Search Results (8,875)

In this study, a three-dimensional Inverse Conjugate Heat Transfer Problem (ICHTP) is numerically and experimentally investigated to estimate the heat-source strength of multiple chips mounted on a printed circuit board (PCB) using the Conjugate Gradient Method (CGM) and infrared thermography. The interfaces between the PCB and the surrounding air domain are assumed to exhibit perfect thermal contact, establishing a fully coupled conjugate heat transfer framework for the inverse analysis. Unlike the conventional Inverse Heat Conduction Problem (IHCP), which typically only accounts for conduction within solid domains, the present ICHTP formulation requires the simultaneous solution of the governing continuity, momentum, and energy equations in the air domain, along with the heat conduction equation in the chips and PCB. This coupling introduces substantial computational complexity due to the nonlinear interaction between convective and conductive heat transfer mechanisms, as well as the sensitivity of the inverse solution to measurement uncertainties. The numerical simulations are conducted first with error-free measurement data and an inlet velocity of u_in = 4 m/s; the recovered heat-sources exhibit excellent agreement with the true values. The computed average errors for the estimated temperatures ERR1 and estimated heat sources ERR2 are as low as 0.0031% and 1.87%, respectively. The accuracy of the estimated heat sources is then experimentally validated under various prescribed inlet air velocities. During experimental verification at an inlet velocity of 4 m/s, the corresponding ERR1 and ERR2 values are obtained as 0.91% and 3.34%, while at 6 m/s, the values are 0.86% and 2.81%, respectively. Compared with the numerical results, the accuracy of the experimental estimations decreases noticeably. This discrepancy arises because the numerical simulations are free from measurement noise, whereas experimental data inherently include uncertainties due to thermal picture resolutions, environmental fluctuations, and other uncontrollable factors. These results highlight the inherent challenges associated with inverse problems and underscore the critical importance of obtaining precise and reliable temperature measurements to ensure accurate heat source estimation. Full article

The accurate short-term forecasting of global horizontal irradiance (GHI) is essential to optimizing the operation and integration of solar energy systems into the power grid. This study evaluates the performance of the Weather Research and Forecasting (WRF) model in predicting GHI over a 48 h forecast horizon at an Italian site: the ENEA Casaccia Research Center, near Rome (central Italy). The instantaneous GHI provided by WRF at model output frequency was post-processed to derive the mean GHI over the preceding hour, consistent with typical energy forecasting requirements. Furthermore, a decomposition model was applied to estimate direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) from the forecasted GHI. These derived components enable the estimation of solar energy yield for both concentrating solar power (CSP) and photovoltaic (PV) technologies (on tilted surfaces) by accounting for direct, diffuse, and reflected components of solar radiation. Model performance was evaluated against ground-based pyranometer and pyrheliometer measurements by using standard statistical indicators, including RMSE, MBE, and correlation coefficient (r). Results demonstrate that WRF-based forecasts, combined with suitable post-processing and decomposition techniques, can provide reliable 48 h predictions of GHI and DNI at the study site, highlighting the potential of the WRF framework for operational solar energy forecasting in the Mediterranean region. Full article

Growing pressure on water resources and mineral fertilizer use calls for innovative and resource-efficient agri-food systems. Aquaponics, integrating aquaculture and hydroponics, represents a promising approach for sustainable greenhouse production. This study, aiming to explore alternative water and nutrient sources for greenhouse tomato production without compromising plant adaptability or yield, evaluated the co-cultivation of grape tomato and rainbow trout in a vertical decoupled aquaponic system under controlled greenhouse conditions. Two aquaponic nutrient strategies were tested: unmodified aquaponic water (AP) and complemented aquaponic water (CAP), with conventional hydroponics (HP) as a control, in a Deep Water Culture hydroponic system. Plant performance was assessed through marketable yield and physiological parameters, while system performance was evaluated using combined-biomass Energy Use Efficiency (EUE), Freshwater Use Efficiency (fWUE) and Nitrogen Use Efficiency (NUE), accounting for both plant and fish production. CAP significantly improved tomato yield (9.86 kg m⁻²) compared to AP (2.40 kg m⁻²), although it remained lower than HP (12.14 kg m⁻²). Fresh WUE was comparable between CAP and HP (9.22 vs. 9.24 g L⁻¹), demonstrating effective water reuse. In contrast, EUE and NUE were lower in CAP, reflecting the additional energy demand of the recirculating aquaculture system and nutrient limitations of fish wastewater. These results highlight aquaponics as a water-efficient production system while emphasizing that optimized nutrient management and energy strategies are critical for improving its overall sustainability and performance. Full article

