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:
Perform transient heat transfer analysis of a solar panel integrated with a passive heatsink and perform shape optimization on heatsink fins to maintain acceptable temperatures, through out a summer day.
Problem identification:
Temperature effect on PV solar module
The temperature demonstrates a significant effect on the output performance curves of PV solar modules when irradiance intensity is kept constant. A minor variation is observed w.r.t 1000 W/m2 when the temperature varies from 10 °C to 70 °C. The voltage shows an increasing trend in I–V performance curve when the atmospheric temperature reduces. Also, the solar cell generates more power, when the atmospheric temperature reduces. Thus solar cell shows an inverse relationship with temperature.
Thus, this work aims to reduce the maximum temperature of the solar panel by optimizing the thermal performance of a passive heatsink using optimization algorithms.
Scope of the project:
Perform transient thermal analysis to determine –
1.Efficacy to cool near to below acceptable temperature limit: ~330K.
3.Analyze heat flux through the model.
4.Inspect temperature gradient over different parts of the solar panel.
5.Analyze heat induced stresses on the FE model.
6.Optimize the parametric model to achieve acceptable temperatures in order to achieve high effiency of the P-V cells.
Geometric analysis of the model: Exploded view
Determination of loads and boundary conditions
Irradiance effects on PV solar module
The effect on solar PV model I–V* and P–V α characteristics curves are depicted in the figure by varying the intensity of irradiance from 200 W/m2 to 1000 W/m2 at a constant temperature of 25 °C. It is observed that the current remains constant with rising voltage up to 30 V after which it decreases. Moreover, the current increases while increasing the irradiance intensity. This demonstrates that irradiance has a substantial effect on short-circuit current, at the same time open circuit voltage is quite low. The maximum power evidence exists on power performance curves. The generation of power by solar PV model is increased by increasing the intensity of solar irradiance.
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.
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
Shape synthesis of the heat sink using optimization techniques using process apps on the 3DEXPERIENCE platform
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 optimization technique used in this study is MISQP
• MISQP - Mixed Integer Sequential Quadratic Programming
• Branch and Bound Integer search Problem and Design Space:
• Well-suited for highly non-linear design spaces
• Well-suited for problems with integer and Boolean variables
• Well-suited for long-running simulations Gradient-based
• Exploits the local area around the initial design point
• Uses branch-and-bound for integer variables
• Rapidly finds a local optimum design
• Handles inequality and equality constraints directly
This method 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 gradually updating it using the BFGS (Broydon-Fletcher-Goldfarb-Shanno) method. On each iteration, a quadratic programming problem is solved to find an improved design, until the final convergence to the optimum design.
It can be observed that the optimization study using the MISQP algorithm minimizes the objective function to get the maximum temperature to come down to 300K.
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.
AMGA- Archive based Micro Genetic Algorithm Classification
•AMGA - Archive based Micro Genetic Algorithm Classification:
•Multi-objective Exploratory Technique Problem and Design Space:
-Well-suited for highly non-linear search spaces
- Well-suited for discontinuous and non-convex search spaces
-Well-suited for highly constrained search spaces
-Designed to handle 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), each objective parameter is treated separately. Standard genetic operation of mutation and crossover are performed 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 takes into account the diversity of the solution. By the end of the optimization run a Pareto set is constructed where each design has the "best" combination of objective values and improving the Paretoone objective is impossible without sacrificing one or more of the other objectives.
Results
It can be observed that shape synthesis using AMGA ran for 220 iterations and the 186th iteration came out to minimize the objective function of the temperature to be 341.439K with the thickness of the heatsink increased to 13.69mm.
