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Multi-objective optimization support tool Product List

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[Case Study] Automatic Fitting of 1D Engine Analysis Tool

Strict fitting completed in a short time! Introducing a case of multi-purpose optimization.

We would like to introduce a case study where we performed automatic fitting of a one-dimensional engine analysis tool using our "modeFRONTIER." We set design variables for parts that could not be accurately read from 2D drawings into 3D shapes, as well as parameters that are difficult to measure in experiments (a total of 14 variables). We established two objective functions: one for minimizing the squared difference between measured and calculated values, and another for expressing variability as standard deviation, with the goal of minimizing both. As a result, a fitting that is difficult to achieve through experience alone was completed in a short time. 【Case Study】 ■ Software Used: modeFRONTIER, GT-POWER ■ Challenge: Fitting between measured and calculated values ■ Method: Executed multi-objective optimization with a total of two objective functions ■ Result: Precise fitting completed in a short time *For more details, please refer to the PDF document or feel free to contact us.

  • Company:IDAJ
  • Price:Other
  • Other analyses
  • simulator
  • Multi-objective optimization support tool

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[Analysis Case] System Identification of Catalyst and Air-Fuel Ratio Sensor Model

We will introduce a case where the fitting parameters for all 6 cases were identified!

This case study presents an example of fitting the actual measured values of the O2 sensor signal and the output values of the air-fuel ratio sensor model using "MATLAB/Simulink" and "modeFRONTIER." In this case study, we explore common fitting parameters (eight parameters for the three-way catalyst model and the O2 sensor model) across six cases. The fitting degree is expressed by the correlation coefficient (R), and the objective function is set to maximize the six correlation coefficients. Additionally, since the matching of the rising edge waveform is important, we impose a constraint on the difference in rising times between the measured values and the model output values. By utilizing the multi-objective optimization algorithm of "modeFRONTIER," the identification of fitting parameters for all six cases was completed in a short time. 【Case Study】 ■ Software Used: modeFRONTIER, MATLAB/Simulink ■ Objective: Fitting the actual measured values of the O2 sensor signal and the output values of the air-fuel ratio sensor model ■ Result: Identification of fitting parameters for all six cases was completed in a short time *For more details, please refer to the external link or feel free to contact us.

  • Company:IDAJ
  • Price:Other
  • Other analyses
  • simulator
  • Multi-objective optimization support tool

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