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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
Application Areas
Optimization is a vast area that has a many applications in diverse areas such as engineering, economics, business and medicine.
Application Area 9: Appliance Design

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Overview
MotionSolve^® is an integrated solution to analyze, evaluate, and optimize the performance of multibody systems.
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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.
  - 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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Application Area 9: Appliance Design

A mechanical accelerometer is used to measure the acceleration of a rocket test sled. It contains a linear spring-damper system. See the image below. By tuning the stiffness and damping, the accelerometer can be designed to estimate the acceleration it feels within an acceptable time.

Figure 1.

Objective: We can compute the deviation of any system response $x$ from its target value $x^{*}$ as follows:; $S T E P (t_{t a r g e t}) = {\begin{matrix} 0 i f t \leq t_{t a r g e t} \\ 1 i f t > t_{t a r g e t} \end{matrix}$

$f (x) = {\int_{t_{s t a r t}}^{t_{e n d}}}_{}^{} S T E P (t_{t a r g e t}) * | x - x^{*} | d t$; The problem is formulated as:; $m i n ({x - x^{*})^{2} |}_{s t e a d y s t a t e}$; $s u c h t h a t f (x) < 1 e - 3$
Design Variables: The stiffness of the spring, $k$ , and its damping, $c$ , are the design variables.
Results: By properly selecting the spring stiffness and damping, the accelerometer can be designed to respond in the desired time frame.; Figure 2.