When designing heavy equipment such as bucket loaders, truck bodies and diggers, finite element analysis tools, such as ANSYS, are a ‘must-have’ in any design engineer toolkit in order to assess the structural integrity of designs and ensure their durability and performance. But while FEA will provide engineers with a wide range of tools for setting up meshes, joints, and boundary conditions, there is one thing missing in this analysis: the bulk material itself that the machine is supposed to handle! DEM (Discreet Element Method) offers additional capabilities to account for bulk materials.
As electronic devices become smaller and more ubiquitous, the printed circuit boards and components that drive them face increasing power densities and evermore complexity. To ensure product reliability and performance, accurate and detailed analysis methodologies are necessary. In a three-part series, Mike Bak and I will discuss modeling approaches for the thermo-mechanical analysis of printed circuit boards and their components. In part one of this series, I will cover modeling approaches for the PCB itself.
A typical PCB will have multiple layers, each one having its own distribution of FR-4 and copper traces and vias. Take the board layout shown in Figure 1 as an example, which has over 16,000 traces and vias across 7 layers. The complex board geometry leads to spatially varying material properties (i.e. modulus of elasticity, density, thermal conductivity, etc.) that must be accurately specified by the analyst for any type of simulation.
Figure 1: Typical PCB Layout Geometry
So, what are some ways that we can model this type of geometry? I’ve outlined below some common approaches: Continue reading
It has been nearly two months since we unveiled ANSYS Discovery Live to the public and made it freely available for download. Discovery Live is the first ever real-time engineering simulation software available to all engineers. Since that time, many things have happened that has made this launch a tremendous success. I’d like to share some of those with you today, and make you aware of some exciting opportunities.
Behind ANSYS developing Discovery Live was the firm belief in the power of simulation and its benefits for everyone. The ability to accurately predict a product’s performance as part of the validation stage, or make adjustments to models to simulate products already in the field are examples of pervasive engineering simulation. But what Discovery Live has done is further advance the reach of simulation to the upfront design exploration stage. ANSYS has had a passion for helping engineers in this space for some time, and Discovery Live represents a true milestone for making this happen even more than it already has. Continue reading
Electronics are everywhere. Amazing innovations such as driver assistance systems (ADAS), IoT, 5G communications, hybrid propulsion and others all depend on electronics. Engineers and designers in almost every industry, must account for electromagnetic fields to design, optimize and deliver products quickly to market.
As radio frequency (RF) and wireless communications components are integrated into compact packages to meet smaller footprint requirements while improving power efficiency, electromagnetic field simulation is the only way to make these trade-offs. Simulation enables innovative ideas, that can push products beyond their traditional limits, to be tested and realized without the burden of prototype costs and time.
The latest issue of ANSYS Advantage features articles from industry leaders who make the most of electromagnetic field simulation to develop next-generation products and deliver them to market quickly.
Have you ever relaxed on the patio on a beautiful autumn day while using your mobile phone to talk to a friend, stream some relaxing music over the phone’s WiFi connection and maybe use the built-in GPS location capability while you map out your next family road trip, all at the same time?
Just think about how amazing it is that you can do all of that — and more — with a device that you hold in the palm of your hand. Your mobile phone has more computing power than the computers that put man on the moon, and more wireless connectivity than we would have thought possible less than a generation ago!
As designs increase in complexity to cater to the insatiable need for more compute power spurred by different AI applications ranging from data centers to self-driving cars, designers are constantly faced with the challenge of meeting the elusive PPA (Power Performance and Area) targets.
PPA over-design has repercussions resulting in increased product cost as well as potential missed schedules with no guarantee of product success. Advanced SoCs pack more functionality and performance which result in higher power density. Traditional approaches of uniformly over-designing the power grid which has worked in the past is no longer an option with routing resources becoming severely constrained. To add to these woes, there are hundreds of combinations of PVT corners to solve for along with the increasing number of applications. Continue reading
Topology optimization has been around for last 20-25 years, however only recently got more attention due to improvements made in additive manufacturing and 3D printing processes (DMLS (DMLM), EBM, SLM, SLS). More importantly, simulation driven topology optimization is rekindled due to more cost effective availability of almost infinite compute capacity in the form of GPUs, TPUs and cloud which makes it easier than ever to iterate over design choices. Modern topology optimization is mixed with machine learning to learn aesthetic styles and further complement the design by volumes of simulation.
ANSYS took its first step in ANSYS 18.0 in the context of ANSYS Mechanical and now it is expanded to the designer community through ANSYS AIM addressing primarily two key issues: abstracting the mechanics of simulation with eager program controlled setup followed by embedded experience with automated geometry reconstruction. You can organically design parts from a single block of material or improve an existing design, both workflows are fully supported and where possible automated. Continue reading
Great products are composed of great individual components that are increasingly assessed from every possible physical perspective. But as you probably know, optimally designed components do not necessarily result in optimal systems. Eventually, the components are assembled, powered, sensed and controlled as an integrated system, and must therefore be simulated as a system to meet peak performance requirements and stringent safety standards. But building and testing integrated product systems and subsystems can be costly and may not identify optimal configurations and/or potential shortcomings. Systems simulation can help to overcome this challenge. Continue reading
In 2014, Student Space Systems (SSS) began at the University of Illinois at Urbana-Champaign as a high-powered rocketry group. In those early days, most of the rocket building was done simply with prefabricated parts. Since then, SSS has progressed to designing and creating its own rocket technology, including power electronics, telemetry and propulsion systems. One of its biggest goals — and challenges — has been to create a liquid-fueled rocket engine built with additive manufacturing techniques.
SSS members prepare Olympus rocket for flight in Mojave Desert Continue reading
Anil Kumar (Senior Engineer – ANSYS) and I thought it would be interesting to share information about integrating ANSYS super-element with the GENESIS structural optimization extension for ANSYS. With ever-increasing computational power, engineers can solve larger FE models in less time. However, optimization is still a serious concern because it is an iterative process and the FE analysis usually needs to be performed multiple times.
Typically, the parts that engineers choose to optimize are only a subset of large assemblies. For example, when optimizing the chassis, the engines and other components attached to it are not designed at the same time. It is not necessary to model all the details of those components not participating in the optimization.