Search Results (1,129)

This paper proposes an experimental, intelligent optimization approach to improve the thermal cooling performance of an overclocked graphics processing unit (GPU). A closed-loop liquid-cooling system was built and tested utilizing deionized water and a silver (Ag) nanofluid coolant (0.015% vol.) across a variety of microchannel heat sink topologies with varying fin spacing. Key thermal performance indicators, including GPU temperature, coolant outlet temperature, and thermal resistance, were measured at different coolant flow rates. Experiments revealed that raising the flow velocity and decreasing the fin gap considerably enhanced cooling performance, while the Ag nanofluid consistently lowered GPU temperature by 1–3 °C compared to water. An Artificial Neural Network (ANN) surrogate model was constructed and trained using experimental data to support predictive analysis and system optimization, achieving excellent predictive accuracy with low RMSE. The trained ANN model was combined with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to perform multi-objective optimization, aiming to minimize GPU temperature and thermal resistance while improving heat removal. The Pareto-optimal solutions revealed that nanofluid-based cooling offered the best trade-off circumstances, with optimal designs occurring at moderate flow rates and small fin spacing. The ANN-NSGA-II multi-objective optimization results indicated that the best thermal performance of the GPU cooling system was achieved when using Ag nanofluid (0.015 vol.%) as the coolant, with an optimal coolant flow rate in the range of 1.30–1.84 LPM and an optimal fin/channel spacing of 0.57–0.71 mm, producing GPU temperatures of 29.18–29.66 °C, coolant outlet temperatures of 29.06–29.41 °C, and a minimized thermal resistance of 0.0106–0.0152 °C/W; thus, overall, the suggested ANN-NSGA-II framework works well as a practical design tool for improving GPU cooling systems and may be used to other high-heat-flux electronic thermal management applications. Full article

The key to metallic fuel development is the fabrication of uranium metal and alloys into fuel forms. U-Nb alloys are one of the best candidates for a metallic fuel alloy with high-temperature strength sufficient to support the core, acceptable nuclear properties, good fabricability, and compatibility with usable coolant media. Melt processing has been a key component of the metallic fuel cycle, and process models require thermophysical parameters at elevated temperatures, particularly above the melting temperatures, regarding which experimental data are scarce, for accurate simulations and process development. By means of ab initio density-functional theory (DFT) quantum molecular dynamics (QMD), we have calculated the main thermophysical parameters—the density, thermal expansion coefficient, specific heat, thermal conductivity, melting temperature, latent heat of fusion, and viscosity—used in the modeling of the U-6 wt.% Nb alloy casting. The melting temperature of the U-6 wt.% Nb alloy at ambient pressure is obtained by means of QMD simulations using the Z-method. The ambient volume change and latent heat of melting of U-6 wt.% Nb are also derived from QMD simulations in conjunction with analytical fitting for the energy and pressure. The thermal conductivity for the solid U-Nb alloy is calculated from the semi-classical Boltzmann transport equation combined with an estimate of the electron relaxation time obtained from DFT simulations. Full article

In molten salt reactors (MSRs), molten salt performs dual essential roles as fuel and coolant. The continuous circulation of the fuel salt in the primary loop inevitably leads to significant neutron activation of loop components, particularly the structural alloys of the heat exchanger (HX) and the coolant salt within the HX. This activation is strongly influenced by delayed neutron fluxes generated by the migration of delayed neutron precursors (DNPs) within the flowing fuel salt. Accurate quantification of the radioactivity of primary HX components is essential for establishing reliable modular replacement strategies, optimizing shutdown maintenance schedules, and ensuring operational safety. To address this requirement, a comprehensive simulation methodology has been developed to model the DNP transport through the primary HX in a small modular molten salt reactor (SM-MSR). It aims to quantitatively evaluate activation levels of HX structural alloys and circulating coolant salt within the HX. Comparative simulations were conducted to contrast scenarios with dynamic DNP migration and static-fuel scenarios excluding it. The results indicate that consideration of DNP migration increases the neutron flux at the top region of the HX by approximately three orders of magnitude compared with the static-fuel scenario. This elevates coolant salt radioactivity by over 50%. Significant increases in irradiation damage parameters (displacements per atom and helium production) are observed in the upper sections of HX structural alloys. These findings highlight the necessity of incorporating DNP migration effects for accurate prediction of primary loop component neutron activation. This provides a reference for future shielding design optimization, irradiation damage assessments, and shutdown dose rate calculations in the SM-MSR. Full article

