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This paper aims to investigate the bonding of the photonic integrated circuit (PIC) chip with the heat sink using the AlNi self-propagating soldering method.

Compared to industrial optical modules, optical modules for aerospace applications require better reliability and stability, which is hard to achieve via the dispensing adhesive process that is used for traditional industrial optical modules. In this paper, 25 µm SAC305 solder foils and the AlNi nanofoil heat source were used to bond the back of the PIC chip with the heat sink. The temperature field and temperature history were analyzed by the finite element analysis (FEA) method. The junction-to-case thermal resistance is 0.0353°C/W and reduced by 85% compared with the UV hybrid epoxy joint.

The self-propagating reaction ends within 2.82 ms. The maximum temperature in the PIC operating area during the process is 368.5°C. The maximum heating and cooling rates of the solder were 1.39 × 107°C/s and −5.15 × 106°C/s, respectively. The microstructure of SAC305 under self-propagating reaction heating is more refined than the microstructure of SAC305 under reflow. The porosity of the heat sink-SAC305-PIC chip self-propagating joint is only 4.7%. Several metastable phases appear as AuSn3.4 and AgSn3.

A new bonding technology was used to form the bonding between the PIC chip with the heat sink for the aerospace optical module. The reliability and thermal resistance of the joint are better than that of the UV hybrid epoxy joint.

Several graphs, streamlines, isotherms and 3D plots are illustrated to enlighten the noteworthy fallouts of the investigation. Embedding flow factors for velocity, induced magnetic field and temperature have been determined using parametric analysis.

Ternary hybrid nanofluids has outstanding hydrothermal performance compared to classical mono nanofluids and hybrid nanofluids owing to the presence of triple tiny metallic particles. Ternary hybrid nanofluids are considered as most promising candidates in solar energy, heat exchangers, electronics cooling, automotive cooling, nuclear reactors, automobile, aerospace, biomedical devices, food processing etc. In this work, a ternary hybrid nanofluid flow that contains metallic nanoparticles over a wedge under the prevalence of solar radiating heat, induced magnetic field and the shape factor of nanoparticles is considered. A ternary hybrid nanofluid is synthesized by dispersing iron oxide (Fe3O4), silver (Ag) and magnesium oxide (MgO) nanoparticles in a water (H₂O) base fluid. By employing similarity transformations, we can convert the governing equations into ordinary differential equations and then solve numerically by using the Runge–Kutta–Fehlberg approach.

There is no fund for the research work.

This kind of study may be used to improve the performance of solar collectors, solar energy and solar cells.

This investigation unfolds the hydrothermal changes of radiative water-based Fe₃O₄-Ag-MgO-H₂O ternary hybrid nanofluidic transport past a static and moving wedge in the presence of solar radiating heating and induced magnetic fields. The shape factor of nanoparticles has been considered in this study.

This study aims to delve into the coupled mixed convective heat transport process within a grooved channel cavity using CuO-water nanofluid and an inclined magnetic field. The cavity undergoes isothermal heating from the bottom, with variations in the positions of heated walls across the grooved channel. The aim is to assess the impact of heater positions on thermal performance and identify the most effective configuration.

Numerical solutions to the evolved transport equations are obtained using a finite volume method-based indigenous solver. The dimensionless parameters of Reynolds number (1 ≤ Re ≤ 500), Richardson number (0.1 ≤ Ri ≤ 100), Hartmann number (0 ≤ Ha ≤ 70) and magnetic field inclination angle (0° ≤ γ ≤ 180°) are considered. The solved variables generate both local and global variables after discretization using the semi-implicit method for pressure linked equations algorithm on nonuniform grids.

The study reveals that optimal heat transfer occurs when the heater is positioned at the right corner of the grooved cavity. Heat transfer augmentation ranges from 0.5% to 168.53% for Re = 50 to 300 compared to the bottom-heated case. The magnetic field’s orientation significantly influences the average heat transfer, initially rising and then declining with increasing inclination angle. Overall, this analysis underscores the effectiveness of heater positions in achieving superior thermal performance in a grooved channel cavity.

This concept can be extended to explore enhanced thermal performance under various thermal boundary conditions, considering wall curvature effects, different geometry orientations and the presence of porous structures, either numerically or experimentally.

