Search Results (57)

Most water supply systems rely on free chlorine residual to ensure disinfection through the network and at the user’s tap. Temperature increase is known to accelerate the chlorine decay process and is typically associated with water quality deterioration. This is a challenging situation under the current climate change scenario, which is bound to increase average temperatures and the intensity and frequency of extreme-temperature events. Moreover, water temperature varies through the supply network due to seasonal changes and thermal interaction, so it is not straightforward to model chlorine evolution through the network considering temperature effects. Previous works have highlighted the importance of considering the Arrhenius formula when accounting for temperature changes in the bulk chlorine decay coefficient (typically characterized through bottle tests), but these studies have never explicitly considered the uncertainty of the bulk decay coefficient itself. Recent studies have identified that the uncertainty of the bulk decay coefficient may be relevant (>15%) and should be considered when cross-comparing bottle test results (e.g., at different temperatures). The aim of this work is to propose a new method that statistically computes the mean and standard deviation of the key parameters in the Arrhenius formula (the reference bulk decay coefficient $k_{b_0}$ and activation coefficient E/R) from free chlorine residual bottle test results (with replicated measurements over samples from the entrance to the network) at different temperatures. This approach (here called the ensemble temperature state estimation approach) ensures that bottle test measurements at different temperatures are jointly assessed to derive an equation that provides the bulk decay coefficient at any water temperature. Therefore, the new method improves the characterization of the bulk decay component (and its associated uncertainty) and could be crucial for improving the understanding and modeling capabilities of complex chlorine dynamics within supply infrastructure. Full article

This study developed and validated a new simple and improved two-parameter high-quality equation of state (EOS) for hydrogen that can be used with less computational burden for the accurate description of various processes regarding underground hydrogen storage. Variation in hydrogen gas density with pressure was described by the modified power-law equation by relating density variation to deviations of density from the low-end and high-end limit values of density. The high-end limit density dependence on temperature was described by an Arrhenius-type asymptotic exponential function. The parameters of this EOS were determined by requiring thermodynamic consistency. This simple two-parameter EOS is thermodynamically consistent because the molar density is equal to zero, and its derivative with respect to pressure is equal to 1/(RT), like an ideal gas EOS when pressure approaches zero (T is absolute temperature, and R is the universal gas constant). This new simple EOS correlates hydrogen density efficiently using experimental data over 0–100 MPa and 220–473 K and molecular simulation data over the 14.03–116.064 MPa and 310.9–470 K ranges of pressures and temperatures. The new EOS is very accurate, as indicated by the coefficients of correlation being almost equal to unity (R² = 0.9999), the relative difference between the correlation and measured density values being very close to zero (RMSE = 0.0032), and the percentage average absolute value of the relative deviation of the EOS correlation density values ρ_EOS from the experimental density values ρ_Data being (ρ_Data/ρ_EOS − 1)100 = 0.15% average uncertainty. Full article

Agricultural cold chain logistics is characterized by inherent challenges—product perishability, high carbon emissions, and stringent time windows—which are further exacerbated by dynamic disruptions. Existing methods suffer from slow adaptability, unstable multi-objective convergence, and severe cold-start issues. This work falls within the broad scope of biomimetics—the science of emulating nature’s time-tested strategies to solve complex engineering problems—and bio-inspired data-driven methods and their applications in engineering control, optimization, and artificial intelligence. The proposed H-MODRL framework embodies core biomimetic principles: the Genetic Algorithm (GA) mimics Darwinian natural selection and genetic inheritance, the Sparrow Search Algorithm (SSA) abstracts the cooperative foraging and anti-predation behaviors of sparrow populations in nature, and the Arrhenius-based freshness-decay model captures the biochemical kinetics governing perishable biological products. By synergistically integrating these biological evolution principles, swarm intelligence, and deep learning, the framework tackles real-world logistics complexity in a manner directly inspired by living systems. This study presents a well-organized hybrid optimization framework (H-MODRL) that couples a three-stage hybrid evolutionary mechanism, synergistically integrating heuristic warm-start, evolutionary policy guidance, and deep reinforcement learning decision-making. First, an improved genetic algorithm combined with the earliest deadline first strategy constructs a feasible initial population satisfying hard time-window constraints. Second, a large neighborhood search-enhanced chaotic sparrow search algorithm builds a high-quality elite guidance set for policy learning. Third, a physics-based multi-objective proximal policy optimization model embedded with Arrhenius equation-derived freshness-decay kinetics performs online decision-making. Experiments demonstrate that pre-computed all-pairs shortest paths and an O(1) hash-based dynamic-disruption indexing mechanism support fast online replanning. On heterogeneous simulated terrains based on real Chinese geospatial data, H-MODRL outperforms state-of-the-art algorithms across four objectives—logistics cost, carbon emissions, terminal freshness, and delivery time—while exhibiting compact, low-variance performance distributions, thereby validating its engineering robustness and practical value in complex agricultural cold chain environments. Full article

