pSeven User Conference 2022 | VIRTUAL 

Digital transformation has become both a major business driver as well as a source of great angst within the product engineering, manufacturing and in-service operations of global enterprises. The need to connect digital models and associated engineering data of today’s complex cyber-physical systems is now seen as a key enabler in achieving the widely promised business benefits of implementing model-based engineering/digital engineering throughout the product lifecycle (i.e., connecting the digital thread). Dealing with the design complexity of cyber-physical products and smart, interconnected systems of systems is driving the need for increased use of advanced digital modeling, performance simulation, and multi-disciplinary analysis and optimization (MDAO) augmented with AI/Machine Learning technologies.

• Engineers can now define and apply multi-disciplinary digital twins leveraging MDAO starting earlier in the conceptual design and development phase as well as apply these technologies in manufacturing and in operations to enable combined physics-based and ML-based “hybrid digital twins”.
• Industry leaders are increasing the use of MDAO, Cloud and “low code” automation environments to enable design innovation; reduce overall product design costs, minimize rework and manufacturing defects, reduce maintenance cycle times, and optimize in-service operational performance and costs.
• The “democratization” of physics-based modeling, performance simulation, design optimization, and data analytics best practices throughout the enterprise is a key enabler of digital transformation initiatives

In this presentation, CIMdata will provide a high level overview of the key industry trends and business challenges related to the implementation of digital engineering technologies.

This presentation attempts to demonstrate the contribution of reproducible research in MDO/AI with several recent open source attempts of Prof Morlier’s group. During the last few decades, surrogate modeling has gained in popularity, especially in engineering fields, where they are often used in design analysis and optimization to replace expensive numerical simulations. Coupled to Multidisciplinary Design Optimization (MDO) this process lead to an engineering design acceleration through AI.

The first part of the talk will present some novelties in AI for Engineers such as the Surrogate Modeling Toolbox (SMT developed conjointly between ONERA, ISAE-SUPAERO/ICA, Nasa, and University of Michigan). SMT is a Python package that contains a collection of surrogate modeling methods (Mainly Kriging also known as Gaussian Processes in Machine Learning community), sampling techniques and benchmarks. SMT is different from existing surrogate modeling libraries because of its emphasis on derivatives that can be used directly in MDO processes. It also includes surrogate models or options that are not available elsewhere: KPLS (Kriging with Partial Least Square) for automatic inputs dimension reduction, mixture of experts (with automatic clustering) for simulation codes that include singularities (such as buckling in structural mechanics) and mixed variable design space (continuous, discrete, and categorical). The 2022 version currently available propose new applications such as multi-fidelity approach, Bayesian unconstrained optimization (EGO), fully inter-operable and automated.

The second part will present some MDO applications in Aerospace sciences will be highlighted, including launcher and UAV, HALE optimal design especially in an environmental perspective.

The engineering in offshore industry is facing more and more challenges due to several factors: more design constraints, increase of the workload post-Covid, global cost reduction, etc… To overcome those issues, TechnipFMC developed Smart Pipeline Design ARENA, a digital web platform, powered by pSeven, which is developed for rigid pipeline engineering (flowlines and risers). The objective of such a platform is to improve engineering cost, engineering time, product robustness and cost. Within that context, ARENA is mainly developed playing on the following four main levers: (i) standardization, (ii) automation, (iii) optimization and (iv) uncertainty management.

ARENA remains a design platform which can be used by a large spectrum of users, i.e. from junior or less experienced engineers to technical experts. In the first usage scenario, the platform proposes standardized and well-framed micro-services gathered in two toolboxes, i.e. respectively for flowline and riser design. Such first usage mainly considers standardized and validated micro-services and does not require any key experience. For the second usage scenario, the platform offers all the freedom to the user to consider standardized tool but also to chain them for any automated process or to develop their own fit-for-purpose tools (including AI capabilities). The latter can be shared in a second step with the team for the benefit of the project.

This study aimed to optimally design a parametrically generated bumper geometry under operating conditions. By changing the bumper geometry at the specified boundary conditions, the mass of the structure, strength and critical lifetimewere examined. As the optimization target, it was determined to meet in minimum mass, maximum critical lifetimeand minimize the stress results where is below theyield stress. This study was carried out with the pSeven workflow; CATIA for parametric CAD model, Patran & Nastran for finite element analysis and nCode for calculating critical lifetimewere used.

The hot rolling of long steel products is a forming process used to provide the final cross section of rolled bars suited to further manufacturing processes. During this process, the microstructure is refined and the defects generated during the casting of steel such as shrinkage pore are reduced.

The finite element modeling of this process is useful to reduce the trial phase necessary to elaborate an industrial rolling sequence suited to the requirements of customers in terms of product soundness and mechanical properties. To reach this objective, the boundary conditions of the model need to be carefully identified to ensure its ability to correctly model the thermomechanical fields involved in the rolled bloom.

In the current study, pSeven is used to optimize the thermal exchange and the friction coefficients of the rolling model of the Rotoforgia rolling mill of ABS. The simulation model is built using the FORGE software and the optimization procedure is based on the minimization of the difference between simulated and measured rolling forces. The workflow implemented in pSeven is using a surrogate-based optimization approach to explore the defined design space and then optimize the response of the simulation. During the workflow, the setting up of the thermal exchanges and friction coefficients in FORGE and the running of the simulations are fully automated by pSeven. The post-processing phase is automatized as well. During this step, the mean rolling force of each stand of the rolling sequence and other process parameters such as the surface temperature are extracted. Using this approach, a large combination of entry parameters is tested without configuring each simulation case individually. The required storage space is also limited to only one computed case at once for the whole study. The use of computation resources is thus optimized, and the large computation time needed can be executed as background tasks.

Using this approach, the performances of the simulation model are improved after 75 runs of the computation.

A new steel ingot casting format had to be designed. Here is the target to propose a feasible and optimal design with reduced segregation and centreline porosity in the as-cast ingot and on the other hand increase the metallic yield. By means THERCAST® the evolution of the filling and the solidification can be simulated and the quality of the ingot can be predicted – given a specific combination of geometry, process parameters and thermal boundary conditions. To investigate the impact of the geometry attributes (the mould, the ingot, the riser, riser insulation), the thermal and physical properties (riser insulation, hot topping), process parameters (steel grade, mass flowrate during filling, superheat) and analyse the responses of the simulation such a study would consume more than six months. In the scope of this investigation pSeven® was used to perform an automated closed-loop workflow in which Inventor® (CAD) and THERCAST® are integrated. PSeven® performs the parametrisation (geometrical, process and thermal data), triggers the automatic generation of CAD as well as the setup (meshing and parameters update) and launching of the THERCAST® computation. The responses from the virtual sensors are automatically acquired and are the basis for the next iteration. Using the design space exploration it was possible to gain a holistic understanding of the parameters interactions and relevance. In the further step the performed surrogate based optimisation enabled to create an optimal design of the ingot. This is performed in a record time of less than three weeks.