HUD informs drivers of their speed, the speed limit, next turn and more — without forcing them to move their eyes off the road.

Any fan of Iron Man knows the power of an effective head-up display (HUD). But you don’t need to be fighting Thanos to benefit from a HUD.

In fact, HUDs can become a significant enabler of advanced driver assistance systems (ADAS) in the automotive industry.

An effective HUD can inform a driver without forcing them to move their eyes off the road. It can show:

• Car speed.
• The road’s speed limit.
• The next turn.
• Traffic.
• Lane suggestions.

For engineers, the challenge is to design a display that doesn’t force the driver to change focus. As a result, the information must remain legible under all road conditions. Simulation can help engineers design HUDs for any condition.

Simulation Optimizes a HUD’s Housing for a Crisp Image

HUDs can become large. The larger they are the more light can sneak in and affect the perceived quality of the image.

Engineers achieve an optimal projected distance for the display by mounting a considerably large device on the dashboard.

The problem is that the larger the device, the greater the unwanted light that manages to creep into the optics.

This unwanted light tends to scatter and cause light artifacts that render the HUD image fuzzy. This fuzzy image could affect the perceived quality of the HUD device.

To address this issue, HUD housings, glare shields and light traps are used to reject these stray beams of light. Optimizing these stray light rejection methods using trial-and-error would likely become too costly. ANSYS SPEOS software can be used to assess these traps and shields during early product development.

HUD housing comparison using classic black plastic (left) and ultrablack coating (right).

As an example, simulation results show that using a classic black plastic housing could still create a fuzzy HUD image.

However, simulation shows that using an ultrablack coating creates a much crisper image.

There are many other factors that can affect the image of a HUD. To learn more, read ANSYS SPEOS Capabilities: HUD and Analysis.

Or, watch the webinar Take the lead on the HUD revolution: windshield as a key optical during its Sept. 27, 10 a.m. (CEST) or 5 p.m. (CEST) timeslot.

The post Simulation Compares How HUD Housings Affect Image Quality appeared first on ANSYS.

Additive manufacturing makes designing a lattice structure for a bracket possible. However, does it have a chance of success? Simulation can tell you.

Traditional prototype techniques take weeks and cost a considerable amount of a product development budget.

It’s true that additive manufacturing has reduced the time and cost to build prototypes. However, the near-instant simulation capabilities of ANSYS Discovery Live can speed up prototyping labs even more.

“Traditional simulation tools are hard to use, take a long time to produce results and are too expensive to be used on a regular basis,” says Tejas Rao, manager of ANSYS’ Discovery Live Technical Team.

“Discovery Live, however, offers instantaneous real-time simulation in an extremely easy-to-use multiphysics interface,” Rao adds. “It also includes integrated geometry manipulation. This means you can do simulation on your own in seconds.”

So how can Discovery Live help a prototyping lab?

Imagine your design team sends you five designs to print. Printing all five is a waste. Instead, import the designs into Discovery Live. In seconds, you can narrow down which designs are worthy of prototyping.

Tip: Test Lattice Structures in Simulation before Additive Manufacturing

Simulation of this lattice part gives engineers an idea how it will perform before printing and prototyping it.

Additive manufacturing makes it possible to drastically reduce a product’s weight by creating unique shapes and forms.

Optimizing these designs through lattice structures — and subsequent testing of their performance — is an important step that is often done by trial and error.

Assessing a lattice’s performance using traditional simulation takes a long time to set up and solve. Additionally, spending time printing models with lattice structures without having an idea of the design’s performance and likelihood of success could be a waste of time.

Alternatively, simulating a model containing a lattice using Discovery live will quickly give you an idea of how it will perform. “The goal here isn’t the highest accuracy possible,” says Rao. “The goal here is to understand the basic flow field.”

Understanding that flow field will help the prototyping lab assess the likelihood of success for a design before they print the prototype. They can then concentrate on printing the lattices they know have a chance of success.

Watch ANSYS Discovery Live for Additive Manufacturing Applications to learn more ways to accelerate your prototyping using simulation software and PNY’s NVIDIA graphics processing units (GPU).

The post Near-instant Simulations Speed Up Prototyping and Additive Manufacturing Labs appeared first on ANSYS.

Learning simulation is a great way to get ahead in the engineering job search.

Simulation is ubiquitous throughout industry, but it wasn’t always that way. What started as a tool for the aerospace and automotive sectors is now spawning an engineering job everywhere.

“You can get an engineering job simulating hair dryers, refrigerators even surgical equipment. It’s really universal,” says Gilles Eggenspieler, Academic Program sales director at ANSYS.

