Search Results (299)

This study focuses on the analysis of the simultaneous impact of inclined magnetohydrodynamic Carreau hybrid nanofluid flow over a stretching sheet, including microorganisms with the effects of chemical reactions in the presence and absence of slip conditions for dilatant $(n>1.0)$ and quasi-elastic hybrid nanofluid $(n<1.0)$ limitations. Meanwhile, the transfer of energy is strengthened through the employment of heat sources and bioconvection. The analysis incorporates nonlinear thermal radiation, chemical reactions, and Arrhenius activation energy effects on different profiles. Numerical simulations are conducted using the efficient Bvp5c solver. Motile concentration profiles decrease as the density slip parameter of the motile microbe and Lb increase. The Weissenberg number exhibits a distinct nature depending on the hybrid nanofluid; the velocity profile, skin friction, and Nusselt number fall when $(n>1.0)$ and increase when $(n<1.0)$. For small values of inclination, the 3D surface plot is far the surface, while it is close to the surface for higher values of inclination but has the opposite behavior for the 3D plot of the Nusselt number. A detailed numerical investigation on the effects of important parameters on the thermal, concentration, and motile profiles and the Nusselt number reveals a symmetric pattern of boundary layers at various angles $\left(\alpha \right)$. Results are presented through tables, graphs, contour plots, and streamline and surface plots, covering both shear-thinning cases $(n<1.0)$ and shear-thickening cases $(n>1.0)$. Full article

Nanotechnology has become a transformative field in modern science and engineering, offering innovative approaches to enhance conventional thermal and fluid systems. Heat and mass transfer phenomena, particularly fluid motion across various geometries, play a crucial role in industrial and engineering processes. The inclusion of nanoparticles in base fluids significantly improves thermal conductivity and enables advanced phase-change technologies. The current work examines Powell–Eyring nanofluid’s heat transmission properties on a stretched Riga plate, considering the effects of magnetic fields, porosity, Darcy–Forchheimer flow, thermal radiation, and activation energy. Using the proper similarity transformations, the pertinent governing boundary-layer equations are converted into a set of ordinary differential equations (ODEs), which are then solved using the boundary value problem fourth-order collocation (BVP4C) technique in the MATLAB program. Tables and graphs are used to display the outcomes. Due to their significance in the industrial domain, the Nusselt number and skin friction are also evaluated. The velocity of the nanofluid is shown to decline with a boost in the Hartmann number, porosity, and Darcy–Forchheimer parameter values. Moreover, its energy curves are increased by boosting the values of thermal radiation and the Biot number. A stronger Hartmann number M decelerates the flow (thickening the momentum boundary layer), whereas increasing the Riga forcing parameter Q can locally enhance the near-wall velocity due to wall-parallel Lorentz forcing. Visual comparisons and numerical simulations are used to validate the results, confirming the durability and reliability of the suggested approach. By using a systematic design technique that includes training, testing, and validation, the fluid dynamics problem is solved. The model’s performance and generalization across many circumstances are assessed. In this work, an artificial neural network (ANN) architecture comprising two hidden layers is employed. The model is trained with the Levenberg–Marquardt scheme on reliable numerical datasets, enabling enhanced prediction capability and computational efficiency. The ANN demonstrates exceptional accuracy, with regression coefficients $R\approx 1.0$ and the best validation mean squared errors of $8.52\times {10}^{-10}$, $7.91\times {10}^{-9}$, and $1.59\times {10}^{-8}$ for the Powell–Eyring, heat radiation, and thermophoresis models, respectively. The ANN-predicted velocity, temperature, and concentration profiles show good agreement with numerical findings, with only minor differences in insignificant areas, establishing the ANN as a credible surrogate for quick parametric assessment and refinement in magnetohydrodynamic (MHD) nanofluid heat transfer systems. Full article

