The automotive industry today is led by consumer perception, understanding, and expectations. With sustainability, AI, and efficiency being the reigning themes in almost every discussion, consumers are becoming increasingly aware, inquisitive, and critical of the vehicles they are purchasing, especially the younger, technically savvy generations. This is enabling particular technologies to rise in popularity and drive the market for at least the next decade.

These emerging technologies have led OEMs, and in turn Tier 1 suppliers, to buckle down and rush to optimize their processes so as not to miss out on market share. As consumer behavior gradually changes, vehicle sales are changing, too – determined by product quality [1], sustainability, purchasing experience, and brand. Moreover, these changes have gone further upstream to impact the design, manufacture, and supply of automotive parts and systems. That is why Tier 1 suppliers are racing to increase engineering efficiency and accelerate innovation.

This is where digital solutions like engineering simulation can play a significant role. And by leveraging the power of cloud computing and AI, the impact of cloud-based simulation on innovation and engineering efficiency can be significant. But let us first take a look at the automotive industry today and find out what trends and technologies are driving the market today.

The Four Trends Driving the Industry: ACES

As we are stepping into a new age of technology led by AI, digitalization, and sustainability, the automotive industry is going through a major transformation. Industry leaders are venturing into new avenues of design and manufacture, incorporating cutting-edge software and digital solutions that can help them keep up with the competition and the ever-growing demand.

As a result, multiple trends have emerged, which not only stem from technological advancements but also consumer perception of and willingness to adopt these technologies. The Center of Automotive Research (CAR) calls these trends ACES, which is an acronym for autonomous, connected, electric, and shared vehicles. Although we have seen major developments in other application areas, such as Hydrogen vehicles (which we will cover in another article), the ACES trends seem to have taken the lead, according to industry experts.

“[ACES] will enable new mobility paradigms, new companies, and new business and revenue models that have the potential to alter the way consumers interact with vehicles.”

Center of Automotive Research (CAR)

1. Autonomous Driving

Autonomous vehicle (AV) technology has emerged as a transformative force in the automotive industry, driven by advancements in AI, sensors, and connectivity. Not only will AV change the way cars navigate the roads, but it will also have a significant impact on the design, manufacture, and supply of components and systems.

“15% of new cars sold in 2030 could be fully autonomous.”

McKinsey & Co. ^[2]

AV will result in a shift from traditional automotive components to advanced sensor systems, computing platforms, and communication devices, which would primarily impact Tier 1 suppliers. As vehicles become more autonomous, the demand for sophisticated sensors, such as LiDAR, radar, and cameras rises exponentially. Tier 1 suppliers need to adapt their product portfolios and the manner by which they design their products to meet the evolving needs of OEMs. For this, they must find solutions that would enhance their engineering efficiency and boost their innovation.

2. Connectivity

The advent of vehicle connectivity has made it possible to process data from a wide range of sensors located at the edge and in the cloud. As a result, the volume of data that connected cars are expected to generate will increase as autonomous driving increases.

“Data traffic from connected vehicles is expected to be over 1,000 times the present volume, exceeding 10 exabytes per month by 2025.”

T-Mobile ^[3]

As such, mobility will change as connectivity is further enabled, especially with the advent of machine learning (ML) and edge AI applications.

3. Electric Vehicles

EVs are arguably the most impactful and pivotal trends in the automotive industry today, marked by a departure from traditional internal combustion engines towards cleaner, sustainable alternatives. The electrification of vehicles is driven by a growing global emphasis on environmental sustainability and the need to reduce carbon emissions. As governments worldwide implement stringent emission standards, the automotive landscape is witnessing a surge in EV adoption.

“Over 55% of all new car sales could be fully electrified by 2030.”

PwC Autofacts ^[4]

With lower battery costs, increasingly available charging infrastructure, and growing consumer approval, EVs will continue to have a strong impetus in the market in the near future. This includes its hybrid, battery-electric, plug-in, and fuel-cell-powered vehicles. In order for automotive suppliers to succeed with EVs, they need to leverage partnerships, especially with service providers that are leading the technological landscape. They need to invest in solutions that enable them to accelerate their innovation and production.

4. Shared Mobility

Motivated by efficiency, sustainability, and social inclusivity, shared mobility has begun to take shape over recent years. Although consumers by large still want to hold on to their private vehicles, many are gradually embracing the concept of shared mobility. In other words, mobility becomes an on-demand service in the form of car sharing, ride sharing, or mobility as a service (MaaS).

“40% of consumers expect to commute using shared mobility services in the next 5-10 years.”

Ericsson ^[5]

This not only is economically viable, especially with the introduction of autonomous vehicles, but it could also lead consumers to become consistently aware of technological advances, given the short life cycles of shared mobility solutions. This would put pressure on OEMs and, in turn, Tier 1 suppliers to augment the upgradability of the vehicles and their technological features.

Race of the Decade: How to Lead the Auto Supply Market

With EVs, AVs, connectivity, and shared mobility driving the automotive industry, suppliers are required to innovate faster while increasing engineering efficiency in order to maintain their competitive edge.

To achieve both requirements, suppliers and OEMs need to transform their engineering workflow, starting as early as the design stage. This can happen by fully digitalizing the design and engineering system and enabling teams to collaborate quickly through multiple design iterations. As such, teams should leverage simulation as a decision-making catalyst that underpins the whole design process.

Here at SimScale, we believe simulating early and broad can transform the product design process as it would inform design decisions efficiently rather than merely validate designs at the end of every cycle.

“The key advantage of using SimScale for us is to extract fast design insights at the early stages. We can then arrive at a final design faster and have more confidence when moving to the physical prototyping stage.”

Massimo Savi, ITW

First to Cloud-Native Simulation Wins

In order to accelerate new product introduction while lifting engineering efficiency on legacy products, OEMs and automotive suppliers must iterate through design runs more quickly and efficiently. For that, they need to incorporate digital solutions that can minimize costs and maximize the speed of sound decision-making. Therefore, those who adopt cloud-native engineering simulation will be one step ahead of the competition.

“Companies that invest 25% of their R&D budget in software applications are rewarded with strong growth.”

