Piero Marcolongo, M.S. Alberto Bassanese Design Optimization Applied to the Solar Industry

Transcription

1 Piero Marcolongo, M.S. Alberto Bassanese Design Optimization Applied to the Solar Industry

2 Process Integration and Desing Optimization The P.I.D.O. (Process Integration and Design Optimization) approach is a recent solution allowing to efficiently manage any design process and to orient it to the product-process optimum The P.I.D.O. approach offers design automation procedures that analyzes and optimizes the entire design process by means of: Design of Experiments (DOE) Optimization Algorithms Decision-Making Procedures Source: Source:

3 What Is Optimization? The Optimization Process Evaluation of a family of possible designs, consider a number of candidates as large as possible from that family Selection of the best option from the possible choices/designs Source: What made it a complex task? The potentially huge number of options to be tested What qualifies an optimization technique? The search strategy

4 Optimization = Max F(x) or Min F(x) F(x) -F(x) Improve passive cooling system for high reliability and low cost Allow thinner wafers and the implementation of higher-speed processing In order to transform a MAX into a MIN: Fnew(x) = - F(x) Wide acceptance angle for high yield and lower cost Decrease optical losses Design to avoid chromatic aberrations and cell mismatching

5 Traditional vs. Innovation Input: Material, Geometry, # of fins for cooling system Output: Temperature, Stress, Mass, Flow Uniformity Temperature Stress Mass Deformation

6 P.I.D.O. Approach = Automation

7 Improving The Design Process At All Industrial Levels modefrontier is a multi-objectives optimization design environment We use modefrontier for consulting and we are the California distributor modefrontier is written to allow easy coupling to almost any computer aided engineering (CAE) tool, whether commercial or in-house Source:

8 P.I.D.O. as applied to CAE Data Flow Logic Flow

9 Design Of Experiment After the DOE table is evaluated, we can post-process the results extracting important information about problem: Which are the most important design variables? Can we reduce the variables space? What is the best design space region to address for the optimization process? What is a reasonable number of objectives or constraints to define?

10 Multi Objective Optimization of a BGA Package Min Plastic Work Min. Displacement A-B Loading History

11 Sensitivity Analysis What is Important and What is Not DOE : 500 Designs Student Chart Parameter Correlation with Plastic Work

12 The Multi-Objective Optimization Process An initial population of designs is generated (DOE) The design space exploration is started The smart algorithms kick in and identify the OPTIMAL design configuration Example of Optimization against two conflicting Objectives, both to be minimized

13 BGA Optimal Solution Optimal Solution: Plastic Work In this case no Pareto Frontier is found since the two objectives are correlated and ONE optimal solution is identified

14 Reverse Engineering - Model Calibration Mechanical parameters calibration of LS-DYNA composite material models with respect to experimental information of fabric reinforced sandwich laminates Results of material model characterization can then be used to predict reliability crashing behaviour of PTW protective and sport equipments

15 Calibration of Models Matching Results from Lab Tests F-t E-t Numerical and experimental drop tests [0/45/0] S laminates Trial and Test best solutions F-t E-t Match modefrontier automatic procedure NUM EXP Multi-objective analysis demonstrates good compliance (D< 5%) between numerical and experimental results

16 Multi-Disciplinary Multi-Objective Optimization of Solar Panel Case Study Optimization & Green Engineering Maximize Area - Maximize Frequency - Minimize Displacements optimization@ozeninc.com

17 Optimization Applied to a Solar Panel OVERVIEW 1. Constraints in Manufacturing Solar Panels 2. The Optimal Design in 3 Steps 3. Multi-Disciplinary Analyses in ANSYS 4. Optimization Problem Definition - Workflow Creation in modefrontier 5. Postprocessing Analyzing the Optimum Configurations 6. Conclusions: Solar Panel Improvements through Optimization

18 Constraints In Manufacturing Solar Panels The cells composing the panel should be: Electrically connected each others Electrically insulated under rainy conditions Mountable on a substructure or building integrated Resist to possible mechanical damage during the manufacturing, transportation, and installation phases Resist to the atmospheric agents attack: hail impact, wind and snow loads. In the traditional way, a lot of money would be invested in the prototyping effort to a) test few configurations, b) defining the most significant variables in the design. With modefrontier you can: - test multiple designs, - carry out sensitivity analysis, - find variable trends - define the optimal solutions to the objectives that has been defined.

