As FEA can be computationally expensive and time-consuming, leveraging the practically unlimited power offered by the cloud grants organizations speed, scale, and accuracy. With a web browser-based user interface easily accessed from anywhere, at any time, engineering teams of all sizes can start adopting engineering simulation earlier in their design and development processes and do more with simulation.

Structural simulation with a cloud-native solution means that, in one platform and using the same model, engineers can use different structural analysis types to holistically simulate and analyze their projects. Structural, thermal, and thermomechanical behavior can be investigated in order to find critical stress regions and unpredictable deformations stemming from the complexity of real-life scenarios.

Structural Analysis and Simulation

Simulations of structures, including linear static and nonlinear quasi-static analyses, enable engineers to assess the structural integrity of a design, predict deformations and stress distributions. With a multiphysics simulation platform, users can simulate plastic behavior in nonlinear analyses but also include thermomechanical behavior in the analyses. Additionally, all types of nonlinearities can also be taken into account, including geometric, contact problems, and material. Applications include heavy machinery, industrial equipment, automotive and aircraft components, pipes, and bridge design. 

SimScale is deployed as a SaaS engineering simulation platform offering a single user interface that supports the following analyses:

• Static – Linear and nonlinear analysis of displacements, stresses, and strains in one or multiple solid bodies
  • Aluminum pipe bending (large deformations)
  • Snap fit permanent deformation (plasticity) 
  • Rubber seal installation (hyperelasticity)
  • Shaft and rotor interference fit (nonlinear frictional contacts)
• Dynamic – Transient calculation of displacements as well as stresses and strains in one or multiple solid bodies
  • Shock
  • Drop test
• Harmonic – Simulation of the steady-state structural response of solids applied to periodical (sinusoidal) loads
  • Shaker table testing – sine swept
  • PCB vibration
  • Bracket and fixation assessment
• Frequency Analysis – Calculation of natural frequencies of oscillation of a structure and the corresponding oscillation mode shapes
  • Modal surveying
  • Prestressed eigenmode analysis (bolt prestress)
  • Support and fixtures due diligence
• Heat Transfer – Calculation of the temperature distribution and heat flux in solids under conduction, convection and radiation)
  • Transient power cycling
  • Thermal packaging
• Thermomechanical – Calculation of the structural and thermal behavior of one or multiple bodies
  • Thermal shock
  • Thermal interference fitting
  • Thermomechanical fatigue

Cloud-Native Engineering Simulation

As a cloud-native engineering simulation platform, SimScale facilitates a high degree of collaboration between team members and automates the most common steps of a simulation workflow, from CAD import and meshing to results analysis. The platform is based on a special meshing technique that ensures the mesh interfaces are both conformal between all parts and robust enough to apply to many types of CAD models the first time around.

Engineers can harness advanced solvers that account for thermal and structural behavior for fast and accurate assessment of deformation, stresses, and other design critical output quantities. Thanks to the huge computational power offered by the cloud, the accuracy of the simulations can be increased by performing more detailed analyses. 

And, engineers can quickly investigate design changes with an intuitive post-processor, for a better understanding of mechanical, thermal, and thermomechanical phenomena. Physical variables including displacements, strain and stress types, reaction forces and moments, or even heat flux can be calculated and visualized. Further quantities including the force history of entire components, stress and temperature distributions, deformation, and much more can be post-processed within SimScale.

Structural Mechanics and Engineering Simulation in the Cloud

With SimScale, engineers can solve structural and thermal problems of industrial components, valves, medical devices, turbomachinery, pressure vessels, and much more. Engineers can benefit from a vast number of existing public projects, spanning multiple industries and applications, to serve as a starting point for their own studies. SimScale makes engineering simulation economically and technically accessible through an easy-to-use, browser-based solution that can be accessed from anywhere, at any time. This means, the scale of simulation projects can range from the analysis of coronary stents all the way to silo superstructures using solid elements without engineers having to worry about disk space, calculation, or post-processing speed.

Learn more in our whitepaper: Structural Mechanics Simulation in the Cloud

Download Whitepaper

The post Cloud-Native Simulation for Structural Mechanics appeared first on SimScale.

Today, the company develops high-performance electric drivetrain and battery systems for many of the world’s largest automotive manufacturers. Its customers include Aston Martin, Porsche, Koenigsegg, Cupra, and many more.

