Search Results (2,745)

The microstructural evolution and tensile behavior of Inconel 617 welded joints produced by gas tungsten arc welding (GTAW) with ERNiCrCoMo-1 filler were systematically investigated. Detailed microstructural characterization revealed that Cr-rich M₂₃C₆ and Ti-rich MC carbides are the dominant precipitates, while Mo-rich M₆C forms locally along grain boundaries after thermal exposure. The fusion and weld zones exhibit fine dendritic morphologies with uniformly distributed precipitates, resulting in significant strengthening through precipitation and dislocation–pinning mechanisms. Owing to the low heat input and compositional compatibility between the weld and base metals, the heat-affected zone remains extremely narrow and free of compositional transitions. The welded joint attains tensile strengths of 920 MPa at room temperature and 605.5 MPa at 750 °C, corresponding to joint efficiencies of 117% and 121%, respectively, with fracture consistently occurring in the base metal. Deformation analysis shows that plasticity at room temperature is governed by planar slip and dislocation entanglement, whereas deformation twinning predominates at elevated temperatures owing to the reduced stacking-fault energy and the pinning effect of M₂₃C₆ carbides. These results provide key insights into the deformation and strengthening mechanisms controlling the high-temperature performance of GTAW-welded Inconel 617 joints and offer guidance for their application in advanced nuclear and high-temperature energy systems. Full article

This study investigates the influence of varying positive–negative polarity ratios (EP:EN) on melt pool evolution during alternating current CMT (VP-CMT) arc additive manufacturing through a combined experimental and numerical approach. A multi-layer single-track droplet-melt pool coupling model was established, revealing the regulatory mechanisms governing melt pool flow, temperature distribution, and dimensional changes. These are driven by differences in arc morphology, heat input, and mechanical forces during EP and EN phases. Results indicate that molten pool flow is primarily governed by wire feed, retraction, and Marangoni forces. During the EP phase, arc divergence and elevated heat input result in significantly higher flow velocities than in the EN phase. Molten pool length increases with rising EP proportion, exhibiting periodic dynamic variations. Lateral flow intensity intensifies as EP ratio increases, directly influencing cladding layer morphology. This study provides theoretical basis for optimising additive manufacturing quality by adjusting the EP:EN ratio. Full article

This study introduces an efficient and accurate two-stage explicit computational scheme for solving partial differential equations (PDEs) containing first-order time derivatives. The suggested method is a modification of the classical Runge–Kutta scheme that introduces a new first-stage formulation. This minimizes numerical error with moderate step sizes while preserving the stability region of the classical method. Spatial discretization is performed using a sixth-order compact finite-difference scheme to obtain high-resolution solutions. The analysis of stability and convergence is strictly determined for both scalar and system forms of convection–diffusion-type equations. To illustrate the suitability of the method, a dimensionless mathematical model of the unsteady, incompressible, laminar flow of a Prandtl-type non-Newtonian nanofluid over a Riga plate is considered, accounting for viscous dissipation, thermophoresis, Brownian motion, and a magnetic field. Here, the Prandtl ternary nanofluid is defined as a non-Newtonian nanofluid that follows the Prandtl rheological model, and it exhibits three critical transport phenomena: heat conduction, viscous dissipation, and nanoparticle diffusion. Representative values of the Prandtl number P_r = 3 and Reynolds number R_e = 5 are used to perform the simulation, and other parameters, including but not limited to the Hartmann number H_a, Williamson number W_e, thermophoresis N_t and Brownian motion N_b, are varied to evaluate the flow behavior. Moreover, an artificial neural network (ANN)-developed surrogate model is used to calculate the skin friction coefficient and the local Sherwood number, using five input parameters: the Reynolds number, Prandtl number, Schmidt number, Brownian motion parameter, and thermophoresis parameter. The governing partial differential equations yield high-fidelity numerical data used to train the surrogate model. The data is split into 80% for training, 10% for validation, and 10% for testing. The ANN is tested using regression analysis and error histograms, which demonstrate high accuracy and generalization capacity. Numerical simulation combined with AI-based prediction is a cost-efficient method for real-time estimation of complex non-Newtonian nanofluid systems. Full article

Accurate thermal conductivity measurement requires temperature control systems to establish stable operating conditions within a limited time. In practical thermal conductivity performance testing systems, large thermal inertia, complex heat transfer paths, and input time delays arising from thermal propagation and sensor placement often degrade dynamic response and control accuracy. To overcome these limitations, a composite PI–RLS–MPC control strategy is proposed for thermal systems with inertia and time delay. A proportional–integral (PI) controller serves as the baseline stabilizing controller, while model predictive control (MPC) is utilized to optimize the control input by explicitly considering system delay and input constraints. To enhance robustness against model uncertainty and parameter variations, recursive least squares (RLS) is adopted for online parameter identification and adaptive PI tuning, and a steady-state parameter freezing mechanism is introduced to suppress unnecessary parameter updates after convergence. Simulation studies are performed on an identified thermal process model with a 20 s input time delay. The results indicate that the proposed strategy reduces overshoot, shortens settling time, and improves disturbance rejection compared with conventional controllers. Overall, the proposed PI–RLS–MPC approach provides a practical solution for improving temperature control performance in thermal conductivity testing systems. Full article