19 Optimization Objective Definition One simple solar panel has been taken into consideration The objective is to find a new solar panel design that would allow: Increasing the area of exposure to sunlight Increasing the Natural Frequency of the panel Decreasing the panel displacements due to thermal cycling or load Solar panel model geometry These objectives are conflicting therefore a certain trade-off will be admitted Since the model is symmetric, one quarter of the full scale panel has been analyzed Solar panel layers

20 Problem Definition Methodology: 1. A parametric solar panel geometry is created 2. One Modal, Structural, and Thermo-Mechanical analysis are carried out in Ansys 3. Starting from the Ansys result, modefrontier will find the best solution testing automatically several configuations Significant results Reduce prototyping costs test only the optimal solutions Gain competitive advantage finding the optimal design solution through the parametric optimization

21 Validation Of The Model in ANSYS The parametric problem analysis is modeled within the solver (ANSYS) Robustness and Thermal behavior Simulation of Solar Panel Modal Analysis is performed to find the frequency of the solar panel for the respective Modes Structural Analysis is performed to find the Deformation of the Solar Panel when subjected to Steel Ball Impact (UL/IEC Requirements) Thermo Mechanical Analysis is performed to find the deformation of the solar panel when subjected to thermal cycling test (EC Requirements) Ansys Thermo-Mechanical Analysis Ansys Modal Analysis Results available from these Analyses are: Frequency, Area, Displacements etc. Ansys Structural Analysis

22 Solar Panel Optimization Definition In modefrontier Create workflow in modefrontier Define the Inputs and their Domains as shown below: Input variables of the parametric model Parameter Thickness Length Width Young s Modulus Domain mm mm mm 6e+10 7e+10 Pa Random as DOE MOSA as Scheduler Set Ansys as an Application Node Set the Logic flow Set the Outputs Set the Objectives: max frequency, max area, min displacement Multi Objectives (functions to be maximized or minimized) Workflow in modefrontier

23 modefrontier - From DOE to Optimum The Design of Experiments algorithm (DOE) creates an initial population of possible designs. ModeFrontier starting from the initial population created with the DOE, explore all the domain of the parameters searching the maximum or minimum of the objective function(s) Initial configurations in the design space through DOE A trade-off curve behavior is typical of problems involving an optimization against conflicting objective, where we don't have an optimal solution, but rather a full set of optimal solutions. The whole process, from the DOE generation to the Pareto FRONTIER identification is carried out in an efficient and automated fashion by modefrontier. Pareto Frontier: the curve representing the optimal designs

24 Frequency Post Processing Bubble Plot #42 Area More than 200 configurations were computed Total CPU time required for the optimization: circa 4 hours

25 ModeFRONTIER Improved All The Parameters Original Parameter Value Optimised Parameter Value % Comparison (Area) 1,244 m 2 (Area) 1,284 m (Frequency) 70 Hz (Frequency) 73 Hz 4.3 (Displacement_1) 0,42 mm (Displacement_1) 0,31 mm 35.5 (Displacement_2) -0,19 mm (Displacement_2) -0,18 mm 5.5 (Displacement_3) -0,20 mm (Displacement_3) -0,19 mm 5.0 NOTE: This optimization has taken into account only 4 geometric parameters to improve the mechanical robustness of solar panel BUT several different parameters can also be optimized simultaneously to improve, for instance, the thermal efficiency and/or the electrical performance etc.

26 Case Study Conclusions In few hours modefrontier tested several configurations, the same task would have taken days for a single operator ModeFRONTIER found the optimum design achieving improvement for all the parameter specified: 3.2% area increase = increase in power output 4.3% frequency increase = increase in the range of applications where the panel can be used 35.5%, 5.5%, 5.0% deformation reduction due to mechanical and thermal loading = increase in product quality modefrontier created an automatic procedure: once the parametric model is set, the optimizator will keep iterating it till it finds the best configurations modefrontier finds the optimum solutions (pareto frontier), therefore the need of testing only the best configurations reducing the experimental phase and controlling the spending

27 Conclusions Optimization Benefits Selection of manufacturing process Reducing weight and material use Improving product performance Material use close to real limits Machining and assembly optimization Applied Materials Dresden Plant Source: Temperature field on a solar cell panel generated by 0.1 [A] current. Reducing time to market (no trial and error) High value components Virtual prototyping approach Market competitiveness ModeFRONTIER short learning curve

28 Cost Factor using modefrontier Optimizing FEM design Optimizing manufacturing design Reducing number of test Optimizing process Reducing energy consumption No scraps = high quality and efficiency Reducing resources (people etc.)

29 Thank You For Your Attention Please let us know if you have any questions on specific topics For further information, please contact: OZEN ENGINEERING, INC E. ARQUES AVE. SUITE: 207 SUNNYVALE, CA (408) We would like to help you achieve your goals