Following a loss-of-coolant accident, the reactor safety injection system is activated, and a large amount of coolant is injected into the reactor pressure vessel (RPV). This induces cold plume phenomena and temperature nonuniformity inside the vessel, which may threaten the structural integrity of the RPV. Transient numerical simulations are performed to investigate the complex cold plumes that arise inside the pressure vessel of a small mobile reactor under safety-injection conditions. By evaluating the flow-field evolution under different injection paths and flow rates, and by innovatively adapting classical plume entrainment theory to define an equivalent coefficient for the nuclear engineering context, this study systematically analyzes the formation and development of the cold plumes and their entrainment–mixing mechanisms. The results indicate that, in such compact RPVs, the entrainment and mixing intensity of the cold plumes is relatively weak, resulting in ineffective thermal mixing during the development stage. Although increasing the injection flow rate enhances heat transfer and reduces thermal gradients, the improvement exhibits diminishing returns. A comparison of different injection paths reveals that a dual-line injection scheme leverages plume–plume interaction to effectively strengthen radial mixing, accelerate temperature-field homogenization, and enlarge the wall-cooling region, thereby alleviating local pressurized thermal shock (PTS). Full article

A design for a small chloride salt fast reactor (sm-MCFR) is presented through the integration of molten salt reactor and small reactor technologies, targeting efficient transmutation of transuranic (TRU) elements in spent nuclear fuel and rapid reactor deployment. The feasibility exploration and research on the design boundaries of sm-MCFR will be conducted in this article. The core adopts a dual-fluid configuration, in which the fuel salt and coolant circulate independently. Chloride salt is selected as the fuel carrier due to its high solubility for heavy metal nuclides and the low neutron absorption cross-section of chlorine, which help to form a hard fast-neutron spectrum and thereby enhance transmutation efficiency. The cooling system employs a direct supercritical carbon dioxide (s-CO₂) cycle, simplifying the overall layout. For the neutronics design, simulations were carried out using the TMCBurnup (TRITON MODEC Coupled Burnup Code). By adjusting the core geometry, fuel salt composition, and reprocessing strategy, the sm-MCFR achieves a hard fast-neutron spectrum but also demonstrates good potential for fuel utilization. In terms of thermal–hydraulic design, the heat exchange effect of the reactor core can be improved by adjusting the proportion of the coolant and the flow direction. The sm-MCFR is expected to become a promising candidate for advanced small reactors that have potential applications in nuclear waste transmutation and distributed energy generation. Full article

The genesis of the conducted research was the intention to determine how the rheological properties of engine oil change upon the addition of a coolant. The aim of the study was to assess the effect of coolant addition to engine oil on its dynamic viscosity. The experiments were carried out for six different fresh engine oils and six of the same oils sampled during routine oil replacement after a vehicle mileage of 10,000 km. Two oils were selected from each of the three primary categories: synthetic, semi-synthetic, and mineral oils. Then, mixtures of engine oils with the addition of coolant were prepared at 0, 5, 10, 20, 30, 40 and 50% (v/v), respectively. The study involved measuring the dynamic viscosity of the samples at 1000 s⁻¹, covering temperatures from 0 to 50 °C. The tests were conducted using a measurement setup equipped with a RHEOLABQC rotational rheometer manufactured by Anton Paar GmbH (Ostfildern, Germany). To investigate the temperature dependence of dynamic viscosity, a GRANT thermostatic bath was coupled with the rheometer. The studies demonstrated that dynamic viscosity strongly depends on temperature, as well as on the type and condition of the engine oil. At 0 °C, the dynamic viscosity of fresh oils ranged from approximately 500 to 700 mPa·s, whereas at 50 °C it decreased to approximately 100 mPa·s. The addition of 5% (v/v) coolant to engine oil resulted in only a slight change in dynamic viscosity. In contrast, a substantial decrease in dynamic viscosity was observed when 50% (v/v) coolant was added to the tested engine oils. The results indicate that coolant, which may enter the engine oil in the event of an engine failure, can significantly deteriorate the rheological properties of this lubricant. Full article

