Unveiling Engineering Insights: A Professional Guide to Mastering Data Analysis with SIMULIA Isight

In the ever-evolving realm of engineering simulation, the need for sophisticated tools that automate and optimize the design process has reached a crucial point. SIMULIA Isight from Dassault Systèmes  is a potent simulation process automation and design optimization software. This blog post unfolds a strategic walkthrough, unravelling the indispensable steps to harness Isight’s prowess for impactful data analysis in engineering projects.

Defining the Simulation Process

It starts with meticulously evaluating the engineering objectives. Then, identify the specific simulations or analyses that Isight will automate or optimize.Understanding stress analysis, fluid flow simulation, and thermal studies is crucial for a successful workflow using SIMULIA Isight. Understanding stress analysis, fluid flow simulation, and thermal studies is crucial for a successful workflow using SIMULIA Isight.This can be demonstrated through a complex engineering problem involving hyperelastic materials such as a rubber bush, highlighting an optimization-based approach using parametric data analysis withIsight

Integrating Simulation Tools

Isight excels at integrating various simulation tools seamlessly into one unified environment. By establishing connections with specific tools such as Abaqus or other third-party software, this integration ensures a cohesive workflow. It enables smooth data transfer between these tools, ultimately boosting efficiency and accuracy in the overall process.

Creating a Workflow

Creating a logical workflow is key to making the most of Isight.This includes outlining the precise sequence Isight will follow to execute simulations seamlessly. It encompasses detailing the transfer of input information among various simulation tools to establish a streamlined and automated simulation procedure. Isight’s intuitive interface facilitates the visual design of workflows, making it accessible to both seasoned engineers and those new to simulation process automation.

Case Studies: Hyperelastic Material

Hyperelastic materials, also termed green elastic materials, possess the unique ability to undergo significant elastic deformations and revert to their original shape upon load removal. These materials, often described using a strain-energy density function like the neo-Hookean model, are used in fields such as biomechanics, rubber-like substances, and the mechanics of soft tissues.In engineering simulations, accurately modelling hyperelastic materials is vital for predicting responses to large deformations, making tools like Isight crucial for design optimization and simulation automation involving such materials.

Step 1: A parametric file was crafted in Abaqus, followed by analyses under diverse loading conditions such as axial, radial, conical, and torsional loads. All associated files, including CAE and ODB files, were consolidated in a single folder.

 

Step 2: Defining Design Variables – In projects geared towards optimization, pinpoint the design variables that Isight will manipulate to achieve desired outcomes. These variables could include material properties, geometric parameters, or any other factors influencing your simulation. Set constraints and allowable ranges, guiding Isight in its optimization process. In our case, geometrical parameters were defined rather than material inputs, as illustrated in the below snapshot of the DOE Editor windows with parameters defined.

 

Step 3: Setting Up Design of Experiments (DOE) – Efficiently navigate the parameter space by definingDesign of Experiments. Isight helps by letting you systematically change input values to check many scenarios.You can specify the number of simulations and the range of values for each variable, enabling Isight to navigate the design space. Different components can be aligned either parallelly or in series for data flow and execution. In our methodology, two Abaqus components were utilized for different loading conditions and physics, and Isight performed the Design of Experiment using optimal Latin hypercube methodology.

Step 4: Running Simulations – Withmeticulously designed workflow in place, execute the Isight workflow and witness the seamless automation unfold. Isight automates simulations with specified parameters, saving valuable time and reducing the likelihood of manual errors. Once the DOE Study is complete, all the results can be saved and further utilized for approximation studies.

Analyzing Results

Upon completion of simulations, Isight equips engineers with robust tools for result analysis. They can visualize data, generate plots, and extract meaningful insights from the simulation results. Isight’s post-processing capabilities empower engineers to delve deep into the system’s behaviour and performance.

 

 

Optimization

For projects focused on optimization, Isight automatically adjusts design variables to meet predefined objectives. Results can be reviewed, improvements can be assessed and iterated further if necessary.

Iterate and Refine

Isight’s flexibility allows for iterative refinement, enabling engineers to progressively enhance their simulation process.

Documentation and Reporting

A step often overlooked is comprehensive documentation. Isight enables the generation of detailed reports covering the simulation process, results, and any optimizations achieved. These reports serve as invaluable resources for communication with project stakeholders, offering a clear overview of the analysis methodology and outcomes.

By following these steps, unlock the full power of Isight, automating and optimizing your engineering simulations. This, in turn, drives efficiency and innovation in your projects. Stay tuned for more insights into the evolving landscape of simulation technology.

Overcoming Electric Vehicle Design Challenges with SaberRD

Introduction: Addressing Electric Vehicle Design Challenges

Designing electric vehicles (EVs) comes with unique challenges, from optimizing battery performance to ensuring efficient power distribution. However, most of these hurdles can be overcome with the right tools and technologies, paving the way for a more sustainable future. In partnership with Synopsys, EDS Technologies offers SaberRD, which addresses some specific design challenges EV manufacturers face. In this blog, we will discuss some challenges and explore key features that can address these challenges.