Lightweight alloys in aerospace precision structures undergo slow but cumulative creep deformation during long-term storage, wherein strain accumulation over years can compromise dimensional stability and operational reliability. However, continuum damage mechanics (CDM) constitutive models, while physically grounded, require extensive parameter calibration and exhibit degraded accuracy during the primary creep stage. Meanwhile, purely data-driven approaches are impractical for the sparse datasets typical of accelerated creep testing, wherein as few as 14 data points may be available per condition. Although physics-informed neural networks (PINNs) have shown promise in computational mechanics, existing PINN-based creep studies predict only scalar life quantities rather than the full strain–time curve $\epsilon \left(t\right)$, and none embed damage evolution equations as differential constraints. This study proposes a damage-coupled PINN framework (termed DC-PINN) that predicts the complete creep strain evolution $\epsilon \left(t\right)$ by embedding CDM damage evolution ordinary differential equations (ODEs) as hierarchical differential constraints within the learning process. The framework couples the predicted strain rate $\mathrm{d}\epsilon /\mathrm{d}t$ with the damage state $D\left(t\right)$ through material-specific constitutive ODEs, supplemented by monotonicity enforcement and boundary conditions. Alloy-specific formulations are developed for 2A12-T4 aluminum (Arrhenius kinetics, no damage) and ZM6 magnesium (Sandström dislocation model with Ostwald-ripening-driven grain coarsening damage). Validated on 13 experimental conditions spanning both alloys (50–100 °C, 20–60 MPa, 14–100 points per condition), DC-PINN achieves $R^2>0.99$ for 2A12-T4 and $R^2>0.97$ for ZM6 across all tested conditions. Ablation studies show that the total physics-driven $R^2$ improvement is 5.8 times larger for the data-sparse ZM6 (14–34 points) than for the data-rich 2A12-T4 (∼100 points), with the CDM damage coupling alone accounting for 22% of the improvement in ZM6. To the best of our knowledge, this represents the first integration of CDM damage evolution ODEs as differential constraints within PINNs for creep strain modeling, providing a physically consistent and data-efficient tool for the storage life assessment of aerospace structures. Full article

Background: Radiofrequency ablation of liver tumors relies on tightly coupled electromagnetic–thermal dynamics. However, conventional computational models oversimplify tissue heterogeneity and the dynamic evolution of biophysical properties, limiting their intraoperative diagnostic utility. Methods: We developed a patient-specific, three-dimensional multiphysics framework for liver RFA that integrates spatially varying tissue porosity with a modified local thermal equilibrium formulation. Advective heat transfer is computed via a supplementary finite-element equation, fully coupled with quasi-static electromagnetic simulations and Arrhenius-based tissue damage kinetics. Results: Simulations revealed three distinct voltage-dependent regimes: stable thermal–electromagnetic coupling at 50 V, optimal lesion expansion at 75 V, and premature electrical conductivity collapse at 100 V. Dynamic conductivity reduction, driven by dehydration and coagulative necrosis, provides a mechanistic basis for interpreting real-time impedance rises as an early indicator of peri-electrode desiccation. Geometry-constrained porosity mapping accurately reproduced anisotropic lesion morphologies, yielding simulated necrotic diameters of 2.8 ± 0.4 cm, closely aligning with MRI-validated clinical benchmarks. Conclusions: By linking microstructural heterogeneity to electromagnetic feedback, this framework transforms intraoperative impedance monitoring into a quantitative, predictive diagnostic tool. Imaging-derived spatial porosity mapping represents a robust biomarker for patient-specific liver RFA planning, significantly reducing procedural uncertainty and improving ablation precision. Full article