Rimac, known for its electric hypercars, is working on highly efficient battery solutions for the most demanding projects and conditions in the automotive industry. In this article, we explore Rimac Automobili‘s approach to simulation and learn how they leverage cloud-native engineering simulation within their battery pack team design and beyond.

Multiphysics Simulation and Product Development at Rimac

SimScale is used by several different departments responsible for designing components for battery packs and for testing the structural integrity of components. Currently, Rimac’s design engineers count on SimScale as a tool in their stack that augments their simulation capabilities. Battery System Engineer Antonio Radenić shares that the team’s main interest lies in heat transfer phenomena. They perform thermal simulations to determine the thermal gradients and hot spot areas they can expect on various concepts and designs.

The cooling performance assessed in the project shown below includes the maximum temperatures reached by the battery pack cells, as well as the battery gradient, which is essential for optimal battery performance. The parameter studies included a variation of the inlet flow velocity as well as the thermal conductivity of the electrical insulator.

These types of studies assure Rimac designers reliability, expected lifespan, and safety of their product. Conducting them with cloud-native engineering simulation means the turn-around time of investigating multiple design iterations is drastically reduced.

As SimScale was born in the cloud, it’s scalable by nature. This gives the design team an edge over traditional CAE tools. Computational power can scale up or down depending on project demand and multiple design options can be explored across multiple teams within Rimac, as cloud-native tools have sharing and collaboration features baked in.

Rimac is interested in exploring the full physical spectrum of the battery packs, which includes testing the structural integrity of their designs. With simulation, they can determine the type of stresses they might expect in their structures and cooling elements and the type of loads that might occur with applied pressure inside various geometries.

With electronics design, investigating thermal aspects is often at the center of the product development process. But Rimac’s work offers many areas of investigation for which multiphysics simulation can be used. A battery pack drop test analysis, for example, represents an opportunity to investigate and improve product safety.

CFD analysis only begins to scratch the surface of the type of design space exploration available to engineers using multiphysics simulation in the cloud. Access to cloud computing enables users to kick off simulations in parallel, meaning in the same amount of time that traditional CAE might require for one simulation run, designers at Rimac can explore the specifics of multiple designs, quickly iterate from there and make fast design decisions.

Simulating Designs for Electric Hypercars 

The introduction of SimScale into the R&D process of Rimac Automobili has empowered them to accurately and efficiently simulate hundreds of virtual battery pack models, including a wide range of complex real-life hypercar driving cycles and scenarios. In the future, they plan to expand the use of SimScale between and across different departments to fully avail themselves of the benefits of a SaaS engineering tool in a highly competitive automotive market. Providing their design team with access to multiphysics simulation allows Rimac to optimize at the top of their field, pushing the boundaries of design with the next generation of electric hypercars. 

Watch our on-demand webinar with Rimac Automobili, where presenters showcase a Design of Experiments (DoE) study seeking to increase battery life while maintaining an optimal operating temperature inside the battery module:

Watch On-Demand

The post Cloud-Native Simulation for the Next Generation of Electric Hypercars appeared first on SimScale.

CAD & Geometry

In electronics cooling design, it is not uncommon to spend significant time preparing CAD models for simulation. Detailed production-ready models in their original state are likely to be unsuitable for most simulation tools, burdening simulation engineers with the tedious process of de-featuring geometry, simplifying parts, closing small gaps in the model, and fixing faulty geometry. And, in the case of geometry studies consisting of multiple variants of one base model, all this work often needs to be repeated. Traditional CAD tools in simulation software are, in many cases, inefficient or ill-equipped for such tasks, as they are primarily optimized for designing models from scratch, not simplifying existing models. The CAD mode feature available in the SimScale platform offers a set of purpose-built CAD editing and simplification tools. For example, the Flow Volume Extraction operation alleviates the engineer from laboriously isolating a flow volume as required in other pre-processing systems. Unlike traditional CAD systems, CAD mode focuses on a core set of simple, intuitive, but also versatile tools that are ideal in making CAD models simulation-ready.

This short demo video illustrates the power of CAD mode:

Electronics Design Performance

A common challenge in early-stage design is the lack of ability to quickly investigate and simulate design performance. Engineers want the capability to receive design feedback in an iterative manner using fast and accurate thermal and flow analyses applied to their designs. A lack of accessible engineering simulation at the early design stages can significantly reduce product performance. With SimScale’s full-stack solution, users can easily go from CAD import and edit to auto-meshing the model and post-processing in a few minutes, all from a web browser.

