Multi-Disciplinary Multi-Objective

Optimization of Solar Panel Case Study

Optimization & Green Engineering

Maximize Area - Maximize Frequency - Minimize Displacements

www.ozeninc.com/Optimization

optimization@ozeninc.com

www.ozeninc.com/Optimization

optimization@ozeninc.com

OVERVIEW

1. Constraints in Manufacturing Solar Panels

2. The Optimal Design in 3 Steps

3. Multi-Disciplinary Analyses in ANSYS

Optimization Applied to a Solar Panel

4. Optimization Problem Definition - Workflow Creation in modeFRONTIER

5. Postprocessing – Analyzing the Optimum Configurations

6. Conclusions: Solar Panel Improvements through Optimization

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:

- test few configurations

- defining which are 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.

Problem 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 frequency of the panel

� Decreasing the panel displacements due to thermal cycling or

load

Thiese 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 – model geometry

Solar panel layers

The parametric model is the starting point: different variables (e.g. Length,

The Optimal Solution in 3 Steps

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

The parametric model is the starting point: different variables (e.g. Length,

Width, Thickness and Youngs Modulus of the Vycor Glass Part) can be varied to

meet the Test requirements and the set Multi-Objectives

Significant results

• Reduce prototyping costs � test only the optimal solutions

• Gain competitive advantage finding the optimal design solution through

the parametric optimization

The parametric problem analysis is modeled within the solver (Ansys)

ANSYS Multi-Disciplinary Analyses

Ansys Modal 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

Results available from these Analyses are: Frequency, Area, Displacements etc.

Ansys Thermo-Mechanical

Analysis

Ansys Structural Analysis

•

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)

Create workflow in modeFRONTIER

• Define the Inputs and their Domains as shown below:

Solar Panel Optimization Problem

Definition In modeFRONTIERTM

Parameter Domain

Thickness - 4.5 – -1.5 mm

Length 1300 – 1900 mm

Random as DOE MOSA as Scheduler

Input variables of the parametric model

Workflow in modeFRONTIER

Width 400 – 1500 mm

Young’s Modulus 6e+10 – 7e+10 Pa

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

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

The whole process, from the DOE generation to the Pareto FRONTIER identification

is carried out in an efficient and automated fashion by modeFRONTIER.

•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.

space through DOE

Pareto Frontier: the curve

representing the optimal designs

Post Processing – Parallel Axis Chart

The Parallel Chart allows the

viewing of all designs

simultaneously, with one vertical

axis for each variable or output

It is most useful for Filtering

Designs, especially in cases with

multiple, conflicting, objectives

Each Jagged Line across the Chart

represents one Design

#42

represents one Design

Configuration

Moving the sliders up or down,

hides all designs outside the range,

allowing the selection of Designs

of interest (RED LINE)

Design 42 is the optimum with high pressure recovery and high flow uniformity

Optimum = Design #42

#42

Post Processing – Bubble Plot

Fre

qu

en

cy

#42

Fre

qu

en

cy

• More than 200 configurations were computed

• ModeFRONTIER identifies 10 designs that are Pareto (non-dominated)

• Total CPU time required for the optimization: circa 4 hours

• More than 200 configurations were computed

• ModeFRONTIER identifies 10 designs that are Pareto (non-dominated)

• Total CPU time required for the optimization: circa 4 hours

Area

ModeFRONTIER Improved All The Parameters

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.

Quantification in dollars.... Or simple example of quantification??

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 • 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

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 www.ozeninc.com

• For more Design Optimization case studies visit: • For more Design Optimization case studies visit: http://ozeninc.com/default.asp?ii=256

• To learn more about modeFRONTIER visit us at: www.ozeninc.com/Optimization

• 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