Accurate prediction of thermal response in biological tissue during microwave ablation (MWA) is essential to ensure treatment safety and efficacy. However, the applicability of classical local thermal non-equilibrium (LTNE) models becomes a concern in the evaporation regime. This study presents a comparative numerical investigation of the classical LTNE model and a proposed δ-modified LTNE model that explicitly accounts for attenuation of tissue–blood heat exchange through a physically motivated δ parameter derived from blood-phase water content. Simulations are performed for liver tissue with varying porosities under 50 W, 75 W, and 100 W and compared with experimental data. A modified W(T) formulation is proposed that preserves evaporation physics with reduced computational complexity. RMSE and MAPE show that, at 50 W, both models provide comparable predictions (RMSE ≤ 2.49 °C, MAPE ≤ 5.85%), indicating limited evaporation effects. At 75 W, RMSE and MAPE values remain comparable between the two models (RMSE ≤ 6.54 °C, MAPE ≤ 7.74%), representing a transitional regime in which evaporation effects begin to emerge. Under 100 W, the classical LTNE model markedly overpredicts tissue temperature near the antenna, with RMSE up to 26.37 °C and MAPE reaching 26.86%. In contrast, the δ-LTNE model substantially reduces prediction errors, maintaining RMSE ≤ 4.23 °C and MAPE ≤ 16.79% through the proposed evaporation-dependent attenuation formulation. These results indicate that the proposed δ-LTNE formulation improves temperature prediction for the investigated high-power liver MWA conditions.

This work investigates optimal magnetic nanoparticle (MNP) injection strategies in three-dimensional (3D) tumor models to enhance the magnetic hyperthermia efficacy. We consider three tumor models with increasing geometric complexities: a spherical tumor, a simple irregular tumor (two connected spheres of different sizes), and a complex irregular tumor (three connected spheres of varying sizes). Centrosymmetric MNP distributions are employed for the spherical model, whereas asymmetric distributions are applied for the irregular models. The rapid convergence of the optimization demonstrates the efficiency and effectiveness of this 3D optimization framework. For the spherical tumor model, multi-site injections significantly enhance therapeutic outcomes under a 20-min waiting limit, whereas a single-site injection with a 114.9-min waiting time achieves 100% tumor ablation without damaging adjacent healthy tissue. Two injection sites suffice for the simple irregular tumor model, while a three-site strategy is optimal for the complex irregular model, indicating a relationship between required injection number and tumor geometry. Furthermore, the optimal MNP injection strategies correlate positively with the locations and sizes of the connected spheres. These findings produce more practical optimal strategies and provide broader, clinically relevant guidance for magnetic hyperthermia treatment.

This study numerically simulates the thermal regulation of a photovoltaic (PV) module by combining phase change materials (PCMs) with various curved metal foam arrangements. In this simulation, both the charging (melting) and discharging (solidification) behaviors of the PCM were investigated. Five arced fin configurations were analyzed, featuring arc numbers of 1, 2, 4, 6, and 8 for three different porosities (epsilon = 0.85, 0.9, and 0.95). The ANSYS Fluent software, based on a finite volume approach, was used to solve the melting process of PCM, and the implemented code was verified against available experimental data. This study illustrates the importance of arced metal foam structures for maximizing thermal regulation and energy storage in a PV module. The results of this study indicate that combining arc metal foam with PV-PCM modules can significantly enhance cooling and heat storage capabilities, improving PV performance compared to conventional modules. Among the considered cases, Case-E achieves the highest temporal enhancement ratio of 43% when epsilon = 0.85 compared to the conventional PV module design.

