Thermal energy storage and entropy generation in NEPCM-water systems: Mixed convection analysis in a double lid-driven enclosure with magnetohydrodynamic flow control

Numerical investigation of transpiration cooling mechanism and entropy generation within gradient porosity plate structure under supersonic condition

This research focuses on examining heat transfer and entropy generation within a porous cavity, which contains a hybrid nanofluid known as MWCNT-Fe3O4/H2O, due to its relevance to improving thermal management systems as well as increasing energy efficiency. The study is based on an advanced Darcy-Forchheimer-Brinkmann computational model that considers inertial forces, the fluid filling the cavity, and the resultant effects of applying a magnetic field. To provide the laminar and incompressible nature of the nanofluids flux, the Darcy-Forchheimer prototype is essential. While accounting for the inertial influence of advection in the permeable coating. To solve non-dimensional equations, we employ the finite element methodology and the Darcy-Forchheimer-Brinkmann prototype. Thus, several parameters, such as ([Formula: see text]); ([Formula: see text]); ([Formula: see text]); ([Formula: see text]); ([Formula: see text]) with the use of isotherm patterns, streamlines, and other graphs, we employed on the fluid flow is evaluated. Using the Finite Element Method to solve the governing equations numerically demonstrates that by increasing the porosity of the cavity, heat transfer rates can be increased by up to 30%; however, the increase in entropy production also increases with cavity porosity while the application of a greater magnetic field helps to reduce this effect on entropy generation by approximately 20%. The results indicate that the length of the cavity and the magnitude of the applied magnetic field also impact thermal performance within the cavity. The results of this study may assist in developing efficient thermal systems based upon the use of hybrid nanofluids, thereby increasing energy efficiency overall.

This study investigates unsteady rotating-disk transport of a water-based Casson nanofluid (Cu/H[Formula: see text]O) and a Casson hybrid nanofluid (Cu+Fe[Formula: see text]O[Formula: see text])/H[Formula: see text]O in a porous medium under magnetic forcing, suction, heat source/sink, and thermal radiation, while accounting for thermal relaxation to model non-Fourier heat conduction. Using similarity transformations, the governing multi-physics equations are reduced to a coupled nonlinear system for the radial and tangential velocities and temperature. To obtain reproducible semi-analytical benchmark solutions, the Homotopy Analysis Method (HAM) is employed with convergence-control parameters selected via [Formula: see text]-curves and residual-error minimization. A central contribution is a controlled comparison between Cu/H[Formula: see text]O and (Cu+Fe[Formula: see text]O[Formula: see text])/H[Formula: see text]O under identical parameter sets, focusing on velocity and temperature characteristics. The results show that Cu/H[Formula: see text]O yields higher velocity levels, whereas the hybrid nanofluid sustains higher temperatures within the thermal boundary layer, revealing a thermal-hydraulic trade-off for aqueous rotating systems. The magnetic parameter M physically quantifies Lorentz-force damping, leading to reduced velocities and modified thermal fields, while the thermal relaxation parameter [Formula: see text] delays the heat-flux response and tends to suppress temperature diffusion, thereby lowering the temperature profile. For the hybrid nanofluid, the skin-friction and Nusselt-number trends obtained from the plots are in good agreement with the tabulated values, confirming the consistency of the solution. Physically, increasing M enhances the skin-friction coefficient because stronger Lorentz braking demands greater wall shear to maintain the flow, while increasing Nr elevates the Nusselt number by strengthening radiative heat transport and steepening the wall temperature gradient. In addition, the thermodynamic irreversibility of the system is characterized through entropy generation and the Bejan number. Physically, increasing Br intensifies entropy generation because stronger viscous dissipation converts more mechanical energy into irreversible thermal energy, whereas increasing [Formula: see text] increases the Bejan number by making thermal irreversibility more dominant relative to frictional irreversibility under stronger non-Fourier heat-flux effects. The findings are relevant to rotating-disk cooling, porous heat-exchanger components, and nano-assisted aqueous lubrication/thermal management in compact and micro-scale rotating devices.

