Thermodynamics-informed neural networks for the design of solar collectors: an application on water heating in the highland areas of the andes




HIGHLIGHTS
  • What: The authors propose using physics-informed neural networks (PINNs) to predict optimal design conditions in a range of data that not only characterized the highlands of Ecuador but also similar geographical locations. The outcomes of this study are anticipated to extend beyond theoretical contributions, opening scope to practical applications and design frameworks that can be implemented within a similar Ecuadorian context and that could serve for solar collector studies , thereby exemplifying a tailored approach to renewable energy design methods. These models integral to the models described inneural the previous sections . The outcomes of this study have several practical applications.
  • Who: Mauricio Cáceres and colleagues from the (GIEMA), Facultad Ciencias have published the article: Thermodynamics-Informed Neural Networks for the Design of Solar Collectors: An Application on Water Heating in the Highland Areas of the Andes, in the Journal: Energies 2024, 4978 of /2024/
  • How: The approach is grounded in a theoretical thermodynamics` framework which was utilized to generate synthetic data sets. The neural model was developed in the software NeuroSolution 7 and da ganized in training (60%) cross-validation (15%) and testing (25%) as shown i 14 of 27
  • Future: This approach bridges the gap between theoretical physics and practical engineering design offering a new perspective on optimizing renewable energy systems. By undertaking this future research the study will address potential limitations and strengthen the overall impact of the proposed approach. Future research should focus on refining and scaling PINNs for broader applications across different climatic regions and renewable energy systems. Future research could explore the expansion of this ANN methodology to other regions with different climatic conditions ensuring broader applicability and validation.
SUMMARY

    This research pivots on to assess the influence of the design parameters of flat-plate solar collectors in the overall efficiency of the collector through the lens of thermodynamics-informed artificial neural_networks (ANN) to improve the efficiency of solar collectors, offering a synergy between traditional engineering design and advanced machine_learning concepts. This gap, particularly in region-specific modeling and thermodynamics-informed ANN optimization, is addressed by this study through the use of physics-informed neural_networks. By incorporating artificial neural_networks into the design process, this study addresses the inherent nonlinearities in solar collector performance, which are often challenging to capture using purely analytical or empirical models. Physics-informed neural_networks are lever-aged to not only account for these nonlinear behaviors, but also to integrate thermodynamic equations directly into the model. Artificial neural_networks represent a cornerstone of computational science, inspired by the biological neural_networks that constitute animal brains. This study addresses two primary objectives: affirming the validity of artificial neural_networks as a method for enhancing the design and efficiency of solar collectors and tailoring this method to the specific environmental conditions of Ecuador. The validation of the physics-informed neural_networks model against experimental data from Alvarez et_al demonstrates its robustness and predictive power. By leveraging the synergy between validated thermodynamic equations and artificial neural_networks, this research provides a novel framework that enhances the precision of solar collector designs. This study shows that physics-informed neural_networks offer a promising and more adaptable alternative to traditional thermodynamic models for predicting flat-plate solar collector performance, especially in handling the nonlinear behavior of the data. @@

LAY DEFINITIONS
  • solar collectors: A solar thermal collector collects heat by absorbing sunlight. A collector is a device for capturing solar radiation
  • artificial neural networks: Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain
  • Design: Design is the creation of a plan or convention for the construction of an object, system or measurable human interaction. Design has different connotations in different fields
  • solar energy: Solar energy is radiant light and heat from the Sun that is harnessed using a range of ever-evolving technologies such as solar heating, photovoltaics, solar thermal energy, solar architecture and artificial photosynthesis. The large magnitude of solar energy available makes it a highly appealing source of electricity
  • water heating: Water heating is a thermodynamic process that uses an energy source to heat water above its initial temperature. Typical domestic uses of hot water include cooking, cleaning, bathing, and space heating

Licence: cc-by

Site reference: https://www.mdpi.com/1996-1073/17/19/4978/pdf?version=1728097135

DOI reference: https://www.doi.org/10.3390/en17194978

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source https://magazine.sciencepod.net/thermodynamics-informed-neural-networks-for-the-design-of-solar-collectors-an-application-on-water-heating-in-the-highland-areas-of-the-andes/

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