Fungal endophytes are microorganisms that inhabit plant tissues without causing disease and emerge as critical mediators of plant stress tolerance, nutrient acquisition, and ecosystem resilience under diverse climate change scenarios. Their unique position within the host allows them to modulate physiological responses more closely than external microbiota. This review explores how endophytic fungi contribute to plant adaptation under climate-induced stresses such as heat, salinity, drought, pollution, and nutrient limitation, with a focus on molecular crosstalk, functional trait modules, and metabolic trade-offs. Key findings emphasize multilayered signaling systems, including MAMP/DAMP recognition, phytohormone regulation, immune tuning, ROS dynamics, and effector deployment, while emerging mechanisms such as cross-kingdom RNA and extracellular vesicle (EV)-mediated exchange are discussed as promising but currently limited in empirical validation within many endophytic systems. Endophytes also enhance nutrient exchange through conditional carbon-for-benefit trade and may shape rhizosphere microbiota and soil activities through plant-mediated inputs. Integrative multi-omics approaches provide predominantly correlational insights into the mechanistic basis of these effects, linking molecular function to ecosystem and community outcomes. These insights have potential applications in climate-resilient agriculture, phytoremediation, and ecosystem restoration; however, their large-scale implementation requires further field-based validation and context-specific assessment. Future priorities should focus on trait-based selection, ecological modeling, and biosafety evaluation to translate microbial functions into reliable field-level strategies that support sustainable crop performance under accelerating environmental stress. Full article

Carryover slag (COS) entrained from the basic oxygen furnace (BOF) during tapping is highly oxidizing and affects secondary steelmaking by increasing deoxidizer consumption, refractory wear, P reversion, and decreasing steel cleanliness. A kinetic COS amount estimation model was developed by using the effective equilibrium reaction zone (EERZ) method. The amount of COS was determined by iteratively adjusting the carryover slag coefficient (CSC) until predicted steel and slag compositions approached industrial measurements. Validation with four industrial heats confirmed that the model effectively predicts COS under both complete and incomplete deoxidation conditions. Further simulation results show that increasing the CSC from 2 to 4 kg per tonne of steel leads to 9.3 ppm P reversion. The calculations also confirmed that larger COS amounts accelerate refractory wear due to the higher input of readily reducible components, particularly FeO and MnO. Full article

The intermittent and variable nature of solar energy poses challenges for maintaining stable thermal performance in solar heating systems. One effective approach to mitigate this limitation is to store surplus thermal energy during periods of high solar irradiance and release it when solar input is insufficient. Phase change materials (PCMs) are particularly suitable for this purpose due to their ability to absorb and release large amounts of latent heat during phase transition. The aim of this work is to develop a mathematical model of a flow-through tank containing a phase change material in the form of a spherical packed bed. Including longitudinal dispersion in the model equations allows for a more accurate description of the heat transfer process in a tank containing PCM elements. Simulation calculations based on the model were carried out to demonstrate its potential applicability to practical problems. The influence of the following parameters on the process was investigated: tank volume, water flow rate, phase change temperature, process duration, dispersion coefficient during water flow, radius of the packed-bed elements, and cyclic variations of the inlet water temperature. A significant influence of the axial dispersion coefficient in the tank containing PCM on the outlet water temperature profile was demonstrated. It was found that the internal heat transfer coefficient within the packing elements containing PCM falls within the range of 58–145 W/(m²K). Full article

Thermal runaway accidents in lithium batteries necessitate effective thermal management. This study proposes a liquid cooling plate with internal spiral-array fins and investigates its performance under electrochemically coupled temperature-dependent heat generation conditions. A pseudo-two-dimensional (P2D) electrochemical model simulates battery discharge at 0.5C–2C rates to obtain heat generation characteristics, which serve as inputs for a fluid–solid coupled heat transfer model. The effects of spiral fin parameters—pitch (S) and height (h)—are systematically analyzed. Three main contributions are presented: spiral fins induce secondary flow that disrupts thermal boundary layer development and enhances fluid mixing, with smaller pitch extending the flow path and increasing radial velocity; a performance evaluation criterion (PEC)-based analysis identifies the optimal parameter range that balances heat transfer enhancement and pressure drop penalty; and increasing the fin height raises the finned area proportion and swirl intensity, suppressing bypass flow and strengthening heat transfer, with effects more pronounced at higher discharge rates. Key quantitative findings show that at 2C discharge, the optimized configuration (S = 3 mm, h = 0.5 mm) achieves a comprehensive performance index of 2.19 and reduces the maximum temperature by 25.32% compared to smooth channels. This work integrates electrochemical and thermal models to provide a new approach for optimizing spiral fin microchannels tailored to lithium battery operation. Full article