Accurate constitutive modeling of TC4 titanium alloy at elevated temperatures is critical for process design and numerical simulation in aerospace manufacturing. However, purely data-driven deep neural networks (DNNs) often suffer from severe overfitting and may yield physically unreasonable predictions in data-sparse or strictly out-of-distribution (OOD) regions. To address this issue, this study proposes a physics-guided two-stage neural network framework, termed NN-PhysicsInit, for the constitutive modeling of TC4 alloy. In Stage I, a large synthetic dataset generated from a strain-compensated Arrhenius-type constitutive equation is used to pre-train the network, thereby introducing analytical prior knowledge into the initial topological space. In Stage II, the pre-trained model is fine-tuned using rigorously corrected experimental data obtained from isothermal compression tests conducted over 800–980 °C and 0.001–1 s⁻¹ to improve material-specific predictive accuracy. To evaluate generalization capability, a rigorous dual-perspective extrapolation validation scheme is designed separately in the temperature (1010 °C) and strain-rate (10 s⁻¹) dimensions. The results demonstrate that, compared with direct black-box training, the proposed framework successfully prevents non-physical divergence and better preserves macroscopic thermodynamic smoothness in unseen domains. Specifically, the extrapolation average absolute relative error (AARE) is significantly reduced from 34.21% to 14.34% in the temperature extrapolation task, and from 27.91% to 8.92% in the strain-rate extrapolation task. These findings confirm that physics-based initialization acts as a powerful implicit regularizer, effectively mitigating the extrapolation catastrophe while maintaining high fitting accuracy. The proposed framework provides a robust and practical strategy for the constitutive modeling of complex alloys under limited-data conditions. Full article

Halloumi cheese is commonly consumed in fried form, yet the effects of frying conditions on its quality and energy performance have not been fully clarified. This study aimed to investigate the color and texture changes of halloumi cheese during deep-fat frying at 140, 150 and 160 °C for 0, 2, 4, 6 and 8 min. It also evaluated the exergy efficiency of the process to clarify how frying temperature and time influence energy use. Based on regression analysis, the reaction kinetics of L*, a*, and b* followed first-order behavior, while changes in ΔE were best described by a zero-order model. The texture parameters chewiness and springiness decreased in accordance with first-order kinetics, whereas the observed increases in hardness and adhesiveness followed a zero-order reaction model. Activation energies for both color and texture changes, calculated using the Arrhenius equation, ranged from 12.976 to 50.857 kJ/mol. Exergy efficiency varied between 31.08% and 46.83%, with the highest value obtained at 150 °C for 8 min. The combined kinetic and exergy approach provides practical information for selecting frying conditions that ensure consistent quality while improving energy use in fried dairy products. Full article

Electrical insulation systems (EISs) are the principal reliability bottleneck of modern electrical machines (EMs). Among the many stresses acting on insulation, thermal stress is the most pervasive because it accelerates chemical reactions that progressively erode dielectric and mechanical integrity, ultimately dictating service life. As EMs migrate into compact, high-power-density platforms—automotive, aerospace, and industrial drives—designers need lifetime models that are not merely explanatory but actionable, linking operating temperatures and missions to quantified ageing and risk. This review article traces the evolution of thermal-ageing modelling from fundamental chemistry to a practical design tool. The historical empirical lineage of Arrhenius equation, Arrhenius–Dakin model, and Montsinger model is first revisited, clarifying their assumptions, parameter definitions, and the construction of thermal endurance curves. A discussion then follows on extensions that address deviations from first-order kinetics and demonstrate how variable temperature histories can be incorporated through cumulative damage formulations suitable for duty-cycle analysis. Since models are required to be anchored in data, accelerated thermal ageing (ATA) practices on representative specimens are outlined, alongside a description of the Weibull post-processing for deriving percentile lifetimes aligned with design targets. Building upon these foundations, the Physics-of-Failure (PoF) approach is introduced as a reliability-oriented design (ROD) methodology, in which validated lifetime models guide material selection and geometry optimisation while supporting prognostics and health management during operation. The emerging trend towards a hybrid PoF–AI approach is also discussed, which integrates artificial intelligence to identify nonlinear degradation patterns and drifting parameter relationships beyond the reach of empirical models, with physical constraints ensuring that predictions remain consistent with known ageing mechanisms. Such integration enables the learning process to adapt to operational variability and coupled stress effects, thereby improving both the accuracy and physical interpretability of lifetime estimation. The review aims to provide a concise view of models, tests, and workflows that convert thermal-ageing knowledge into robust, design-time decisions. By linking empirical and physics-based insights with modern data-driven learning, these developments support proactive maintenance, sustainable asset management, and extended operational lifetimes for next-generation EMs. Full article