Accurate volatility forecasting in energy markets is paramount for risk management, derivative pricing, and strategic policy planning. Traditional econometric models like the Heterogeneous Auto-regressive (HAR) model effectively capture the long-memory and multi-component nature of volatility but often fail to account for non-linearities and complex, unseen dependencies. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, excel at capturing these non-linear patterns but can be data-hungry and prone to overfitting, especially in noisy financial datasets. This paper proposes a novel hybrid model, HAR-LSTM-GARCH, which synergistically combines the strengths of the HAR model, an LSTM network, and a GARCH model to forecast the realized volatility of crude oil futures. The HAR component captures the persistent, multi-scale volatility dynamics, the LSTM network learns the non-linear residual patterns, and the GARCH component models the time-varying volatility of the residuals themselves. Using high-frequency data on Brent Crude futures, we compute daily Realized Volatility (RV). Our empirical results demonstrate that the proposed HAR-LSTM-GARCH model significantly outperforms the benchmark HAR, GARCH(1,1), and standalone LSTM models in both statistical accuracy and economic significance, offering a robust framework for volatility forecasting in the complex energy sector. Full article

Local energy systems (LESs) are becoming larger and more heterogeneous as distributed energy resources, electrified loads, and active prosumers proliferate, increasing the need for reliable coordination of operation, markets, and community governance. This Perspective synthesizes recent literature to map how artificial intelligence (AI) supports forecasting and situational awareness, optimization, and real-time control of distributed assets, and community-oriented markets and engagement, while arguing that adoption is limited by system-level credibility rather than model accuracy alone. The analysis highlights interlocking deployment barriers, such as governance-integrated explainability, distributional equity, privacy and data governance, robustness under non-stationarity, and the computational footprint of AI. Building on this diagnosis, the paper proposes principles-as-constraints for sustainable, trustworthy LES AI and a deployment-oriented validation and reporting framework. It recommends evaluating LES AI with deployment-ready evidence, including stress testing under shift and rare events, calibrated uncertainty, constraint-violation and safe-fallback behavior, distributional impact metrics, audit-ready documentation, edge feasibility, and transparent energy/carbon accounting. Progress should be judged by measurable system benefits delivered under verifiable safeguards. Full article

This study addresses the fundamental problem of representing the rheological properties of heterostructured materials composed of metals that differ significantly in their crystal structure, stacking fault energy, and related characteristics. The necessity of accounting for essential strengthening mechanisms is highlighted. The study is based on experimental results related to the fabrication of a multilayer, heterogeneous system via multistage wire drawing, supported by microstructural analysis, microhardness measurements, and numerical simulations employing various flow-stress models. A discussion is presented regarding the effectiveness of these models in representing the deformation behavior of the investigated materials. The primary materials examined were a multilayer system composed of microalloyed steel and titanium. The obtained results indicate that, in addition to incorporating strengthening mechanisms, it is necessary to consider significant microstructural changes affecting microstructure evolution—particularly grain refinement induced by continuous recrystallization and the effects of strain hardening. Moreover, the findings point to the potential intensification of strengthening associated with pile-up mechanisms, linked to the development of dislocation substructures and the possible fragmentation of the hard phase in the vicinity of the more ductile microalloyed steel phase. In conclusion, the discussion integrates measurements of rheological properties obtained through tensile tests, supported by microstructural analysis, digital image correlation (DIC), and microhardness measurements, which collectively demonstrate the effectiveness of the adopted analytical approach. Full article