“Companies tell us that they look for two things in the engineers they hire,” adds Eggenspieler. “Did they build anything, and do they have any special skills?”

Many graduates and young engineering professionals have skills similar to their peers. This isn’t surprising as engineering curricula are very similar from college to college. But these colleges tend not to teach simulation.

“Universities know the value of simulation for research and often teach simulation in graduate schools,” says Eggenspieler. “But not all use simulation in a pervasive manner in their engineering undergrad curriculum. Simulation can be used in capstone projects and many engineering classes.

“Nonetheless, many students learn simulation thanks to online learning tools, webinars and free student downloads,” adds Eggenspieler. “When adding simulation skills to their resumes, they already are a step ahead in finding an engineering job.”

Simulation Basics to Land Your Dream Engineering Job

CFD analysis to optimize the aerodynamics of a race car.

So, what are the simulation basics engineers should know before entering the workforce

First there are many types of simulation software.

Students looking to place an antenna on a drone need to learn electromagnetic simulation software.

As for students working to design a car for a competition, they need to make the parts as light as possible while maintaining structural integrity. That will require finite element analysis (FEA).

Students might also want to ensure their car is aerodynamic. That will require computational fluid dynamics (CFD) software.

Many of these simulation techniques can also be linked together in multiphysics problems where the results of one simulation feed into another linearly or iteratively.

Try to learn which parts of the design should be simulated and what type of simulation software you need to model the appropriate physics. You will also need to learn which assumptions need to be made for the simulation to run efficiently.

When working on a multiphysics problem, Eggenspieler suggests that you don’t start simulating everything at once. Concentrate on simulating one physics at a time and work your way up. This will make it easier to fix any bugs that might come up in the model.

“In the meantime, you should learn about simulation terminology,” says Eggenspieler. “This allows students to learn simulation, demonstrate their skills and be able to share their results with both engineers and simulation experts.

“Most importantly, you need to learn how to interpret the results of the simulation,” stresses Eggenspieler. “Do your results make sense or is something wrong with the simulation like a typo in the boundary condition? What is the simulation teaching you? How can you use the results to improve your design? Can you perform more simulations to iterate toward a new design?”

How to Learn Simulation to Land an Engineering Career

There are a lot of options online to learn simulation

Now the question is where do you gain a basic understanding of simulation?

“Where do you start? That depends on your interests,” says Eggenspieler. “Some engineers specialize in one topic — like an electrical engineer who is an expert in electromagnetic simulations but has only basic knowledge of structural simulations. Other engineers will broaden their horizons and specialize in simulating multiphysics applications.”

A great way to learn simulation is by jumping into it. There are many courses, training videos and best practices online. You can access them from the ANSYS Student Community.

“Students should use simulation in conjunction with a project they have, like a research project, student team or capstone project,” suggest Eggenspieler. “Use what you have to build and apply the simulation, so you have real world experience solving problems. You can then relate your simulation results to what you designed and iterate further to optimize the design.

“Every company wants makers,” adds Eggenspieler. “I talked to an aerospace company recently that only hires people who build stuff. They said hiring people from classes only was something you did in the year 2000, it’s very theoretical. And to build stuff optimally you need simulation.”

To help get your feel wet with simulation, check out a live series of free online webinars offered by ANSYS. To sign up, follow these links:

• Simulation for electric vehicles.
• Fluid dynamics simulation.
• Structural analysis simulation.
• Simulation for autonomous vehicles.

The post How to Prepare for Engineering Jobs in Simulation appeared first on ANSYS.

Simulations make the model patient. They volunteer for any clinical trial, save lives and tell us a lot about medical procedures.

The healthcare industry is slow to innovate. One cure can wait for decades of clinical trials in the time it takes to design 20 distinct iPhone models. The only way healthcare providers can deliver the promise of personalized medicine is with simulation.

“Leading companies have made great innovations to save lives, but many are never realized as treatments because it’s too expensive to provide the required evidence to get them approved by regulatory authorities,” says Thierry Marchal, global industry director for healthcare at ANSYS.

It’s a frightening thought — the only thing between a patient and a known cure sitting in someone’s lab is years — sometimes decades — of testing.

You can barely blame the pharmaceutical companies. If they come out with a new drug that cures millions but seriously endangers one due to an allergic reaction what would the headlines say?

Some could also be tempted to blame the Food and Drug Administration (FDA) for approving a treatment that fails only once. So, the FDA might tighten the regulatory process and require the testing of another 1,000 people — just to be safe. With this increased cost many companies may think twice and opt to scrap promising treatments.

