASA’s Jet Propulsion Lab assumed the conversion had been made. This navigation mishap pushed the spacecraft dangerously close to the planet’s atmosphere where it presumably burned and broke into pieces, killing the mission on a day when engineers had expected to celebrate the craft’s entry into Mars’ orbit.

The contributing factors that led to the disaster, as reported by the Mars Climate Orbiter failure board, were eight-fold. According to NASA’s board, errors were undetected within ground-based computer models of how small thruster firings on the spacecraft were predicted and then carried out on the spacecraft during its interplanetary trip to Mars. Furthermore, the board added that the operational navigation team was not fully informed of the details of the way that Mars Climate Orbiter was pointed in space, as compared to the earlier Mars Global Surveyor mission.

The initial error was made by contractor Lockheed Martin Astronautics in Colorado, which, like the rest of the U.S. launch industry, used English measurements. The contractor, by agreement, was supposed to convert its measurements to metrics. The systems engineering function within the project, whose responsibility was to track and double-check all interconnected aspects of the mission, was not robust enough. The board added that this was exacerbated by the first-time handover of a Mars-bound spacecraft from a group that constructed it and launched it to a new, multi-mission operations team.

NASA's Miscalculations Other Instances of Conversion Errors

Gimli Glider

This was not the only disaster in history that was directly caused by conversion errors. 1983 is famous for the “Gimli Glider” incident, in which Air Canada’s Boeing 767 jet ran out of fuel mid-flight because of a mistake in figuring out the fuel supply of the airline’s first aircraft using metric measurements.

Canada was one of the countries that employed the imperial system until 1970 when the nation began to change over to metric. Metrication (as it was called) took some time—about fifteen years or more. One of the industries that were late to change over was the airline industry, which was mainly due to the expense and longevity of the equipment.

The pre-flight fueling protocol of the flight required to convert volume (liters) into mass (kilograms or pounds, depending on the system in use) to estimate the amount of fuel required. Instead of figuring out how many liters the plane needed to hit the required payload of 22,300 kg, the crew calculated how many liters were needed to hit 22,300 pounds. This was half the quantity of fuel required, which meant that the flight only had enough fuel to make it halfway to the destination. This turned out to be a major—and potentially life-threatening—problem as the vehicle in question was an airplane cruising at 12,500 meters above the ground.

Luckily for all on board, the pilot had ten years of glider training under his belt, and his co-pilot knew the surroundings quite well. The skilled pair was able to land the 767—gliding the last 100 kilometers, ensuring the safety of everyone on board.

Lesser Known Incidents

There have been several lesser-known occurrences of conversion mishaps. The Institute for Safe Medication Practices reported an instance where a patient had received 0.5 grams of Phenobarbital (a sedative) instead of 0.5 grains because the recommendation was misread. A grain is a unit of measure equal to about 0.065 grams. The Institute emphasized that only the metric system should be used for prescribing drugs.

In yet another event, an aircraft was more than 13,000 kilograms overweight. In 1994, the FAA received an anonymous tip that an American International Airways (now Kalitta Air, a cargo airline) flight had landed 15 tons heavier than it should have. The FAA investigated and discovered that the problem was in a kilogram-to-pounds conversion (or lack thereof).

Finally, it is worth mentioning that even Columbus had conversion problems. He miscalculated the circumference of the earth when he used Roman miles instead of nautical miles, which is part of the reason he unexpectedly ended up in the Bahamas on October 12, 1492, and assumed he had hit Asia.

NASA’s Lost Spacecraft Units in FEM

As one would have noticed, there are no predefined units when using FEM software. It is left to the user to ensure that the right conjugates are used. If the unit used for length is meter, then the right units for other aspects of mechanical units are kilograms and seconds. In contrast, if the units are millimeters, then the right units are milligrams and milliseconds, and so on. Every time you think about setting up your simulations, you have to give the units a thought!

When using an FEA or CFD simulation software, be aware that not all the solutions on the market offer both the metric and the imperial units. To help our users avoid making design mistakes, SimScale supports both the metric and the imperial systems, which you choose in the first step of creating your simulation. All you have to do is make sure your collaborators use the same system.

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