Analysis of passively cooled Solar Panel solution: Shape synthesis using optimization algorithms: This simple example demonstrates the optimization of heat-sink design parameters for maintaining high power output.

Objective:

The study aims to perform transient heat transfer analysis of a solar panel integrated with a passive heatsink and optimize the heatsink fins’ shape to maintain an acceptable temperature limit over 24 hours on a summer day.

Problem identification: 

Temperature effect on PV solar module

Temperature significantly affects the output performance curves of PV solar modules when irradiance intensity remains constant. Specifically, when the temperature varies from 10 °C to 70 °C, only minor changes occur at 1000 W/m². Moreover, the voltage in the I–V curve increases as atmospheric temperature decreases, and the solar cell generates more power under lower temperatures. Therefore, the solar cell exhibits an inverse relationship with temperature. Consequently, this work seeks to reduce the maximum temperature of the solar panel by optimizing the thermal performance of a passive heat sink using advanced optimization algorithms.

Scope of the project:

The project involves performing transient thermal analysis to:

• Assess the heatsink’s efficacy in cooling the panel below the acceptable temperature limit (~330 K).

• Analyze heat flux throughout the model.

• Inspect temperature gradients across different parts of the solar panel.

• Evaluate heat-induced stresses on the finite element (FE) model.

• Optimize the parametric model to achieve acceptable temperatures and enhance PV cell efficiency.

Geometric analysis of the model: Exploded view

Determination of loads and boundary conditions :

Irradiance effects on PV solar module

The figure shows the I–V and P–V curves of the solar PV model as we vary the irradiance intensity from 200 W/m² to 1000 W/m² at a constant temperature of 25 °C. The current remains constant as voltage rises up to 30 V, then decreases. Increasing the irradiance intensity also increases the current. These results demonstrate that irradiance strongly affects the short-circuit current, while the open-circuit voltage stays relatively low. The power performance curves show the maximum power, which rises as the solar irradiance intensity increases.

Determination of simulation conditions

Following assumptions are made for simulation 

1.Initial temperature of panel is 300K

2.Power due to solar irradiance 1000W/m2

A.The solar irradiance on a solar panel can be depicted be the following curve:

3.Film Coefficient is 5W/m2

4.Acceptable surface temperature upper limit is 330-350 K.

Initial conditions

• Initial Temperature: 300K (~26°C)
• Material: Aluminum
• Heat flux Amplitude: 1000W/m2
• Heat Flux function: Sinusoidal
• Film Coefficient: 5W/K.m2
• Mesh element type: DC3D4
• Emissivity: 0.25

Determination of simulation conditions

Following assumptions are made for simulation –

1.Initial temperature of panel is 300K

2.Power due to solar irradiance 1000W/m2

A.The solar irradiance on a solar panel can be depicted be the following curve:

1.Film Coefficient is 5W/m2

2.Acceptable surface temperature upper limit is 330-350 K.

Figure 3: Solar Irradiance 
 

Results of Transient transfer

It can be observed that the temperatures exceed the optimal temperature limit i.e. 330 K when subjected to time variable heat flux. Thus optimizing the shape and geometry of the heat sink is quintessential for the optimal cooling of the Solar panel, thus ensuring highest efficiency.

Results of Von Mises stresses due to Transient Heat Transfer  

Results of displacement due to Transient Heat Transfer  

Objective: Optimization is the process of finding the best solution to a problem, given a set of constraints. It is a powerful tool that can be used to improve the performance of a wide range of systems, including engineered products.

Single objective Shape synthesis of the heat sink using optimization algorithms

The study employs MISQP (Mixed Integer Sequential Quadratic Programming), which is suitable for:

• Highly non-linear design spaces

• Problems with integer and Boolean variables

• Long-running, gradient-based simulations

Additionally, MISQP:

• Exploits the local area around the initial design point

• Uses branch-and-bound for integer variables

• Rapidly identifies a local optimum design

• Handles inequality and equality constraints directly

The algorithm builds a quadratic approximation to the Lagrange function and linear approximations to all output constraints at each iteration, starting with the identity matrix for the Hessian of the Lagrangian and updating it using the BFGS method. On each iteration, it solves a quadratic programming problem to improve the design until convergence. As a result, the MISQP algorithm reduces the maximum temperature to 300 K.

Multi-Objective Shape Synthesis of passive heatsink using archive-based micro-genetic algorithm

• The objective of this optimization study is to lower the maximum temperature of the solar panel while also decreasing the amount of material needed for the manufacturing of the heatsink.
• Therefore the parameter “thickness” has also been added as an objective to be minimized with the previous objective, Max. Temperature.

Archive-based Micro Genetic Algorithm (AMGA):

• AMGA – Archive-based Micro Genetic Algorithm Classification

• Purpose: Multi-objective exploratory technique for complex problems and design spaces

Applications:

• • It works well in highly non-linear search spaces

  • This method effectively handles discontinuous and non-convex search spaces

  • The algorithm is suitable for highly constrained search spaces

  • Additionally, it is designed to manage highly multi-modal functions with many local optima

Gradient-Based: No Features: Each objective is treated separately and a Pareto front is constructed by selecting feasible non-dominated designs.

In the Archive-based Micro Genetic Algorithm (AMGA), the algorithm treats each objective parameter separately. It performs standard genetic operations, such as mutation and crossover, on the designs.

The algorithm maintains a search history and the Selection process is based on a myriad of different heuristics. It uses the first tier fitness is assigned based on the domination level of a solution in the population.

-The second tier fitness is based on the contribution of the solution to the search history of the algorithm and  

-The third tier of fitness considers the diversity of the solution. By the end of the optimization run, the algorithm constructs a Pareto set, where each design achieves the ‘best’ combination of objective values. Improving one objective in the Pareto set requires sacrificing one or more of the other objectives.

Results

Figure 10: AMGA synthesis

The AMGA shape synthesis ran for 220 iterations and minimized the temperature to 341.439 K on the 186th iteration, while increasing the heatsink thickness to 13.69 mm.

• Author
• Recent Posts

Debaditya Chakraborty

Industry Process Consultant at Dassault Systèmes

Industry Process Consultant at Dassault Systèmes. Debaditya is passionate about innovation in multi-body dynamics, CAD, and FEA, specializing in multi-link mechanism design using data-driven methods. He has experience in Metal Additive Manufacturing simulations, ship structural analysis, and FEA process automation.

Latest posts by Debaditya Chakraborty (see all)

• ABAQUS Pipe Intersection: Hex vs Shell Elements - 10 November 2025
• SIMULIA Surrogate Modelling 101: AI & ML in Physics Simulations - 8 October 2025
• Optimized Thermal Analysis of Solar Panels with MODSIM on 3DEXPERIENCE - 15 September 2025