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Design Optimization Applied to the Solar Industry
Piero Marcolongo, [email protected]
Alberto [email protected]
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
Source: www.solyndra.com
Source: www.skyline-solar.com
The P.I.D.O. approach offers design automation procedures thatanalyzes and optimizes the entire design process by means of:
• Design of Experiments (DOE)
• Optimization Algorithms
• Decision-Making Procedures
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
What Is Optimization?
Source: www.skyline-solar.com
• What made it a complex task?The potentially huge number of options to be tested
• What qualifies an optimization technique?
The search strategy
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)
Optimization = Max F(x) or Min F(x)
• Wide acceptance angle for high yield and lower cost
• Decrease optical losses
• Design to avoid chromatic aberrations and cell mismatching
Temperature
Stress
Mass
Deformation
Input: Material, Geometry, # of fins for cooling system Output: Temperature, Stress, Mass, Flow Uniformity
Traditional vs. Innovation
P.I.D.O. Approach = Automation
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
Source: www.solfocus.com
modeFRONTIER is written to allow easy coupling to almost any computer aided engineering (CAE) tool, whether commercial or in-house
Logic Flow
Data Flow
P.I.D.O. as applied to CAE
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?
Loading History
Multi – Objective Optimization of a BGA Package
Min Plastic Work Min. Displacement A-B
DOE : 500 Designs
Sensitivity Analysis – What is Important and What is Not
Student Chart – Parameter Correlation with Plastic Work
Example of Optimization against two conflicting Objectives, both to be minimized
The Multi-Objective Optimization Process
The smart algorithms kick in and identify
the OPTIMAL design configuration
The design space
exploration is started
An initial population of
designs is generated
(DOE)
Optimal Solution: Plastic Work
BGA Optimal Solution
In this case no Pareto Frontier is found since the two objectives are correlated and ONE optimal solution is identified
Mechanical parameters calibration of LS-DYNA composite material models with respect to experimental information of fabric reinforced sandwich laminates
Reverse Engineering - Model Calibration
Results of material model characterization can then be used to predict reliability crashing behaviour of PTW protective and sport equipments
NUM
EXP
F-t E-t
Multi-objective analysis demonstrates good compliance (D< 5%) between numerical and experimental results
Numerical and experimentaldrop tests [0/45/0]S laminates
Match
Trial and Test best solutions
F-t E-t
Calibration of Models Matching Results from Lab Tests
modeFRONTIER automatic procedure
Optimization & Green Engineering Maximize Area - Maximize Frequency - Minimize Displacements
Multi-Disciplinary Multi-ObjectiveOptimization of Solar Panel Case Study
www.ozeninc.com/[email protected]
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
Optimization Applied to a Solar Panel
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.
Constraints In Manufacturing Solar Panels
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
Solar panel layers
Optimization Objective Definition
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
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
Problem Definition
The parametric problem analysis is modeled within the solver (ANSYS)
Results available from these Analyses are: Frequency, Area, Displacements etc.
Ansys Modal Analysis
Ansys Thermo-Mechanical Analysis
Ansys Structural Analysis
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 61646 Requirements)
Validation Of The Model in ANSYS
Create workflow in modeFRONTIER
• Define the Inputs and their Domains as shown below:
Parameter Domain
Thickness - 4.5 – -1.5 mm
Length 1300 – 1900 mm
Width 400 – 1500 mm
Young’s Modulus 6e+10 – 7e+10 Pa
Random as DOE MOSA as Scheduler
Multi Objectives (functions to be maximized or minimized)
• Set Ansys as an Application Node
• Set the Logic flow
• Set the Outputs
• Set the Objectives: max frequency, max area, min displacement
Input variables of the parametric model
Workflow in modeFRONTIER
Solar Panel Optimization Definition In modeFRONTIER
The whole process, from the DOE generation to the Pareto FRONTIER identification is carried out in an efficient and automated fashion by modeFRONTIER.
•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)
•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.
Initial configurations in the design space through DOE
Pareto Frontier: the curve representing the optimal designs
modeFRONTIER - From DOE to Optimum
Fre
qu
en
cy
• More than 200 configurations were computed
• Total CPU time required for the optimization: circa 4 hours
#42
Area
Post Processing – Bubble Plot
Original Parameter Value Optimised Parameter Value % Comparison
(Area) 1,244 m2 (Area) 1,284 m2 3.2
(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.
ModeFRONTIER Improved All The Parameters
• 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
Case Study Conclusions
• Selection of manufacturing process• Reducing weight and material use• Improving product performance• Material use close to real limits• Machining and assembly optimization
Conclusions – Optimization Benefits
Applied Materials Dresden Plant
Source: www.appliedmaterials.com
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
• 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.)
Cost Factor using modeFRONTIER
Thank You For Your Attention
For further information, please contact:
OZEN ENGINEERING, INC.
1210 E. ARQUES AVE. SUITE: 207
SUNNYVALE, CA 94085
(408) 732-4665
www.ozeninc.com
• Please let us know if you have any questions on specific topics
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