Cost Reduction

Simulation tools that utilize cloud computing eliminate much of the costs associated with traditional simulation. They not only require no hardware installation or space for a workstation on-premise but are inherently scalable—enabling users to run multiple design iterations in parallel and deploy additional licenses organization-wide. The costs of traditional simulation stacks usually add up to big up-front investments before proving the added value of all the acquired hardware. With that also comes an enormous amount of responsibility in terms of maintaining the systems and a considerable know-how barrier requiring teams to conduct specialized training. Integrating cloud-native simulation into thermal management design also holds the potential for reducing enormous costs in the product development process. The more quickly a design study converges on its final version, the less costly it is. With simulation tools, designers can identify a problem before money is spent on producing physical prototypes. This is why Raycore Lights uses SimScale to shorten the design and prototype lifecycle of their products. In one example, Raycore ran a design study with eight simulations, each simulation taking five hours to complete.

Using legacy software, these would have been completed sequentially over a forty-hour period. With parallel simulation capabilities, all eight simulations were run concurrently, yielding a total simulation time of five hours.

Product Performance and Lifespan

In the absence of the right cooling strategy, the lifespan of components can decrease and the respective electronic device will operate at a suboptimal level. Failure to effectively cool electronics can lead to loss of efficiency and even system malfunction. Therefore, high-performance microchips and electronic devices of all shapes, sizes, and levels of functionality need innovative thermal design techniques to maintain design operating conditions and remain within peak performance range. The life of LED systems, for example, shortens significantly when exposed to prolonged heat.

Following standardized physical testing procedures is expensive, time-consuming, and becomes prohibitive for each LED design. Using thermal simulation means that engineers can quickly evaluate the thermal performance of multiple LED designs at many operating points and use the results to predict component performance and lifespan.

Electronics Design with Simulation

A fully cloud-native simulation platform allows engineers to simulate and analyze high-fidelity models with complex physics by making High-Performance Computing (HPC) accessible, giving unprecedented accuracy in results, efficiency in design collaboration, and versatility in the vast range of electronics cooling applications that can be solved. Engineering managers rely on the accessibility of SimScale to provide their team with immediate access to electronics cooling digital prototyping, early in the design stage, throughout the entire R&D cycle, and across the entire enterprise.

Learn more in our whitepaper: Electronics Cooling Multiphysics Simulation

Download Whitepaper

The post Electronics Design Challenges: Solved with Simulation appeared first on SimScale.

These FSAE engineering competitions allow students to design, test, develop, and produce a single-seater prototype to race against other university teams. The teams then compete across a range of different categories, from acceleration to endurance performance, to design, and even cost evaluation of the project.

The Challenge:

RFR had been using an aerodynamic package for several years now. The team undertook a new project this year; the inclusion of a footplate on the front wing of the car. A CAE program like SimScale was essential to their application as they needed to analyze the flow emerging from the front wing. These insights would allow the team to understand the vortices generated by airflow and determine if the footplate would increase the performance of the undertray, by sealing it and preventing flow leakage.

Before SimScale, computing iterative simulations on personal computers was a major challenge. They would consume a lot of time, due to the limited computational power of the computers available. It was thus very difficult to run multiple iterations, and each design change would cause large delays in the design flow. SimScale solved this problem. Being a cloud-based computational solution, RFR could perform various design iterations in a shorter amount of time and utilize their computers for other design work, in the meantime. SimScale’s easy-to-use interface enabled the team to visualize the vortices and the changes in downforce and drag values after the introduction of the footplates. Necessary design changes could be quickly enacted and the final design easily integrated with their complete aero package.

How They Solved It With SimScale

The simulations were based on the applications of CFD in Formula Student and Formula SAE workshop sessions. The team found these workshops very clear, informative, and useful in setting up their own simulations.