In this contribution, we propose and study long-time behaviors of a new class of N-dimensional delayed Kirchhoff–Carrier-type problems with variable transfer coefficients involving a logarithmic nonlinearity. We take into account the dependence of diffusion and damping coefficients on the position and direction, as well as the presence of different types of delays. This class of nonlocal anisotropic and nonlinear wave-type equations with multiple time-varying mixed delays and dampings, of a fairly general form, containing several arbitrary functions and free parameters, is of the following form: ${\displaystyle \frac{{\partial }^{2}u}{\partial t^2}-{div\left(\mathcal{K}\right(\Vert \sigma \nabla u\Vert }_{L^2\left(\Omega \right)}^2)A_{\sigma }{\left(\mathbf{x}\right)\nabla u)+\mathcal{M}(\Vert u\Vert }_{L^2\left(\Omega \right)}^2)u-div(\zeta \left(t\right)A_{\sigma }\left(\mathbf{x}\right)\nabla \frac{\partial u}{\partial t})+d_0\left(t\right)\frac{\partial u}{\partial t}+{\mathbb{D}}_r(\mathbf{x},t;\frac{\partial u}{\partial t})=\mathbb{G}\left(u\right),}$ where $u(\mathbf{x},t)$ is the state function, $\mathcal{M}$ and $\mathcal{K}$ are the nonlocal Kirchhoff operators and the nonlinear operator $\mathbb{G}\left(u\right)$ corresponds to a logarithmic source term. The symmetric tensor $A_{\sigma }$ describes the anisotropic behavior and processes of the system, and the operator ${\mathbb{D}}_r$ represents the multiple time-varying mixed delays related to velocity ${\displaystyle \frac{\partial u}{\partial t}}$. Our problem, which encompasses numerous equations already studied in the literature, is relevant to a wide range of practical and concrete applications. It not only considers anisotropy in diffusion, but it also assumes that the strong damping can be totally anisotropic (a phenomenon that has received very little mathematical attention in the literature). We begin with the reformulation of the problem into a nonlinear system coupling a nonlocal wave-type equation with ordinary differential equations, with the help of auxiliary functions. Afterward, we study the local existence and some necessary regularity results of the solutions by using the Faedo–Galerkin approximation, combining some energy estimates and the logarithmic Sobolev inequality. Next, by virtue of the potential well method combined with the Nehari manifold, conditions for global in-time existence are given. Finally, subject to certain conditions, the exponential decay of global solutions is established by applying a perturbed energy method. Many of the obtained results can be extended to the case of other nonlinear source terms. Full article

The article presents issues related to fossil fuel energy consumption and CO₂ emissions from motor vehicles. It identifies the main areas of research in this field in the context of motor vehicles, namely driver behavior, fuel consumption, and OBD systems. The research sample consisted of experimental data containing records of a series of test drives conducted with a passenger vehicle equipped with a gasoline-powered internal combustion engine, collected via an OBD diagnostic interface. Three subsets related to engine operation and energy demand patterns were distinguished for the study: during vehicle start-up and low-speed driving (vehicle start-up mode), during urban driving, and during extra-urban driving. Multiple regression models were constructed for the analyzed subsets to predict CO₂ emissions based on engine energy output parameters (power, load) and vehicle kinematic parameters. The developed models were subjected to detailed evaluation and mutual comparison, taking into account their predictive performance and the interpretability of the results. The analysis made it possible to identify the variables with the most substantial impact on CO₂ emissions and fuel energy consumption. The models allow individual drivers to monitor and optimize vehicle energy efficiency in real-time. The extra-urban driving model achieved the highest predictive accuracy, with a mean absolute error (MAE) of 19.62 g/km, which makes it suitable for real-time emission monitoring during highway driving. Full article