The findings are applicable across diverse fields, including biomedical systems, heat exchanging devices, electronic cooling systems, food processing, drying processes, crystallization, mixing processes and beyond.

This work provides a novel exploration of CuO-water nanofluid flow in mixed convection within a grooved channel cavity under the influence of an inclined magnetic field. The influence of different heater positions on thermomagnetic convection in such a cavity has not been extensively investigated before, contributing to the originality and value of this research.

The purpose of this study is to adapt the incompressible smoothed particle hydrodynamics (ISPH) method with artificial intelligence to manage the physical problem of double diffusion inside a porous L-shaped cavity including two fins.

The ISPH method solves the nondimensional governing equations of a physical model. The ISPH simulations are attained at different Frank–Kamenetskii number, Darcy number, coupled Soret/Dufour numbers, coupled Cattaneo–Christov heat/mass fluxes, thermal radiation parameter and nanoparticle parameter. An artificial neural network (ANN) is developed using a total of 243 data sets. The data set is optimized as 171 of the data sets were used for training the model, 36 for validation and 36 for the testing phase. The network model was trained using the Levenberg–Marquardt training algorithm.

The resulting simulations show how thermal radiation declines the temperature distribution and changes the contour of a heat capacity ratio. The temperature distribution is improved, and the velocity field is decreased by 36.77% when the coupled heat Cattaneo–Christov heat/mass fluxes are increased from 0 to 0.8. The temperature distribution is supported, and the concentration distribution is declined by an increase in Soret–Dufour numbers. A rise in Soret–Dufour numbers corresponds to a decreasing velocity field. The Frank–Kamenetskii number is useful for enhancing the velocity field and temperature distribution. A reduction in Darcy number causes a high porous struggle, which reduces nanofluid velocity and improves temperature and concentration distribution. An increase in nanoparticle concentration causes a high fluid suspension viscosity, which reduces the suspension’s velocity. With the help of the ANN, the obtained model accurately predicts the values of the Nusselt and Sherwood numbers.

A novel integration between the ISPH method and the ANN is adapted to handle the heat and mass transfer within a new L-shaped geometry with fins in the presence of several physical effects.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This study investigates the impact of the fire decay phase on structural damage using the sectional analysis method. The primary objective of this work is to forecast the non-dimensional capacity parameters for the axial and flexural load-carrying capacity of reinforced concrete (RC) sections for heating and the subsequent post-heating phase (decay phase) of the fire.

The sectional analysis method is used to determine the moment and axial capacities. The findings of sectional analysis and heat transfer for the heating stage are initially validated, and the analysis subsequently proceeds to determine the load capacity during the fire’s heating and decay phases by appropriately incorporating non-dimensional sectional and material parameters. The numerical analysis includes four fire curves with different cooling rates and steel percentages.

The study’s findings indicate that the rate at which the cooling process occurs after undergoing heating substantially impacts the axial and flexural capacity. The maximum degradation in axial and flexural capacity occurred in the range of 15–20% for cooling rates of 3 °C/min and 5 °C/min as compared to the capacity obtained at 120 min of heating for all steel percentages. As the fire cooling rate reduced to 1 °C/min, the highest deterioration in axial and flexural capacity reached 48–50% and 42–46%, respectively, in the post-heating stage.

The established non-dimensional parameters for axial and flexural capacity are limited to the analysed section in the study owing to the thermal profile, however, this can be modified depending on the section geometry and fire scenario.

The study primarily focusses on the degradation of axial and flexural capacity at various time intervals during the entire fire exposure, including heating and cooling. The findings obtained showed that following the completion of the fire’s heating phase, the structural capacity continued to decrease over the subsequent post-heating period. It is recommended that structural members' fire resistance designs encompass both the heating and cooling phases of a fire. Since the capacity degradation varies with fire duration, the conventional method is inadequate to design the load capacity for appropriate fire safety. Therefore, it is essential to adopt a performance-based approach while designing structural elements' capacity for the desired fire resistance rating. The proposed technique of using non-dimensional parameters will effectively support predicting the load capacity for required fire resistance.

The fire-resistant requirements for reinforced concrete structures are generally established based on standard fire exposure conditions, which account for the fire growth phase. However, it is important to note that concrete structures can experience internal damage over time during the decay phase of fires, which can be quantitatively determined using the proposed non-dimensional parameter approach.

Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the reliability of electronic devices. The purpose of this study is to propose a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and thus provide reference information for the reliability evaluation of solder joints.

The temperature distribution and current density distribution of the interconnect structure of electronic devices were investigated through finite element simulations. During the experimental process, the actual temperature of the solder joints was measured and was used to optimize the finite element model. A large amount of simulation data was obtained to analyze the neural network by varying the height of solder joints, the diameter of solder pads and the magnitude of current loads. The constructed neural network was trained, tested and optimized using this data.

Based on the finite element simulation results, the current is more concentrated in the corners of the solder joints, generating a significant amount of Joule heating, which leads to localized temperature rise. The constructed neural network is trained, tested and optimized using the simulation results. The ANN 1, used for predicting solder joint temperature, achieves a prediction accuracy of 96.9%, while the ANN 2, used for predicting solder joint current density, achieves a prediction accuracy of 93.4%.

The proposed method can effectively improve the estimation efficiency of temperature and current density in the packaging structure. This method prevails in the field of packaging, and other factors that affect the thermal, mechanical and electrical properties of the packaging structure can be introduced into the model.

This paper aims to study the influence of cylindrical texture parameters on the lubrication performance of static and dynamic pressure thrust bearings (hereinafter referred to as thrust bearings) and to optimize their lubrication performance using multiobjective optimization.

The influence of texture parameters on the lubrication performance of thrust bearings was studied based on the modified Reynolds equation. The objective functions are predicted through the BP neural network, and the texture parameters were optimized using the improved multiobjective ant lion algorithm (MOALA).

Compared with smooth surface, the introduction of texture can improve the lubrication properties. Under the optimization of the improved algorithm, when the texture diameter, depth, spacing and number are approximately 0.2 mm, 0.5 mm, 5 mm and 34, respectively, the loading capacity is increased by around 27.7% and the temperature is reduced by around 1.55°C.

This paper studies the effect of texture parameters on the lubrication properties of thrust bearings based on the modified Reynolds equation and performs multiobjective optimization through an improved MOALA.

This paper aims to derive a reduced-order model for the heat transfer across the interface between a millimetric thermocapillary liquid bridge from silicone oil and the surrounding ambient gas.

Numerical solutions for the two-fluid model are computed covering a wide parametric space, making a total of 2,800 numerical flow simulations. Based on the computed data, a reduced single-fluid model for the liquid phase is devised, in which the heat transfer between the liquid and the gas is modeled by Newton’s heat transfer law, albeit with a space-dependent Biot function Bi(z), instead of a constant Biot number Bi.

An explicit robust fit of Bi(z) is obtained covering the whole range of parameters considered. The single-fluid model together with the Biot function derived yields very accurate results at much lesser computational cost than the corresponding two-phase fully-coupled simulation required for the two-fluid model.

Using this novel Biot function approach instead of a constant Biot number, the critical Reynolds number can be predicted much more accurately within single-phase linear stability solvers.

The Biot function for thermocapillary liquid bridges is derived from the full multiphase problem by a robust multi-stage fit procedure. The derived Biot function reproduces very well the theoretical boundary layer scalings.

The purpose of this paper is to develop a coupled lattice Boltzmann model for the simulation of the freezing process in unsaturated porous media.

In the developed model, the porous structure with complexity and disorder was generated by using a stochastic growth method, and then the Shan-Chen multiphase model and enthalpy-based phase change model were coupled by introducing a freezing interface force to describe the variation of phase interface. The pore size of porous media in freezing process was considered as an influential factor to phase transition temperature, and the variation of the interfacial force formed with phase change on the interface was described.

The larger porosity (0.2 and 0.8) will enlarge the unfrozen area from 42 mm to 70 mm, and the rest space of porous medium was occupied by the solid particles. The larger specific surface area (0.168 and 0.315) has a more fluctuated volume fraction distribution.

The concept of interfacial force was first introduced in the solid–liquid phase transition to describe the freezing process of frozen soil, enabling the formulation of a distribution equation based on enthalpy to depict the changes in the water film. The increased interfacial force serves to diminish ice formation and effectively absorb air during the freezing process. A greater surface area enhances the ability to counteract liquid migration.