PwC Autofacts ^[4]

With SimScale’s next-generation simulation that leverages the power of cloud computing and AI, automotive suppliers can circumvent the long lead times and siloed approach of legacy simulation. They can employ aggressive simulation by utilizing parallel testing, which can reduce lead time from weeks to minutes.

Contact us below for more information on how to adopt cloud-native simulation in your workflow, or visit SimScale’s Product Overview section to have a look for yourself.

The post The Automotive Race to Innovation & Efficiency appeared first on SimScale.

In this article, we will delve into the intricacies of drafting in racing, shedding light on how it works and the speeds at which it is most effective. We will also highlight the effects via a small CFD study using the SimScale simulation platform to visualize and capture the effects at different trailing distances.

What is Drafting in Racing?

Drafting refers to a strategic racing technique where a vehicle closely follows behind another, taking advantage of reduced air resistance or drag created by the lead vehicle. This aerodynamic phenomenon allows the trailing vehicle to experience a decrease in wind resistance, enabling it to achieve higher speeds or improve fuel efficiency compared to running independently.

Drafting is also interchangeably referred to as slipstreaming. Effective drafting requires a delicate balance between proximity to the lead vehicle and maintaining control, as getting too close can result in turbulent air and compromise stability.

How Does Drafting Work?

Drafting exploits a key principle of viscous, bluff-body fluid dynamics called “Boundary Layer Separation.” This phenomenon occurs when the airflow over an object loses its ability to follow the contour of the object’s surface and separates away from it, creating a turbulent and chaotic wake. This turbulent wake leads to reduced pressure or a vacuum region on the rear of the vehicle, creating a drag force that acts against the vehicle’s motion and necessitates additional energy to overcome. The net effect is a reduction in fuel (or electrical energy) efficiency or, in the case of a race car, a reduction in top speed.

When a following vehicle slips into this turbulent wake, its nose augments this wake, and the tandem vehicles will start to behave more like a longer, single aerodynamic body. Depending on the distance between the vehicles, the wake of the first car can be nearly eliminated. The corresponding pressure changes reduce the drag force on both vehicles compared to when traveling alone (raised rear pressure on the lead vehicle and reduced nose pressure on the trailing vehicle).

Thus, the overall convoy experiences significantly reduced air resistance, enabling it to achieve higher top speeds and more fuel efficiency with the same power output. Adding more cars into the drafting situation (as is typical at the Indianapolis 500 or on a NASCAR superspeedway, such as Daytona or Talladega) can further amplify the effect for the whole platoon. Also, drafting can be used by a trailing car to gain a short closing boost, which can be exploited to “slingshot” with momentum to overtake the lead car upon braking and corner entry, as is common in F1.

Other Effects of Drafting

Dirty Air

Drafting can also cause a very pronounced aerodynamic force balance shift. When you hear a race car driver say that they were in “dirty air” or had the “air taken off of their nose,” they are referring to this shift in aerodynamic force balance. In essence, the center of pressure (or neutral moment point) moves front to rear (and possibly laterally and vertically) in response to the net change in pressure due to the draft. Too much of a shift rearward for the trailing car results in an understeer condition. The lead car may see the opposite effect, with a reduction in rear spoiler or underbody downforce, leading to an oversteer condition.

Overheating

Aerodynamic performance is not the only consequence of this “dirty air.” Getting adequate air cooling can often be a big issue for the following car. Drivers are constantly pushing hard and putting mechanical components, like the engine and brakes, to their thermal limits. When the total pressure (or ability to do work) of the air is greatly reduced to the trailing car, it starts to affect all of the cooling systems, which are designed for “clean” airflow; the radiators would not be able to work sufficiently, the airflow going through the brake ducts would be insufficient, and EV battery cooling would be suboptimal, etc. All of that causes overheating, and the drivers have to generally back off to manage those systems.

At What Speed Does Drafting Work?

The effectiveness of drafting hinges on multiple factors, including the overall velocity of both the lead and trailing vehicles, the spacing between them, and the shape of the vehicles involved. Drafting is most potent at higher speeds, generally exceeding 50 mph (80 km/h). At these velocities, the aerodynamic forces become more prominent, and the advantages of drafting become more pronounced.

Let’s take a closer look at the drag force equation below. Here, we can see that drag is proportional to the velocity squared, so a pair of race cars traveling 200 mph (~ 320 km/h) see 16x more drag than one at 50 mph (80 km/h) highway speeds. This greatly magnifies the drag change due to drafting.

$$ D = \frac{1}{2} \rho V^2 C_D A $$

where:

• \(D\) is the drag force acting on the car,
• \(\rho\) is the air density,
• \(V\) is the relative velocity between the vehicle and air,
• \(C_D\) is the drag coefficient,
• \(A\) is the reference surface area of the vehicle.

The overall aerodynamic shape of the vehicles and any aerodynamic devices (splitter, spoiler, wings, etc.) also greatly affect their drafting ability. Race vehicles can often be very dependent on the performance of these discrete aero components, so augmenting the airflow they feel can have an abrupt and often undesirable effect on the handling balance. This is especially true when cars are cornering and grip-limited. Here, you will often hear the drivers complain about the ‘dirty air’. Motorsports-sanctioning bodies are always exploring aerodynamic packages and overall car designs to limit this sensitivity, as it hampers competition and overtaking.

In addition to the vehicle shape and features, the ground clearance and underbody design elements (such as the diffuser) are also critical drivers of drafting performance. The low pressure suction produced by the underfloor is very sensitive to the airflow ingested and expelled. When a car is trailing another in the draft, the lead car effectively uses up the energy of the oncoming air and leaves much less to drive the underfloor performance of the trail car. Again, this can have detrimental effects on the handling balance and reduce the maximum grip the trailing car has available when cornering.

Simulation Analysis: Analyzing Aerodynamic Drafting Using CFD

To gain deeper insights into the intricate dynamics of drafting, engineers have turned to computational fluid dynamics (CFD) simulations, usually instead of wind tunnel experiments. This choice is often driven by the high costs of wind tunnels, but in this case, the overall physical size limitations of most wind tunnels prohibit multi-car testing.

