Structuring Experimenting

Esmée BertensTim de RidderHerman de Vos

/Department of Mechanical EngineeringSystems Engineering Group

Masters Team Project

Supervisor: Prof. dr. ir. J.E. RoodaAdvisors: Dr. ir. A.T. Hofkamp

Dr. ir. L. F. P. Etman

Contents

• Introduction• Inventory• Design• Final Product• Conclusions and recommendations• Demonstration

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Introduction

Performing simulation experiments with χ-models.Knowledge often not recorded or shared with others.Developing own method, which takes time and effort.

Objective:Establish a structured method for performing multiple simulation experiments with χ-models.

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InventoryDesign

Final ProductConclusions

Introduction

Demonstration

InventoryInterview subjects Running simulations

SE-rack systems / SE ClusterRepetitions / Varying parameters

OutputText file / Updating values

Software and ToolsProcessing Output: GREP, Matlab, PythonVisualization: Matlab, Excel, Gnuplot, Simplot,

Labview Desires and recommendations

Limiting file I/O.Method for a statistical test.

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InventoryDesign

Final ProductConclusions

Introduction

Demonstration

DesignAnalysis of the inventory functionality

Investigation into available software packages. Investigation into possibilities with Python.

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InventoryDesign

Final ProductConclusions

Introduction

Demonstration

DesignFinal design: Framework with three parallel processes connected through buffers.

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InventoryDesign

Final ProductConclusions

Introduction

Demonstration

Final ProductFramework in Python with external scripts and tools.

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InventoryDesign

Final ProductConclusions

Introduction

Demonstration

Conclusions and RecommendationsConclusions Start has been made with a structured method for performing multiple simulation experiments with χ-models. Framework in Python with external scripts and tools. Simple best practices.

Recommendations Improvement of external scripts. Connection to Matlab. Collection of improvements/extensions on SE-wiki page.

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InventoryDesign

Final ProductConclusions

Introduction

Demonstration

Demonstration demo1.chi 2 input, 1 output frame.py

main_demo1.py Input parameters: P1 = [0,1,2] P2 = [2,5,7] Number of repetitions: 5

run_demo1.py Run for all combinations of input parameters

calc_demo1.py Calculate and update mean value

vis_demo1.py Plot values with Gnuplot

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InventoryDesign

Final Product

/Department of Mechanical Engineering

DemonstrationConclusions

Introduction

Questions ?Inventory

DesignFinal Product

/Department of Mechanical Engineering

DemonstrationConclusions

Introduction

Python code

Why Python? Possibilities for parallel processes and buffers. Well known whithin SE Group. Runs on Linux systems. Interaction with other software.

InventoryDesign

Final Product

/Department of Mechanical Engineering

DemonstrationConclusions

Introduction

Frame.py

Demo1.chi

calc_demo1.py

main_demo1.py

run_demo1.py

vis_demo1.py

Feedback example - 1 output value - Calculate mean - Calculate max and min of Ncheck last updated mean values - Compare with Xcheck - Run new simulation or stop

InventoryDesign

Final Product

/Department of Mechanical Engineering

DemonstrationConclusions

Introduction

Commercial Software PackageWhy not? Too many options and functions are integrated Most software packages cannot function in a Linux environment Quite some effort and time to learn such a software package Most software packages are expensive

InventoryDesign

Final Product

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DemonstrationConclusions

Introduction