Altair OptiStruct is a leading state of the art finite element solver for linear and non-linear structural problems, it has optimization at its core, basically any analysis solution we solve, can be further Optimized using wide range of responses and constraints which OptiStruct has, this empowers engineers to enhance their designs by leveraging these powerful techniques. In this blog post, we will dive into Altair OptiStruct and explore the different optimization techniques it offers.
Topology optimization is a process that aims to find the optimal material distribution within a given design space. We’ve discussed this in another post found here as well. It helps engineers identify the best configuration for a structure, eliminating unnecessary material while ensuring structural integrity and performance. Altair OptiStruct, outside of its structural solver capabilities, employs advanced topology optimization algorithms, such as Level Set Method, DTPL and DSIZE for Composite and Free-size optimization and Failsafe Topology Optimization (FSO) for structures.
Free size optimization is for Shell structures, where it finds the optimum thickness on an element-by-element basis that meets the constraints and objective. In the below figure we can notice that the design suggested has element with least thickness in regions where no material is needed and with higher thickness where the material is needed.
Fig 1: Free-size optimization on shell structure
Given a structure and a number of user-defined shapes, shape optimization finds the optimum fractional summation of those shapes that meets the constraints and objective. The shape variables are created using HyperMorph, a tool inside HyperMesh. In the below example we see the ability to reduce the stresses in the tube structure by changing the shape of the tube
Fig 2: Stress changes in tube structure
Topography optimization is a special class of shape optimization, which can be used to change shapes of shell structures by introducing stamped beads for a better structural performance. These beads help modify the stiffness distribution, enabling engineers to improve the overall performance of the design. Bead optimization is particularly effective in achieving multi-objective optimizations, where various conflicting objectives need to be balanced. In Fig 2 below, you can see a shape change in the top of the stamped design that requires no additional material but adds strength.
Fig 3: Initial and Optimized stamped design.
Given a structure, size optimization finds the optimum component thickness or values that meets the constraints and objective. This can be applied to any property card which has a variable i.e. sheet thickness, diameter of 1D beam elements etc. In the below example we have performed gauge optimization on a cradle assembly, the optimized design has reduced the mass by changing the part thicknesses.
Fig 4: Cradle Assembly before and after optimization
Multi-Model Optimization (MMO) involves simultaneously exploring and optimizing multiple design models, that have linked design variables, to identify the best trade-off solutions based on multiple performance criteria. Unlike traditional single-model optimization, which focuses on a single design alternative, MMO broadens the scope to consider a range of possible designs, offering engineers more flexibility and insights into the design space. In the below figure we have an excavator arm run with single model optimization and multi model optimization, the idea here is we want to come up with a arm which can be used across different variants, so we are trying to account for different loadings to come up with a common design.
Fig 5: Application example for multi-model optimization
Lattice Structure Optimization
Lattice structures consist of an interconnected network of repeating unit cells, often in the form of complex geometric patterns. These structures are ideal for many applications like lightweighting, energy absorption and thermal management. The optimization of lattice structures involves finding the best configuration of lattice cells, strut thickness, and lattice density to achieve specific design goals such as weight reduction, mechanical performance enhancement, or targeted material properties. OptiStruct offers various lattice generation techniques, including Voronoi, regular, and stochastic algorithms.
Fig 6: Comparison of OSSRMSH on the Lattice Structure
Multi-Material Optimization involves optimizing the distribution and allocation of multiple materials within a design to achieve specific design objectives. Engineers can create designs that leverage the unique characteristics of each material to meet performance goals, such as weight reduction, improved structural integrity, or enhanced functionality. Engineers can explore the blending or mixing of different materials within a design.
Fig 7: Multi-material topology optimization of an automotive cradle
Embracing topology optimization is an important part of a modern engineering process. It enables design freedom, reduces material waste, and costs, and opens the door to new manufacturing techniques. If you want to learn more about OptiStruct, or any of the Altair solutions, don’t hesitate to let us know at email@example.com.