Structuring Experimenting Esmée Bertens Tim de Ridder Herman de Vos /Department of Mechanical Engineering Systems Engineering Group Masters Team Project Supervisor: Prof. dr. ir. J.E. Rooda Advisors: Dr. ir. A.T. Hofkamp Dr. ir. L. F. P. Etman

Contents Introduction Inventory Design Final Product Conclusions and recommendations Demonstration /Department of Mechanical Engineering2/9

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. 3/9/Department of Mechanical Engineering Inventory Design Final Product Conclusions Introduction Demonstration

Inventory Interview subjects  Running simulations SE-rack systems / SE Cluster Repetitions / Varying parameters  Output Text file / Updating values  Software and Tools Processing Output: GREP, Matlab, Python Visualization: Matlab, Excel, Gnuplot, Simplot, Labview  Desires and recommendations Limiting file I/O. Method for a statistical test. 4/9/Department of Mechanical Engineering Inventory Design Final Product Conclusions Introduction Demonstration

Design Analysis of the inventory  functionality  Investigation into available software packages.  Investigation into possibilities with Python. 5/9/Department of Mechanical Engineering Inventory Design Final Product Conclusions Introduction Demonstration

Design Final design: Framework with three parallel processes connected through buffers. 6/9/Department of Mechanical Engineering Inventory Design Final Product Conclusions Introduction Demonstration

Final Product Framework in Python with external scripts and tools. 7/9/Department of Mechanical Engineering Inventory Design Final Product Conclusions Introduction Demonstration

Conclusions and Recommendations Conclusions  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. 8/9/Department of Mechanical Engineering Inventory Design Final Product Conclusions Introduction Demonstration

 demo1.chi2 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 9/9 Inventory Design Final Product /Department of Mechanical Engineering Demonstration Conclusions Introduction

Questions ? Inventory Design Final Product /Department of Mechanical Engineering Demonstration Conclusions Introduction

Python code Why Python?  Possibilities for parallel processes and buffers.  Well known whithin SE Group.  Runs on Linux systems.  Interaction with other software. Inventory Design Final Product /Department of Mechanical Engineering Demonstration Conclusions Introduction

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 Inventory Design Final Product /Department of Mechanical Engineering Demonstration Conclusions Introduction

Commercial Software Package Why 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 Inventory Design Final Product /Department of Mechanical Engineering Demonstration Conclusions Introduction