2022.2

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MotionSolve User Guide
MotionSolve is a system level, multi-body solver that is based on the principles of mechanics.
MotionSolve Optimization Guide
Optimization Capabilities in MotionSolve
Optimization capabilities in MotionSolve include equations of motion for a multibody system, design variables and limits, and analysis support.
Optimization Problem Formulation and Solution
Topics in this section include optimization problem types and optimization search goal and methods.

What's New
Overview
Tutorials
MotionSolve User Guide
MotionSolve Reference Guide

2022.2

What's New
View new features for MotionSolve 2022.2.
Overview
MotionSolve^® is an integrated solution to analyze, evaluate, and optimize the performance of multi-body systems.
Tutorials
Discover MotionSolve functionality with interactive tutorials.
MotionSolve User Guide
MotionSolve is a system level, multi-body solver that is based on the principles of mechanics.
- Introduction to MotionSolve
- Platform Support, Recommended Hardware, and Licensing
- Run MotionSolve
- Model Systems
- Model Simulations
- Visualize Results - Animation and Request Plots
- Couple MotionSolve with Other Software
- Customization Capabilities
- Analysis Tips
- User Subroutines in MotionSolve
- MSolve API
- MotionSolve Verification Manual
- NLFE Validation Manual
- MotionSolve Optimization Guide
  - Introduction
    The MotionSolve Optimization Guide explains the design sensitivity and optimization capabilities in MotionSolve.
  - Optimization Capabilities in MotionSolve
    Optimization capabilities in MotionSolve include equations of motion for a multibody system, design variables and limits, and analysis support.
    - Equations of Motion for a Multibody System
      This section describes equations of motion.
    - The Optimization Problem Formulation
      Optimization algorithms work to minimize (or maximize) an objective function subject to constraints on design variables and responses.
    - Design Variables and Limits
      Design variables are used to define parametric models.
    - Response Variables
      Model behavior is captured in terms of Response Variables.
    - Sensitivity Calculation
      The sensitivity of an output y (u) to an input u is defined as the change in y due to a unit change in u.
    - Scaling Mechanism
      Both Dv and Response can be scaled in msolve.
    - Analysis Support
      MotionSolve supports DSA only for Kinematic, Static, and Quasi-static analyses
    - License Usage for Optimization Jobs in MotionSolve
      The optimization capability in MotionSolve typically involves several solver simulations - one to determine sensitivities for the initial design and several consequent design simulations to determine and adjust design variables based on responses to satisfy the overall optimization criteria.
    - Optimization Problem Formulation and Solution
      Topics in this section include optimization problem types and optimization search goal and methods.
      - Optimization Problem Types
        Optimization problems types include unconstrained optimization, simple bound constraints, and nonlinearly constrained optimization.
      - Optimization Search Goal
        The search goal refers to the objective of the optimization algorithm.
      - Optimization Search Methods
        The search method refers to the approach taken in the optimization algorithm to locate a new design point that has a lower objective function or is more feasible than the current design point.
  - Application Areas
    Optimization is a vast area that has a many applications in diverse areas such as engineering, economics, business and medicine.
  - Optimization Input Data
    The input data for an optimization is organized in a python parametric model class with methods that operate on the class.
  - Optimization Output Data
    Every optimization run generates a variety of files.
  - Advanced Topics
    Advanced topics include a scaling optimization problem and tips on how to debug optimization runs.
  - SLA Suspension Example
    This example, referred to as MV3010 in this section, describes the optimization of an SLA suspension and is also available in MotionSolve tutorial topics/solvers/ms/optimization_using_mv_hs_intro_r.html#optimization_using_mv_hs_intro_r.
  - PID Controller Example
    In this example, we are designing a PID controller whose job is to minimize the effects of a disturbance force on the position, velocity, and acceleration of a block.
  - Glossary
- Track Model
  A continuous track plug-in for tracked vehicles, such as bulldozers, excavators, robots, snowmobiles, and military vehicles.
- MotionSolve Environment Variables
- References
MotionSolve Reference Guide
This manual provides a detailed list and usage information regarding command statements, model statements, functions and the Subroutine Interface available in MotionSolve.

View All Altair Simulation Help

2022.2

Home
MotionSolve User Guide
MotionSolve is a system level, multi-body solver that is based on the principles of mechanics.
MotionSolve Optimization Guide
Optimization Capabilities in MotionSolve
Optimization capabilities in MotionSolve include equations of motion for a multibody system, design variables and limits, and analysis support.
Optimization Problem Formulation and Solution
Topics in this section include optimization problem types and optimization search goal and methods.

What's New
Overview
Tutorials
MotionSolve User Guide
MotionSolve Reference Guide

Optimization Problem Formulation and Solution

Topics in this section include optimization problem types and optimization search goal and methods.

Optimization Problem Types
Optimization problems types include unconstrained optimization, simple bound constraints, and nonlinearly constrained optimization.
Optimization Search Goal
The search goal refers to the objective of the optimization algorithm.
Optimization Search Methods
The search method refers to the approach taken in the optimization algorithm to locate a new design point that has a lower objective function or is more feasible than the current design point.

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