The following settings were used for the simulations:

• Incompressible flow analysis
• K-omega SST turbulence model
• Steady-state flow
• Air density = 1.225kg m-3 (density at sea level)
• Velocity inlet of 15m s-1 (estimated speed of the car at the end of a straight at Formula Bharat)
• Fixed pressure outlet
• Moving wall condition on the floor at 15m s-1 so a non-zero surface velocity boundary layer is developed since the floor is moving relative to the wing 
• No-slip wall condition on wing surfaces so a zero surface velocity boundary layer is developed
• Tetrahedral meshing algorithm

Simulation Results 

To design the footplate of the front wing, RFR ran a total of 20 simulations, slightly varying the geometries of the footplate and the flap configurations until they obtained optimal performance from the front wing.

The meshing for each simulation took around 30 minutes and the solving took three hours, rendering the entire simulation complete within four hours on average. The team utilized a maximum of 16 processor cores to aid in their simulations.

Velocity streamlines and pressure contours were used to study the vortices generated by the footplate.

RFR was able to achieve up to a 5% increase in downforce from their undertray after including the footplate in their front wing. The initial design gave them around 68 N of downforce from their undertray, and after analyzing the flow around it, and making necessary changes, the team was able to bring it up to around 72 N.

Overall, SimScale provided RFR with an intuitive interface and a powerful platform to run demanding simulations on the cloud — meaning minimal expenditure of the team’s on-site computational resources.

The Aerodynamics Subsystem at Raftar Formula Racing found SimScale to be very intuitive, and found the plethora of available tutorials and workshops to be very useful in getting to know the software swiftly and comprehensively.

Raftar Formula Racing

On account of the fast and accurate simulation results achieved through SimScale, RFR plans to continue using the software for future development on its aerodynamic package. Moreover, the workshops and vast online resources offered by SimScale help the team build a strong theoretical foundation that is crucial for component design.

Raftar Formula Racing: Next Steps

Due to COVID restrictions throughout most of 2020, RFR did not have access to high-spec computers in their design phase. However, with engineering simulation in the cloud, the team had no problems performing demanding simulations with large meshes. This also meant they could invest more time into documenting their design changes. RFR’s future plan is to validate the results obtained from SimScale with on-track testing. The team plans to verify their drag and downforce values and the corresponding coefficients using shock potentiometers placed in their car. They also plan on verifying pressure distributions by placing pressure sensors across the wings and other aerodynamic elements of the car. Finally, small set-up changes will be made in case of any discrepancy between simulation and real-world results.

The post SimScale Student Success Story: Raftar Formula Racing appeared first on SimScale.

What is Thermal Management for Electronics?

There is much debate over the best strategies for managing thermal conditions of electronics but the question most often overlooked is: Why do electronics need cooling in the first place? The second law of thermodynamics dictates that energy usage results in waste heat. In electronic devices, an electric control current causes transistors to switch on and off or amplify electronic signals.

Modern processors can contain millions of transistors, meaning the additional current needed to power these transistors generates additional heat. Most electronic circuits also contain resistors that work to control the flow of the current to certain components. The energy resisted by these parts is converted into heat and adds to the thermal energy present within an enclosure.

Overheating within an electronic device can result, at its worst, result in the melting of components. Most often, however, electrical engineers are not able to see immediate damage or failure but, rather, products experience degradation over time. This is why accounting for effective thermal management solutions, early in the design process, is critical for performance. For example, in LED chips a difference of only a few degrees cooler can result in thousands of extended hours of product life.

With adequate heat management becoming a central issue in product development it is important to revisit the misconceptions that remain from earlier eras of electronics design.  These are the 4 biggest myths we see in electronics cooling today and how engineers can counteract these mistakes with the best tools for the job. 

Myth One: Adding Extra Heat Sinks or Fans Can Fix Any Problem

As heat needs to be dissipated or moved away from sensitive electronic components, thermal interface material is used to conduct thermal energy to components like heat sinks. Heat sinks work by dissipating heat into the surrounding air and fans increase the airflow to support this heat loss mechanism, so one might be inclined to believe that simply adding more will equate to improved thermal management.

However, engineers designing devices that are increasingly smarter, and smaller, understand that there is always a tradeoff between space, cost, and energy consumption. By installing an additional component, space on the board is occupied, energy demand is increased and overall production cost goes up. With the installation of additional parts to a circuit, the probability of failure increases, as well. 

These considerations show that the answer to smarter, better, and more cost-effective thermal management lies not in additional parts but in early-stage design optimization. Simulation is an accurate tool that allows teams of designers to identify problems before incurring the cost associated with traditional physical prototyping. Forwiz System used simulation to test and validate CPU cooler design for optimal cooling performance.