CFD provides a platform to accurately predict and analyze how tandem cars will behave as they approach one another. A map of various relative positions can be explored to understand the handling effects, and steps can be taken to optimize drafting performance. Furthermore, engineers can understand why these changes are happening by visualizing the airflow, which would accelerate the cars’ development and inform design changes. This powerful tool empowers engineers and designers to foresee the performance of their designs under different conditions and optimize them before hitting the track.

Aerodynamic Drafting in SimScale

On the SimScale platform, there are two different CFD modules that could be employed to simulate external vehicle aerodynamics and assess drafting performance. The Incompressible module utilizes the computationally efficient and practical finite volume approach (FVM), using the Reynolds Averaged Navier-Stokes (RANS) k-w SST turbulence model, which is prevalent in industry. The other approach leverages the advanced Incompressible Lattice Boltzmann Method (LBM), which can quickly solve high-fidelity, transient turbulence utilizing the power of GPUs.

Generally, LBM is the better option with regard to accuracy (particularly in the rear wake), scalability, and geometry robustness. Nowadays, DES and IDDES turbulence modeling (as is deployed in the LBM solver) is considered state-of-the-practice for accurate external vehicle aerodynamics simulations. However, if a quick early screening is all that is required, a simplified model using the Incompressible RANS approach still has merit.

A drafting study was conducted in SimScale using the geometry from the 2019 Formula 1 regulations, as shown in Figure 5. This geometry was imported from a dirty .stl surface mesh directly into the platform. The poor quality of this starting geometry is not an issue for the LBM module, as it is able to handle non-manifold surface mesh geometries in this format.

A single-car simulation was first conducted to get baseline values for drag, lift (downforce), and lift balance coefficients and a corresponding surface pressure plot. This “virtual wind tunnel” CFD simulation was conducted at 180 mph (~ 290 km/h) and assumed a rolling road and spinning tires via a rotational wall velocity. Force coefficients are summarized in Table 1 below.

The vehicle was shown to have a relatively high drag coefficient (\(C_D\)) of 0.805, which is expected for a race vehicle. The downforce was much lower than would be expected for an F1 racer, with a \(C_L\) of only -0.945. Also, the aerodynamic balance is heavily biased towards the rear, with a 14 to 86% front/rear balance. These differences are mainly driven by inaccuracies in the CAD model, particularly around the aero devices, ride heights, and interior structures. This serves to highlight the sensitivity of aerodynamic design.

For this study, it is more interesting to explore the aero differences once an identical second car is introduced into the draft, at a trailing distance of 1 wheelbase (~ 3.5 m). This is shown in Figure 6. The tandem pair is still traveling at 180 mph (~ 290 km/h), so this would be akin to slipstreaming down a straightaway just prior to deciding to overtake under braking.

Here, we can assess the aerodynamic force and moment changes due to the drafting effect. In this scenario, the lead and trail cars see a -0.066 and -0.180 reduction in \(C_D\), respectively. This is a drastic drag reduction of more than 22% for the following car, compared to when traveling alone! When viewing the frontal surface pressure, it becomes apparent that the trail car will exhibit much less drag, as it sees much less overall static pressure to act against the forward motion. This is very evident on the wings, engine, duct inlets, and even the tires.

As a consequence of less activation of the aerodynamic devices (particularly the front and rear wings and the rear diffuser), the tandem cars experience an overall reduction in downforce. This is especially true for the trailing car, which sees its downforce cut down by more than half! It would be imperative for this driver to slip past the lead car and get into some fresh air under braking into the corner.

There is so much more that could be explored in this drafting CFD study, including the reduction in duct inlet flow, the effect of drafting distance, and the effects of stepping slightly out of the draft (just to name a few). You may try that for yourself and explore these effects by accessing, copying, and running this “F1 2019 Drafting Study” project in SimScale.

With its online CFD toolset, SimScale enables engineers and designers to easily simulate aerodynamic cases like this one early in the design process directly in their web browser without the hassle of hefty hardware and expensive prototyping. The scalable high-performance computing platform in SimScale enables automotive and motorsports aerodynamicists to quickly and easily conduct vehicle drafting CFD studies. Try it for yourself by clicking the “Start Simulating” button below.

The post Aerodynamic Drafting (Slipstreaming) in Racing appeared first on SimScale.

Design Build Fly (DBF) at UCLA, a team passionate about engineering, takes flight in the annual AIAA Design Build Fly competition, alongside 110 teams worldwide. Their mission involves crafting fixed-wing RC aircraft that master complex challenges such as carrying the longest possible wingtip-mounted antenna, delivering packages onto a section of the runway after each lap, or towing a banner. Despite their young talent, they landed 15th in the 2023 competition, hungry to soar even higher, aiming for a spot in the top 10.

“SimScale has changed our mindset in terms of CFD by allowing us to run significantly more simulations in significantly less time, and its cloud-based design has been a game-changer for teaching new members.”

– Aiden Taylor, Aerodynamics Lead (DBF)

The Design Build Fly team has a core of young and talented returning members who have experience in competition and are determined to improve. The rapidly growing team is hoping to push into the top 10 within the next couple of years.

Aircraft design involves tricky optimization issues. DBF at UCLA is dealing head-on with these issues as they refine their process to reach the top 10. This involves randomization scripts and genetic algorithms to optimize aircraft sizing and performance, vortex lattice models to determine stability, and detailed CFD analysis to optimize the form of the aircraft.

Prior to switching to SimScale, the team faced many computational and logistical issues. Most team members, as university students, used laptops for work. However, their laptops were slow for simulations with only about 6 CPU cores and 16-32 GB of RAM. This computational power could not even be fully dedicated to CFD, as university students often completed homework simultaneously on the same devices. Also, different devices and systems made it hard to work together because most CFD programs don’t run on both Windows and MacOS.

“SimScale is the CFD tool of choice for DBF at UCLA. It makes sharing and copying simulations easy, simplifying the process of teaching new members and reviewing their results. Since DBF at UCLA has adopted SimScale, the team has been able to run a much higher number of simulations in a short amount of time.”

– Aiden Taylor, Aerodynamics Lead (DBF)

To fix these problems, the team switched to SimScale at the start of the 2022-23 competition year. With SimScale, each student has received access to 16 cores in the cloud, more than what their laptops had. Simulations became available from any operating system simply through a web browser, and running them in the cloud made them faster and allowed multiple simulations at once, boosting the amount of analysis they could do.