But if it takes this long to validate treatments, how is personalized medicine possible?

A key to personalized medicine is to validate treatments based on computer models of patients by partially replacing clinical trials with in silico clinical trials. In silico clinical trials are run on computers using a large cohort of virtual patients. The researchers can run thousands of simulations in the computer to ensure that a new treatment is safe and beneficial for the target population.

Simulations Create Clinical Trials of Personalized Medicine

Pressure wall simulation of a cardio system. Courtesy of Sheffield Teaching Hospitals NHS Foundation Trust.

“The biggest problems with lab testing and clinical trials is that they take a long time and create models that are not totally accurate,” says Marchal. “You can test 50,000 people but you will never be able to capture the variability of the 7.4 billion people on this planet.”

Simulation, on the other hand, is much more affordable, faster and versatile.

You can run a decade-long traditional clinical trial on 1,000 people — hoping the data applies to everyone.

Or, you can perform computed tomography (CT) scans of you, your children, your parents, your neighbors and your aunt Betty. You then create a computer model for everyone in your network and run tens of thousands of simulations on this large digital population to ensure that none of them would suffer from the treatment if they needed it in the real life.

That is the beauty of a properly verified and validated (V&V) in silico clinical trial. It could run a simulated clinical trial on thousands of virtual patient in weeks without risking any human or animal.

These simulations will be able to show how some treatments would improve your aunt Betty’s condition, worsen her condition, trigger any side effects, maintain her health for the future or lead to dramatic consequences.

The regulatory authorities and the government won’t go after you if a hundred digital Betty clones suffer to save the life of a real-world Betty. They won’t be as kind if the real Betty’s condition worsened dramatically due to ignorance about your medical product’s unfortunate interaction with her specific body.

Simulations Can Verify Treatments for Whole Populations

Simulation of an aortic valve by ADMEDES and CADFEM.

Simulations can go far beyond traditional clinical trials. What if it could ensure that a treatment will be safe for all 7.4 billion people on this planet and anyone who may live in the next 2,000 years? This is theoretically possible.

Let’s say you know the historic world record for the largest and smallest human heart. You can use this data to determine the variations of key parameters, or material properties, of the organ.

You can use this data in a parameter study to verify a treatment for every heart that lands between those world records.

At this point, you can effectively verify that the procedure would work for everyone without a clinical trial.

“In the future, I think we may completely replace clinical trials for numerous cases,” says Marchal. “This is already discussed in medical conferences now that the industry and regulatory bodies are gaining confidence in simulation. We are not there yet, but the seed has been planted. Today’s in silico clinical trials are already saving a lot of money for the industry — they also help manufacturers release treatments years faster.”

Since clinical testing could cost up to $40,000 a person, even partially replacing clinical trials with simulation could save tens of millions of dollars. It’s beginning to happen already. Marchal explains that companies have reduced their clinical trials by hundreds, even thousands, of people based on simulation results.

Regulatory bodies are also accepting these computer models as a form of validation — assuming the V&V of the models and simulations. In fact, that might be the biggest hurdle to the dream of personalized medicine — validating every simulation and model to a level acceptable to the regulatory authorities.

Clearly, as Marchal points out, the FDA and other regulatory organizations are starting to embrace simulations. So, he might be right. Perhaps it’s only a matter of time and commitment until most clinical trials are a thing of the past.

Click here to learn other ways simulation is seeding innovation into the healthcare industry.

Banner image:

A comparison between simulation results and per operative in vivo data. The accuracy of in silico results gave surgeons confidence to routinely use the model to plan surgeries. Courtesy of CHU Hospital Rennes (France).

The post How Simulation Will Unlock the Dream of Personalized Medicine appeared first on ANSYS.

This engine intake simulation was post-processed using EnSight Enterprise. This allowed for the processing of a large data set to be shared among servers.

Simulation data sets have a funny habit of ballooning as engineers move through the development cycle. At some point, post-processing these data sets on a single machine becomes impractical.

Engineers can speed up post-processing by spatially or temporally decomposing large data sets so they can be post-processed across numerous servers.

The idea is to utilize the idle compute nodes you used to run the solver in parallel to now run the post-processing in parallel.

In ANSYS 19.2 Ensight Enterprise you can spatially or temporally decompose data sets. Ensignt Enterprise is an updated version of EnSight HPC.

Post-Processing Using Spatial Decomposition

EnSight is a client/server architecture. The client program takes care of the graphical user interface (GUI) and rendering operations, while the server program loads the data, creates parts, extracts features and calculates results.