Birch stands, dominated by Betula pendula Roth, are a common feature of boreal forests. Within the Krasnoyarsk (central) group of regions, they are concentrated in the taiga, subtaiga and forest steppe zones of actively managed forests, represented by stands of seed and shoot origin. The health and productivity of birch forests is often determined by the activity of wood-decay fungi, which leads to rot and decay in trees. The objective of the research is to evaluate the impact of stem rot on birch forests in the study area, with a focus on key ecological and silvicultural factors. The research methods employed included a reconnaissance survey of birch forests, a detailed forest pathology survey of forest stands on research plots (31 pcs.), comprehensive macroscopic diagnostics of stem rot, identification of xylotrophic fungi by basidiomes, integrated assessment of forest health, graph analytics and statistical data analysis. Stem rot has been identified in all birch forests in the study area. In shoot origin stands, the incidence rate has reached the stage of the disease center (i.e., more than 10% of trees are infected). The following wood-decay fungi have been detected on the trunks of living trees affected by rot: Fomes fomentarius, Fomitopsis pinicola, Inonotus obliquus, Phellinus igniarius, and Trametes versicolor. The infection typically infects trees via spores, finding entry through dying branches or mechanical and thermal wounds on trunks. In trees of shoot origin, stem rot is frequently transmitted via mycelium from stumps left after felling. This, in conjunction with diminished immunity, contributes to a substantially elevated incidence of stem rot in comparison to stands of seed origin. The research has not established a reliable correlation between the incidence of stem rot and forest stand characteristics due to the impact of human activity on birch forests (e.g., cutting, fires, tree injury). At the same time, no reliable connection has been established between the spread of stem rot and the stage of recreational disturbance. Trees of various sizes are affected by stem rot, usually proportional to their representation in the structure of the forest stand. The disease has a detrimental effect on the trees, which is clearly evident in the decline of forest health. Full article

Building performance simulation is crucial for the design and optimization of sustainable buildings. However, the increasing complexity of building systems necessitates advanced modeling techniques capable of handling multi-domain interactions. This paper presents a novel application of the bond graph (BG) methodology to simulate and analyze the thermal behavior of an integrated trigeneration system within an experimental test cell. Unlike conventional simulation approaches, the BG framework enables unified modeling of thermal and hydraulic subsystems, offering a physically consistent and energy-based representation of system dynamics. The study investigates the system’s performance under both dynamic and steady-state conditions across two distinct climatic periods. Validation against experimental data reveals strong agreement between measured and simulated temperatures in heating and cooling scenarios, with minimal deviations. This confirms the method’s reliability and its capacity to capture transient thermal behaviors. The results also demonstrate the BG model’s effectiveness in supporting predictive control strategies, optimizing energy efficiency, and maintaining thermal comfort. By integrating hydraulic circuits and thermal exchange processes within a single modeling framework, this work highlights the potential of bond graphs as a robust and scalable tool for advanced building performance simulation. Full article

Earth–air heat exchangers (EAHXs) use the soil’s thermal capacity to dampen the amplitude of outdoor air temperature oscillations. This effect can be used in hot and dry climates for room cooling with no or very little need for resources other than those used during the EAHX construction, an obvious advantage compared to the significant operational costs of refrigeration machines. Contrary to the streamlined process applied in conventional HVAC design (using refrigeration machines), EAHX design lacks straightforward and well-established rules; moreover, EAHXs struggle to achieve office room design cooling demands determined with conventional indoor thermal environment standards, hindering designers’ confidence and the wider adoption of EAHXs for standalone room cooling. This paper presents a graph-based method to assist in the design of a large-diameter EAHX. One year of post-occupancy monitoring data are used to evaluate this method and to investigate the performance of a large-diameter EAHX with up to 16,000 m³/h design airflow rate. Considering an adaptive standard for thermal comfort, peak EAHX cooling capacity of 28 kW (330 kWh/day, with just 50 kWh/day of fan electricity consumption) and office room load extraction of up to 22 kW (49 W/m²) provided evidence in support of standalone use of EAHX for room cooling. A fair fit between actual EAHX thermal performance and results obtained with the graph-based design method support the use of this method for large-diameter EAHX design. Full article