The Problem: Formation of Vortices at the Wing Root

Using SimScale for CFD analysis has led to significant performance benefits for DBF at UCLA. For example, during flight testing of a prototype of the 2022-23 aircraft, it was determined that significantly less lift was produced than was expected and that there was a lack of control authority from the tail surfaces. Incompressible CFD analysis in SimScale revealed that leaving a gap between the two removable sides of the wing, rather than having one continuous wing, resulted in vortices forming at the wing root. These vortices not only reduced lift due to high-pressure air leaking into the low-pressure region above the wing but also disturbed the airflow over most of the span of the tail surfaces. 

“The CFD process has essentially been transformed from finding values for an existing design to an iterative process in which there is time to redesign parts multiple times based on CFD results. The team has also seen significant positive feedback from members regarding the learning process, as they are able to quickly share simulations, and they can access their work from any OS.”

– Aiden Taylor, Aerodynamics Lead (DBF)

How They Solved It: Adding a Wing Center Section and Improving Control

Based on the results from CFD, the team added a center section connecting the two halves of the wing in the region where it would not interfere with the landing gear. Post-processing results showed that the new center section of the wing significantly reduced the wing root vortices, causing cleaner flow over the tail surfaces, which resulted in better pitch and yaw authority. Furthermore, adding this lightweight component constructed of foam and carbon fiber resulted in an additional 4.62 lbf of lift at the aircraft’s cruise speed of 66 ft/s. This directly increased the team’s competition score by allowing the aircraft to carry more weight.

“We’re leveraging SimScale’s capacity to run multiple simulations concurrently in the cloud, enabling us to meet our simulation targets efficiently. Additionally, we’re sharing simulations with team members to adjust parameters and conduct similar studies with varying velocities or geometry.”

– Aiden Tayor, Aerodynamics Lead (DBF)

They’ve employed a Hex-dominant algorithm generating approximately 15 million nodes at a cost of around 30 core hours. Each simulation takes about 4 hours to run, at a cost of around 76 core hours. They examined the total lift and drag force produced by the entire aircraft, along with the specific lift and drag forces attributed to the wing, horizontal stabilizer, vertical stabilizer, and fuselage. Additionally, they gauged C_l and C_d. In certain simulations, they factored in lift and drag forces from other components, such as slotted flaps or landing gear wheel fairings.

Through ongoing refinement using the SimScale platform, they made impressive strides in their design and performance!

Next Steps for Design Build Fly

For the 2023-24 competition year, the team is going even more in-depth with their analysis. So far, they have run a number of design studies, including AoA sweeps at numerous air speeds to determine the lift and drag produced by the aircraft at different angles of attack, and to identify stall characteristics. Further analysis has been completed on the 2023-24 aircraft to test the effects of various subsystems. For example, the effect of endplates on the aircraft’s overall lift and drag has been determined during takeoff and cruise.

Another application of SimScale simulations to the 2023-24 DBF aircraft has been the optimization of wheel fairings to reduce drag caused by the landing gear. A series of designs were iteratively modeled in CAD and tested using CFD in SimScale. Compared to a wheel with no fairing, the wheel with the final iteration of fairing makes 60% less drag!

By constructing the fairings from a specialized lightweight, PLA plastic, the reduction in drag relative to the increase in weight is significant, justifying the addition of the fairings to the competition aircraft.

We’re confident that SimScale’s diverse simulation capabilities will greatly benefit the Design Build Fly 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 AIAA Design Build Fly competition or any other competition – make sure to check out our Academic Plan for students who are joining design competitions.

The post Design Build Fly Student Team: Student Success Story appeared first on SimScale.

Waseda Formula Team, a dedicated group of 20 members, competes in Formula Student Japan. This esteemed event gathers around 70 national teams alongside 30 international contenders. In 2022, they secured a commendable 6th place in Autocross, advancing to the Final 6 endurance event. In 2023, despite significantly improving their Autocross performance, they narrowly missed the Final 6 due to ongoing endurance challenges, showcasing their remarkable progress and determination.

“SimScale opened our engineering possibilities and revolutionized our workflow”.

-Ryu, Waseda Formula Team

The Waseda Formula Student team may be smaller in size compared to other teams in Formula Student, yet they shine brightly with an incredible sense of unity. Driven by their love for combustion vehicles, they delight in discovering fresh ways to get better even with limited resources.

The Problem: Engine overheating

While maintaining a steady pace in Autocross, the team encountered a significant challenge—endurance remained unconquered in both 2022 and 2023 due to persistent engine overheating issues. After further investigation, the root cause was identified: inadequate airflow through the radiator and suboptimal side pod design, hindering maximum cooling efficiency.

Addressing this obstacle demanded a deep analysis of airflow dynamics through the radiator and optimization of the side pod configuration for enhanced cooling capacity. For a team like theirs, limited in computational resources and CFD expertise, conducting a precise car simulation mirroring radiator characteristics and fan behavior proved challenging.

In their pursuit of a solution, the team found SimScale. Through a few simulation runs, they witnessed the impressive capabilities of the platform, realizing its potential to significantly aid their efforts.

“First, it allowed us to run a full model car simulation at ease, which normally took days to finish with our weak computer resources.  Second, it has one of the best user-friendly interfaces and rich supporting environment from the SimScale team. “

-Ryu, Waseda Formula Team

How They Solved It: SimScale Incompressible Simulations

The simulation of airflow around the FSAE car was set up based on the tutorial “Incompressible Flow around a Formula Student Car” provided by SimScale. The tutorial, which they found to be reliable and easily comprehensible, helped a smooth transition from the previous CFD software to SimScale. In their simulation runs, they used the incompressible simulation type with the k-omega SST turbulence model to simulate the car’s operation at 11 m/s. To mimic the radiator, Porous media: Darcy-Forchheimer medium was utilized, adopting coefficient values from the tutorial. For the radiator fan, they used Momentum sources: Fan model, integrating the performance data of the specific fan employed for their car.