Carbon geological storage is critical for mitigating greenhouse gas emissions. This study is aimed at lowpermeability carbonate reservoirs during CO2 injection and investigates the Hydro-Mechanical-Chemical (HMC) coupling mechanisms by using an integrated approach combining theory, experiments, and simulation. In this work, a coupled mathematical model considering nonlinear flow, effective stress and mineral reaction kinetics was established. The core experiments provided variation curves of permeability with effective stress and mechanical parameters to establish a coupled HMC relationship, and the process of CO2 injection into carbonate rock reservoirs was numerically simulated. The results of the rock stress sensitivity test indicate that the reservoir permeability decreases by up to 33.3% with the effective stress increases. The injection of carbon dioxide increased the pore pressure of the reservoir and reduced the effective stress, and resulted in 1.67% and 15% increase in porosity and permeability of the reservoir within two years. The mineralization reaction of the reservoir rock led to a decrease in the contents of calcite and dolomite from 78.3% and 13.6% to 57% and 1.6%, respectively, while the content of chlorite increased from trace amounts to 34.9%. which are mainly attributed to the processes of mineral dissolution and precipitation. By adopting a five-point well pattern with a daily injection volume of 10,000 m3/day, and a 60-day cyclic cycle, higher oil recovery efficiency and CO2 sequestration effect can be achieved. This work provides systematic theoretical and engineering foundations for the safe CO2 geological storage and enhance oil recovery efficiency in low-permeability carbonate reservoirs.

Reducing shipping carbon emissions requires absorbents that combine low regeneration energy with practical onboard applicability. However, for blended monoethanolamine and 2-amino-2-methyl-1-propanol solvents, effects of liquid-phase non-ideality on carbon dioxide loading, regeneration energy, and feasible operating windows have not been quantified systematically. In this study, a non-ideal electrolyte thermodynamic model was developed for the aqueous monoethanolamine-2-amino-2-methyl-1-propanol-carbon dioxide-water system by coupling gas-liquid partitioning, temperature-dependent chemical equilibria, ionic speciation, and activity coefficients. The model was calibrated and validated against literature data, reproducing carbon dioxide loading with a symmetric mean absolute percentage error of 8.3042%, a root mean square error of 0.0489, and a coefficient of determination of 0.94605. Within a unified thermodynamic framework, the regeneration energy was decomposed into sensible heat, water-vaporization heat, and reaction heat. Sensible heat was the dominant contribution, accounting for about 55-80% of the total regeneration energy over the investigated range, whereas the water-vaporization contribution increased markedly at higher regeneration temperatures. Relative to ideal-solution calculations, neglecting liquid-phase non-ideality led to a systematic underestimation of regeneration energy. This penalty was governed primarily by solvent composition, became most pronounced in monoethanolamine-rich blends, and was further amplified at relatively high regeneration temperatures, where the deviation reached 150-200 kJ per mole of carbon dioxide. The results identify monoethanolamine-lean, 2-amino-2-methyl-1-propanol-rich blends as the most favorable region, with a recommended monoethanolamine content of 0-10 wt% and a regeneration temperature of 410-425 K. The novelty of this work lies in explicitly quantifying non-ideality penalties and translating them into composition-temperature operating windows for shipboard carbon capture.

Magnetic hyperthermia therapy (MHT) in glioblastoma requires accurate modeling of nanoparticle transport and heat deposition across highly heterogeneous tumor regions. Traditional numerical approaches remain limited by high computational cost and sensitivity to complex tumor geometry, reducing their suitability for rapid clinical evaluation. To address these challenges, we introduce a genetically optimized physics-informed neural network (GA-PINN) that directly solves the bioheat transfer equation, while its governing parameters are dynamically coupled to nanoparticle transport, Darcy flow, and Arrhenius damage kinetics. Unlike prior PINN implementations, our approach integrates automatic genetic tuning of learning rates and loss-term weights, ensuring balanced convergence across coupled physics. Furthermore, tumor-focused collocation sampling uniquely enhances resolution of steep gradients near injection sites, a critical feature for patient-specific modeling. Results show that single-port injection restricts heating to the necrotic core, yielding central temperatures of 40 °C, 42.5 °C, and 48 °C for nanoparticle doses of 2.5, 5, and 10 kg/m3, respectively, but produces <10% necrosis in the viable rim. Increasing the magnetic field amplitude-frequency product to 8.4 × 108 A/(m.s) raises peak temperatures to ∼47 °C and significantly accelerates damage accumulation. A multi-port injection strategy improves peripheral nanoparticle coverage, elevates rim temperatures to ∼41.5 °C, and reduces the dispersion index by more than 30%, indicating markedly more uniform ablation. These findings demonstrate that GA-PINN provides a stable, efficient, and physics-consistent surrogate for MHT, enabling rapid assessment and optimization of dosing conditions, magnetic field parameters, and multi-site injection strategies for patient-specific treatment planning.