The progressive deployment of renewable energy systems has engendered a considerable increase in the generation of glycol-based coolant waste, specifically ethylene glycol (EG) and propylene glycol (PG), thereby raising significant environmental apprehensions. This review analyses the critical environmental challenge and examines the feasibility of microbial degradation as a viable and sustainable alternative to glycol waste treatment, while highlighting significant gaps in current hazardous glycol waste management practices. Present waste management practices are largely founded on incineration or membrane filtration approaches, both of which exhibit significant energy demands and inefficiencies in large-scale waste handling. Reported performance ranges from >99% EG recovery at 10–16 kWh/m³ by electrodialysis and 80–95% recovery at 2–4 MJ/kg by vacuum distillation, to ~17 MJ/kg combustion heat from incineration; biological methods, though promising, currently operate below 10% glycol concentration, an order of magnitude below the 10–100% range in real coolants. We analyze the current understanding of metabolic pathways involved in glycol biodegradation, drawing on the peer-reviewed literature, bioinformatics, and patent databases. Special attention is given to the challenges of high glycol concentrations in industrial coolants and the formation of toxic oxidation products during thermal aging. The review also explores recent advances in genetic engineering approaches to enhance microbial degradation efficiency. Finally, we discuss the potential integration of biological recycling methods into existing waste management systems and future prospects for converting glycol waste into value-added products through microbial biotransformation. Full article

Blade tip leakage flow in gas turbines is associated with aerodynamic loss and local heat transfer variation in the tip region. In this study, the flow structure, total pressure loss coefficient, heat transfer coefficient (HTC), and film cooling effectiveness (FCE) were numerically investigated for a plane tip (PLN) and five squealer tip geometries: a conventional squealer tip (SQR), cutback squealer tip (CBS), multi-cavity squealer tip (MCS), triangular-grooved suction-side squealer tip (GSS), and multi-cavity triangular-grooved suction-side squealer tip (MGS). All configurations were compared under the same cascade geometry, tip-clearance condition, and inlet/outlet boundary conditions to examine the geometry-dependent relationship among aerodynamic loss, heat transfer, and film cooling performance. Film cooling was evaluated at blowing ratios of M = 1 and 2 using a camber-line hole arrangement, and the effect of hole rearrangement was further examined at the same blowing ratio and with the same number of cooling holes. The results indicate that the aerodynamic and thermal characteristics of the tip region vary with the leakage-flow path, cavity recirculation, and reattachment behavior formed by each tip geometry. Under the present conditions, SQR showed the lowest downstream total pressure loss coefficient, with a 7.27% reduction relative to PLN, whereas MGS showed the lowest geometry-normalized heat transfer rate among the tested geometries. Increasing the blowing ratio tended to increase FCE, although local cooling performance was affected by high-pressure or reattachment-dominated regions where coolant ejection, surface attachment, or lateral spreading was limited. Compared with the camber-line arrangement, the rearranged hole configuration increased local FCE by up to 29.6% for CBS and 23.3% for MGS at the same blowing ratio. These results may be used as comparative data for evaluating squealer tip geometries and cooling-hole placement during preliminary blade tip cooling design. Full article

Building upon our previous aerodynamic characterizations of skewed jets, this study extends the investigation to systematically evaluate their thermal performance. Turbulent air jets are produced by unilaterally supplying coolant and forcing it through a series of concave perforated blockages having varying relative inner diameters (D_in/D_j = 3.0, 4.0 and 5.0) or relative thicknesses (t/D_j = 0.5, 2.0, 4.0, 6.0 and 8.0), with the jet diameter and Reynolds number fixed at D_j = 21 mm and Re_j = 20,000, respectively. The results demonstrate that the skewed jets exhibit pronounced asymmetric velocity profiles in both the x-z and y-z planes. Unlike the Gaussian distributions characteristic of conventional axisymmetric jets, these profiles manifest as distinctly skewed or saddle-shaped topologies. This topological distortion is exacerbated by reducing either D_in/D_j or t/D_j, albeit through fundamentally different mechanisms: the former only leads to jet deflection from the geometric axis, with the deflection angle increasing non-linearly from α = 4°, 5° to 12°; whilst the latter induces asymmetric internal flow development and exit momentum redistribution. The thermal performance of these jets on an iso-flux target flat plate, characterized by Nusselt number distributions at different jet-to-target spacings (H/D_j = 0 to 8.0), is shown to significantly differ from conventional axisymmetric jets. Full article