High regeneration energy demand remains a critical barrier to the large-scale deployment of ethanolamine-based (MEA-based) CO₂ capture. This study adopts an Al₂O₃ ceramic-membrane heat exchanger (CMHE) to recover both sensible and latent heat from the stripped gas. Experiments confirm that heat and mass transfer within the CMHE follow a coupled mechanism in which capillary condensation governs trans-membrane water transport, while heat conduction through the ceramic membrane dominates heat transfer, which accounts for more than 80%. Guided by this mechanism, systematic structural optimization was conducted. Alumina was identified as the optimal heat exchanger material due to its combined porosity, thermal conductivity, and corrosion resistance. Among the tested pore sizes, CMHE-4 produces the strongest capillary-condensation enhancement, yielding a heat recovery flux (q value) of up to 38.8 MJ/(m² h), which is 4.3% and 304% higher than those of the stainless steel heat exchanger and plastic heat exchanger, respectively. In addition, Length-dependent analyses reveal an inherent trade-off: shorter modules achieved higher q (e.g., 14–42% greater for 200-mm vs. 300-mm CMHE-4), whereas longer modules provide greater total recovered heat (Q). Scale-up experiments demonstrated pronounced non-linear performance amplification, with a 4 times area increase boosting q by only 1.26 times under constant pressure. The techno-economic assessment indicates a simple payback period of ~2.5 months and a significant reduction in net capture cost. Overall, this work establishes key design parameters, validates the governing transport mechanism, and provides a practical, economically grounded framework for implementing high-efficiency CMHEs in MEA-based CO₂ capture. Full article

External gear pumps are widely used in industrial hydraulic systems, but their volumetric efficiency can deteriorate significantly because of internal leakage, especially under high-pressure operating conditions. Conventional lumped parameter models typically assume fixed clearances and therefore cannot accurately capture the leakage behavior associated with pressure-induced deformation and wear. In this study, a dynamic clearance model for an external gear pump is developed and experimentally validated. Radial and axial clearances are measured in situ using eddy-current gap sensors over a range of operating conditions, and empirical correlation equations are identified as functions of pressure and rotational speed. These correlations are embedded into a tooth-space-volume-based lumped parameter model so that the leakage flow is updated at each time step according to the instantaneous dynamic clearances. The proposed model is validated against experimental measurements of volumetric efficiency obtained from a dedicated test bench. At 800 rev/min, the average prediction error of volumetric efficiency is reduced to 1.98% with the proposed dynamic clearance model, compared with 9.43% for a nominal static-clearance model and 3.35% for a model considering only static wear. These results demonstrate that explicitly accounting for dynamic clearance variations significantly improves the predictive accuracy of volumetric efficiency, and the proposed model can be used as a design tool for optimizing leakage paths and enhancing the energy efficiency of external gear pumps. Full article

This work investigates methods for predicting low-cycle fatigue life by employing new energy-based fatigue damage measures. The primary goal of this research is to evaluate whether fatigue life can be predicted based on an energy accumulation graph, proposed as a generalization of the isodamage lines concept. The efficiency of fatigue life predictions using this approach, derived from the empirical linear Palmgren–Miner hypothesis, is compared against the physically grounded Unified Mechanics Theory thermodynamic approach, which allows for general understanding of material degradation, in contrast to empirical approaches. The study also accounts for the influence of anisotropy resulting from the sheet rolling process on the fatigue response of S420M steel. Samples were tested in orientations both parallel to the rolling direction and perpendicular to the sheet surface. Microstructural analysis revealed a visible banded structure in the perpendicular samples, which is a consequence of anisotropy. The fatigue life of samples taken perpendicular to the sheet surface was lower than that of parallel samples. Verification of the linear Palmgren–Miner damage summation hypothesis, using both the classical fatigue chart and the cumulative energy chart, resulted in calculated fatigue life consistently higher than the experimental fatigue life in all cases. The reduction in fatigue life ranged from 40% (for total strain amplitude equal to 1.0%) to almost 290% for a strain amplitude of 0.25%. A comparative analysis of the unit loop energy shows that at all tested levels of strain amplitude, the unit loop energy of parallel samples is higher than that of samples perpendicular to the surface. Full article