This study investigates the effect of surface wettability on nucleate boiling performance in a two-phase immersion cooling system, using static contact angles of 160 degrees, 90 degrees, and 30 degrees to represent hydrophobic, intermediate, and hydrophilic surfaces. A dual-chip configuration is employed to capture the influence of surface orientation on boiling behavior and thermal response. Volume of Fluid (VOF) simulations are conducted to evaluate vapor distribution, temperature fields, liquid coverage, and heat transfer coefficients. Results show that decreasing the contact angle from 160 degrees to 30 degrees significantly enhances boiling performance. The heat transfer coefficient on the lower chip increases from 600 to 5700 W/m2 center dot K, while the upper chip improves from 440 to 3600 W/m2 center dot K, representing gains of 850 % and 700 %, respectively. Surface temperatures are reduced by up to 2.5 K. However, stronger boiling activity at lower contact angles increases vapor accumulation near the upper chip, resulting in greater temperature asymmetry between the two surfaces. These findings highlight that while enhanced wettability substantially improves boiling heat transfer, it also intensifies orientation-driven vapor effects. Optimizing performance in immersion-cooled systems requires not only surface engineering but also consideration of vapor management in multi-surface and vertically arranged configurations.

Low-permeability reservoirs include well-developed fracture networks and significant variations in pore structure across multiple scales that are characterized by pronounced heterogeneity. The heterogeneity of the storage structure may evolve dynamically during CO2 flooding processes. Consequently, CO2 transport exhibits distinct anomalous diffusion behavior. Conventional advection-diffusion equation models fail to accurately capture this complex transport phenomenon. To fill this knowledge gap, the variable-order fractional derivative serves as a non-local operator expressed in a differential-integral form, which can effectively describe the global spatial correlation and temporal memory effects inherent in particle movement within heterogeneous media structures or complex flow fields. This study aims to investigate the mechanism of CO2 transport in low-permeability reservoirs using a variable-order fractional advection-diffusion equation (V-FADE) model. The finite difference method is well applied to numerically solve the variable-order fractional differential equation. Numerical experiments and field applications of the V-FADE model effectively capture the apparent positive skewness and the accelerating sub-diffusion phenomenon observed in gas breakthrough curves (BTCs). Furthermore, simulation studies demonstrate a strong correlation with experimental data reported in previous literature. The decrease in variable order leads to a heavier late-time tailing in BTCs. Notably, the time-dependent variable order alpha(t) serves as a key parameter that characterizes the temporal evolution of reservoir pore structure and microfracture connectivity during transport processes; meanwhile, the anomalous diffusion dynamics and the associated concentration tailing in BTCs are clearly explained. Therefore, aiming at the process and dynamic characteristics of CO2 transport in low-permeability reservoirs, an analysis of the influence of external environmental factors and pore-scale structural properties on oil displacement efficiency can provide valuable theoretical and technical guidance for reservoir production. This study not only aims to enhance oil recovery but also contributes to carbon storage and environmental protection.

This study examines the application of large-scale phase change material (PCM) packed beds for compressor inlet air cooling to enhance gas turbine power generation during high-temperature periods. The proposed system employs encapsulated PCM installed in covered underground trenches to provide latent thermal energy storage and maintain an approximately constant inlet air temperature, without the use of water injection. Three PCMs—RT31, RT35hc, and lithium nitrate trihydrate—were evaluated, with lithium nitrate trihydrate demonstrating superior thermal performance and more compact system dimensions. The method was applied to a gas turbine with a rated capacity of 123.4 MW and average capacity of 85 MW. The average air mass flow for this turbine is 281.63 kg/s. The simulation results indicate that the system can increase annual electricity generation by more than 4,700 MWh. Compared with water-based cooling, the PCM system achieves comparable power enhancement while saving approximately 4,300 tons of water annually. Unlike conventional evaporative cooling systems, the proposed PCM packed-bed approach enables water-free compressor inlet air cooling, making it particularly suitable for humid climates where wet- and dry-bulb temperatures are close and for regions experiencing water scarcity.