The purpose of this study is to investigates the complex magneto-thermo-diffusive transport and entropy generation characteristics in unsteady third-grade nanofluid flow over a stretching surface, driven by the need for accurate prediction of mixed convective heat-mass transfer mechanisms in advanced thermal systems. This work aims to quantify how magnetohydrodynamic behavior under the simultaneous influences of Cattaneo-Christov heat and mass diffusion, viscous dissipation, thermal radiation, nonuniform heat generation/absorption, Brownian motion, thermophoresis, and activation energy influences thermal performance within the Buongiorno nanofluid framework. The governing nonlinear partial differential equations are transformed into non-dimensional ordinary forms and solved numerically via a fourth-order Runge-Kutta method integrated with the shooting algorithm. To enhance predictive fidelity, the resulting datasets are trained through advanced ANN algorithms, and model accuracy is verified using regression metrics, error histograms, and mean square error analysis. The obtained graphical results evaluations reveal that intensified magnetic field intensity and unsteadiness significantly suppress fluid velocity, whereas thermal radiation, Eckert number, Brownian motion, and thermophoretic diffusion substantially enhance heat transport. Entropy generation increases significantly with higher Brinkman and radiation parameters. Quantitatively, increasing radiation parameter from 0.2 to 1.0 elevates the Nusselt number by 14.28%, while higher activation energy parameter from 0.5 to 2.0 suppresses the thermal transport rate by 12.63% and amplifying the mass transfer rate by 29.9%. The ANN framework achieves superior predictive accuracy and computational stability, enabling a robust platform for optimizing thermal systems and supporting advanced biomedical diagnostics such as early stage cancer detection and tumor classification.

The thermal control of peristaltic blood flow in a curved duct under a magnetic field is a relatively unexplored phenomenon. This research fills this gap by proposing the use of a blood-based hybrid nanofluid. The blood flow is assumed to be a non-Newtonian fluid, modelled by the Casson fluid model. It is mixed with gold (20 nm) and iron oxide (25 nm) nanoparticles. The equations are expressed in a cylindrical coordinate system in order to account for the curved nature of the duct. Peristaltic waves are considered to propagate along the wall. The governing equations are obtained using the low Reynolds number approximation (Reynolds number Re) and long wavelength approximation. The finite element method (FEM) with Python programming is applied for solving the resulting strongly nonlinear partial differential equations. The obtained results are also utilized to analyze the irreversibility of the system. The main findings indicate that increasing the aspect ratio enhances the blood flow velocity at the central part. It is found that the mono nanofluid has more improvement in central velocity compared to the hybrid nanofluid. Optimal control of the magnetic parameter and duct wall elasticity can enhance the flow efficiency. It also results in a reduction of thermal losses and thermodynamic irreversibilities.

Methyl chloride (CH₃Cl) is an essential intermediate in the manufacture of silicones, agrochemicals, amines, refrigerants, and synthetic rubber; however, conventional production routes are constrained by substantial energy inefficiencies and exergy destruction. This study seeks to enhance the hydrochlorination of methanol to methyl chloride by integrating heat exchangers (HE) as a waste‑heat recovery strategy. Simulation software was used to simulate both the baseline and heat‑integrated process configurations, employing the Peng–Robinson EOS to represent thermodynamic behavior. In the baseline system, the process required 12,302.48 kW of energy input and produced 9,028.60 kW of useful output, achieving a conversion of 73.4%, with unrecovered hot streams contributing significantly to entropy generation. The modified configuration introduced three heat exchangers (E‑100, E‑101, E‑102) to recover reaction and condensation heat, enabling feed preheating and reducing external utility demand. This integration increased conversion from 73.4% to 95%, raised energy output to 11,912 kW, and reduced both energy losses and exergy destruction. The resulting dataset from the optimized system was subsequently evaluated using machine learning models, among which Bayesian Ridge Regression (BRR) demonstrated the highest accuracy and stability, exhibiting superior MSE, MAE, and R² performance. Overall, the findings show that coupling heat‑integration strategies with machine‑learning analysis provides a robust pathway for improving energy efficiency, product quality, and predictive reliability in methyl chloride production. Copyright © 2026 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

