Workshops

Description
It seems there is a spectrum in FEA world. On one end, you can learn mathematics related to FEA on an expert level, and on the other end you can learn to use the software with most sophisticated options. But for me, it's a triangle, and at the top of it is the actual understanding on how things work, and what you're trying to achieve. This seems to be often omitted in how we teach FEA (and engineering in general).

In this workshop I want to dive deeper into this idea, and have a discussion about this.

Description
Virtualization of engineering has the potential to address strategic goals like reduction of time-to-market, managing an increasing product complexity and variety, or raising the flexibility to react on the market. Companies can realize it at various levels. But how shall a successful transformation look like?

The workshop aims to provide an impulse for successful engineering virtualization and to identify potentials and fields of activity by analyzing and discussing industry examples with the participants. It will cover key aspects such as strategic goals, boundary conditions, process, organization, technology, data, or mindset. Depending on the discussion, experience-based recommendations will be added to the conclusion.

Description
In this workshop we will inform interested participants about the work and tasks of the NAFEMS Technical Working Groups and how to become a member.

Description
Engineering Simulation is a crucial capability for developing, refining and optimizing products. It is therefore, vital that organisations engineering simulation capability is fast and efficient, but can also be trusted to provide reliable results to enable important engineering decisions to be made with confidence. This is especially true with the increased use of AI techniques, as simulation is a critical source of trustworthy data used to train AI models. Is your engineering simulation capability ready?

Engineering Simulation is like a complex machine. It depends on many components working together in harmony to be effective, efficient, and reliable. To assess the status of their Engineering Simulation, organisations can benefit from conducting a Maturity Assessment. This enables them to identify their strengths, but importantly to identify areas to target improvement across.

This mini workshop will present an approach and framework that organisations can use to assess the status of their organisations engineering simulation capability, addressing the following topics:

- Organisation Goals
- Core Components of Engineering Simulation
- Simulation Maturity Assessment Types
- Approach to conducting a Maturity Assessment
- An Example Maturity Framework
- Some Practicalities
- Taking Action
- The Benefits of Conducting a Maturity Assessment
Share your experiences and learn from others via interactive mini surveys and workshop discussions:
- Your goals and challenges
- Your experiences and lessons learnt
- Benefits of maturity assessment
Andy is a Fellow of the Institution of Mechanical Engineers. He has over 30 yrs experience in Automotive Engineering at Jaguar Land Rover, with 20 years at Senior Management level, including 10 years as Head of Engineering Simulation.
In 2019 formed PHRONESIM Ltd to provide consulting in all aspects of Engineering Simulation Strategy helping multinational organisations with; simulation capability maturity assessment, simulation strategy development, improvement roadmaps, and simulation strategy training and coaching.
Andy is a member of the NAFEMS UK Steering Committee, Assess Business Theme, and the Simulation Governance and Management Working Group .

Description
AIAA has recently published a recommended practice for code verification for CFD, R-141-2021, and is now working on the publication of a full new international standard. This standard will be the first international standard for code verification (to the authors knowledge) in any field of engineering simulation and, as such, it represents an important step forward in terms of how to achieve confidence in the simulation tools that the NAFEMS membership uses.

Of key importance is the concept of the observed order of accuracy. Code verification requires the error in a simulation to be quantitatively evaluated, which by implication requires the solution to the simulation to be known. The error is calculated on a series of progressively refined meshes so that the trend in the error with the spatial discretization can be determined. Specifically, this trend is plotted on a log log graph and the observed trend should conform to a straight line, the gradient of which is the observed order of accuracy. This should then be compared with the formal order of accuracy claimed by the developer of the code.

NAFEMS SGM and CFD working groups have been working on some code verification exemplars to demonstrate the processes set out by AIAA: specifically, they are real cases using real commercial software tools where the observed order of accuracy is found not to correspond to the formal order claimed by the software developer. In each case, the reason for the discrepancy is explored and identified, so that suitable modifications to how the software tool is used can be made, and the tool can be used with confidence. The exemplars demonstrate the essence of the new standard and why considering the observed order of accuracy is important. Whilst the focus of the standard is on the performance of the simulation solver, as the exemplars show, considering the observed order of accuracy can also identify potential issues with the pre and post processing steps.

Description
Members will present and discuss current activities of the MPWG. There will be members onsite, while a couple of us will be joining online.