“We were pleasantly surprised with the high capabilities of the SimScale CAD editing tool. We were able to switch to SimScale from previous CFD software without any problems whatsoever, and were surprised with the rich variations of CFD tools that were prepared.”

-Ryu, Waseda Formula Team

They’ve conducted approximately 30 simulations for radiator cooling analysis, each taking around 4 to 5 hours and consuming approximately 80 core hours. Their focus on development speed over quality during the initial design phase is reflected in their relatively lightweight mesh settings. They employed a standard meshing algorithm set at level 5 fineness and activated the hex mesh core settings, generating approximately 3 million nodes.

At this point, compared to the initiation of their project, they’ve achieved a notable 40% increase in radiator airflow—a figure aligned with their initial calculations. Obtaining accurate airflow volume data posed challenges with previous CFD software. Yet with SimScale, making use of the Cutting Plane and Statistics features made this task effortless. Moreover, the extensive customization options in result filters enabled them to precisely identify and comprehend issues in each simulation.

By continuously refining their design through the SimScale platform, they managed to achieve remarkable progress!

They noticed that SimScale improved the flexibility, productivity, and time efficiency of their aerodynamics development, completely transforming their engineering process. Within a short period, it allowed them to achieve their initial engineering goals.

“With SimScale, once the simulation begins, you’re free to close the tab and continue working on other tasks using your PC. We frequently created CAD models for other simulations concurrently while running SimScale. Plus, because the simulations are cloud-based and not limited to specific computer resources, they can be accessed and reviewed from any device and location. This significantly boosted team productivity, making CFD simulations available anytime and anywhere.”

-Ryu, Waseda Formula

Next Steps for Waseda Formula Student

The Waseda Formula Student team plans to persist in their efforts, continuing to further explore and refine the rapid rough design to final shape. Their aim is to enhance precision and elevate mesh quality for a more refined final product.

We are sure that the wide range of simulation capabilities within SimScale will be beneficial for the Waseda Formula Student team for future applications, and we are looking forward to cooperating with them in the future. If your team is also interested in an academic sponsorship to enhance the performance of your vehicle – no matter if it is in Formula Student or any other competition – make sure to check out our Academic Plan for students who are joining design competitions.

The post Waseda Formula Student: Student Success Story 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.

Tampere Formula Student is a motorsport designing team comprised of student members at Tampere Universities. In 2022, they competed in four different competitions and have recovered from their 6-week competition trip and are now ready for more challenges in 2023.

Tampere Formula Student team is relatively small, compared to other European teams. However, they possess an amazing team spirit and everyone in the team knows each other which helps the communication between departments. Their common passion is in combustion vehicles and they enjoy finding new ways to improve their performance with limited resources. They have team members from several grades, and their oldest teammates now are graduating and becoming alumni of the community after almost four years together. In the previous year’s recruitment event, they expanded their population with 1st grades as well. Since they’re always recruiting new students who need training in using CAE software, they stated that SimScale came in very handy considering the ease of use of the platform.

“Our aerodynamics department consists mostly of new team members, so SimScale’s simple interface and visual results helped new members to keep workflow and learn from the results.”

Samuli Harjula

The Problem: Underbody Design

Now that the Tampere Formula Student team has started designing next year’s aerodynamic design, they’ve faced some challenges with the efficiency of the underbody of their design. They inspected the design using post-processing tools within SimScale such as particle traces and iso-volumes, and soon they noticed that the upwash greatly reduced the amount of air flowing to the underbody intakes. The problem was that the underbody intakes were too low to use with the front wing so they extended the underbody’s intake much higher and redesigned the intakes’ overall shape. Although optimization of the design is still in progress, they’ve already learned a lot about underbody aerodynamics and behavior. The change in velocity contours and turbulent kinetic energy values for two different design alternatives can be seen below.

How They Solved It: SimScale Incompressible Simulations

In their SimScale simulation runs, they used the incompressible simulation type with the k-omega SST turbulence model. They studied aerodynamics in a free-flow region using boundary conditions such as velocity inlet, moving walls for the ground, and rotating walls for the tires to simulate a moving car. As underbody aerodynamics was unfamiliar with the team’s current aerodynamics department, they chose to pursue two different concepts simultaneously to keep their minds open in the early concept phase.

“SimScale’s server-based CFD software allowed us to keep working on two different projects simultaneously and additionally run multiple simulations at the same time. As having limited man hours, this was very helpful.”

Samuli Harjula

While performing design iterations, Tampere Formula Student team took advantage of Result Control items in order to monitor the forces not only on the entire car but also on individual parts of the car. In the end, they were able to increase the downforce by 200% at 10 m/s, and 250% at 20 m/s with the new design! Such a great difference was achieved by adding more wing profiles and iterating the underbody design within the SimScale platform.

Next Steps for Tampere Formula Student

Tampere Formula Student team is going to continue developing their aerodynamic package even further and inspect possible design alternatives. They are aiming to add more details and features to their simulation models to make them more detailed with the wheels, hubs, and a more realistic engine mockup. They are also interested in simulating brake discs and radiators in addition to aerodynamic devices. 

We are sure that the wide range of simulation capabilities within SimScale will be beneficial for the Tampere Formula Student team for future applications and we are looking forward to cooperating with them in the future. If your team is also interested in an academic sponsorship to enhance the performance of your vehicle, no matter if it is in Formula Student or any other competition, make sure to check out our Academic Plan for students who are joining design competitions.

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

Nearly all lithium batteries are required to comply with the UN 38.3 international transportation standard. Ever wondered why you cannot have your laptop or any electronic equipment stored in your check-in baggage while flying? Well, that’s because batteries are sensitive to low-pressure conditions due to potential leakage which may cause a fire. Batteries are also sensitive to shocks and vibration. Batteries are also sensitive to…well the list will keep going but no worries, we have the UN 38.3 standard that helps us mitigate those risks.

Under-pressurized situations are definitely not anything pleasant so let’s leave it for now and just focus on how simulation in the cloud can help us design better batteries from the vibration perspective. SimScale can be used to understand and improve battery product design to comply with vibration test standards relating to the transport of dangerous goods. SimScale helps designers and engineers create reliable battery systems by virtually replicating multiple vibration tests within their web browser. This article touches upon a virtual shaker table test of an electric vehicle (EV) battery module and its housing, trying to replicate the UN 38.3 T3 standard using SimScale.