The concept of “material effort” appears in continuum mechanics wherever the response of a material to the currently existing state of loads and boundary conditions loses its previous, predictable character. However, within the material, which still descriptively remains a continuous medium, new physical structures appear and new previously unused physical features of the continuum are activated. The literature is dominated by a simplified way of thinking, which assumes that all these states can be characterized and described by one and the same measure of effort—for metals it is the Huber–Mises–Hencky equivalent stress. Quantitatively, perhaps 90% of the literature is dedicated to this equivalent stress. The remaining authors, as well as the author of this paper, assume that there is no single universal measure of effort that would “fit” all operating conditions of materials. Each state of the structure’s operation may have its own autonomous measure of effort, which expresses the degree of threat from a specific destruction mechanism. In the current energy sector, we are increasingly dealing with “low-cycle thermal fatigue states”. This is related to the fact that large, difficult-to-predict renewable energy sources have been added. Professional energy based on coal and gas units must perform many (even about 100 per year) starts and stops, and this applies not only to the hot state, but often also to the cold state. The question arises as to the allowable shortening of start and stop times that would not to lead to dangerous material effort, and whether there are necessary data and strength characteristics for heat-resistant steels that allow their effort to be determined not only in simple states, but also in complex stress states. Do these data allow for the description of the material’s yield surface? In a previous publication, the author presented the results of tension and compression tests at elevated temperatures for two heat-resistant steels: St12T and 26H2MF. The aim of the current work is to determine the properties and strength characteristics of these steels in a pure torsion test at elevated temperatures. This allows for the analysis of the strength of power turbine components operating primarily on torsion and for determining which of the two tested steels is more resistant to high temperatures. In addition, the properties determined in all three tests (tension, compression, torsion) will allow the determination of the yield surface of these steels at elevated temperatures. They are necessary for the strength analysis of turbine elements in start-up and shutdown cycles, in states changing from cold to hot and vice versa. A modified testing machine was used for pure torsion tests. It allowed for the determination of the sample’s torsion moment as a function of its torsion angle. The experiments were carried out at temperatures of 20 °C, 200 °C, 400 °C, 600 °C, and 800 °C for St12T steel and at temperatures of 20 °C, 200 °C, 400 °C, 550 °C, and 800 °C for 26H2MF steel. Characteristics were drawn up for each sample and compared on a common graph corresponding to the given steel. Based on the methods and relationships from the theory of strength, the yield stress and torsional strength were determined. The yield stress of St12T steel at 600 °C was 319.3 MPa and the torsional strength was 394.4 MPa. For 26H2MH steel at 550 °C, the yield stress was 311.4 and the torsional strength was 382.8 MPa. St12T steel was therefore more resistant to high temperatures than 26H2MF. The combined data from the tension, compression, and torsion tests allowed us to determine the asymmetry and plasticity coefficients, which allowed us to model the yield surface according to the Burzyński criterion as a function of temperature. The obtained results also allowed us to determine the parameters of the Drucker-Prager model and two of the three parameters of the Willam-Warnke and Menetrey-Willam models. The research results are a valuable contribution to the design and diagnostics of power turbine components. Full article

We introduce a synchronization procedure for clocks based on the Einstein–Landauer framework. Clocks are modeled as discrete, macroscopic devices operating at a thermal equilibrium temperature T. Synchronization is achieved by transmitting photons from one clock to another; the absorption of a photon by a clock reduces the uncertainty in its timekeeping. The minimum energy required for this reduction in uncertainty is determined by the Landauer bound. We distinguish between the time-bearing and non-time-bearing degrees of freedom of the clocks. A reduction in uncertainty under synchronization in the time-bearing degrees of freedom necessarily leads to heat dissipation in the non-time-bearing ones. The minimum energy dissipation in these non-time-bearing degrees of freedom is likewise given by the Landauer limit. The same is true for mechanical synchronization of clocks. We also consider lattices of clocks and analyze synchronization using a Ramsey graph approach. Notably, clocks operating at the same temperature may be synchronized using photons of different frequencies. Each clock is categorized as either synchronized or non-synchronized, resulting in a bi-colored complete graph of clocks. By Ramsey’s theorem, such a graph inevitably contains a triad (or loop) of clocks that are either all synchronized or all non-synchronized. The extension of the Ramsey approach to infinite lattices of clocks is reported. Full article

Robotic positioning accuracy is critically affected by both geometric and non-geometric errors. To address this dual error issue comprehensively, this paper proposes a novel two-stage compensation framework. First, a Memory based red billed blue magpie optimizer (MRBMO) is employed to identify and compensate for geometric errors by optimizing the geometric parameters based on end-effector observations. This memory-guided evolutionary mechanism effectively enhances the convergence accuracy and stability of the geometric calibration process. Second, a tuned graph neural network (AMPSO-GNN) is developed to model and compensate for non-geometric errors, such as cable deformation, thermal drift, and control imperfections. The GNN architecture captures the topological structure of the robotic system, while the adaptive momentum PSO dynamically optimizes the network’s hyperparameters for improved generalization. Experimental results on a six-axis industrial robot demonstrate that the proposed method significantly reduces residual positioning errors, achieving higher accuracy compared to conventional calibration and compensation strategies. This dual-compensation approach offers a scalable and robust solution for precision-critical robotic applications. Full article

