Computation

25 pages, 1819 KB

Open AccessArticle

AI-Driven Thermodynamic Evaluation of Beta-Type Stirling Engine Using CFD Simulation and Numerical Calculations

by Amir H. Shahriari, Majid Monajjemi and Fatemeh Mollaamin

Computation 2026, 14(6), 119; https://doi.org/10.3390/computation14060119 - 22 May 2026

This study presents an AI-assisted thermodynamic and computational fluid dynamics (CFD) evaluation of a β-type Stirling engine to improve its thermal efficiency and indicated power output. The engine performance was investigated using Restricted Dimensions Thermodynamics (RDT), the Schmidt thermodynamic model, and three-dimensional CFD simulations under various operating and geometric conditions. Key parameters including rotational speed, phase angle, piston diameter, displacer stroke, porosity, and charged pressure were systematically analyzed to determine their influence on engine behavior. A feed-forward artificial neural network (ANN) trained using the Levenberg–Marquardt optimization algorithm was integrated with CFD-generated datasets to predict engine performance and accelerate the optimization process. The AI-assisted optimization was coupled with the Variable Step-size Simplified Conjugate Gradient Method (VSCGM) to identify near-optimal operating conditions while reducing computational cost. Simulation results demonstrated that the optimization process improved the indicated power from 180.33 W to 185.44 W and increased thermal efficiency from 10.32% to 11.54%. The results also showed close agreement between predicted and experimental pressure–temperature profiles, confirming the reliability of the proposed methodology. Furthermore, CFD analyses revealed that increasing piston diameter and optimizing porosity enhanced heat transfer and pressure distribution within the engine chambers, resulting in improved thermodynamic performance. The proposed AI-driven framework provides a reliable and computationally efficient approach for the design and optimization of advanced β-type Stirling engines operating under realistic thermal conditions. Full article

(This article belongs to the Section Computational Engineering)

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22 pages, 4406 KB

Open AccessArticle

Numerical Investigation on Cathode Gas Diffusion Layer with Conical Frustum Grooves for Enhancing Performance of Proton Exchange Membrane Fuel Cell

by Wei Zuo, Xiongwei Yao, Yimin Li and Qingqing Li

Computation 2026, 14(6), 118; https://doi.org/10.3390/computation14060118 - 22 May 2026

Abstract

To address performance limitations in proton exchange membrane fuel cells (PEMFCs), this work proposes and numerically investigates a cathode gas diffusion layer (GDL) with conical frustum grooves. A systematic comparison is performed across three GDL configurations: a baseline structure without grooves, a design with cylindrical grooves, and the proposed conical frustum grooves. The results demonstrate that the conical frustum grooves effectively enhance liquid water removal, oxygen mass transport, membrane current density, and peak power density. This improvement arises as the grooves expand transport pathways for both liquid water and oxygen, facilitating more robust electrochemical reactions. A parametric analysis is further conducted to evaluate the effects of groove spacing, depth, top radius, and bottom radius. Reduced groove spacing, together with increased groove depth, top radius, and bottom radius, consistently improves water management and oxygen delivery. However, membrane current density and power density do not vary monotonically with groove depth and bottom radius. The optimal values for these two parameters are identified as 0.3 mm and 0.5 mm, respectively. Full article

(This article belongs to the Special Issue Computational Modelling of Transport Phenomena in Advanced Energy Systems)

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16 pages, 589 KB

Open AccessArticle

A State-Space Agent-Based Model for Infectious Disease Spread

by Durward A. Cator, Martial L. Ndeffo-Mbah and Ulisses M. Braga-Neto

Computation 2026, 14(6), 117; https://doi.org/10.3390/computation14060117 - 22 May 2026

Abstract

We present a novel framework for epidemiological disease spread modeling that combines agent-based simulation with Boolean state-space representations and optimal filtering for state estimation under noisy observations. Our approach models individual agents in discrete Susceptible-Exposed-Infected-Recovered (SEIR) states using a compact 2-bit Boolean representation, with agent interactions governed by scheduled contact patterns. To address the challenge of inferring latent infection states from limited and noisy testing data, we develop two complementary inference approaches: (1) a Boolean Kalman particle filter for small populations that tracks the full joint distribution over agent states, and (2) a mean-field approximation for large populations that factorizes the posterior into independent marginal distributions, enabling scalability to realistic population sizes. Unlike continuous-state Kalman filters, our methods naturally handle the discrete nature of epidemiological states while accommodating realistic observation models where only a subset of agents are tested at each time step, with test results subject to false positive and false negative errors. We demonstrate that this framework enables accurate reconstruction of population-level infection dynamics and individual agent states from sparse, noisy observations across populations from 100 to 50,000 agents, providing a computationally tractable approach for real-time epidemic monitoring. Full article

(This article belongs to the Section Computational Social Science)

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Computation, Volume 14, Issue 6 (June 2026) – 3 articles

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