Myth Two: Tools for Thermal Management Have Not Evolved

As mentioned previously, the rules that dictate the need for electronics cooling are universal laws that have been recognized for centuries. And, similarly, the mathematical problems created by waste heat can be solved by equations that already exist. But it is incorrect to believe that because the mathematical theories have not changed, neither have the tools used for heat design. 

For example, computational fluid dynamics can be used for early-stage simulation of heat management in electronics. This type of simulation uses the Navier-Stokes equation which was first developed in 1821 and has been used since the first thermal CFD software was released in the 1990s.

Despite this, modern tools allow engineers to unleash simulation in their design process without needing to understand complex mathematical equations in great detail. Setting up simulation no longer necessitates complex equations for all physical variables and laborious mesh tweakers.

CFD software today allows engineers to orient their simulation around real, physical problems like open boundaries for natural convection supplemented by smart default values. And, tools used in thermal management designs have advanced by continuously updating the capabilities of their solvers.

SimScale’s Conjugate Heat Transfer v2.0 improves on the original algorithm and now calculates the temperature field through all regions at once in a fully coupled manner, no matter how many solid components and flow volumes or cavities one has. This speeds up convergence and allows for a more efficient parallel computation, which provides engineers with more accurate results, faster.

Myth Three: Simulation Is Only for Experts

It is clear that thermal management design benefits greatly from simulation, however, many may still believe that it is a practice reserved only for those specially trained in modeling and simulation. Cloud-based tools, like SimScale, reduce the barrier to access for engineers allowing powerful, high-fidelity simulations to be performed with nothing more than an internet connection and web browser.

Integrated plug-ins allow users to model their CAD in OnShape and directly import model iterations seamlessly into the same project within the SimScale workbench. For thermal engineers, this can mean testing different component placements or heat sink and fan orientations and evaluating their effect on thermal performance. They can also test multiple variations of heat sink material or designs, different fans, and casing designs, and optimize convective flow behavior. SimScale enables designers to run multiple simulations in parallel, offloading the high-performance computing power to the cloud, so users’ machines are freed up for other operations.  

Myth Four: Simulation Tools Are Expensive

Simulation tools that utilize cloud computing do away with much of the costs associated with traditional simulation. Not only do they require no hardware installation or space for a workstation on-premise, but they are also inherently scalable; users only pay for what they use. Integrating cloud-based simulation into thermal management design also holds the potential for reducing enormous costs in the product development process. The more realized a product becomes to its final version the more costly modifications become. With simulation tools, designers can identify a problem before money is spent on producing physical prototypes. This is why Raycore Lights use SimScale to shorten the design and prototype lifecycle of their products. In addition to reducing the overall time-to-market, simulation can mitigate unforeseen costs.

Dispelling myths about electronics cooling can open up the world of simulation to engineers and help them analyze thermal management and optimize their design for best results and improved products.

The post The 4 Biggest Myths About Thermal Management for Electronics appeared first on SimScale.

SFR is among the UK’s top ten teams. It is made up of approximately 40-50 students from a variety of engineering disciplines including mechanical, aerospace, electrical, and materials. The team is run as a purely extra-curricular activity meaning the students invest a lot of their spare time to design and manufacture a new race every year.

Each year, their third-year mechanical engineering students engage in a semester-long group design project. The project aims at developing students’ teamwork and communication skills, as well as putting into practice the theory taught across their degree program. SFR typically proposes a number of future development projects for students to select from. These projects would typically be beyond the scope for the team to carry out in the normal design phase (September to December) and use the outcome of the project to gauge feasibility for future cars.

One of these projects in 2020 was to improve the track performance of the race car through aerodynamic development. Lap time simulation helped identify a drag reduction system (DRS) as one method to achieve this aim.

Part of the development of the DRS required the rear wing to be studied using computational fluid dynamic (CFD) tools. The team is familiar with CFD and has used it for numerous years to develop the current aerodynamic package. Traditionally, this would have been achieved using traditional on-premise simulation tools on the university campus. However, when lockdown struck in 2020, midway through the group design project, an alternative solution was needed.