The low-temperature toughness of a coarse-grained heat-affected zone (CGHAZ) is a critical factor governing the service safety of welded joints in X70 pipeline steel. This study systematically investigated the influence of nitrogen content (ranging from 0.0018 to 0.0120 wt%) on the microstructure and low-temperature impact toughness of the CGHAZ in X70 pipeline steel using welding thermal simulation tests with a heat input of 12.5 kJ/cm. The results indicate that the CGHAZ microstructure predominantly comprises lath bainite (LB) and minor martensite–austenite (M/A) constituents. With increasing nitrogen content, the austenite-to-ferrite transformation start temperature (Ar₃) increased while the transformation finish temperature (Ar₁) decreased, resulting in coarsening of the lath bainite packet structure. The M/A volume fraction rose from 2.11% to 5.23%, the average particle size grew from 0.17 to 0.71 μm, and the high-angle grain boundary (HAGB > 15°) fraction declined from 67.5% to 52.2%. These microstructural alterations collectively caused the Charpy impact energy of the CGHAZ to decrease from 269 J to 48 J. The deterioration in toughness is primarily attributed to blocky M-A constituents lowering the resistance to crack nucleation and the reduced HAGB fraction diminishing the resistance to crack propagation. This work provides a theoretical foundation for optimizing the performance of X70 pipeline steel welded joints, and it is recommended that the nitrogen content in the base metal be strictly maintained below 0.005 wt% to ensure superior CGHAZ toughness. Full article

Machine learning was employed to make fluid agnostic laminar heat transfer prediction in supercritical conditions, encompassing three fluids (sCO₂, sH₂O, sC₁₀H₂₂) representing a wide range of operating conditions. High-fidelity training data, consisting of both non-dimensional and dimensional (operating parameter) as inputs and Nu and T_wall as outputs, were generated from grid-converged, steady-state, computational fluid dynamic (CFD) simulations. The Random Forest (RF) algorithm outperformed the artificial neural networks (ANNs) across all scenarios on the small multi-fluid dataset (~1600 data points) employed during the training process. When using non-dimensional parameters as inputs, Nu prediction fidelities were better than T_wall predictions for both ML algorithms across both horizontal and vertical configurations. The RF model trained on data from a specific flow configuration (horizontal/vertical) could predict T_wall within an accuracy of +/−1% with dimensional, operational parameters as inputs while being agnostic to the working fluid. Furthermore, by including the gravity vector as an additional variable during the training process, the RF model could predict T_wall accurately in a mixed, multi-fluid dataset containing data from both horizontal and vertical configurations. Full article

At temperatures higher than 5 °C in the cooling chambers of refrigeration systems, bacteria multiply rapidly on fresh fishes, thereby leading to an increased risk of foodborne diseases. Maintaining the storage temperature within the recommended bounds of 0 °C and 5 °C is needed to maintain food safety and quality. This study presents model predictive control of a data-driven medium-temperature cold storage system using subspace system identification techniques. The identified linear model presents a holistic view of the whole system, with each subsystem cohesively linked together. The data-driven model was developed from synthetic data derived from a high-fidelity simulation benchmark model of a supermarket refrigeration system from Aalborg University, Denmark. The benchmark model consists of a medium-temperature closed display case, the suction manifold, and the compressor rack. The data of the expansion valve, suction pressure, compressor capacity, heat transfer rate, and ambient temperature were taken as inputs, while the data of the air and goods temperatures were taken as outputs based on expert knowledge. A linear model predictive controller was designed to control the temperature outputs of the identified linear model, and the outputs were compared with the proportional–integral dead band control used in the benchmark. Simulation results for 24 h showed that the model predictive controller was able to achieve an air temperature and a goods temperature within the recommended temperature range of 0 °C and 5 °C that guarantees safe storage of fresh fishes. These results imply that a reduced-order model of a commercial refrigeration system that is robust, reliable, and stable can be developed and controlled to achieve the goal of food safety, thereby guaranteeing food security and reducing costs. Full article