Hyperthermia using magnetic nanoparticles (MNPs) provides a minimally invasive way to heat deep brain regions for Parkinson's neuromodulation and to deliver proper thermal doses to cancerous tumors. By applying both the classical Pennes' bioheat equation and a local thermal non-equilibrium equation (LTNE) model, this work explores how heartbeat-driven oscillations in blood flow shape the resulting temperature field. The simulations are performed for various MNPs concentrations, blood flow velocities, and tissue porosities; the results show that pulsatile blood velocity causes no changes in both peak and average temperatures. Arrhenius-based damage analysis then identifies specific MNPs concentrations that achieve gentle heating around 43-44 degrees C with minimal tissue injury for neuromodulation, or more intense heating induce widespread cancer cell death with damage fractions between 63-99 %. To enable rapid prediction of thermal outcomes under varying treatment conditions, multilayer perceptron (MLP) neural networks were trained on simulation data to estimate average temperature and tissue damage directly from input parameters, achieving high accuracy and supporting real-time treatment planning. The findings support the use of steady-flow assumptions in hyperthermia modeling to use for brain tissue and offer clear guidance on dosing for each therapeutic application.

Heat exchangers are vital components of industrial thermal systems, requiring high heat transfer efficiency, compact geometry, and cost-effectiveness. Twisted tubes, as a passive heat transfer enhancement technique, offer a practical and economical solution due to their simple manufacturing and robust performance. These tubes induce secondary swirling flows that disrupt thermal and hydrodynamic boundary layers, thereby enhancing convective heat transfer. Notably, twisted tubes with lobed and polyhedral cross-sections generate stronger secondary vortices compared to elliptical or oval counterparts. This review comprehensively analyzes experimental and numerical studies on twisted tubes with lobed and polyhedral geometries, considering both straight and curved configurations. It also explores hybrid approaches that combine twisted tubes in conjunction with additional passive enhancement approaches including tape insert devices and nanofluid mixtures, as well as active enhancement mechanisms like magnetic field-assisted heat transfer. Furthermore, the review summarizes and correlates key geometric parameters with Nusselt numbers and friction factors, providing predictive insight regarding the thermal-hydraulic behavior of these systems. By integrating extensive experimental and computational findings, this review advances the understanding and optimization of high-performance heat exchanger designs.

The rapid development of electric vehicle (EV) technology has intensified the need for efficient thermal management of critical components, including insulated-gate bipolar transistor (IGBT) traction inverters, batteries, and fuel cells. These components are compact yet generate substantial heat, which can reduce performance, reliability, and lifespan if not effectively managed. This review systematically examines the application of micro-channel heat sinks (MCHSs) for cooling these EV components. The literature synthesis follows a structured methodology, including the collection, categorization, and comparative analysis of experimental, numerical, and hybrid studies on MCHS designs, configurations, and thermal performance. The scope of this review encompasses the cooling principles, design innovations, and practical applications of MCHSs for IGBTs, batteries, and fuel cells. Key comparative findings reveal that MCHSs can significantly reduce peak temperatures and temperature non-uniformity, enhancing component reliability, lifespan, and performance. Among different MCHS configurations, hybrid jet-microchannel and two-phase micro heat sinks consistently demonstrate superior thermal performance across multiple EV components. The review also identifies current challenges in fabrication, integration, and optimization, highlighting research gaps that require further investigation. By explicitly defining the review scope, applying a systematic synthesis methodology, and providing comparative insights across various EV components and MCHS designs, this study offers a comprehensive reference for researchers and engineers seeking to optimize thermal management solutions in electric vehicles.