“SimScale was approached so we could take advantage of their cloud-based computer-aided engineering (CAE) simulation package. SimScale allowed the team to complete simulations of the rear wing with 94 cores on their cloud application, using just their personal PCs. Not only did this mean a high amount of computing power could be utilized, but other work can be completed in parallel as the demand is not coming from the user’s device. The team found the software intuitive and simple to use and yielded good results,” says George Poulter, Chassis and Driver Environment Lead.

The simulations were based on the “Applications of CFD in Formula Student and Formula SAE” workshop sessions. The team found these workshops very clear, informative, and useful in setting up their own simulations.

The following procedure was used for the simulations:

• Incompressible flow analysis
• K-omega SST turbulence model
• Steady-state flow
• Air density = 1.225kg m^-3 (density at sea level) 
• Velocity inlet of 28m s^-1 (estimated speed of the car at the end of a straight at FSUK) 
• Fixed pressure outlet 
• Slip wall condition applied to outboard wall and ceiling so no boundary layer is developed 
• Symmetry condition on the centerline plane 
• Moving wall condition on the floor at 28m s^-1 so a non-zero surface velocity boundary layer is developed since the floor is moving relative to the wing 
• No-slip wall condition on wing surfaces so a zero surface velocity boundary layer is developed 
• Hex-dominant meshing algorithm 

Simulations were run with the rear wing flaps in a “closed” and “open” configuration. Closed refers to a high angle of attack, whilst open refers to 0° angle of attack.

The Results

Meshing for each simulation took approximately 47 minutes (12.5CPUh). Simulation solving, for each simulation, took approximately 534 minutes (285.3CPUh). 94 cores were used.

Velocity and pressure plots were studied to check for flow detachment on the rear elements. Forces and moments were also integrated across each of the elements, respectively, to determine how much downforce and drag changed across the configurations. These were also used for the structural design and analysis later on in the project. Table 1 summarizes the total downforce and drag across all rear wing elements in the “closed” and “open” configurations. Drag was found to reduce by 90%.

SFR found the software intuitive and simple to use and yielded good results. As mentioned, the workshops were also very helpful for first time users. SFR continues to use the software and hopes to expand its knowledge of the toolsets offered by SimScale.

Next Steps for Sheffield Formula Racing

SFR has been able to compute simulations while working from home during COVID restrictions where access to high specification computers is limited.

As the project did not have any form of validation, the next steps for SFR before they commit to installing the DRS would be to manufacture a prototype using experimental methods. This would include building a small-scale model to be studied in a wind tunnel. Hopefully, results can be correlated with the SimScale simulations and the set-up procedure adjusted where relevant.

Testimonial

George and the SFR team feel that Simscale offered a “simple-to-use and intuitive interface that showed promising results from the get-go. Their online tutorials and workshops were incredibly useful on getting up to speed with the software”.

Sheffield Formula Racing is a purely extra-curricular activity that is run by students. We wouldn’t be in the position we are today without the continuous support of our sponsor, SimScale.

George Poulter, Chassis and Driver Environment Lead

The post SimScale Student Success Story: Sheffield Formula Racing 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. 

This paper addresses the difference between on-premises software and SaaS
solutions for computer-aided engineering, explaining how SaaS came to be and its
key benefits for students and professionals alike.

Download White Paper

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

As well as our customer success stories here. 

The post Brunel Unmanned Aerial Systems Student Success Story appeared first on SimScale.

These FSAE engineering competitions allow students to design, test, develop, and produce a single-seater prototype to race against other university teams. The teams are then matched up in groups of different disciplines ranging from acceleration, endurance, performance, and design to cost evaluation of the project.

The Challenge: Evaluating for Electric Mobility 

In recent years, the team has obtained several wins all over Europe at different events, and has continued to develop their prototypes to ultimately improve their performance year over year. For the upcoming season, the project has shifted its focus towards electric mobility, giving them the chance to start from scratch, and design a prototype like never before. This includes a new monocoque, as well as assessing new aerodynamic properties for the first fully electric prototype of Dynamis PRC, the DP12.

How Dynamis PRC Solved It With SimScale 

The basis of the design process is performed through CAE systems. After the 3D model is developed, a simplified version for computational fluid dynamics or CFD simulations is then created. The next step is the actual simulation using SimScale, followed by the post-processing of the results, and finally, some modifications being made to the design based upon the findings. Once the process has been completed, the cycle starts again from the beginning until the team is satisfied with the results of their design’s aerodynamic properties.