If your model is too large to post-process on a single machine, you can utilize the spatial decomposed parallel operation to assign each spatial partition to its own EnSight Server. A good server-to-model ratio is one server for every 50 million elements.

Each EnSight Server can be located on a separate compute node on any compute resource you’d like. This allows engineers to utilize the memory and processing power of heterogeneous high-performance computing (HPC) resources for data set post-processing.

The engineers effectively split the large data set up into pieces with each piece assigned to its own compute resource. This dramatically increases the data set sizes you can load and process.

Once you have loaded the model into EnSight Enterprise, there are no additional changes to your workflow, experience or operations.

Post-Processing Using Temporal Decomposition

Keep in mind that this decomposition concept can also be applied to transient data sets. In this case, the dataset is split up temporally rather than spatially. In this scenario, each server receives its own set of time steps.

A turbulence simulation created using EnSight Enterprise post-processing

EnSight Enterprise offers performance gains when the server operations outweigh the communication and rendering time of each time step. Since it’s hard to predict network communication or rendering workloads, you can’t easily create a guiding principle for the server-to-model ratio.

However, you might want to use a few servers when your model has more than 10 million elements and over a hundred time steps. This will help keep the processing load of each server to a moderate level.

How EnSight Speeds Up the Post-Processing of Large Simulation Data Sets

Another good tip to ensure you are post-processed optimally within EnSight Enterprise. Engineers achieve the best performance gains by pre-decomposing the data and locating it locally to the compute resources they anticipate using. Ideally, this data should be in EnSight Case format.

To learn more, check out Ensight or register for the webinar Analyze, Visualize and Communicate Your Simulation Data with ANSYS EnSight.

The post Post-Processing Large Simulation Data Sets Quickly Over Multiple Servers appeared first on ANSYS.

Illini EV Concept Team Photo at Shell Eco Marathon 2018

Weight is the enemy of all teams that design electric cars for the Shell Eco Marathon.

Reducing the weight of electric cars improves the vehicle’s acceleration and power efficiency. These performance improvements make all the difference come race day.

However, if the car’s weight is reduced too much, it could lead to safety concerns.

Illini EV Concept (Illini) is a Shell Eco Marathon team out of the University of Illinois. Team members use ANSYS academic research software to optimize the chassis of their electric car without compromising safety.

Where to Start When Reducing the Weight of Electric Cars?

Front bump composite failure under a load of 2000N.

The first hurdle of the Shell Eco Marathon is an initial efficiency contest. Only the best teams from this efficiency assessment even make it into the race.

Therefore, Illini concentrates on reducing the most weight in the shortest amount of time to ensure it makes it to the starting line.

Illini notes that its focus is on reducing the weight of its electric car’s chassis.

“The chassis is by far the heaviest component of our car, so ANSYS was used extensively to help design our first carbon fiber monocoque chassis,” says Richard Mauge, body and chassis leader for Illini.

“Several loading conditions were tested to ensure the chassis was stiff enough and the carbon fiber did not fail using the composite failure tool,” he adds.

Competition regulations ensure the safety of all team members. These regulations state that each team must prove that their car is safe under various conditions. Simulation is a great tool to prove a design is within safety tolerances.

“One of these tests included ensuring the bulkhead could withstand a 700 N load in all directions, per competition regulations,” says Mauge. If the teams’ electric car designs can’t survive this simulation come race day, then their cars are not racing.

Iterate and Optimize the Design of Electronic Cars with Simulation

Front bump deformation under a load of 2000N.

Simulations can do more than prove a design is safe. They can also help to optimize designs.

Illini uses what it learns from simulation to optimize the geometry of its electric car’s chassis.

The team found that its new designs have a torsional rigidity increase around 100 percent. This is after a 15 percent decrease in weight compared to last year’s model.

“Simulations ensure that the chassis is safe enough for our driver. It also proved that the chassis is lighter and stiffer than ever before. ANSYS composite analysis gave us the confidence to move forward with our radical chassis redesign,” notes Mauge.

The story optimization story continues from Illini. It plans to explore easier and more cost-effective ways to manufacture carbon fiber parts. For instance, the team wants to replace the core of its parts with foam and increase the number of bonded pieces.

If team members just go with their gut on these hunches, they could find themselves scratching their heads when something goes wrong. However, with simulations, the team makes better informed decisions about its redesigns and manufacturing process.

To get started with simulation, try our free student download. For student teams that need to solve in-depth problems, check out our software sponsorship program.

The post How to Increase the Acceleration and Efficiency of Electric Cars for the Shell Eco Marathon appeared first on ANSYS.