 

The Complexities of Electric Vehicle Design

Designing electric vehicles brings new challenges compared to traditional combustion-engine vehicles. The complexities lie in the powertrain and battery systems and other crucial components such as motor controllers, sensors, and charging infrastructure.

 

Driving Range

One of the primary concerns in EV design is range anxiety. EV manufacturers strive to extend the range of their vehicles to alleviate customer concerns about running out of power. Achieving a balance between range, battery size, and weight is a delicate task that requires advanced modellingand simulation tools.

 

Charging infrastructure

In the future, we expect improved charging infrastructure and faster chargers to make electric vehicles (EVs) competitive with gas cars. Long-distance travel poses a challenge due to sparse charging stations along routes. While expanding this infrastructure requires significant investment, daily recharging in home garages, workplaces, and commercial areas could eliminate the need for regular stops at filling stations for EV drivers.

 

Reliability

Ensuring the reliability of powertrain elements like the battery, motor, and power electronics while in use poses a significant challenge for engineers in powertrain design. These components are susceptible to various environmental stressors, including temperature fluctuations and mechanical impacts. Designers of automotive power ICs prioritize meticulous design and manufacturing of integrated power devices. The effectiveness of thermal management systems is crucial in ensuring the efficient and dependable operation of e-powertrain components. Suppliers and original equipment manufacturers (OEMs) must carefully consider material properties and the non-uniform distribution of current, voltage, magnetic flux, and component temperature. The performance of a single component can significantly affect the distribution of flux in others.

 

Introducing Synopsys SaberRD: The Solution to EV Design Challenges

The Saber® platform by Synopsys offers robust capabilities in design, modeling, and simulation to analyze and validate system interactions spanning various physical domains thoroughly. Saber encompasses an extensive array of models and utilities designed for simulating Hybrid Electric Vehicle (HEV) systems, encompassing:

  • Motors (utilizing both analytical and Finite Element Analysis (FEA)-based models)
  • Power devices such as IGBTs, MOSFETs, and BJTs
  • Batteries, ultracapacitors, and charging systems
  • Inverters, DC/DC converters, switches, speed controllers, and capacitors
  • Mechanical components

 

Robust Design and Electric Vehicle Design Challenges

A comprehensive design approach, known as robust design, is critical in enhancing vehicle safety and reliability. This approach ensures that reliability concerns are integrated into the design process itself. Design teams rely on robust design methodologies to effectively handle and enhance complex system interactions, particularly when faced with operational and environmental variations. This makes such methods ideal for the development of hybrid and electric vehicles. The following outlines a typical flow of robust design.

Moreover, SaberRD provides advanced analytics and visualization tools that allow engineers to effectively interpret and communicate simulation results. This facilitates collaboration and decision-making throughout the design process.

  • Simulate the complete system: Capture all the device effects and multi-domain interactions critical to power system design
  • High accuracy results, faster: Robust simulation technology and distributed processing capabilities come standard with SaberRD
  • Design for robustness and reliability: Built-in capability for analyzing effects of variation, parameter sensitivity, worst-case behaviours, faults and more

In conclusion, the key features and benefits of SaberRD position it as the ultimate solution for overcoming design challenges faced by the electric vehicle industry. In the next section, we will explore how SaberRD integrates seamlessly into the existing design workflow, making it easily accessible and adaptable for manufacturers.

 

Case Studies: Success Stories of Overcoming Design Challenges with SaberRD

One of the most compelling aspects of SaberRD is its proven track record in helping manufacturers overcome electric vehicle design challenges. In this section, we will delve into a few case studies that highlight the real-world benefits of using SaberRD.

Case Study 1: Optimizing Battery Performance

An electric vehicle manufacturer struggled to maximise their vehicles’ range while ensuring optimal battery performance. By utilizing SaberRD’s comprehensive modelling and simulation capabilities, engineers could analyse various factors accurately, such as battery capacity, voltage levels, and power distribution. With this information, they could fine-tune the battery system, resulting in vehicles that offered an extended range without compromising overall performance.

Case Study 2: Enhancing Vehicle Safety

Safety is paramount in the electric vehicle industry, and one manufacturer faced challenges in detecting and mitigating potential electrical faults. With SaberRD, engineers could simulate numerous safety scenarios and fault analyses, stress-test the electrical system, and identify potential weaknesses. By implementing necessary improvements, such as redundant safety features and enhanced insulation, the manufacturer significantly improved the overall safety of their electric vehicles.

Conclusion:  SaberRD for EV

In conclusion, SaberRD has proven to be a game-changer in the electric vehicle industry, enabling manufacturers to overcome various design challenges. Through case studies focused on optimizing battery performance and enhancing vehicle safety, we have seen the real-world benefits of utilizing SaberRD’s modelling and simulation capabilities.

By using SaberRD, manufacturers can design high-performance, safe, and sustainable electric vehicles. The seamless integration of SaberRD into the existing design workflow, with its user-friendly interface and compatibility with industry standards, makes it an invaluable tool for engineers.

 

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