This study evaluates the impact of four drying methods—open sun drying, solar drying, infrared drying, and microwave drying—on the quality attributes and elemental retention of apricots (Prunus armeniaca L.). Experimental trials were conducted in June 2024 at the Tashkent Institute of Chemical-Technology using equal quantities of fresh apricots. Drying was continued until the moisture content, measured gravimetrically, dropped below 20% (wet basis), followed by spectroscopic analysis to determine macro- and microelement concentrations. Solar-dried apricots showed higher retention of essential nutrients in this experimental trial: potassium (2.37%), silicon (0.538%), magnesium (0.145%), calcium (0.176%), and sulfur (0.152%). In contrast, open sun drying led to significant nutrient degradation and poor visual quality. Microwave drying preserved some micronutrients but resulted in surface scorching due to uneven heating. Infrared drying yielded acceptable results but required substantial energy input. Among all methods, solar drying provided the optimal balance of high product quality and energy efficiency. The drying process required negligible electrical energy owing to exclusive reliance on solar radiation. This method supports sustainable food processing by reducing energy demand and greenhouse gas emissions while preserving nutritional quality. The results highlight solar drying as a promising, eco-friendly technique for preserving the nutritional integrity of agricultural products. These findings offer valuable scientific guidance for selecting appropriate drying technologies in the food processing industry, especially in regions with high solar potential. However, the study is limited to a single fruit variety and seasonal conditions. Full article

High-entropy alloy (HEA) coatings are widely recognized for their excellent hardness and wear resistance. Heterogeneous cabled wire welding (HCWW) combined with gas metal arc welding (GMAW) has emerged as an efficient approach for fabricating HEA coatings; however, severe arc instability inherent to HCWW often deteriorates coating quality. In this study, the effects of axial magnetic fields (AMFs) with different orientations on the HCWW–GMAW process were systematically investigated. High-speed imaging revealed that the HCWW arc without magnetic assistance exhibits pronounced instability, characterized by asymmetric morphology and rotational behavior. The application of AMFs significantly altered arc dynamics. An upward axial magnetic field (N-AMF, 2 mT) effectively suppressed arc rotation, resulting in a stable bell-shaped arc and more uniform heat input, whereas a downward axial magnetic field (S-AMF) caused arc contraction and promoted dendrite coarsening. Consequently, the N-AMF condition led to a refined and homogeneous microstructure, yielding a high microhardness of 825 ± 15 HV. Tribological tests demonstrated that the wear rate of the N-AMF-assisted coating was reduced by 55% compared with that produced by conventional GMAW. These results highlight that magnetic-field-induced arc stabilization plays a critical role in achieving high-performance HEA surface coatings. Full article

This paper proposes a simplified mechanical model to estimate transverse shrinkage and angular distortion in multi-pass butt welding. The simplified mechanical model is first derived for an I-groove joint by representing the heated weld region with one-dimensional bar elements and by enforcing force equilibrium to obtain closed-form expressions for pass-by-pass deformation increments and cumulative deformation. For non-I-groove joints, the same simplified mechanical model is applied by updating the layer partition and geometric parameters for each pass based on the pass-wise high-temperature region; the inherent shrinkage of each pass is evaluated from the heat input and an equivalent heated-layer thickness. The simplified mechanical model is validated for V-groove multi-pass joints by comparison with thermo-elastic-plastic finite element (FE) analyses and available experimental data, and for X-groove multi-pass joints by comparison with thermo-elastic-plastic FE analyses. In addition, a parametric study on the V-groove angle (40°–70°) for SUS316L demonstrates that the model captures the increasing trend of final transverse shrinkage with groove angle without a pronounced degradation in prediction accuracy. The results show that the simplified mechanical model reproduces both deformation histories and final values with good accuracy while using only a small set of input parameters and negligible computational cost, making it useful for early-stage welding procedure planning and quick parameter studies. Full article

The integration of hydrogen into natural-gas-fired gas turbines represents a promising transitional pathway for reducing greenhouse gas emissions in industrial power generation. This study presents a comparative thermodynamic and environmental assessment of a Solar Titan 130 gas turbine operating in combined heat and power (CHP) mode under two fueling conditions: conventional natural gas and a hydrogen-enriched CH₄/H₂ (80/20 vol.%) blend. The analysis combines validated operational data for natural gas with analytical thermodynamic modeling for the blended-fuel scenario to evaluate key performance indicators, including thermal efficiency, specific fuel consumption, power output, and carbon dioxide emissions. The results indicate that hydrogen enrichment leads to an increase in thermal efficiency from 34.1% to 36.6% and a reduction in specific CO₂ emissions by approximately 13.7%, while maintaining similar thermal input within the adopted steady-state modeling framework. Compressor power consumption decreases, and net electrical output increases slightly under hydrogen-enriched operation, contributing to improved overall energy performance. Although the hydrogen-blended regime is assessed through modeling, the findings suggest that moderate hydrogen addition can enhance efficiency and environmental performance in industrial gas turbines without fundamental structural redesign of the turbine core, assuming appropriate fuel supply and control system adaptation. The study provides practical insights into the feasibility of hydrogen-assisted operation in existing CHP installations and supports its role in near-term decarbonization strategies. Full article