The application of ceramic particle-reinforced metal matrix composites (CPRMMCs) in the nuclear power sector is primarily dependent on their mechanical and thermal properties. A comprehensive understanding of the structure–property (SP) linkages between microstructures and macroscopic properties is critical for optimizing material properties. However, traditional studies on SP linkages generally rely on experimental methods, theoretical analysis, and numerical simulations, which are often associated with high time and economic costs. To address this challenge, this study proposes a novel method based on Materials Informatics (MI), combining the finite element method (FEM), graph Fourier transform, principal component analysis (PCA), and machine learning models to establish the SP linkages between the microstructure and thermodynamic properties of CPRMMCs. Specifically, FEM is used to model the microstructures of CPRMMCs with varying particle volume fractions and sizes, and their elastic modulus, thermal conductivity, and coefficient of thermal expansion are computed. Next, the statistical features of the microstructure are captured using graph Fourier transform based on two-point spatial correlations, and PCA is applied to reduce dimensionality and extract key features. Finally, a polynomial kernel support vector regression (Poly-SVR) model optimized by Bayesian methods is employed to establish the nonlinear relationship between the microstructure and thermodynamic properties. The results show that this method can effectively predict FEM results using only 5–6 microstructure features, with the R² values exceeding 0.91 for the prediction of thermodynamic properties. This study provides a promising approach for accelerating the innovation and design optimization of CPRMMCs. Full article

In the current paper, an analysis of magnetohydrodynamic Williamson nanofluid boundary layer flow is presented, with multiple slips in a porous medium, using a newly designed human-brain-inspired Ricker wavelet neural network solver. The solver employs a hybrid approach that combines genetic algorithms, serving as a global search method, with sequential quadratic programming, which functions as a local optimization technique. The heat and mass transportation effects are examined through a stretchable surface with radiation, thermal, and velocity slip effects. The primary flow equations, originally expressed as partial differential equations (PDEs), are changed into a dimensionless nonlinear system of ordinary differential equations (ODEs) via similarity transformations. These ODEs are then numerically solved with the proposed computational approach. The current study has significant applications in a variety of practical engineering and industrial scenarios, including thermal energy systems, biomedical cooling devices, and enhanced oil recovery techniques, where the control and optimization of heat and mass transport in complex fluid environments are essential. The numerical outcomes gathered through the designed scheme are compared with reference results acquired through Adam’s numerical method in terms of graphs and tables of absolute errors. The rapid convergence, effectiveness, and stability of the suggested solver are analyzed using various statistical and performance operators. Full article

Coal-fired boilers significantly contribute to nitrogen oxides (NOx) emissions, posing critical environmental and health risks. Effective prediction of NOx emissions is essential for optimizing control measures and achieving stringent emission standards. This study applies a Multiscale Graph Convolutional Network (MSGNet) designed to capture multiscale dynamic relationships among operational parameters of a 660 MW coal-fired boiler. MSGNet employs Fast Fourier Transform (FFT) for automatic periodic pattern recognition, adaptive graph convolution for dynamic inter-variable relationships, and a multihead attention mechanism to assess temporal dependencies comprehensively. Compared with the existing state of the art, the proposed structure achieves a good performance of 2.176 mg/m³, 1.652 mg/m³, and 0.988 of RMSE, MAE, and $R^2$. Experimental evaluations demonstrate that MSGNet achieves superior predictive performance compared with traditional methods such as LSTM, BiLSTM, and GRU. Results underscore MSGNet’s robust accuracy, stability, and generalization capability, highlighting its potential for advanced emission control and environmental management applications in thermal power generation. Full article