Wireless Sensor Networks (WSNs) operate under strict energy constraints and are therefore highly vulnerable to radio interference, particularly jamming attacks that directly affect communication availability and network lifetime. Although jamming and anti-jamming mechanisms have been extensively studied, energy is frequently treated as a secondary metric, and analyses are often conducted in partial isolation from system assumptions, protocol behavior, and deployment context. This fragmentation limits the interpretability and comparability of reported results. This article presents a systematic literature review (SLR) covering the period from 2004 to 2024, with a specific focus on energy-aware jamming and mitigation strategies in IEEE 802.15.4-based WSNs. To ensure transparency and reproducibility, the literature selection and refinement process is formalized through a mathematical search-and-filtering model. From an initial corpus of 482 publications retrieved from Scopus, 62 peer-reviewed studies were selected and analyzed across multiple dimensions, including jamming modality, affected protocol layers, energy consumption patterns, evaluation assumptions, and deployment scenarios. The review reveals consistent energy trends among constant, random, and reactive jamming strategies, as well as significant variability in the energy overhead introduced by defensive mechanisms at the physical (PHY), Medium Access Control (MAC), and network layers. It further identifies persistent methodological challenges, such as heterogeneous energy metrics, incomplete characterization of jamming intensity, and the limited use of real-hardware testbeds. To address these gaps, the paper introduces an energy-centric taxonomy that explicitly accounts for attacker–defender energy asymmetry, cross-layer interactions, and recurring experimental assumptions, and proposes a minimal set of standardized energy-related performance metrics suitable for IEEE 802.15.4 environments. By synthesizing energy behaviors, trade-offs, and application-specific implications, this review provides a structured foundation for the design and evaluation of resilient, energy-proportional WSNs operating under availability-oriented adversarial interference. Full article

Foundries represent complex exposure scenarios where metals, particulate matter, and combustion by-products coexist, posing potential cumulative biological effects. Urinary metabolic profiles from 64 foundry workers and 78 residents living in surrounding areas were investigated using multivariate statistical modeling. Differences in urinary metabolite patterns were observed between the two groups, including lower levels of several amino acids (e.g., valine, alanine, tyrosine, and tryptophan) and tricarboxylic acid intermediates (e.g., citrate and succinate), together with higher levels of selected branched-chain amino acid catabolites (e.g., 3-hydroxyisobutyrate and erythro-2,3-dihydroxybutyrate) in workers. Variations in gut microbiota-related metabolites, such as phenylacetylglycine and p-cresol sulphate, were also detected. Based on these metabolic patterns, potential molecular mechanisms related to energy metabolism, oxidative stress and host–microbiome interaction are discussed as interpretative hypotheses. The comparison between workers and residents was interpreted, taking into account differences in demographic and lifestyle characteristics between groups. Overall, the results indicate that occupational exposure in foundries is associated with measurable differences in urinary metabolic profiles, demonstrating that the applied NMR-based metabolomic strategy is capable of capturing early biological effects and supporting its potential as a non-invasive and holistic biomonitoring tool for evaluating the health impact of complex occupational exposures. Full article

We analyze the formal equivalence between the electromagnetic energy conservation law derived from Maxwell’s equations in an optical microcavity and the conservation of a probability fluid associated with the de Broglie–Bohm theory for an effective massive particle describing a photon in this cavity. This work is part of a critical analysis of recent experiments by Sharoglazova et al. carried out with a view to refuting the de Broglie–Bohm theory. Furthermore, the consequences of our analysis for microphotonics go far beyond these experiments. In particular, extensions that take into account photon spin and stochastic aspects associated with radiative or absorption losses are considered. From the point of view of symmetries and probability current, here the effective photon behaves like a spin-1/2 particle. Full article

Achieving optimal daylighting is a cornerstone of sustainable architectural design, impacting energy efficiency and occupant well-being. Fast and accurate prediction during the conceptual phase is crucial but challenging. While physics-based simulations are accurate but slow, existing machine learning methods often rely on restrictive parametric inputs, limiting their application across free-form designs. This study presents a novel, geometry-agnostic framework that uses only building facade unfolding diagrams as input to a Generative Adversarial Network (GAN). Our core innovation is a 2D representation that preserves 3D facade geometry and orientation by “unfolding” it onto the floor plan, eliminating the need for predefined parameters or intermediate features during prediction. A Pix2pixHD model was trained, validated, and tested on a total of 720 paired diagram-simulation images (split 80:10:10). The model achieves high-fidelity visual predictions, with a mean Structural Similarity Index (SSIM) of 0.93 against RADIANCE/Daysim benchmarks. When accounting for the practical time of diagram drafting, the complete workflow offers a speedup of approximately 1.5 to 52 times compared to conventional simulation. This work provides architects with an intuitive, low-threshold tool for rapid daylight performance feedback in early-stage design exploration. Full article