The continuously improving power density of Li-ion batteries and the widespread application of fast charging and discharging have rendered thermal management an increasingly critical task. Cold plates are among the most important means for such a task, and their channel structure significantly affects battery performance. Aiming to further improve the thermohydraulic performance of cold plate, this study proposes a cold plate with sinusoidal wave-shaped channel. Using channel quantity, amplitude, wavelength, diameter, and coolant mass flow rate as variables, the orthogonal experimental scheme is employed to design combinations of different variables for numerical simulation. The numerical simulation results are used to train a deep neural network for cold plate performance prediction. The trained neural network can accurately predict the maximum temperature, comprehensive performance indicators, and entropy generation rate with errors below 5.0%, 5.0%, and 10.0%, respectively. Multi-objective optimization design (MOOD) is implemented by combining a deep neural network with the NSGA-II genetic optimization, yielding two sets of Pareto fronts as follows: one for maximizing comprehensive performance indicator and minimizing entropy generation rate, and the other for minimizing maximum temperature and entropy generation rate, and TOPSIS decision points are provided. This study provides a new method and valuable MOOD results for the thermal management of Li-ion batteries and cold plate engineering while offering theoretical guidance for practical applications. Full article

Water-miscible metalworking fluids are widely used in industrial processes. Despite the fact that they typically contain biocides, they are almost always colonized by microorganisms, which degrade different components of the liquid, may clog machines due to biofilm formation, and might pose a health risk to workers. In this study, samples from four metalworking machines operated with the same metalworking concentrate were analyzed with respect to microbial growth. Twenty-seven bacterial species and one fungus were identified. From these, twenty species were not observed before as colonizers of metalworking fluids. Growth of microorganisms, putative contamination sources, metabolic pathways involved in biodegradation, and resulting health risks are analyzed and discussed in this study. Full article

Frost buildup on copper tube-fin heat exchangers reduces their performance in cold, humid conditions. Fin length plays a key role in balancing heat transfer and frost resistance. This study experimentally examines how fin length affects thermal and frosting behavior. Four heat exchangers with fin lengths of $15.1$ $\mathrm{m}$$\mathrm{m}$, $18.53$ $\mathrm{m}$$\mathrm{m}$, $20.3$ $\mathrm{m}$$\mathrm{m}$, and $23.5$ $\mathrm{m}$$\mathrm{m}$ were tested at 2 °C/1 °C dry-bulb/wet-bulb air temperature and $-6$ °C coolant temperature under constant static pressure. Results show that longer fins increase total heat transfer—peak capacity rose from 512 $\mathrm{W}$ to 566 $\mathrm{W}$—but reduce heat transfer per unit area by about 30%. Operating time before defrosting increased by 30.6%, from $45.7$ $\min$ to $59.6$ $\min$, due to lower frost density. Total frost mass grew, but unit-area frost decreased by 12.7%. During defrosting, longer fins achieved greater absolute airflow recovery (from 195 to 213 ${\mathrm{m}}^3$h⁻¹), though defrosting efficiency per gram of frost declined. Short fins (15 $\mathrm{m}$$\mathrm{m}$) suit space-limited systems needing high surface efficiency. Long fins (23 $\mathrm{m}$$\mathrm{m}$) benefit large systems requiring long run times and strong post-defrost performance. Medium lengths (17 $\mathrm{m}$$\mathrm{m}$ to 20 $\mathrm{m}$$\mathrm{m}$) offer a practical balance for general use. These findings support better heat exchanger design in frost-prone applications. Full article