Starting with an introduction of the working group, we will present and discuss topics like next events (e.g., the Multiphysics Conference in 2026) and publications (e.g., The NAFEMS Journal of Multiphysics Case Studies '“ Volume 2). Additionally, we will discuss with the audience the future of Multiphysics simulation.

Description
Automation is a very strong trend in engineering and FEA calculations. We try to automate as many things as possible, simply to make them easier/faster/cheaper to do.

But can engineering design be really automated, and to what extent this is possible? Also, what would be the consequences of this? Or to put it differently, is engineering an art, or a procedure?

In this workshop we will dive deep into this issue, and have a discussion about this.

Description
Introduction (10 min.)
Peter Froeschle (CEO ARENA2036)

DigiTain - Digitalization for Sustainability (40 min.)
Processes, Methods and Models for the Fully Digital Product Development of Sustainable Electric Drive Architectures

? Digital Methods & Models - Overview of Applied and Developed Methods (20 min)
Dr. - Ing Karsten Keller (University of Stuttgart - ISD)
? Digital Certification - Status of Homologation / Certification by Analysis for Crashworthiness (20 min)
Prof. Dr. -Ing André Haufe (Dynamore / Ansys)

AI Act & TEF AI Matters Services (40 min.)
Muhammad Saeed (ARENA2036)

Key takeaways include:
? Understanding the AI Act's impact on engineering and simulation workflows
? Exploring TEF AI Matters services for AI validation and deployment
? Engaging in discussions on AI compliance, governance, and risk mitigation

This workshop is ideal for engineers, researchers, and decision-makers looking to align their AI-driven innovations with European regulations while benefiting from TEF AI's expertise.

Description
In Manufacturing Process Simulation, we use multi-scale, multi-physics models to obtain high-fidelity predictions of the transformations in the material during the production of metal or composite parts. This often requires material data for which there is no standardised way of testing. Also, the material modelling and manufacturing process simulation approaches that exploit these data to predict what will happen during manufacturing, are rarely standardised.

The consequence of this for industry is low re-use of material data and modelling: in complex supply chains, material characterisations are often repeated and models often created again from scratch. Worse: opportunities to use simulation are lost because people are not confident re-using existing data and models and therefore simply refrain from using simulation.

The objective of this session is to reflect on the current state of standardisation in this particular field, and identify opportunities to improve things. The discussion will be started by four short presentations.

The need for standardisation for manufacturing process simulation in the aerospace industry
Sjoerd van der Veen (Airbus)

Due to a lack of standardisation today, material characterisations are often repeated and models often created again from scratch. Worse: opportunities to use simulation are lost because people are not confident in re-using existing data and models and therefore simply refrain from using simulation.

Development of Modelling Guidelines for Welding in the Nuclear Industry
Paull Hurell (Amentum), Michael Roy (TWI)

Weld modelling guidelines and measurement validation benchmarks are a valuable part of the analyst's toolkit, to aid good decision making. This presentation describes weld simulation examples for austenitic and ferritic steels, including comparisons with experimental measurements, developed for the civil nuclear industry.

Generation, validation and management of material data and models in composites process simulation
Goran Fernlund (Convergent Manufacturing Technologies), Martin Roy, Alastair McKee (Convergent Manufacturing Technologies)

Process simulation often falls short of its full potential due to the absence of established and efficient methods. To unlock its capabilities, we must transition from 'one-off artistry' to standardized, validated workflows. This presentation explores the current state of composites process simulation, focusing on the generation, validation, and management of material data and models. It also highlights future opportunities and pathways to achieve them.

NAFEMS Benchmark Examples of Buckling Occurrence, Residual Stresses and Bending Distortion in Additive Manufacturing
Yongle Sun (Cranfield), Oliver Found (TWI), Anas Yaghi (TWI)

An important part of standardisation procedures is to make available representative benchmark examples to the modelling and simulation community. In this presentation, we provide two benchmark examples that compare experimental measurements and numerical simulations of additive manufacturing processes.

Description
During our 'œSpeaking of Simulation Live '“ Machine Learning' session at the NAFEMS World Congress, we will discuss how Machine Learning (ML) can help engineers to tackle everyday challenges. Experts will share practical examples of ML speeding up simulations and saving time. We will discuss how blending physics-based and data-driven models can offer real benefits, but also examine where such approaches can run into problems.

