Software & Consulting
Our engineering consultancy team has assisted companies to optimise their products and process since we started structural analysis services over 30 years ago. However, computational methods are evolving to help automate the optimisation process when using simulation, identifying options that manual methods may miss and efficiently assessing the very large number of permutations to satisfy multiple objectives.
Through parametric integration between geometry (CAD) and simulation, most of our simulation software offer in-built optimisation capabilities based on Design of Experiments (DOE).
For example, we supply their Ansys DesignXplorer & optiSlang capabilities for general structural, fluid and electromagnetic design challenges, while similar functionality for Manufacturing Process Simulation is available in Moldex3D and DEFORM.
These products offer a toolkit of methods to create response surfaces characterising the behaviour of a design subject to multiple parameter changes, inhttp://Manufacturing processcluding geometric features, input loading conditions and material properties. From this envelope, an optimum solution can be found depending on the objectives, such as minimum weight, natural frequency ranges or increased fatigue life. When objectives conflict and a decision has to be made between candidate designs, ‘trade-off’ or Pareto analysis can be undertaken.
Furthermore, the ability to understand the change in performance when parameters are altered enables us to undertake Robust Design studies, identifying how sensitive a design is to manufacturing tolerances, environmental conditions and other real-world probabilistic variations.
DOE inevitable requires a large number of individual simulations to build the response surface. However, High Performance Computing (HPC) and parametric licensing now enables such studies to be run automatically and efficiently within our clients’ IT infrastructure or by us on our clusters.
DOE methods are ideal for refining designs, choosing alternatives and measuring sensitivities but are restrictive when considering new geometric designs at concept stage. For example, pre-defined features such as radii or use of ribs remain throughout the optimisation sequence although their quantity and dimensions can be changed.
With increasing use of additive manufacturing / 3D printing, injection moulding and other processes that are more flexible with the geometric forms that can be produced, topological and generative optimisation techniques provide greater freedom for innovation.
We support the adjoint, shape and topology optimisation technologies within Ansys and have undertaken projects using these capabilities. The adjoint solver within Ansys CFD calculates the performance sensitivities of the design with respect to geometric changes during the original fluid dynamics simulation, providing insight to manual or automated shape morphing that derives an optimised solution. Applications include pressure-drop reduction, drag/lift ratio, surface heat transfer and aerofoil optimisation studies.
» Brochure: ANSYS optiSLang
» Brochure: Optimus Product Overview
» Demo Video: Topology Optimisation with ANSYS 18
» Feature Story: Optimising Forward Tooling on Subsea Trenching Vehicle for Forum Energy
» White Paper: How Design Optimisation Can Help Vault Your Product Ahead of Competitors’