A Computer Aided Validation (CAV) framework for digital design qualification
Lead Research Organisation:
CCFE/UKAEA
Department Name: Culham Centre for Fusion Energy
Abstract
Every day we rely on products which have been designed by engineers, from the modes of transport we use, through to the power plants that deliver electricity to our homes. For all of these applications engineers have used a process called design qualification to ensure that the components of these systems are able to perform their function without structural failure. For the new technologies required to achieve and maintain net-zero carbon emissions, such as fusion energy, design qualification is challenging. Given the extreme environmental conditions that fusion components must sustain it is expensive, time consuming and difficult to test the components under representative loads.
With increased computing power engineers are increasingly using simulations for design qualification. Simulating the performance of components and their structural integrity allows engineers to evaluate many designs that would be costly to test physically. However, simulations make assumptions about the component geometry, the loading and the response of the materials it is made from. Therefore, simulations must be quantitatively validated against experimental data to ensure these assumptions are reasonable.
While there have been significant advances in engineering simulation, there have not been similar advances in the experimental and data science technologies required to validate simulations. Digital imaging technology is now common with many of us carrying a camera in our smart phones. This has led to the development of image-based methods for measuring the structural response and temperature of components using cameras. Image-based diagnostics are commercially available, but the data science methods required to use them for quantitative validation do not yet exist, meaning they do not yet have widespread application in engineering practice.
There are two software tools which are used for design qualification: Computer Aided Design (CAD), which allows engineers to define the geometries and assemblies of parts to form systems; and Computer Aided Engineering (CAE), which allows engineers to simulate the physical response of the components to different types of loading. However, a software tool does not exist that allows engineers to connect the output from CAD/CAE to the experiment required for validation.
The aim of my fellowship is to develop a new methodology which I have called a Computer Aided Validation (CAV) framework. The idea behind the CAV framework is to create smart experiments by digitally linking the output from simulations to the physical experiments using image-based diagnostics and machine learning. I will deploy an implementation of the CAV framework as open-source software which I have split into 3 modules, each of which is an objective of my project. To demonstrate the CAV framework, I will also develop 3 test cases which will build in complexity over the project in line with CAV framework methodology. The objectives of my fellowship are to:
1) Build and lead a multi-disciplinary experimental/computational team to create the framework.
2) Create new methods for experiment optimisation to maximise information for simulation validation.
3) Develop efficient tools to quantify and minimise the impact of uncertainty on measurements.
4) Generate a workflow to connect the experimental data to the simulation in a quantitative manner.
Using the resources of my fellowship I will build the research team and global network required to deploy the CAV framework and use it to deliver a wide range of net-zero engineering solutions and enable sustainable commercial fusion technology in the UK.
With increased computing power engineers are increasingly using simulations for design qualification. Simulating the performance of components and their structural integrity allows engineers to evaluate many designs that would be costly to test physically. However, simulations make assumptions about the component geometry, the loading and the response of the materials it is made from. Therefore, simulations must be quantitatively validated against experimental data to ensure these assumptions are reasonable.
While there have been significant advances in engineering simulation, there have not been similar advances in the experimental and data science technologies required to validate simulations. Digital imaging technology is now common with many of us carrying a camera in our smart phones. This has led to the development of image-based methods for measuring the structural response and temperature of components using cameras. Image-based diagnostics are commercially available, but the data science methods required to use them for quantitative validation do not yet exist, meaning they do not yet have widespread application in engineering practice.
There are two software tools which are used for design qualification: Computer Aided Design (CAD), which allows engineers to define the geometries and assemblies of parts to form systems; and Computer Aided Engineering (CAE), which allows engineers to simulate the physical response of the components to different types of loading. However, a software tool does not exist that allows engineers to connect the output from CAD/CAE to the experiment required for validation.
The aim of my fellowship is to develop a new methodology which I have called a Computer Aided Validation (CAV) framework. The idea behind the CAV framework is to create smart experiments by digitally linking the output from simulations to the physical experiments using image-based diagnostics and machine learning. I will deploy an implementation of the CAV framework as open-source software which I have split into 3 modules, each of which is an objective of my project. To demonstrate the CAV framework, I will also develop 3 test cases which will build in complexity over the project in line with CAV framework methodology. The objectives of my fellowship are to:
1) Build and lead a multi-disciplinary experimental/computational team to create the framework.
2) Create new methods for experiment optimisation to maximise information for simulation validation.
3) Develop efficient tools to quantify and minimise the impact of uncertainty on measurements.
4) Generate a workflow to connect the experimental data to the simulation in a quantitative manner.
Using the resources of my fellowship I will build the research team and global network required to deploy the CAV framework and use it to deliver a wide range of net-zero engineering solutions and enable sustainable commercial fusion technology in the UK.
