Optimize Engineering Designs with the Optimization Module
Improving Your COMSOL Multiphysics Models
The Optimization Module is an add-on package that you can use in conjunction with any existing COMSOL Multiphysics Product. Once you have created a COMSOL Multiphysics model of your product or process, you always want to improve on your design. This involves four steps. First, you define your objective function – a figure of merit that describes your system. Second, you define a set of design variables – the inputs to the model that you would like to change. Third, you define a set of constraints, bounds on your design variables, or operating conditions that need to be satisfied. Last, you use the Optimization Module to improve your design by changing the design variables, while satisfying your constraints. The Optimization Module is a general interface for defining objective functions, specifying design variables, and setting up these constraints. Any model input, whether it be geometric dimensions, part shapes, material properties, or material distribution, can be treated as a design variable, and any model output can be used to define the objective function. It can be used throughout the COMSOL Multiphysics product family and can be combined with the LiveLink™ add-on products to optimize a geometric dimension in a third-party CAD program.
Derivative-Free and Gradient-Based Algorithms
The Optimization Module includes two different optimization techniques: Derivative-Free and Gradient-Based optimization. Derivative-Free optimization is useful when your objective functions and constraints may be discontinuous and do not have analytic derivatives. For example, you may want to minimize the peak stress in your part by changing the dimensions. However, as the dimensions change, the location of the peak stresses can shift from one point to another. Such an objective function is non-analytic and requires a derivative-free approach. Five such methods are available in the Optimization Module: the Bound Optimization by Quadratic Approximation (BOBYQA) method, the Constraint Optimization by Linear Approximation (COBYLA) method, the Nelder-Mead method, the coordinate search method, and the Monte Carlo method.
The Optimization Module will compute an approximate gradient to evolve the design variables towards an improved design. You may want to minimize the total mass of your part which can also be done using this approach. The mass of the part is usually directly differentiable with respect to the part dimensions, permitting use of a gradient-based approach. The Optimization Module will compute the exact analytic derivative of your objective and constraint functions using the adjoint method of the SNOPT optimizer, developed by Philip E. Gill of the University of California San Diego and Walter Murray and Michael A. Saunders of Stanford University, to improve the design variables. A second gradient-based algorithm is a Levenberg-Marquardt solver. You can use this solver when the objective function is of a least-squares type, typically for parameter estimation and curve fitting applications. A third method, the Method of Moving Asymptotes (MMA), is a gradient-based optimization solver written by Professor K. Svanberg at the Royal Institute of Technology in Stockholm, Sweden. It is designed with topology optimization in mind. The method is called GCMMA in the literature and is available in the Optimization Module under the name MMA.
The advantage of the gradient-based method is that it can address problems involving hundreds, or even thousands, of design variables with very low increase in computational cost as the number of design variables increases. The Adjoint method simultaneously computes all analytic derivatives, whereas the derivative-free method has to approximate each derivative, and will take more time as the number of design variables increases. The gradient-based methods can also include more complex constraint functions.
The advantage of the derivative-free method is in its simplicity. It does not require finding a differentiable objective function, and it requires less user interaction to set up. However, due to the computational costs, derivative-free methods are most attractive when the number of design variables is around 10 or less. In practice, this covers a wide range of practical engineering optimization problems.
Parameter Optimization involves optimizing any scalar input to the model, such as flow rates and load magnitudes. This is usually the easiest kind of optimization and can be addressed with any technique.
Parameter Estimation is more complex, and involves correlating a COMSOL model to experimental data. Typically, the objective is to use a model to estimate material properties used.
Dimension, Shape, and Topology Optimization
Optimization methods can be further classified by the types of variables being optimized. Dimension, Shape, and Topology optimization are all addressed within the Optimization Module, and each has its own place in the design process.
Dimensional optimization involves defining design variables that can be directly translated to manufacturing. Typical design variables may be hole sizes, or length, width, and height of structural members. Dimensional optimization is usually used as the last step in the design process and is performed once the design is more or less fixed in terms of the overall shape. The derivative-free method is usually used here.
Shape optimization typically occurs earlier in the design process, and involves a more free-form alteration of the object. More care is usually required for choosing the design variables, as the objective is to improve the shape without over-constraining the design. The gradient-based method is preferred if an analytic objective function can be found.
Topology optimization is used very early in the design process, typically in the conceptual stage. Topology optimization treats the distribution of material as a design variable and inserts or removes structures to improve the objective function. Due to the high number of design variables, only gradient-based optimization is practical.
- Curve fitting
- Derivative-free optimization
- Dimension and shape optimization
- Gradient-based adjoint method optimization
- Levenberg-Marquardt solver
- Bound Optimization by Quadratic Approximation (BOBYQA) method
- Constraint Optimization by Linear Approximation (COBYLA) method
- Nelder-Mead method
- Coordinate search method
- Monte Carlo method
- Optimization for electrical, mechanical, fluid, and chemical models
- Parameter estimation
- SNOPT solver
- Topology optimization
Numerical Simulation-Based Topology Optimization Leads to Better Cooling of Electronic Components in Toyota Hybrid Vehicles
Toyota Research Institute of North America, MI, USA
The Toyota Research Institute in Ann Arbor drives cutting-edge research in energy efficiency, mobility infrastructure, and safety. Here, Ercan Dede and his team are designing heat sinks (or cold plates), which are affixed to fragile electrical systems and other applications in Toyota’s hybrid vehicles. Their simulations simulate the cold plate’s ...
Simulating the Release Mechanism in Drug-Eluting Stents
T. Schauer, I. Guler Boston Scientific Corporation, MN, USA
Stent insertion through the coronary artery is a common procedure used to treat restricted blood flow to the heart caused by stenosis. Following the procedure, restenosis may occur due to excessive tissue growth around the stent. Researchers at Boston Scientific are using multiphysics simulation to better understand how drug-eluting stents ...
Shape Optimization of a Tuning Fork
When a tuning fork is struck, it vibrates in a complex motion pattern that can be described mathematically as the superposition of resonant modes, also known as eigenmodes. Each mode is associated with a particular eigenfrequency. The tuning fork produces its characteristic sound from the specific timbre that is created by the combination of all ...
Mooney-Rivlin Curve Fit
This presentation shows how to use the Optimization Module to fit a material model curve to experimental data. It is based on the hyperelastic Mooney-Rivlin material model example given in the Structural Mechanics users guide.
Optimizing a Flywheel Profile
The radial stress component in an axially symmetric and homogeneous flywheel of constant thickness exhibits a sharp peak near the inner radius. From there, it decreases monotonously until it reaches zero at the flywheel’s outer rim. The uneven stress distribution reveals a design that does not make optimal use of the material available. Given ...
Topology Optimization of a Loaded Knee Structure
Imagine that you are designing a light-weight mountain bike frame that should fit in a box of a certain size and should weigh no more than 8 kg. Given that you know the loads on the bike, you can achieve this by distributing the available material while making sure that the stiffness of the frame is at a maximum. This way you have formulated the ...
Topology Optimization of an MBB Beam
A demonstration of topology optimization using the Structural Mechanics Module and the Optimization Module. The classical MBB beam is solved in 2D using a Helmholtz filter and Solid Isotropic Material Penalization (SIMP) technique to recast the original combinatorial optimization problem into a continuous optimization problem.
Minimizing the Flow Velocity in a Microchannel
Topology optimization of the Navier-Stokes equations is encountered in different branches and applications, such as in the design of ventilation systems for cars. A common technique applicable to such problems is to let the distribution of porous material vary continuously. In this model, the objective is to find the optimal distribution of a ...
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