MODELLING AND SIMULATION g
than the traditional simulation method. The ability to incorporate manufacturing variation into the simulation and predict what proportion of builds will squeal is another major advantage. TRW is moving to implement the new method into its design process for future brake programs. The company is confident that it will be able to substantially improve brake quality while reducing engineering costs and lead time, Hebbes added.
Multidisciplinary design optimisation Another use of optimisation can be found in multidisciplinary design optimisation (MDO). This method, as the name suggests, combines multiple disciplines simultaneously that can produce impressive results but also increases the complexity of the challenge facing engineers. An example of MDO studies can be
found in a recent paper, Optimization and Model Order Reduction on Launch Vehicle Aerodynamic Design, written by Cédric Dupont, Sophie Missonnier, Gilles Bouré and Michel Onori, from CT Engineering group. The paper explores the use of MOD for a Space Launch Vehicle (SLV), a complex system with a large number of closely interdependent disciplines including propulsion, structure, aerodynamics, flight mechanics, and thermal control. The engineers at CT Paris developed
their own internal methodology to assess aerodynamic calculations of a launch vehicle and optimise aerostructures. The methodology relies on the use of an internal CFD tool, CPS_C, which has been modified to allow its use in an optimisation environment. Historically, launch system designers
would apply an analytic approach to initiate an iterative design process where the only objective of the procedure was to maximise the performance. While this process can be successful, it requires several iteration loops and the optimisation of certain parameters performance may affect other systems – which increases costs and time to market. The paper states that ‘an MDO
approach allows the handling of relevant technical disciplines and economic drivers and in the meantime reducing human intervention. In this way, the optimal solution can be identified through a rapid exploration of several design concepts – ensuring a final design with optimised performance together with significant costs saving and risks mitigation.’ CT Paris’ strategy is to foster automation
by coupling aerodynamics, geometry and mesh model generation in order to
22 Scientific Computing World February/March 2020
“The entire simulation process is contained within a single environment, which saves time by automating many aspects of the process and setting up batch runs for design optimisation or manufacturing variation analysis”
run a parametric study case allowing to provide specific surrogate models. The workflow integrates an automatic mesh procedure, pre-treatment calculations, CFD calculation and post-processing, all integrated into HADES optimisation environment based on modeFRONTIER software developed by Esteco. Using this framework the study optimised the aerodynamics database, the structural mass and geometry of the launch vehicle, and surrogate models for aerodynamic coefficients evaluation. ‘One aim was to widen and enhance
its reduced model building capacities, by implementing state-of-the-art methods on an industrial case,’ states the paper. The researchers report that the first results are encouraging: ‘There is a big gain in computation time, from hours to milliseconds, together with weak prediction error (lower than 0,5 per cent
in most results) provided that new case stays inside variation range or reduced order model (ROM) is adaptively enriched with extended full order model (FOM) simulations. ‘Ultimately, the aim would be to replace
aerodynamic coefficient tables with reduced aerodynamic models in all relevant steps of the SLV design process, such as general loads computations, trajectory optimisation and thermal loads prediction,’ states the paper. This project demonstrates that this
process can be used to optimise multiple objectives: defining the aerodynamics database through the to the design of experiments algorithms; optimising structural mass and geometry of the launch vehicle; and developing models for aerodynamic coefficients evaluation. While these techniques are varied in their approach each demonstrates the benefits of applying engineering simulation to optimise designs. In an increasingly competitive market, simulation engineers can make use of advanced simulation techniques to drive new products to market faster and cheaper than was previously possible. With increased computational resources
these projects can begin to tackle more complex challenges. Over time this will continue to expand to include adjacent and interactive systems to give a more comprehensive picture of how changes may affect the overall performance of a given component or product.
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