“The extensive use of SimScale ensures a flawless workflow for the daily development of the aero package and, more importantly, for Aero Maps. Throughout the whole year, several full car RANS simulations have been performed to develop the basic concepts and to decide on the wing shapes.” – Luca Galimberti, Head of the Aerodynamics Department

In order to extract the maximum potential from the car in each situation, multiple simulations with different ride heights are made to compute aerodynamic loads in each part of the race lap. These studies help to evaluate the overall behavior of the car in a variety of conditions on the racetrack, and with different setups; so that the team is prepared for whatever is around the corner. If implemented with dynamic models, this data can give a holistic view of the vehicle’s performance, and therefore improve the electric vehicle’s lap time.

A Look Under the Hood: Evaluating Ride Heights

For Dynamic PRC, the most extensive analysis has been conducted for the ride height of the car, both for the front and rear axle. By imposing a certain translation from the normal value, which is 30 mm of ground clearance, it’s possible to have a positive or a negative rake angle. The dependence of this angle is very important for this context. In particular, the next table resumes the different ride heights and corresponding rake angles tried.

The goal of this evaluation is to keep a stable value of downforce in different conditions (rolling, braking, acceleration, crosswind, and with the wheels steered) to optimize the overall performance.

At first, the prototype was simulated in various height configurations in a straight line stationary condition at 16 m/s. These simulations were the simplest, yet require a lot of man-hours as there are multiple combinations. For this reason, the use of SimScale proved to be beneficial to the team as it provides a lot of computational power, allowed the team to run simulations in parallel, and in a quick and user-friendly manner.

After some convergence studies, a mesh with around 20 – 25 million cells was created on the platform and then used for further investigations. The level of refinement of the cells within the vicinity of the car was very high.

It was possible to obtain between 5 and 6 boundary layer cells (depending on the area of the car), with an extrusion percentage always higher than 87%. Furthermore, averaged y + values of around 30 were obtained. To reduce the computational costs, half of the straight-line car simulations were run with a symmetry plane splitting the car.

Dynamis PRC: Car Simulation Results

The results of the simulations were targeted towards the maximum possible downforce. This is because of this type of competition, in which not very high speeds are reached. In the pictures, it’s possible to visualize the pressure distribution in the form of a pressure coefficient on the surface of the car.

The plot of all the aerodynamic data at the different ride heights (the Aero map), allows one to easily comprehend the dynamic behavior of the vehicle. In this particular case, we can evaluate the lift coefficient, in order to see the downforce contribution in each rake configuration. Such a result is obtained through a linear interpolation of the data computed with various simulations.

At the same time, the results were dynamically analyzed with a complete model of the car including suspensions, mass distribution, and several parameters that contribute to the overall performance of the vehicle.

Overall, the team successfully performed with SimScale more than 125 simulations with around 20000 CPU hours used. Moreover, the work with the software has helped to improve the downforce from the previous car by about 12.5% and in general, has given a better understanding of the car prototype’s aerodynamics.

As the season has come to an early ending, the team is now looking forward to starting again where it stopped and will surely work hard with SimScale in its virtual pit crew, to bring the best possible vehicle for 2021.

The post SimScale Formula Student Success Story: Dynamis PRC appeared first on SimScale.

For the past four years, Evgenii Varseev, an expert in his field from Rosatom Technical Academy, has been conducting international training courses with a focus on specific simulation applications for nuclear reactor safety analysis. During the course in Indonesia, he used computational fluid dynamics (CFD) with the SimScale online platform to demonstrate different approaches to solve application-related problems using a best-estimate thermal-hydraulic simulation for a group of 16 trainees with diverse backgrounds and experiences.

The course is part of a hands-on tutorial series, a training module called “Practical session on simulation using open source CFD software” which consisted of classes explaining the four main stages of the cloud-based CFD simulation process: 

1. Preparing the problem (model, mesh, etc.)
2. Configuring the case (simulation setup) 
3. Simulation runs with iterative design changes
4. Post-processing results 

“It was fun to demonstrate my case on the screen for everyone in the class and not worrying about licensing, workplace configuration, and problems with hardware available on personal laptops of participants. I was focused on the training process itself; the case structure and explanation of physical aspects, best practices in preparing and setting up the case, and being confident that no essential aspects are excluded.” Evgenii Varseev

For this training course, Evgenii presented the case of OECD/NEA-Vattenfall T-Junction benchmark. He invited participants to copy a public project accessible through SimScale to explain the basic stages of convective heat transfer simulation, from case configuration to post-processing the results via the online simulation platform.