This work provides a self-contained derivation of several fundamental results in stochastic thermodynamics, including the Jarzynski equality, Crooks fluctuation theorem, and the Clausius inequality. Although the principal theoretical conclusions are well established in the literature, the present approach departs from conventional formulations of stochastic entropy by employing a trajectory-independent probability density constructed as the marginal of the full path-space distribution. This construction establishes a direct connection between microscopic path statistics and macroscopic state probabilities, thereby providing a natural framework for quantifying the relative statistical weights of distinct dynamical histories leading to the same microstate. We demonstrate that entropy production and the fluctuation theorems emerge directly from the Gibbs inequality under the assumption of microscopic reversibility of trajectories along with the extension of the Gibbs-Shannon entropy to time-evolving ensembles. For reduced stochastic descriptions such as Langevin dynamics, the counterpart of microscopic reversibility is local detailed balance, which is inherited from the underlying time-reversal symmetry of the full system-reservoir dynamics and is the condition under which the present framework applies to effective open-system descriptions. We show that the change in microscopic entropy can be attributed to both underlying stochastic fluctuations and the statistical uncertainties inherent in thermodynamic ensembles. This dual perspective ensures that the average entropy production remains non-negative, providing a consistent microscopic basis for thermodynamic irreversibility through the statistical preference of forward over time-reversed trajectories. The connection between microscopic entropy flow and heat, as well as the subsequent derivation of the Clausius inequality and Jarzynski-Crooks relations, requires the additional assumption of an ideal thermal reservoir at a fixed temperature. While this formulation introduces no new physical or mathematical insights, it illuminates the universality of fluctuation theorems across diverse systems, offering a unified perspective that may serve a pedagogical purpose for those studying the statistical-mechanical foundations of non-equilibrium thermodynamics.

The present study investigates transient nanofluid flow in a vertical porous channel with buoyancy effects and Navier slip at the walls, with particular emphasis on thermodynamic irreversibility, entropy generation, and mixed convection. The study formulates dimensionless, coupled nonlinear governing equations for velocity, temperature, and entropy generation. These equations are solved numerically using a finite-difference scheme (FDS) that is second-order accurate in space and first-order accurate in time, capturing both transient and steady-state behaviors. Parametric analyses are conducted for permeability (Darcy number), buoyancy (Grashof number), pressure gradient, Biot number, Prandtl number, Brinkman number, and distinct Navier slip coefficients at the heated and cooled walls. Increasing permeability and buoyancy accelerate the flow and enhance convective heat transfer, but intensify entropy generation near the walls where velocity and temperature gradients are highest. Stronger pressure forcing sharpens the centerline velocity and increases near-wall irreversibility. Higher Biot and Prandtl numbers reduce velocity and temperature levels across the channel, while potentially increasing entropy generation due to steeper thermal gradients. Increased viscous dissipation raises temperatures near the heated wall and amplifies entropy generation near both plates.

Biological nervous systems are theorized to operate in the edge of chaos (EOC), a stable and locally active regime enabling energy-efficient information processing. Electronic implementations of such behavior have mainly relied on electrothermal phase transitions, which are power-intensive and unlike biological dynamics. Diffusive memristors, by contrast, harness ionic drift and diffusion resembling biological dynamics, yet their potential for EOC operation has remained unexplored. Here, we experimentally demonstrate ionically moderated EOC behavior in diffusive memristors. At the hundred-nanoampere level, the device exhibits sharp negative differential resistance (NDR) enhanced by a Ridley entropy production minimization transition, noise amplification and spontaneous oscillatory dynamics consistent with operation near the EOC. These emergent behaviors arise from coupled ionic transport, filament evolution and circuit dynamics, as confirmed by compact modeling. This system achieves power efficiency over three orders of magnitude greater than electrothermal EOC devices, introducing low-power neuromorphic elements that emulate brain-like dynamics through nanoionics.

Deep deoxygenation, defined as oxygen level ≤ 1%, is increasingly pursued to suppress oxidation-driven spoilage and side reactions in applications such as food preservation, yet industrial practice still relies on nitrogen purging enabled by pressure swing adsorption. Because oxygen removal by dilution and displacement depends on maintaining a nitrogen-oxygen concentration gradient, this approach requires substantial nitrogen throughput and high power input. This burden is especially pronounced in cold chain logistics, where refrigeration already dominates operating cost and emissions. Here we report an electrochemical environmental deoxygenation system that selectively transports oxygen from the controlled environment to ambient air through coupled oxygen reduction and oxygen evolution reactions. This pathway reduces entropy generation and operates closer to the thermodynamic minimum than pressure swing adsorption. When integrated with low-oxygen, moderate-temperature preservation, the system reduces total energy use by ~ 36% versus conventional low-temperature operation while maintaining or improving preservation quality, supporting scalable, energy-efficient oxygen management.

