How to leverage the Functional Mock-up Interface (FMI) for Model Based System...Siemens PLM Software
This presentation focusses on the use cases and motivations behind FMI and provide some tips on when to use Model Exchange or Co-Simulation. It illustrates how FMI helps covering all the phases of product design in a scalable way by connecting LMS Imagine.Lab Amesim™ models with models of various levels of detail such as 3D/MBS tools or advanced FEA or CFD codes. Parallelized heterogeneous co-simulation of Functional Mock-up Units (FMUs) is described from MiL, through SiL, towards HiL applications, for instance on recent FMI compliant multiprocessor real-time targets. The use of “surrogate FMUs” for controls validation, or for evaluating global product performance attributes such as Vehicle Fuel Economy is discussed. Then it is explained why FMI enables the management of complex product architectures and their associated scenarios at high level, and how this can be achieved thanks to Siemens PLM Software's LMS Imagine.Lab product family. Lastly, Siemens PLM Software provides its view and perspectives on promising evolutions of the FMI standard.
Using FMI (Functional Mock-up Interface) for MBSE at all steps of System DesignSiemens PLM Software
This presentation describes several FMI use-cases addressed by LMS Imagine.Lab Amesim covering all the phases of MBSE.
For more information, please visit our website: www.siemens.com/plm/simcenter-amesim
How to leverage the Functional Mock-up Interface (FMI) for Model Based System...Siemens PLM Software
This presentation focusses on the use cases and motivations behind FMI and provide some tips on when to use Model Exchange or Co-Simulation. It illustrates how FMI helps covering all the phases of product design in a scalable way by connecting LMS Imagine.Lab Amesim™ models with models of various levels of detail such as 3D/MBS tools or advanced FEA or CFD codes. Parallelized heterogeneous co-simulation of Functional Mock-up Units (FMUs) is described from MiL, through SiL, towards HiL applications, for instance on recent FMI compliant multiprocessor real-time targets. The use of “surrogate FMUs” for controls validation, or for evaluating global product performance attributes such as Vehicle Fuel Economy is discussed. Then it is explained why FMI enables the management of complex product architectures and their associated scenarios at high level, and how this can be achieved thanks to Siemens PLM Software's LMS Imagine.Lab product family. Lastly, Siemens PLM Software provides its view and perspectives on promising evolutions of the FMI standard.
Using FMI (Functional Mock-up Interface) for MBSE at all steps of System DesignSiemens PLM Software
This presentation describes several FMI use-cases addressed by LMS Imagine.Lab Amesim covering all the phases of MBSE.
For more information, please visit our website: www.siemens.com/plm/simcenter-amesim
9. 科学的方法
F. James Rutherford and Andrew Ahlgren, Science for All Americans , 1989
1. 物事を調査し、結果を整理し、新
たな知見を導き出し、知見の正し
さを立証するまでの手続きであっ
て、(仮説検証)
2. その手続きがある一定の基準を満
たしているもののことである。
(査読)
研究の世界入門B
21. RSM : Response Surface Methodology
• A response surface approximates a response y which
is estimated using n design variables(x1,x2,…,xn) as
– y=f(x1,x2,…,xn) + ε
• There is no restriction on the function form, and
quadratic polynomial functions are often employed
• RSM is applied to an optimization of a product
development process or an decrease in dispersion
研究の世界入門B
22. Construction of a response surface
using a least square method
• A response surface approximates a response y
which is estimated using two design
variables(x1,x2) as
– y=β0+ β1x1 + β2x2 + β3x12 + β4x22 + β5x1x2
• A substitution is made as
– x12 =x3 x22 =x4 x1x2 =x5
– y=β0+ β1x1 + β2x2 + β3x3 + β4x4 + β5x5
Coefficients(β0 β1β2 β3β4β5) are estimated from more
than six design points (y, x1 x2 x3 x4 x5)
研究の世界入門B
24. Estimation of a coefficient vector β
• The coefficient vector which minimizes the squared error
summation is estimated as
L ( y X )T ( y X )
T
( yT T X T )( y X ) yT y T X T y yT X T X T X
y y 2 X y X X
T T T T T
L
2 X T y 2 X T X b 0
b
yT y 2 T X T y ( X )T X
b = (XTX)-1XTy
研究の世界入門B
25. A model construction process using RSM
Set a parameter range
応答値
Select design points x2 x1
0
Calculate responses
Estimate a surface Min
x2 x1
Calculate a minimum 0
研究の世界入門B
27. Exercise using a climate dataset
• Obtain a dataset on average temperature from a climate data server in Japan
Meteorological temperature Agency
(http://www.data.jma.go.jp/obd/stats/etrn/index.phpe )
• Construct a response surface of the which is estimated using two design
variables(Longitude, Latitude)
• Estimate a Longitude and a Latitude which minimize the temperature
• Specify a location using the estimated Longitude and Latitude in Google map
• Obtain an average temperature at the specified location to confirm the accuracy
Average temperature at Dec. 1, 2000 緯度 経度
盛岡 -0.3 39.69833 141.165
仙台 3.6 38.26167 140.8967
青森 0.5 40.82167 140.7683
山形 3.2 38.255 140.345
秋田 2.7 39.71667 140.0983
福島 4.5 37.75833 140.47
角館 0.9 39.60333 140.5567
八戸
研究の世界入門B -0.1 40.52667 141.5217