This study investigates the thermal characteristics of two hybrid nanofluids, single-walled carbon nanotubes with titanium dioxide ($SWCNT-Ti{O}_2$) and multi-walled carbon nanotubes with copper ($MWCNT-Cu$), as they flow over an inclined, porous, and longitudinally stretched cylindrical surface with kerosene as the base fluid. The model takes into consideration all of the consequences of magnetohydrodynamic (MHD) effects, thermal radiation, and Arrhenius-like energy of activation. The outcomes of this investigation hold practical significance for energy storage systems, nuclear reactor heat exchangers, electronic cooling devices, biomedical hyperthermia treatments, oil and gas transport processes, and aerospace thermal protection technologies. The proposed hybrid ANN–numerical framework provides an effective strategy for optimizing the thermal performance of hybrid nanofluids in advanced thermal management and energy systems. A set of coupled ordinary differential equations is created by applying similarity transformations to the governing nonlinear partial differential equations that reflect conservation of mass, momentum, energy, and species concentration. The boundary value problem solver bvp4c, which is based in MATLAB (R2020b), is used to solve these equations numerically. The findings demonstrate that, in comparison to the $MWCNT-Cu/kerosene$ nanofluid, the $SWCNT-Ti{O}_2$/kerosene hybrid nanofluid improves the heat transfer rate (Nusselt number) by up to $23.6\%$. When a magnetic field is applied, velocity magnitudes are reduced by almost $15\%$, and the temperature field is enhanced by around $12\%$ when thermal radiation is applied. The impact of important dimensionless variables, such as the cylindrical surface’s inclination angle, the medium’s porosity, the magnetic field’s strength, the thermal radiation parameter, the curvature ratio, the activation energy, and the volume fraction of nanoparticles, is investigated in detail using a parametric study. According to the comparison findings, at the same flow and thermal boundary conditions, the $SWCNT-Ti{O}_2$/kerosene hybrid nanofluid performs better thermally than its $MWCNT-Cu$/kerosene counterpart. These results offer important new information for maximizing heat transfer in engineering systems with hybrid nanofluids and inclined porous geometries under intricate physical conditions. With its high degree of agreement with numerical results, the ANN model provides a computationally effective stand-in for real-time thermal system optimization. Full article

The escalating demand for efficient thermal management in lithium-ion batteries necessitates precise characterization of their thermal behavior under diverse operating conditions. This study develops a three-dimensional (3D) electrochemical–thermal coupling model grounded in porous electrode theory and energy conservation principles. The model solves multi-physics equations such as Fick’s law, Ohm’s law, and the Butler–Volmer equation, to resolve coupled electrochemical and thermal dynamics, with temperature-dependent parameters calibrated via the Arrhenius equation. Simulations under varying discharge rates reveal that high-rate discharges exacerbate internal heat accumulation. Low ambient temperatures amplify polarization effects. Forced convection cooling reduces surface temperatures but exacerbates core-to-surface thermal gradients. Structural optimization strategies demonstrate that enhancing through-thickness thermal conductivity reduces temperature differences. These findings underscore the necessity of balancing energy density and thermal management in lithium-ion battery design, proposing actionable insights such as preheating protocols for low-temperature operation, optimized cooling systems for high-rate scenarios, and material-level enhancements for improved thermal uniformity. Full article

This study aimed to explore innovative process technologies for producing milk liqueurs with balanced and stable formulations. Milk liqueurs are known to pose significant technological challenges due to phase separation, which compromises product stability and reduces shelf-life. Interactions between milk proteins, alcohol, carbohydrates, temperature, and ionic strength play a crucial role in such destabilization. Pectin, known for its stabilizing effect, can mitigate phase separation, enhancing both shelf-life and sensory quality. This research focused on developing stable formulations of liqueur milk based on fresh buffalo milk by incorporating the pectin extracted from lemon peels. Rheological properties, particularly viscosity, were assessed in formulations containing varying percentages of pectin. The most stable formulation was identified as the one containing 0.10% pectin. Accelerated shelf-life testing, modelled using the Arrhenius equation, predicted a shelf-life of 15 months at 25 °C under standard lighting. The findings demonstrate that lemon peel-derived pectin, obtained from agri-food waste, sustainably improves product stability. Further studies are needed to characterize the pectin structure and optimize extraction methods for industrial-scale applications. Full article