Our speakers will talk about what it takes to prepare teams for ML, handle data concerns, and ensure that new tools are trustworthy. We will aim to highlight cases where ML has worked well, while also looking at issues like black-box models and the risk of overconfidence.

Attendees can expect a clear view of how ML can support simulation engineers with tasks such as setting up models or analysing results. We will be realistic about the effort involved in adopting these methods, focusing on why validation and careful planning are so important. This session aims to share real-world experience on how ML can help with practical engineering needs, without losing sight of its limits.

We look forward to an open conversation about how to make the best use of ML in engineering simulation.

Description
Product verification and validation constitutes a major share of the overall product development effort in many industries. This workshop will be an open discussion about the benefits, changing role and increasing significance of simulation in the V&V of engineered products.
Before the emergence of computer simulation, the verification and validation of engineered products relied on physical testing. The main benefit of simulation was initially seen as speeding up product development in a “first time right” paradigm. In this paradigm, a hardware driven product V&V phase is expected to confirm what is already known through computer simulation.
Simulation methods have gotten more and more accurate with the creation of a Simulation Governance quality management framework that allows us to assess the predictive power of a simulation and quantify the uncertainty of the results. Increasingly, this is enabling simulation to take a leading role in product V&V. At the same time, Simulation Governance principles require that we validate simulation models, preferably with physical tests that provide information about aspects of the simulation model that need additional clarification.
The following questions will be discussed:
    • How are test and simulation activities used individually or in combination to validate products in different industries today? What changes are expected in the next years?
    • What are best practices for using simulation in product V&V across different industries?
    • How can the predictive powers of test and simulation be assessed and how can they each contribute to product V&V?
    • What collaboration models are there between test and simulation in product V&V?
    • How can physical test and simulation benefit from each other?
    • How can we define virtual V&V processes that provide at least the same level of assurance and certainty as the established test driven processes?
    • To what extent can expensive physical testing be replaced by simulations with the same or even higher predictive power?
    • What acceptance issues need to be resolved when replacing physical tests by simulations?
    • What are the benefits and challenges of transitioning from a test-driven to a simulation-driven approach to product V&V?
    • What (general or industry-specific) regulatory changes are needed to enable simulation-driven product V&V?
    • How should tests for validating simulation models be defined to ensure comprehensive coverage of the application domain?
    • Is certification by analysis an attainable long-term vision, or is it already reality in some applications?
    • What role does uncertainty quantification of simulation or test play in product V&V?

Description
1) Introduction to uncertainty, statistical modeling, and learning from data
● Bayesian and Frequentist concepts of probability
● Aleatory and epistemic uncertainty
● Tools for statistical machine learning
● Statistical modeling: data generating model and prior predictive simulation
● Bayes' rule as the foundation for statistical simulation
● (Machine) Learning from data and posterior predictive simulation

2) Examples used
● A real-life application of Bayes' Rule
● An engineering application with uncertain data

3) Tools used
● R
● Rstudio
● Stan

What will you learn?

● You will learn about the basics of Uncertainty Quantification and Statistical Machine Learning starting from first principles such as concepts of probability and the application of Bayes's Rule.
● You will see how statistical simulations can be run and evaluated using freely available software such as R, Stan, and RStudio.
● You will learn about application use cases of Statistical Machine Learning in industry.

What questions will this course answer?

● What is probability and how can I use it to assess and enhance the predictive power of simulations?
● How can I use statistical modeling to learn from data?
● How can statistical simulation bridge the gap between traditional simulation and 'œphysics-agnostic' machine learning?
● How can I make predictions and decisions in the face of uncertainty?

Who should attend?

Everyone is welcome to attend. The seminar will be most interesting for

● Simulation engineers that would like to understand the predictive power of their models by quantifying uncertainty
● Engineers that would like to broaden their understanding of potential applications of statistical machine learning in their field
● Anyone interested in the practical application of physical modeling, machine learning, and data science

The workshop/short course is code independent. Examples will be presented with the use of the open-source statistics tools R, RStudio, and Stan.

Description
This short workshop will provide information on the NAFEMS Professional Simulation Engineer (PSE) Certification scheme, presenting why it is useful, how it works, the application process and some client case studies.

PSE enables simulation engineers to demonstrate competencies acquired throughout their professional career, located in the PSE Competency Tracker which will be described at https://www.nafems.org/professional-development/certification/.

Description
More details and panellists will be announced soon.