The participants praised the overall course as well as the practical exercises. The training highlighted the simplicity of the platform to participants, and the power of HPC under the hood of SimScale allowed them to simulate relatively large meshes fast enough to post-process within the time-frame of the class. 

“SimScale is a perfect tool to demonstrate essentials of CFD simulations for newcomers to the field, and we are very happy to have the opportunity to use it,” mentions Varseev.

A Note From Our Author:

SimScale currently supports over 100 educators all over the world in class from universities across the globe, including MIT, John Hopkins University, Pennsylvania State University, University of Glasgow, and many more! If you want to use SimScale in your classroom, please feel free to reach out to me at jmurad@simscale.com.

I am constantly looking for people with a keen interest in computational fluid dynamics (CFD), finite element analysis (FEA) or thermal analysis and want to use a cloud-based simulation ecosystem in their classes or for research.

Want to Read More About SimScale’s Academic Workshops? 

• SimScale’s Student Ambassador Holds Biomedical Engineering Workshop
• Aircraft Design Workshop
• DIY 3D Printer Workshop

The post Applying SimScale to the Nuclear Engineer’s Training Process appeared first on SimScale.

“Our combustion vehicle secured an overall rank 3rd in Formula Bharat. Following this, our upcoming goal is to compete in the international event such as FSG or FSA and do our best.” – Sujay Bhaumik

Formula Manipal is a student project which involves a handful of students to work together to build a Formula style race car. Their team is involved in making one combustion engine car as well as one electric car. They first learned about SimScale from attending a webinar about CFD simulations.

The Problem: Simulating Aerodynamic Properties

The team wanted to run simulations of the various aerodynamic devices such as the front wing, the rear wing, and the undertray that could be used by a formula-style race car. The goals were to:

• Obtain the downforce and drag values of the aero devices.
• Optimize these devices by pursuing the iterative design and analysis process.
• Observe the varying downforce and drag values along with pressure and velocity contours and streamlines.

Through CAE and namely SimScale, Formula Manipal was able to keep an eye on the future by predicting accurate results in most cases by using their physics models. This saved a lot of time, money, and resources before going to wind tunnel testing and proceeding to manufacturing.

How They Solved It With SimScale: CFD Simulation

“We used CFD, particularly the “incompressible flow analysis” module inside SimScale. This included the meshing and simulations of the full car with aerodynamic devices.”

The basic setup involved using CATIA V5 for CAD by making use of wireframe surface design, part design, and assembly design. SimScale aided the team through not only the meshing of the domain along with the race car but also through the CFD analysis. The meshing was done facing a trade-off between high-quality mesh (finer feature, surface refinements, boundary layer, etc.) and conserving core hours.

“With the help of SimScale, we were able to do the required simulations, both with better quality and commensurate quantity.”

The team was unable to run other CFD software as they would require more computation power which was not being met by their system’s requirement. This hindered their analysis process, allowing no way out of this problem. SimScale was the only software that provided Formula Manipal with cloud computation, which helped them finally overcome this issue efficiently.

Results and Next Steps

In total, the team ran 43 simulations on the rear wing, 93 on the front wing and undertray, and 11 full-body simulations. It took 208 minutes for the meshing to be completed, and 380 minutes for the simulation of one full-body that involves rotating tires.

Formula Manipal obtained the pressure contours on several sections using the “Cutting Plane” option in SimScale, which helped in the analysis of the aero devices in the essential sections.

“Due to the computation power made available by SimScale, it was possible to meet the deadlines before our design freeze and move to the manufacturing phase without facing any delays. It also helped us to optimize the designs a lot in the limited time available. It gave us a huge boost in our design and analysis process.”

The current goal for the aero team is to work on the cornering simulation of the full-body, as the aerodynamics is beneficial in cornering. This is a big step for the Formula Manipal team, as the car which wins the corner wins the race. Future designs will be based on the outcome of these simulations.

If your team is interested in an academic sponsorship to enhance the performance of your vehicle, no matter if it is in Formula Student or any other competition that we sponsor, make sure to check out the Academic Plan.

The post SimScale Student Success Story: Formula Manipal appeared first on SimScale.