Conventional heat transfer fluids often fail to meet the thermal demands of advanced industrial systems. This study investigates electromagnetohydrodynamic (EMHD) flow of a trihybrid nanofluid containing copper, gold and silver nanoparticles dispersed in water over a porous stretching surface. The model incorporates temperature-dependent viscosity, convective heating, velocity slip, suction, viscous and thermal dissipation, buoyancy, electromagnetic forces, internal heat generation, mass diffusion and chemical reactions. The governing nonlinear partial differential equations are transformed to ordinary differential equations using a suitable similarity transformation and then solved using the fourth-order Runge-Kutta shooting method. Entropy generation and Bejan number analyses to show thermodynamic performance. An Artificial Neural Network (ANN) employing the Scaled Conjugate Gradient (SCG) algorithm is used to predict skin friction, Nusselt number and Sherwood number. A dataset of 200 points is used and divided into 70% training, 15% testing and 15% validation. The ANN demonstrates high predictive accuracy with overall correlation coefficients of 0.97209, 0.97951 and 0.97080 for skin friction, Nusselt number and Sherwood number, respectively. Results show that radiation has little effect on entropy generation under variable viscosity conditions but decreases the Bejan number. The investigation confirms the potential of trihybrid nanofluids better choice for nanotechnology and energy applications.

ABSTRACT This study investigates thermo-hydraulic and second-law performance of ethylene glycol – water (EG/W) mixtures in a heat exchanger tube with conical wire inserts. Experiments were performed under turbulent conditions for Re = 3300– 21,000 using three EG/W ratios and five insert spacings. Nusselt number, friction factor, THPF, entropy generation number, Bejan number, and second-law efficiency were evaluated. Increasing EG content enhanced Nusselt number but increased frictional irreversibility. Maximum entropy generation (0.9221) occurred for CW-0 with 40:60 EG/W at Re = 21,000, whereas maximum second-law efficiency (0.428) was obtained for CW-0 with water at Re = 3300.

Reshaping interfacial heat supply in the Boudouard reaction through variable-frequency microwave fields.

Bacterial persisters are antibiotic-tolerant phenotypes that underlie chronic and recurrent infections. Motility is a functional trait in the persister lifecycle, yet the physical dynamics of the flagellar motor in persistence remain poorly understood. Here, we systematically characterize the motor behavior of rifampin-induced E. coli persisters under high mechanical load, in comparison with mid-exponential phase cells. While persister motors show reduced overall activity and torque-generating capacity, they retain the rotation directional bias and key kinetic architecture. However, nonequilibrium thermodynamic analysis reveals a divergence: mid-exponential phase motors operate near a regime of quasi-detailed balance with minimal dissipation, whereas persister motors shift into a dissipative, nonequilibrium state. This state is characterized by measurable entropy production, increased time-irreversibility, and a more significant deviation from Poissonian switching statistics. Collectively, these findings demonstrate that the motor's kinetic machinery is largely preserved in persisters, but the energetic optimization in mid-exponential phase cells is compromised, revealing a distinct thermodynamic cost of antibiotic stress.

The Gibbs free energy generated from the mixing of seawater and freshwater across a salinity gradient is considered one of the most significant yet underutilized renewable energy sources. Membrane-based reverse electrodialysis (RED) enables direct electricity generation from osmotic energy by harnessing the net ion flux driven by concentration gradients across ion-selective membranes. However, entropy generation caused by non-selective ion mixing significantly limits the power density of RED systems. Therefore, enhancing membrane ion selectivity is critical. 2D covalent organic frameworks (COFs) demonstrate remarkable potential for osmotic energy conversion due to their aligned 1D nanochannel, high porosity, and organized ionic groups. Herein, we present a strategy leveraging electrostatic repulsion to controllably fabricate TpPa-(SO3H)X COF (X = 0.5, 1, 1.5, 2) membranes with varied ionic group density. Via stoichiometric modulation during COF synthesis, we achieved variation in sulfonic acid group density within nanochannels, enabling optimized charge-governed ion selectivity. Under salinity gradients mimicking seawater/freshwater conditions (0.5m/0.01m, NaCl), the device delivered an exceptional power output density of 24.53W m-2, representing a 4.9-fold enhancement over commercial benchmarks (5W m-2). This study presents a novel method and strategy for the design and application of ion-selective membranes in mass transport and efficient energy conversion.

The nonlinear dynamics of transverse and polarization modes of a broad-area vertical-cavity surface-emitting laser (BA-VCSEL) exhibit, without any external perturbation, chaos with high correlation dimension, large bandwidth (BW), and good spectral flatness over a wide range of currents. We leverage this for high bit-rate entropy generation and random number generation (RNG), passing the NIST tests with rates up to 150 Gb/s, and observe a correlation between the correlation dimension and the number of passed NIST tests. The RNG shows consistent performance across a wide range of parameters. In contrast to other setups, our system does not require optical feedback or optical injection to generate chaos, making it simple, compact, and robust.