The dielectric relaxation behavior of polymeric materials is critical to their performance in electronic, insulating, and energy storage applications. This study presents an electrical fractional model (EFM) based on fractional calculus and the complex electric modulus ($M^{*}\mathit{=}{M}^{\prime }\mathit{+}i{M}^{″}$) formalism to simultaneously describe two key relaxation phenomena: $\alpha$-relaxation and interfacial polarization (Maxwell–Wagner–Sillars effect). The model incorporates fractional elements (cap-resistors) into a modified Debye equivalent circuit to capture polymer dynamics and energy dissipation. Fractional differential equations are derived, with fractional orders taking values between 0 and 1; the frequency and temperature responses are analyzed using Fourier transform. Two temperature-dependent behaviors are considered: the Matsuoka model, applied to $\alpha$-relaxation near the glass transition, and an Arrhenius-type equation, used to describe interfacial polarization associated with thermally activated charge transport. The proposed model is validated using literature data for amorphous polymers, polyetherimide (PEI), polyvinyl chloride (PVC), and polyvinyl butyral (PVB), successfully fitting dielectric spectra and extracting meaningful physical parameters. The results demonstrate that the EFM is a robust and versatile tool for modeling complex dielectric relaxation in polymeric systems, offering improved interpretability over classical integer-order models. This approach enhances understanding of coupled relaxation mechanisms and may support the design of advanced polymer-based materials with tailored dielectric properties. Full article

Analytical solutions for the temperature change of a lithium-ion battery during thermal runaway were derived by the equation of linearizing thermal decomposition reaction. This study focuses on the representative temperature of the battery cell (zero-dimensional) during the heating test. First, the thermal decomposition reaction was modeled from DSC tests data of the electrode assuming Arrhenius-type temperature dependency. Subsequently, the reaction was simplified by a linear function of temperature and the analytical solution was derived as the exponential function with respect to time. The validity and applicability of the analytical solution are discussed by comparing it with a one-dimensional thermal runaway simulation. Further study was carried out for multiple batteries in consideration of cell-to-cell propagation of the thermal runaway and the applicability was discussed. As results, the single-cell predictions agreed generally with numerical results, especially with higher heating and lower latent heat. A delay in thermal runaway onset in multiple cells, linearly dependent on inter-cell conductivity, was quantified analytically. Parameter adjustments improved the alignment of analytical and numerical results for multiple cells, enabling quick thermal assessments. While numerical simulation is needed for high accuracy, this analytical framework offers new insights and facilitates initial analyses. Full article

Reducing the environmental impact is a key reason for developing recyclable insulation materials for high-voltage industries. In this study, polypropylene (PP) blends were prepared via melt mixing with styrene–ethylene–butylene–styrene (SEBS), a thermoplastic elastomer, to improve breakdown strengths at various cooling speeds. A systematic investigation was conducted to evaluate the influence of crystal size, degree of crystallinity, and nucleation growth rate on the breakdown strength. Crystallization behavior was analyzed using isothermal and non-isothermal methods based on the Avrami model. Increasing SEBS content reduced crystallinity, with the lowest nucleation growth rate observed at 35% SEBS. Breakdown strength correlated with crystallization behavior and was further validated by Weibull distribution method. Notably, PP/SEBS blends containing 35% SEBS exhibited the highest breakdown strength of 66.4 kV/mm at a cooling speed of 10 °C/mm. This improvement reflected a reduction in the degree of crystallinity from 36.0% to 22.9% and the lowest growth rate constant (k) at 35% SEBS. Furthermore, the predicted lifetime of PP/SEBS blend containing 35% SEBS, calculated using the oxidation induction time and the Arrhenius equation, was 42 years. These findings demonstrate that SEBS content and cooling rate effectively modulate crystallization and breakdown strength, enabling recyclable PP/SEBS with XLPE-comparable performance for sustainable high-voltage insulation. Full article

The effect of thermo-oxidative aging on the hyperelastic behavior of ethylene propylene diene monomer (EPDM) rubber was investigated by a combined experimental and theoretical modeling approach. Firstly, the uniaxial tensile test of aged and unaged EPDM rubber was carried out. The test results show that the unaged EPDM rubber had the nonlinear large deformation characteristic of a “S” shape. The stiffness of the EPDM rubber was found to increase with the aging time and aging temperature. Then, in order to quantitatively characterize the hyperelastic behavior of unaged EPDM rubber, the fitting performances of the Mooney–Rivlin, Arruda–Boyce, and Ogden models were compared based on a uniaxial tensile stress–strain curve. The results show that the Ogden model provided a more accurate representation of the hyperelastic behavior of unaged EPDM rubber. Subsequently, the Dakin dynamic equation was adopted to associate the parameters of the Ogden model with the aging time, and the Arrhenius relationship was utilized to introduce the aging temperature into the rate term of the Dakin dynamic equation, thereby establishing an improved Ogden constitutive model. This improved model expanded the Ogden model’s ability to explain aging time and aging temperature. Finally, the improved model prediction results and the test results were compared, and they indicate that the proposed improved Ogden constitutive model can accurately describe the hyperelastic behavior of aged and unaged EPDM rubber. Full article