Simulation Strategy and Setup Within SimScale

According to the UN 38.3 T3 requirements, a battery module should show no leakage, venting, rupture, or fire under a sinusoidal vibration test spanning from 7 Hz to 200 Hz. Key test details are summarized below:

• Sinusoidal vibration waveform
• Tests are to be performed in all X, Y, and Z directions
• Test range from 7 Hz up to 200 Hz
  • 7 Hz – 18 Hz: 1 G acceleration applied
  • 18 Hz – 50 Hz: Peak acceleration gradually raised to 8G at 50 Hz
  • 50 Hz – 200 Hz: Peak acceleration maintained at 8G until 200 Hz

Leveraging the SimScale platform, designers and engineers can perform structural analyses which will help them ensure that stresses on their product will not exceed certain limits and that deformation magnitudes will not entail any clearance issues. Let’s look a bit more in detail at how this process may look like and what a reasonable simulation strategy can be.

Using simulation, we would be interested to examine whether or not there are “resonance” modes for our components within the test frequency range (7 Hz to 200 Hz). If such modes exist, we would then be interested to see the response of our structure under the required loading conditions (acceleration 1G to 8G). The above strategy will help us identify whether or not there is a risk of resonant behavior and if yes, where the peak stresses and deformations are located. Knowing the above, we can then take corrective action through design changes to improve the performance of our battery product before having to go to the actual test bench in order to achieve compliance with the UN 38.3 standard.

CAD Upload

All simulations start from a CAD model. SimScale is CAD agnostic, meaning that it can handle almost any CAD model in its native format as well as neutral files. We can import our models into CAD mode, inspect them, do any modifications necessary, and then proceed with the simulation setup.

Setup

Since our CAD is ready, we are good to continue with the next steps. Setting up a simulation was never easier. Simply by picking the desired analysis type we get a simulation tree prepopulated in our workspace. All we have to do is define a few essential pieces of information for the simulation to run.

Modal Analysis

The first step is to conduct a Modal Analysis. This will help us identify any modes laying below 200 Hz. As the geometry is imported into the simulation tree, all bonded contacts are automatically identified. Special care should be taken in material definition. We can either pick materials from our prepopulated default material library or create our own customized materials and save them in our user materials for future use. The final step would be to fix our model, in this case at the holes located in the battery’s outer shell, and request the number of modes we need to calculate. Before hitting solve, we can choose to go with the robust automated meshing of SimScale or maybe add a couple of manual controls, such as Sweep Meshing which will help build higher quality elements reducing the overall cell count.

Running the frequency analysis for the battery module leads to no significant modes within the frequency range of interest. The first mode is at 233 Hz. On the other hand, the housing seems a bit more prone, as at least 3 resonance frequencies exist (72 Hz, 80 Hz, and 138 Hz). It looks like there is a higher chance that the housing might experience resonance during the vibration test.

Harmonic Analysis

To perform a Harmonic Analysis we follow a very similar procedure. The materials and boundary conditions definition are identical to the Modal Analysis. However, we now have to also include a Base Excitation covering the desired frequency range according to the UN 38.3 T3 requirements. This can be input in a table format to depict the actual test requirements.

The Harmonic Analysis should be performed in all three directions (X, Y, and Z). This is made extremely easy by taking advantage of the templated simulation approach offered by SimScale and since SimScale runs in the cloud, all three directions can be simulated in parallel.

Results

The Modal Analysis provided us with valuable information about the potential frequencies that are worth investigating. More specifically, the EV battery module has no eigenmodes within the test range, with the first eigenmode being at 233 Hz. The housing, though, has at least three eigenmodes below 200 Hz. The results can be nicely summarized and exported in table format.

Now, we are ready to examine the structural response of our components by checking the Harmonic Analysis results. Despite not having any eigenmode within the test range, with the closest one being at 233 Hz, it still might be worth having a look at the excitation results of the EV battery module at 200 Hz.

A maximum displacement of 1.8 mm is not worrying as it is not going to cause any clearance issues given the model tolerances. On the other hand, we can see some quite high peak stresses being developed near the mounting holes of the module. While these are within the material structural limits it may be worth having a deeper look to see whether those are real, or it may be a product of numerical errors, often called stress singularities.

Moving on with the housing, things get a little bit more “dangerous” since now there are significantly higher deformations and stresses present.

The structure is highly sensitive to z-axis excitation. Checking the displacement at 144 Hz, we get large portions of the model, especially in the center baffle, being deformed by more than 4 mm. Given the small overall size of the battery housing, 4 mm of deformation, may definitely increase the chances of structural damage within the battery components due to clearance issues. In addition to that, peak stress appears to be well above the yield point of aluminum (casing material), especially around the corner mounting holes. This is extremely worrying and we, as designers, should definitely take action and try to improve/stiffen the housing design before conducting any actual vibration tests.

SimScale Simulation Design Insights

SimScale has been helpful in simulating Modal and Harmonic analyses on two different designs related to an EV battery module. Battery modules need to meet certain requirements/pass certain tests before being released to the market. We have seen how we can successfully implement a structural analysis simulation strategy within SimScale and be able to extract valuable insights about our designs. These and many more simulation capabilities are available through your browser and SimScale platform.

Be sure to watch our on-demand webinar if you would like to know more about how engineers and designers can leverage cloud computing using SimScale to build successful and reliable products:

The post Transporting Dangerous Goods: Vibration Analysis of EV Batteries appeared first on SimScale.

This article presents a design and simulation study of battery cold plate technology for electric vehicles. Engineering simulation is used to perform a fully-coupled conjugate heat transfer analysis of a cold plate for dynamic thermal management. Furthermore, using an advanced Subsonic CFD solver, a design study is performed for evaluating pressure-flow characteristics across the cold plate flow channel. Parallel simulations in the cloud are used for scenario analysis both for geometric variants and multiple coolant flow rates. In this sample case, our simulation workflows show users how to set up and run a complete heat transfer and flow analysis of a cold plate, including pressure drop and temperature at various coolant flow rates. Engineers can follow this example to learn how to quickly complete a parametric design study in SimScale and answer key design questions. 