The growing power density and miniaturization of electronic equipment make thermal management critical to ensure reliability and performance. Among the passive methodologies, heat pipes are a relevant option thanks to their high equivalent thermal conductivity, absence of any moving part, and versatility. The present review offers an overview on the application of heat pipes for thermal management of electronic equipment by analysing the operating principles, the different typologies, and the approaches for the analytical and numerical modelling. Furthermore, strategies for enhancing heat pipe performances are also analysed such as the coupling with phase change materials, nanofluids, and fins. The review highlights how these combinations enhance the performances by notably increasing the thermal dissipation capacity. Finally, a set of practical cases are illustrated where heat pipes are used to cool electronic chips or high heat density consumer electronic devices such as smartphone, tablets, and laptops. Future research directions are also proposed to support the development of more and more compact and efficient heat pipes for high heat density applications.

The goal of this research is to improve the cooling performance of IGBT power modules by combining microchannel heat sinks (MCHS) with high-conductivity thermal interface materials (TIMs). Two easy-to-manufacture MCHS designs, parallel rectangular channels and circular pin-fins, are examined to emphasize practical feasibility, while newly developed TIMs, specifically graphite sheet and liquid metal, are incorporated to further improve heat dissipation. Additionally, a double-layer channel structure is proposed to reduce pressure drop with minimal impact on cooling efficiency. The cooling performance of the proposed MCHS and TIM configurations is evaluated using a three-stage coupled CFD simulation under a wide range of electrical loading conditions to ensure realistic operating scenarios. It is found that the double-layer pin-fin design provides the best thermal performance among the heat-sink configurations, and both graphite sheet and liquid metal TIMs significantly enhance cooling; however, considering feasibility, the graphite sheet is preferred over liquid metal. The combined heat-sink and TIM solution reduces the maximum device temperature and temperature non-uniformity by more than 60 °C and 5 °C, enabling a 25 % increase in maximum power-handling capability. Consequently, up to 50 % higher switching frequency, together with a 17 % increase in acceleration factor, demonstrates the achieved enhancement in both reliability and AC output quality. Overall, this study presents a practical and high-performance cooling system design for IGBT power modules that surpasses current solutions while maintaining manufacturing feasibility.

This study proposes a hybrid optimization method for enhancing the thermal efficiency of on-board multi-chip systems, integrating target detection with machine learning algorithms. Experiments demonstrate that the Slimneck + EMA network adopted in the fusion stage achieves a 25.9% improvement in precision (P) and an 11.7% increase in Recall (R) compared to the original YOLOv8 algorithm, significantly enhancing target localization and component recognition efficiency. The research approach identifies chip variables with minimal impact on thermal dissipation through dimensionality reduction analysis, and predicts deviation by comparing temperatures across multiple post-fusion algorithms during layout optimization. Further analysis reveals that when the tolerance levels for the fitness function and constraint function reach 1e-6 and 1e-4 respectively, further increasing the iteration accuracy yields limited improvement in hotspot temperature optimization, with an enhancement rate below 0.85%. Validation via Nusselt number analysis confirms the thermally superior performance of the optimized chip layout under forced convection. The proposed optimization scheme achieves a 4% actual improvement over the Initial chip Layout, while the SVR-GA framework identifies an 8.4% theoretical optimization potential within the search space. This method effectively enhances the thermal performance of multi-chip circuit boards, providing an effective approach for high-power multi-device board-level design and thermal management.

A numerical study focuses on the temporal evolution of fractional-order convective nanofluid flow along with entropy generation characteristics within a wavy square porous enclosure containing a circular cylinder. The application of fractional derivatives facilitates a more accurate representation of fluid flow dynamics, thermal transport, and entropy production. The governing equations are formulated as fractional partial differential equations, with momentum transport modeled using the Darcy–Brinkman–Forchheimer approach. The complete mathematical framework is solved using a robust numerical technique that integrates the implicit finite difference scheme (L1-scheme) for temporal discretization and the penalty finite element method for spatial discretization. The numerical investigation is carried out for various emerging parameters, including fractional-order parameters (α), Rayleigh number (Ra), Darcy number (Da), and porosity (ε). The results are displayed through contour plots of streamlines, isotherms, and local entropy generation, along with graphical plots of the mean Nusselt number, Bejan number, and total entropy generation. These findings offer valuable insights into the interplay between fractional-order parameter and flow parameters in influencing flow dynamics, thermal transport, and entropy generation. The study reveals that the fractional-order parameter (α) plays a pivotal role in governing the system's temporal evolution, with higher values of α significantly accelerating the rate of evolution.