ABSTRACT Mathematical modelling of biological fluids is essential for understanding physiologically relevant transport mechanisms in micro-scale biomedical systems. This study examines the peristaltic transport of an ionized non-Newtonian biological fluid, modelled as a fractional second-grade fluid, through a ciliated micro-vessel under electro kinetic effects. The analysis emphasizes the role of the electric double layer formed near the peristaltic wall and its influence on fluid motion. The governing nonlinear equations are simplified using the long-wavelength and low-Reynolds-number approximations together with the Debye-Hückel linearization. Thermal effects are incorporated through a modified bio heat equation accounting for viscous dissipation and heat conduction. Thermodynamic irreversibility is evaluated by quantifying entropy generation due to temperature gradients, viscous effects, and electric field interactions. Exact analytical solutions of the resulting boundary value problem are derived and illustrated using Mathematica. The results reveal that increasing the Helmholtz-Smoluchowski velocity U Hs and Debye length parameters m significantly enhances axial velocity due to intensified electroosmotic forces. Parameter polarity strongly influences near-wall and core flow behavior. Additionally, increased cilia length ( ε ) and electrokinetic width retard core flow while accelerating transport near the walls. These findings provide valuable insights for the modelling-based design and optimization of biomedical microfluidic devices, including artificial cilia systems.

In this study, heat transfer and friction factor were experimentally investigated for laminar, transitional, and turbulent flow regimes in circular mini-micro channels carrying pure water flow. Unlike many studies in the literature, a systematic experimental study was conducted under a constant surface temperature condition, encompassing multiple hydraulic diameters (381, 500, 750, and 1000μm) and a wide range of Reynolds numbers (from 228 to 3120, with different flow rate 0-50 mL/min). The experimental setup consisted of a piston pump with precise flow and pressure control, a pressure regulator, heating-cooling baths, and high-accuracy temperature and pressure sensors. Thermo-hydraulic performance, including heat transfer, friction factor, pumping power, and entropy generation, were determined by simultaneously measuring the inlet and outlet temperatures and the pressure drop in the mini-micro channel. The obtained results were found to be in good agreement with the experimental data studied in the literature with similar hydraulic diameters. Based on the results at a flow rate of 50 mL/min, the maximum heat transfer was 45.96W at a hydraulic diameter of 750μm, while the minimum heat transfer at the same flow rate was 37.20W at a hydraulic diameter of 381μm. In terms of pressure drop, the highest was 16.358bar at a flow rate of 50 mL/min and a hydraulic diameter of 381μm, while the lowest was 0.089bar at a flow rate of 50 mL/min and a hydraulic diameter of 1000μm. These findings reveal the balance between heat transfer performance and pumping power requirements in mini-micro channel systems, offering comprehensive and original contributions to the literature on operational performance and efficiency optimization.

This study presents the wavelet-based physics-informed neural networks (PINNs) simulation to analyse entropy generation in hybrid nanofluid peristaltic flow through a curved porous channel. The flow and heat transfer characteristics of a water-based hybrid nanofluid containing multi-walled carbon nanotubes (MWCNTs) and magnetite left( {Fe_{3} O_{4} } right) nanoparticles are examined in a Darcy porous medium. The model accounts for nonlinear thermal convection, nonlinear radiation, and entropy generation, providing a comprehensive assessment of transport and thermodynamic irreversibility’s. The governing equations are formulated in curvilinear coordinates, transformed into dimensionless form, and solved using wavelet-PINNs. Unlike conventional numerical schemes, the PINN approach embeds boundary conditions and physical constraints directly into the training process, ensuring accuracy, mesh-free implementation, and generalization across parameter ranges. The results indicate that variations in key physical parameters, including buoyancy forces, nonlinear convection, porous medium resistance, thermal radiation, curvature, and viscous dissipation, significantly influence the flow, thermal and entropy generation characteristics. The velocity, temperature, and entropy generation profiles exhibit non-uniform behaviour across the channel, with notable variations near the walls due to stronger gradients. Entropy generation is observed to be higher in regions of intensified velocity and temperature gradients, while relatively lower values occur in the core region. The present PINN framework provides a stable and efficient approach for analysing hybrid nanofluid transport in complex geometries, with potential application in the design and optimization of thermal systems involving peristaltic transport in porous channels.