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MotionSolve User Guide
MotionSolve is a system level, multibody solver that is based on the principles of mechanics.
MotionSolve Optimization Guide
Optimization Input Data
The input data for an optimization is organized in a python parametric model class with methods that operate on the class.
Parametric Model Class
Contains the model design parameter values and the methods for generating a model, adding responses, performing the optimization, and printing results.
__init__ Method
The __init__ method for a parametric model instantiates a parametric model and stores the design data for the model with the instance.

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What's New
View new features for MotionSolve 2024.1.
Overview
MotionSolve^® is an integrated solution to analyze, evaluate, and optimize the performance of multibody systems.
Tutorials
Discover MotionSolve functionality with interactive tutorials.
MotionSolve User Guide
MotionSolve is a system level, multibody 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
- NLFE Bodies Introduction
- User Subroutines in MotionSolve
- msolve API
- MotionSolve Verification 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.
  - 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.
    - Parametric Model Class
      Contains the model design parameter values and the methods for generating a model, adding responses, performing the optimization, and printing results.
      - __init__ Method
        The __init__ method for a parametric model instantiates a parametric model and stores the design data for the model with the instance.
      - createModel Method
        Creates a model object.
      - addResponses Method
        adds Response objects to the model.
      - simulate Method
        Performs a simulation on the model.
      - optimize Method
        Performs an optimization on the model.
      - printResults Method
        Prints the final optimization results to your specifications.
      - Main Program
        The main program performs all the tasks required to do the optimization.
  - 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.
  - 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
- 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.

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init Method

The __init__ method for a parametric model instantiates a parametric model and stores the design data for the model with the instance.

The __init__ method for the FourBar class.

def __init__ (self,
    label       = "4bar", {The default lable for the model}
    ax          = -45, {ax-cx: The default values for design variables}
    ay          = +45,
    bx          = +65,
    by          = +260,
    cx          = +300,
    cy          = +500,
    dx          = +515,
    dy          = -85,
    ax_lim      = (-50 ,50 ), {ax_lim-cy_lim: The default values for design limits}
    ay_lim      = (-50 ,50 ), 
    bx_lim      = (+20 ,80 ),  
    by_lim      = (+180,280),
    cx_lim      = (+240,380),
    cy_lim      = (+400,620),
    dx_lim      = (+180,520),
    dy_lim      = (-100,20 ),
    
  ):

    self.label  = label {self.label-self.dy-lim: Store the design data with the object}
    self.ax     = ax     
    self.ay     = ay     
    self.bx     = bx     
    self.by     = by     
    self.cx     = cx    
    self.cy     = cy    
    self.dx     = dx    
    self.dy     = dy    
    self.ax_lim = ax_lim
    self.ay_lim = ay_lim
    self.bx_lim = bx_lim
    self.by_lim = by_lim
    self.cx_lim = cx_lim
    self.cy_lim = cy_lim
    self.dx_lim = dx_lim
    self.dy_lim = dy_lim
    self.bx_lim = bx_lim
    self.by_lim = by_lim
    self.cx_lim = cx_lim
    self.cy_lim = cy_lim
    self.dx_lim = dx_lim
    self.dy_lim = dy_lim