This study has established and resolved a new mathematical model of a homogeneous, generalized, magnetothermoelastic half-space with a thermally loaded bounding surface, subjected to ramp-type heating and supported by a solid foundation where these types of mathematical models have been widely used in many sciences, such as geophysics and aerospace. The governing equations are formulated according to the Green–Lindsay theory of generalized thermoelasticity. This work’s uniqueness lies in the examination of Maxwell’s time-fractional equations via the definition of Caputo’s fractional derivative. The Laplace transform method has been used to obtain the solutions promptly. Inversions of the Laplace transform have been computed via Tzou’s iterative approach. The numerical findings are shown in graphs representing the distributions of the temperature increment, stress, strain, displacement, induced electric field, and induced magnetic field. The time-fractional parameter derived from Maxwell’s equations significantly influences all examined functions; however, it does not impact the temperature increase. The time-fractional parameter of Maxwell’s equations functions as a resistor to material deformation, particle motion, and the resulting magnetic field strength. Conversely, it acts as a catalyst for the stress and electric field intensity inside the material. The strength of the main magnetic field considerably influences the mechanical and electromagnetic functions; however, it has a lesser effect on the thermal function. Full article

The transition to electric vehicles is a critical step toward achieving carbon neutrality and environmental sustainability. This shift relies on advancements across multiple technological domains, driving the need for strategic technology intelligence to anticipate emerging technology convergence opportunities. To address this challenge, this study aimed at providing an analytical framework for identifying technology convergence opportunities using node2vec graph embedding. A dual-level prediction framework that combines similarity-based scoring and machine learning-based classification was proposed to systematically identify new potential technology linkages between previously unrelated technology areas. The patent co-classification network was used to generate graph embeddings, which were then processed to calculate edge similarity among unconnected nodes and to train the classifier model. A case study in the EV market demonstrated the framework can reliably predict future patterns across disparate technology domains. Consequently, advancements in battery protection, thermal management, and composite materials emerged as relevant for future technology development. These insights not only deepen our understanding of future innovation trends but also provide actionable guidance for optimizing R&D investments and shaping policy strategies in the evolving electric vehicle market. The findings contribute to a systematic approach to forecasting technology convergence, supporting innovation-driven growth in the evolving EV sector. Full article

Microfluidic technology is an emerging interdisciplinary field that uses micropipes to handle or manipulate tiny fluids in chemistry, fluid physics, and biomedical engineering. As one of the rapid prototyping methods, the three-dimensional (3D) printing technique, which is rapid and cost-effective and has integrated molding characteristics, has become an important manufacturing technology for microfluidic chips. Polymethyl-methacrylate (PMMA), as an exceptional thermoplastic material, has found widespread application in the field of microfluidics. This paper presents a comprehensive process study on the fabrication of fused deposition modeling (FDM) 3D-printed PMMA microfluidic chips (chips), encompassing finite element numerical analysis studies, orthogonal process parameter optimization experiments, and the application of 3D-printed integrated microfluidic reactors in the reaction between copper ions and ammonium hydroxide. In this work, a thermal stress finite element model shows that the printing platform temperature was a significant printing parameter to prevent warping and delamination in the 3D printing process. A single printing molding technique is employed to fabricate microfluidic chips with square cross-sectional dimensions reduced to 200 μm, and the microchannels exhibited no clogging or leakage. The orthogonal experimental method of 3D-printed PMMA microchannels was carried out, and the optimized printing parameter resulted in a reduction in the microchannel profile to Ra 1.077 μm. Finally, a set of chemical reaction experiments of copper ions and ammonium hydroxide are performed in a 3D-printed microreactor. Furthermore, a color data graph of copper hydroxide is obtained. This study provides a cheap and high-quality research method for future research in water quality detection and chemical engineering. Full article

The real-time temperature prediction of chips is a critical issue in the semiconductor field. As chip designs evolve towards 3D and high integration, traditional analytical methods such as finite element software and HotSpot face bottlenecks such as high difficulty in modeling, costly computation, and slow inference speeds when dealing with large-scale, multi-hotspot chip thermal analysis. To address these challenges, this paper proposes a real-time temperature prediction model for multi-core chips based on Graph Convolutional Neural Networks (GCNs) that includes the following specific steps: First, the multi-core chip and its temperature power information are represented by a graph according to the physical pattern of heat transfer; Second, three strategies—full connection, setting a truncation radius, and clustering—are proposed to construct the adjacency matrix of the graph, thus supporting the model to balance between computational complexity and accuracy; Third, the GCN model is improved by assigning learnable weights to the adjacency matrix, thereby enhancing its representational power for the temperature distribution of multiple cores. Experimental results show that, under different node numbers and distributions, our proposed method can control the Mean Squared Error (MSE) error of temperature prediction within 0.5, while the single inference time is within 2 ms, which is at least an order of magnitude faster than traditional methods such as HotSpot, meeting the requirements for real-time prediction. Full article