Reaching Optimum Battery Reliability with CFD and Heat Transfer

SimScale has many analysis types available depending on the application. In this example, two analysis types have been used to analyze battery cold plate optimization for electric vehicle batteries. The Subsonic analysis type in SimScale produces an automated and robust hexahedral cell mesh, using the body-fitted Cartesian meshing technique that significantly reduces mesh generation times by an order of magnitude. The highly parallelized meshing algorithm gives a higher quality mesh requiring much fewer cells to attain comparable accuracy to traditional discretization schemes. This leads to faster convergence and hence, faster simulations.

The Subsonic analysis type is used to simulate both incompressible and compressible flow, with turbulence modeled using the RANS equations and the k-epsilon turbulence model. A powerful feature of this analysis type is the built-in parametric capability for defining velocity inlet boundary conditions. At the simulation setup stage, users can define multiple inlet flow rates at once, that are then simulated simultaneously. The conjugate heat transfer (CHT) analysis type in SimScale enables heat transfer analysis between solid and fluid domains. Typical applications of CHT analysis type include analysis of heat exchangers, cooling of electronic equipment and electronics enclosures, and LED luminaire design. A fluid/solid mesh is required for a CHT simulation with clear definitions of the fluid and solid interfaces, also referred to as contacts. The mesh is automatically generated in SimScale, with local refinement available for advanced users. In the case of the battery cold plate, the subsonic analysis is used to simulate coolant flow through the cold plate channel to ascertain optimal flow rates with regard to pressure drop. The CHT solver is used to demonstrate heat exchanger effectiveness and thermal performance.

Improving Battery Cold Plate Design

The purpose of this study is to maximize the dynamic thermal management of a new battery cold plate design, used to cool electric vehicle battery packs. We will consider two versions of a cold plate heat exchanger. The original design (V1) is a common component widely used in industry and has a single serpentine cooling channel. The design is known to cause hotspots that lead to battery longevity issues. A thermal engineer has come up with a new design (V2) that uses a labyrinth cooling channel with a much-increased surface area for thermal energy exchange. This is an early-stage proof of concept design that requires simulations to validate its predicted performance and compare it with the original version. 

Using SimScale, an engineer can quickly and accurately assess both designs. Both battery cold plate designs have one inlet and one outlet. A velocity inlet is used to define the mass flow rate of the coolant (water) which is the primary heat removal mechanism for extracting heat from the batteries. The battery cold plate is assigned Aluminum from the SimScale materials library, water for the coolant, and a power source is applied to represent heat generated by the battery. When importing the V2 CAD file, associativity between cad files is applied automatically in SimScale, maintaining naming conventions for parts/faces from the V1 CAD model. This means that when swapping CAD files for comparative studies, users don’t have to reassign boundary conditions, mesh settings, or result control outputs, making comparing two or more CAD variants of a single product much faster. 

The heat transfer analysis shows that despite a larger contact surface area in V2, thermal results are much worse compared to the original V1 design, as V2 exhibits higher temperatures across the battery cold plate. The V2 design needs further simulation and optimization. To help resolve these counterintuitive results, the design engineer analyzes the flow in isolation.

Results of the Subsonic fluid flow analysis that takes just four minutes to solve, yields further insight. The flow in V1 maintains a steady flow of 3.5 m/s throughout the single channel. In the V2 design, highly varied coolant velocity is seen throughout the labyrinth channels. In many areas, almost stagnant flow is observed in the areas of peak temperature, hence the cooling channels are not effectively removing heat from those areas which leads to poorer thermal performance. We can use the fluid flow analysis to look purely at flow velocity to better understand pressure drop across the cold plate. The converged pressure drop across any two points can be extracted from SimScale using the results control menu. In this case, for a constant coolant flow rate of 0.13 kg/s, V1 has a 292 KPa pressure drop between inlet and outlet and, V2 is ten times less at 22.7 KPa. This might have some advantages if the thermal performance of V2 was comparable to V1, such as less pump power and hence energy needed to operate the pump. First, we need to try and increase its thermal performance, however. Since we have enough pressure drop to play with, we might try and increase the coolant flow through V2.

Using the parametric inputs in the Subsonic analysis type, we can specify multiple flow rates in the inlet boundary condition to run in parallel. Simscale will recognize that we have a parameterized study setup and all runs (all flow rates) are simulated in parallel in the cloud on SimScale servers. This frees up your local PC/laptop and does not consume machine resources. SimScale will send users an email notification when all the runs have been completed successfully. 

A higher mass flow rate of 0.3 kg/s gives increased flow velocities in V2 and improved thermal performance. The pressure drop across the cooling plate has increased considerably but is still 60% lower than the original V1 design. The new V2 cooling plate design has achieved a lower average temperature, although less uniform, with a higher flow rate but a much lower pressure drop than V1. Additional design iterations would be necessary to improve the temperature distribution and battery reliability further in V2. Engineers looking to optimize this battery cooling plate can parametrize the CAD model, coolant flow rate, and inlet temperatures for more detailed design studies. Swapping materials is also simple by using the extensive SimScale materials library.

Heat Transfer Simulation in the Cloud

Access to simulation in the early product design stages is essential to achieving the required thermal performance of battery cold plate designs, all while reducing physical prototyping costs. With the cloud-native SimScale platform engineers and designers can harness advanced solvers that account for coupled analyses of conduction, convection, and radiation to deliver accurate results across a variety of problem domains and scales. 

Watch our on-demand demo, Cooling Performance Optimization of Electric Vehicle Batteries, to see how you can quickly complete a parametric design study and run a complete thermal analysis of a cooling plate including pressure drop and temperature at various coolant flow rates:

The post Simulating and Optimizing an Electric Vehicle Battery Cold Plate appeared first on SimScale.

