Solar Flux Case Study

Transcription

1 Solar Flux Case Study q" s Surface q" emit P Mass Vs Temperature Multi-Objective Optimization of a Solar Flux Problem optimization@ozeninc.com

2 Solar Flux Case Study OVERVIEW 1. The Physics of the Problem 2. Solar Flux Problem Definition in EXCEL 3. Optimization Problem Definition - Workflow Creation in modefrontier 4. Postprocessing Optimum Configuration Analysis 5. Conclusions

3 Physics of the Problem and Objectives Problem definition: Solar Flux Problem is defined with Inputs such as surface area of electronic package ( = 1 m 2 ) power dissipation by the electronics (1000 w) q" emit q" s Surface P Surface Emissivity (= 1) and Absorptivity ( = 0.25) Solar Flux ( = 750 w/ m 2 ) Objective: This is a Multi-Objective Optimization: Mass Vs Temperature Use modefrontier to find the best solution to minimize both the temperature and the mass of the body.

4 Solar Flux Problem Definition in Excel Introduce the Problem in EXCEL The parametric problem analysis is modeled within the solver (EXCEL) The spreadsheet provides the following calculations - surface temperature with and without incident solar radiation - material mass - etc. The user aims to an automatic procedure to iterate these calculations in order to find the best solution given the design constraints modefrontier

5 Solar Flux Optimization Problem Definition Create workflow in modefrontier The optimization problem is modeled in modefrontier Define the Inputs with the Lateral Limits as shown Surface_Area m 2 (6 Levels) Surface_Emissivity (5 Levels) Set Excel as an Application Node Set the Logic flow Set Surface Temperature and Mass as Outputs Set the Objectives Random as DOE MOGA-II as Scheduler Multi Objective (Minimize both Surface Temperature and Material Mass)

6 Finding the Optimum Design Configurations Initial Design Point and Few Design Points of the Pareto Curve are shown in the table. Initial Design Design ID Surface_Area Surface_Emissivity Material_Mass Surface_temperature Few Pareto Design Points Design ID Surface_Area Surface_Emissivity Material_Mass Surface_temperature

7 Post processing - Bubble plot The Bubble Scatter shows the trade-off curve among the conflicting objective (Temp. vs Mass.) More than one Pareto curve exists. It is seen that the Pareto curves are function of the Emissivity Variable. The Multi Criteria Decision Making tool (MCDM) provided in modefrontier assists the designer in finding the best solution among the set of reasonable alternatives. Mate erial Mass Pareto Shifts as emissivity reduces Pareto Frontier Curve Initial Design Surface Temperature

8 Why ModeFRONTIER Creates automatic procedures to find the optimum It is easy to define which are the most significant variables in a model, as a consequence the designer speeds up the development phase ModeFRONTIER is uses of direct interfaces to integrate CAE tools (EXCEL) Fast to build workflow and precise specification of optimization DOEs and statistical tools to detect sensitivity Efficient algorithm(modefrontier found the pareto frontier very fast) Powerful postprocessing tools, guiding in deciding the best rade-off decision Many other capabilities are available (Such as Response Surface Modelling techniques, Multi-Criteria-Decision-Making, Robustness Design verification and Optimization

9 Stay Ahead During Challenging Times To learn more about how Ozen Engineering can help you incorporate simulation into your design and testing processes, please visit us at For more Design Optimization case studies visit: To learn more about modefrontier visit us at: If you would like us to create a demo for your specific case or for any other question, please contact us at: optimization@ozeninc.com