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MBD System Level Response Optimization |
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MBD System Level Response Optimization |
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The original ESLM implemented in OptiStruct is for the optimization of flexible bodies in MBD systems through the control of structural responses, such as stress or deformation. The original ESLM is not capable of handling system level responses, such as joint force of a joint or velocity of a node of a body. System-level responses referred to in this section can be displacement, velocities, acceleration, joint force, and functions of those 4 quantities defined by MBVAR. In addition to the original ESLM, MBD system level response optimization is also available in OptiStruct. ESLM and MBD system level optimization can be combined together to optimize MBD systems. One of typical optimization formulations that can be solved by OptiStruct could be:
Minimize joint force of a joint
subject to stress < allowable value
deformation < allowable value
velocities of a node < allowable value
MBD system level responses can be controlled by the properties of PRBODY, CMBUSH(M), CMBEAM(M), and CMSPDP(M) as well as usual design variables in structural optimization such as thickness of bodies, shape of bodies, etc. DVMBRL1 and DVMBRL2 are available to define relationships between design variables and properties of PRBODY, CMBUSH(M), CMBEAM(M), and CMSPDP(M).
MBD system level responses are approximated by adaptive response surfaces. The basic solution strategy for MBD system level optimization is the same as that implemented in HyperStudy. In general, response surface based optimization cannot handle large number of design variables. This issue has been resolved in OptiStruct through the introduction of intermediate design variables for bodies. All the design variables defined on rigid/flexible bodies are converted to some predefined intermediate design variables. Adaptive response surfaces are built based on those intermediate design variables. As a result, even if there are hundreds of design variables defined on bodies, adaptive response surfaces for MBD system level responses can be built with only several intermediate design variables. This way, OptiStruct can handle MBD system level responses with large number of design variables.
When you try to achieve a specific velocity value less than an allowed value, changing the length of bodies is an efficient way to achieve it. You can set up a shape optimization problem to change the length of bodies. The designable bodies can be either rigid bodies or flexible bodies. When defining shape perturbation vectors with DVGRID, make sure that each perturbation vector does not break down the original joint configuration as the design changes. For example, a revolute joint must have two coincident nodes attached to two different bodies. Be aware while defining shape perturbation vectors, that the perturbation vector could cause a location change for only one of the two coincident nodes so that the revolute joint definition would not be valid after one design iteration.
Design variables associated with PRBODY, CMBUSH(M), CMBEAM(M), and CMSPDP(M) cannot control structural responses, such as deformation or stress. They can only control MBD system level responses such as MBDIS, MBVEL, MBACC, MBFRC, and MBEXPR. Design variables associated with structures such as length, shape, and thickness can control MBD system level responses. Thus, if interaction between structural responses and MBD system level responses are mutually strong, this feature is not applicable.
Rigid bodies cannot have structural design variables (shape, properties, thickness, etc.) and the design variables associated with DVMBRL1/2 with TYPE=PRBODY at the same time.
Currently, system level responses must be scalar values. OptiStruct provides an option to pick up the maximum, minimum, maximum absolute, or minimum absolute value of displacement/velocity/acceleration/joint force when defining MBD system level responses using the DRESP1 card. If a system level response is an objective function, and the time when the objective function is picked up jumps around during the optimization process, convergence could be slow or even diverge in some cases.