This article describes the structural assessment and vibration analysis of an electric motor support bracket to calculate and verify if its critical response frequencies lie within the intended operating range. In addition to the vibration analysis, we show engineers how to perform a load analysis on the electric motor shaft under an applied torque. The design goal of this simulation study is to optimize the electric motor support bracket to ensure that its eigenfrequencies remain outside of the motors’ operating speeds, avoiding possible part damage, bolt loosening, and reduction in clearances between parts and unwanted noise. In many cases, there is a danger that resonance effects might excite vibrations in the support bracket or other components. Meticulously analyzing this uncertainty is needed to reduce the risk of unwanted issues and, simulation at the early stages provides this guarantee. Geometry changes are made to mitigate dangerous vibration at shaft speeds close to the calculated first eigenfrequencies of the support bracket. Note, that the motor in question has a fixed rotational speed. Vibration analysis is even more important when variable speed drive motors are used for example and more in-depth studies are needed in those cases. We can also perform a quick safety factor check of the motor shaft under an applied torque to represent real-world loads and ensure a minimum safety factor of two is validated under normal operating conditions. A factor of two is a good starting point as it means the stresses are half of the yield stress of the shaft material (mild steel), which is getting close to the material endurance and performance limits that affect fatigue and component life. The entire engineering simulation workflow is implemented via a web browser and performed in the cloud.

Fast and Accurate Structural Simulation

The FEA capabilities in SimScale enable engineers to virtually test and predict the behavior of structures and components, and to solve complex engineering problems subjected to static and dynamic loading conditions. SimScale uses scalable numerical methods that can calculate physical variables otherwise very challenging due to complex loading, geometries, or material properties. Another structural analysis software feature, modal (frequency) analysis can help determine the eigenfrequencies (eigenvalues) and eigenmodes (mode shapes) of a structure due to vibration. The results are important parameters to understand and simulate structures and products that are subject to unsteady conditions. The resulting frequencies and deformation modes are dependent on the geometry and material distribution of the structure, with or without the displacement constraints. A static analysis type allows time-invariant calculation of displacements, stresses, and strains in one or multiple solid bodies and the results are a consequence of the applied constraints and loads. SimScale has integrated the Code_Aster solver into the platform for frequency and static analysis simulations. A much broader study can use a multidisciplinary physics approach to simulate and optimize the loading, modal, thermal, and rotating aspects of the electric motor. In this example, we will focus on the vibration analysis of the electric motor support bracket and load stresses on the motor shaft.

Vibration Analysis of an Electric Motor Support Bracket

A simple workflow is required to complete the analysis for both the support bracket and shaft.

Import CAD – Users can import many types of common CAD file formats and use CAD connectors with tools such as Onshape, Solidworks, AutoCAD, and more. The feature CAD mode in SimScale allows users to perform basic CAD operations for editing without leaving the platform. The motor geometry is imported from Onshape. The number of faces increases with support bracket geometry modifications with a total of six bracket CAD variants defined. The motor shaft is also a single volume. 

Simulation set up & mesh for the support bracket – The bracket has a frequency analysis type selected in SimScale. The mesh is generated automatically with 23.5K cells and the element sizing is set to automatic which takes the geometry fineness into account when deciding size. In this case, a moderately fine mesh has been chosen (the user has manual control over mesh settings). A point mass boundary condition is applied to represent the mass/moments of inertia of the whole motor eliminating the need to include the fully detailed motor geometry in the simulation. Our goal is to find the first eigenmode of the support bracket that needs the least energy and is thus the easiest mode to excite. An eigenfrequency plot is generated automatically.  

Simulation set up and mesh for the shaft  – The motor shaft has a static (linear) analysis applied to it and from the materials library, we have selected mild steel as the primary material with linear elastic behavior. In a static analysis, we can define constraints and loads. The shaft has fixed support connected to the motor and the main shaft can be deformed due to remote load forces that represent applied torque using moments around the center of rotation. Deformable here means that the shaft is allowed to deform without applying additional stiffness. Undeformable would mean the shaft was a rigid entity which is not what we want. A centrifugal force is applied to the whole volume of the shaft and a rotational speed is set in radians/second. Automatic mesh settings are again used and a second-order tetrahedral mesh is generated for the shaft with 170K cells.

Post-processing – The SimScale platform’s field results allow us to visualize the deformed shape for each mode showing the displacement magnitude in meters. Statistical data is available to further interrogate the eigenmode number, eigenfrequency, Modal Effective Mass (MEM), Normalized Modal Effective Mass, and cumulative Normalized Modal Effective Mass (CNME). Multiple plots are pre-populated showing the eigenfrequency plot, modal effective mass, and accumulated normalized modal effective mass. From the static analysis, we can evaluate standard outputs such as Von Mises and Cauchy stresses, displacement, strain, and a new output variable that is the safety factor.

Design Insights

Bracket Vibration Analysis

The support bracket vibration assessment shows us that the first eigenfrequency (21.8 Hz) of the original bracket (CAD 1) design is identified as being dangerous as it is too close to the shaft rotation speed equivalent in Hz (19 Hz), causing dangerous resonance effects and possibly leading to severe deformation. This could easily excite the first eigenmode just by running the motor and the excitation could cause resonance in the bracket itself —the displacement would move the motor beyond operating parameters and could cause damage. Although the first eigenmode is the most important to reconcile, different eigenmodes cause more displacement and deformation. They can all be visualized in SimScale. Geometry optimization by defining several CAD variants (1-6) of the support bracket can be simulated in parallel to achieve successful inflation of the first eigenfrequency value, away from the shaft speed. The full results are shown in the table below:

Shaft Safety Factor

The shaft structural integrity is simulated under real-world operating conditions, ensuring that the factor of safety did not drop below 2. Under two loading conditions with a nominal torque of 0.5 Nm and 10 Nm respectively we found the following:

1. Load Case 1 – Nominal Torque of 0.5 Nm
   1. Results show a peak stress value 4.01 MPa.
   2. Results show a minimum factor of safety of 62.35, achieving the design goal.
2. Load Case 2 – Maximum Torque of 10 Nm
   1. Results show a peak stress value 80.19 MPa. 
   2. Results show a minimum factor of safety of 3.118, achieving the design goal.

Engineers must embed structural simulation in the cloud early in their product development processes to ensure that performance of critical design parameters is met using sound engineering analysis. Using parametric analysis, engineers can increase their confidence that the final product will operate and function within expected limits.

The post Vibration Analysis of an Electric Motor Bracket appeared first on SimScale.