Szczegóły publikacji
Opis bibliograficzny
Artificial neural network surrogate for entropy generation analysis in PCM-based thermal energy storage / Łukasz ŁACH // Energies [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1073 . — 2026 — vol. 19 iss. 21 art. no. 5164, s. 1–16. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 15–16, Abstr. — Publikacja dostępna online od: 2026-10-30
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 170973 |
|---|---|
| Data dodania do BaDAP | 2026-11-02 |
| Tekst źródłowy | URL |
| DOI | 10.3390/en19215164 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Energies |
Abstract
Latent heat thermal energy storage (LHTES) systems based on phase change materials (PCMs) are central to improving energy efficiency in solar–thermal, waste-heat recovery, and building energy management applications; however, minimizing the thermodynamic losses that degrade their exergetic performance requires fast and accurate prediction of entropy generation across wide operating envelopes. This study develops a coupled numerical–surrogate framework for predicting entropy generation during one-dimensional conduction-dominated melting of a PCM slab. An explicit finite-difference enthalpy solver is formulated in fully dimensionless variables governed by five independent parameters: the Stefan number (Ste), Biot number (Bi), initial subcooling (𝜃𝑖 ), dimensionless temperature difference (Π ), and solid-to-liquid conductivity ratio (𝑟𝑘 ). The solver is verified against the analytical Neumann solution, achieving mean front-position errors below 0.7%. A Latin Hypercube Sampling campaign of 400 parameter combinations yields 378 converged cases spanning nearly three orders of magnitude in the entropy generation number 𝑁𝑠 . A compact multilayer perceptron (5→64→64→32→1 ; 6657 parameters) trained on these data reproduces 𝑁𝑠 with a test-set coefficient of determination 𝑅2=0.991 and a mean absolute percentage error of 5.73% while delivering a speed-up of approximately four orders of magnitude over the full solver. A global sensitivity analysis reveals that the dimensionless temperature difference Π is the dominant driver of entropy generation (Spearman 𝜌=+0.80 ), followed by the Stefan number (𝜌=−0.51 ) and the Biot number (𝜌=+0.25 ), whereas initial subcooling and conductivity ratio exert only marginal influence. The framework enables rapid, second-law-aware screening of PCM operating conditions, supporting energy-efficient design and optimization of thermal energy storage systems.