Computer Numerical Control (CNC) machining centers are critical assets in discrete manufacturing, yet many shop floors still rely on periodic expert judgment for machine selection and workload allocation. This practice is unsuitable for high-mix production because machine condition and risk can change rapidly due to tool wear, thermal drift, coolant variation, and alarms. Moreover, decision evidence is fragmented and often incomplete across controller and programmable logic controller signals, production records, and inspection results, making manual evaluation time-consuming and prone to misjudgment. Static rankings can also break down under unforeseen shop-floor disruptions, requiring rapid event-driven re-prioritization and rescheduling. To address these challenges, this research proposes a shop-floor decision intelligence pipeline that executes a rolling-window, uncertainty-aware ranking-and-dispatch loop directly on the shop floor. The industrial compute node continuously collects multi-source operational evidence, normalizes it into a unified event representation, and aggregates rolling-window indicators for each machine. A mapping structure then converts these indicators into neutrosophic triplets that separate performance from evidence credibility. Using this representation, a shop-floor decision procedure continuously updates machine priority scores using a TOPSIS procedure, which are further translated into workload allocation and persistence-confirmed protective action requests. A case study demonstrates end-to-end operation. It shows that the top-ranked machines remain stable under risk-aversion and weight-uncertainty analyses, while the protective logic prevents unsafe dispatching when reject-level conditions persist under reliable evidence. Overall, the proposed pipeline reframes CNC machine selection as a rolling-window, evidence-driven decision process and provides a pathway toward near-real-time and safety-aware shop-floor coordination. Full article

Efficiency improvements in gas turbines have been realized in recent decades by raising the turbine inlet temperature. This work devotes attention to pressure-gain combustion (PGC), which is a technology capable of yielding the same time-averaged combustor outlet temperature as conventional Brayton–Joule cycles but at a higher pressure. Here, PGC is implemented in a thermodynamic cycle wherein the compression system operates at a lower pressure ratio compared to the reference Brayton–Joule cycle. Focusing on E-, F- and H-class gas turbines, representative of three different technologies, the possible PGC advantages in both simple- and combined-cycle modes are investigated by means of in-house simulation code. Specifically, this work includes the energy penalty related to the PGC system cooling in the cycle analysis. In detail, the effects of different coolant amounts on the PGC system, as well as the lower efficiency at the first expansion stage compared to conventional gas turbine systems, are analyzed. Among the three classes of gas turbines, E is the one wherein the advantages are more significant, with ultimate efficiency values in simple-cycle mode calculated in the range of 38% to 41%. The higher the gas turbine technology and power class, the lower the benefit, and current H-class gas turbines already start from a higher efficiency level. Anyway, focusing on the latter, performance improvements for the PGC combined cycle seem to be possible, with efficiency greater than 65%, exceeding the current state-of-the-art systems. Full article

This study proposes a multi-objective optimization framework for a cooling plate-based indirect liquid cooling system applied to a 6S2P lithium-ion battery module during 3C fast charging. A three-dimensional computational fluid dynamics (CFD) model coupled with the multi-scale multi-domain (MSMD)–Newman–Tiedemann–Gu–Kim (NTGK) battery heat generation model was developed to investigate the system thermal–hydraulic behavior. The numerical model was experimentally validated through single-cell charging tests, with temperature deviations below 5%, confirming its reliability. A systematic parametric analysis was conducted to evaluate the effects of coolant channel number, channel width, channel spacing, and coolant mass flow rate on maximum temperature (T_max), temperature difference (ΔT), and pressure drop (ΔP). The results indicated that increasing the coolant flow rate significantly enhanced thermal performance but caused a substantial increase in hydraulic losses, whereas geometric parameters had comparatively smaller effects. To improve optimization efficiency, 30 design samples were generated using Latin hypercube sampling and used to train ANN surrogate models, which demonstrated high predictive accuracy with test R² values of 0.9931, 0.9960, and 0.9842 for T_max, ΔT, and pumping power (P_pump), respectively. Subsequently, NSGA-II combined with TOPSIS identified the optimal design with a channel width of 6.22 mm, channel spacing of 4.84 mm, and coolant flow rate of 2.55 LPM. Under these conditions, the optimized system achieved a T_max of 30.47 °C, a ΔT of 4.50 °C, and a P_pump of 0.05879 W. The relative deviations between ANN predictions and CFD results were all below 1%, demonstrating the robustness of the proposed optimization framework. These findings provide an effective design methodology for enhancing heat transfer while minimizing pumping power in advanced battery thermal management systems. Full article