Typical damage in multi-layer composite materials of wind turbine blades, such as delamination and crack defects, are critical factors in the evolution of blade fracture accidents. Current detection methods predominantly rely on ultrasonic testing during manufacturing and visual inspections during operation. However, these methods lack the capability to detect typical internal damage within blades during operation. Infrared thermal wave detection, characterized by its non-contact, rapid, and efficient nature, offers a promising solution. It can operate in challenging environments (e.g., wind, light, sound) and accurately detect defects at specific depths within composite materials, thereby enabling structural health monitoring and maintenance of wind turbine blades, ensuring their safe operation. This study addresses two common defects in multilayer composite materials: delamination and cracks. We employ an infrared thermal wave detection technique based on solving the inverse problem of heat transfer. By capturing the surface temperature distribution and solving the inverse heat conduction problem, the method determines internal defect characteristics (shape, size, and location) from the time-dependent thermal measurements. Unlike traditional methods that utilize time-frequency transformation for infrared thermal wave image processing, our approach integrates three-dimensional heat transfer simulation with intelligent optimization algorithms. This method allows forward numerical simulation of the internal temperature field's dynamic behavior under thermal disturbance. After validating the accuracy of the heat transfer model, we compare the surface temperature distributions of defective and defect-free samples. These distributions serve as input data for the optimization model's fitness function, facilitating the accurate prediction of defect parameters. Key findings include: (1) detectability increases with defect thickness and shallower depth; (2) delamination defects are more detectable than cracks, with an overall detection error of less than 5%; (3) crack defects with greater depth and thinner thickness present challenges, with the highest detection error reaching 12.73%.

PurposeThis study aims to investigate the mechanism underlying erosion caused by solid-liquid-gas multiphase flow in pipeline transportation. In CO2 flooding projects, the process of pipeline oil transportation is often accompanied by the presence of a small quantity of sand particles. Owing to the interaction of multiphase fluid flow (solid-liquid-gas), the inner walls of pipelines, particularly at bend locations, frequently experience significant erosion wear.Design/methodology/approachIn this study, experimental data from existing literature are integrated with computational fluid dynamics to systematically analyze the influence of key parameters, including particle concentrations, flow rates and velocities, on the erosion rate within coupling pipes. Through a rigorous comparative analysis of multiple erosion prediction models, the volume of fluid (VOF) model and the discrete phase model (DPM) are identified and used as the most appropriate methods for the current investigation.FindingsThe findings indicate that particle concentration and flow rates are the primary influencing factors on erosion rates, with the outer wall of curved pipes identified as the primary area of erosion and the maximum erosion rate is 1.539 x 10-2kg/m2/s. Additionally, this study integrates 90 degrees bend pipes with reducer pipes and compares their performance to standalone pipe structures. The results indicate that the maximum erosion rate of the coupling pipe decreases by up to 40% under various working conditions.Research limitations/implicationsThrough a rigorous comparative analysis of multiple erosion prediction models, the VOF model and the DPM are identified and used as the most appropriate methods for the current investigation.Practical implicationsThis study provides theoretical foundations and technical support for the engineering design and maintenance of coupling pipes, offering scientific guidance to reduce pipeline erosion and prolong equipment lifespan.Social implicationsOwing to the interaction of multiphase fluid flow (solid-liquid-gas), the inner walls of pipelines, particularly at bend locations, frequently experience significant erosion wear.Originality/valueThe present work provides theoretical foundations and technical support for the engineering design and maintenance of coupling pipes, offering scientific guidance aimed at reducing pipeline erosion and prolong equipment lifespan.