Search Results (676)

Mode veering is a modal-interaction phenomenon found in vibration systems. For inertially coupled structures, the combined influence of coupling delay, nonlinear restoring force, and stochastic coupling perturbation remain insufficiently understood. This work analyzes an inertially coupled two-coordinate prototype in which a discrete delay, a delayed cubic stiffness, and positive multiplicative stochastic modulation all enter through the same relative-coordinate coupling channel. We formulate the delayed linear spectrum through a quasi-polynomial characteristic equation. We also characterize the veering by the two positive-frequency characteristic-root branches descending from the mechanical modes. Coupling delay shifts the veering center, alters the minimum frequency gap, and moves the tracked rightmost roots toward the stability boundary. An analytical imaginary-axis-crossing criterion is derived to determine the delay-induced stability boundary of the deterministic linearized system, and the resulting boundary is independently validated by direct multi-start characteristic-root searches and Chebyshev-collocation approximation of the DDE generator. A fixed-reference modal-coordinate representation identifies the off-diagonal modal terms associated with branch exchange while retaining the full delayed characteristic equation. A first-harmonic treatment of the delayed cubic term can yield an amplitude-dependent nonlinear veering backbone. For the stochastic problem, frozen lognormal coupling samples and a time-dependent Ornstein–Uhlenbeck-driven multiplier are constructed from the same unit-mean positive lognormal marginal law. The former is used to quantify realization-wise spectral broadening, whereas the latter retains temporal correlation and is used to evaluate finite-time branch residence and pathwise delayed-work statistics. The pathwise energy balance reveals that the delayed relative-coordinate work rate is sign-indefinite. This provides a common energy-transfer mechanism through which delay, nonlinearity, and stochastic modulation reshape mode veering in the inertially coupled system. Full article

Mg-Gd-Y-Zr alloys, with strong age-hardening and thermal stability, are ideal for lightweight load-bearing components, yet forming large complex parts is limited by high sensitivity to hot deformation parameters. This work investigates the hot deformation behavior and microstructure evolution of a Mg-9Gd-4Y-0.5Zr (wt.%) alloy via hot compression at 400 to 510 °C and strain rates of 0.001 to 10 s⁻¹. An Arrhenius constitutive equation with an activation energy Q of 158.63 kJ/mol was established, and a hot processing map was constructed. EBSD characterization revealed the dynamic recrystallization, grain size evolution, and texture transition. The results show that flow stress depends strongly on temperature and strain rate. At strain rates of 0.001~1 s⁻¹, a dynamic balance between work hardening and dynamic softening is achieved, and the post-peak flow stress gradually stabilizes. At a high strain rate of 10 s⁻¹, the flow stress continues to decrease because the competition between softening from dynamic recrystallization and work hardening is disrupted by deformation-induced heating. Low strain rates (≤0.01 s⁻¹) and high temperatures (≥470 °C) promote dynamic recrystallization and significant grain refinement. Two optimal processing windows were determined: 400 to 430 °C at 0.001 to 0.01 s⁻¹, giving fully recrystallized fine equiaxed grains, and 440 to 460 °C at 0.01 to 0.1 s⁻¹ with a power dissipation efficiency η of 0.43 to 0.51, balancing processing efficiency and microstructural uniformity. This work provides systematic theoretical and data support for optimizing hot forming parameters of large Mg-Gd-Y-Zr load-bearing components and offers guidance for applying high-strength magnesium alloys in high-end equipment. Full article

Renewable energy communities (RECs) are increasingly recognised as important instruments for accelerating the transition towards decentralised, low-carbon energy systems. However, technological progress and supportive policies alone cannot ensure their success, as participation depends largely on citizens’ behavioural readiness. This study investigates the behavioural determinants of participation in renewable energy communities by extending the Theory of Planned Behaviour (TPB) with four motivational dimensions: environmental, economic, technical, and social. A quantitative cross-sectional survey of 174 household electricity users in Slovenia was analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings show that environmental and economic motivations were positively associated with Attitudes, Technical Motivation was positively associated with Perceived Behavioural Control, and Social Motivation was positively associated with Subjective Norms. These TPB constructs are positively associated with behavioural readiness to participate in renewable energy communities. A supplementary exploratory multi-group analysis suggested possible differences between prosumers and conventional consumers. Prosumer status reflected individual household renewable electricity production and not verified REC membership, while Behavioural Readiness captured prospective stated intention and willingness rather than observed participation. However, because the prosumer subgroup comprised only 15 respondents, these group-specific patterns should be interpreted cautiously and require confirmation in larger and more balanced samples. The study extends the Theory of Planned Behaviour by integrating a multidimensional motivational framework and conceptualises participation in renewable energy communities as a socio-technical behavioural process. The findings provide empirically informed insights for policymakers, municipalities, and renewable energy community developers seeking to support citizens’ behavioural readiness to participate in renewable energy communities. Full article

This study presents a numerical model of electromagnetic and thermal processes characteristic of a submerged arc furnace. Because direct modeling of a full-scale industrial furnace is complex and difficult to validate experimentally, a laboratory system without an electric arc is considered at this stage. The system reproduces the main features of current supply and energy distribution in the conductive region of the furnace bath. The model is implemented in ANSYS Fluent 2020 R1 using user-defined scalar equations for the electric potential, the components of the magnetic vector potential, and their time derivatives. The implementation was assessed in terms of mesh independence, time-step sensitivity, current and energy balances. The calculations yielded consistent distributions of electric potential, current density, magnetic flux density, Joule heat generation, and temperature. Heating was described using a two-stage scheme: the transient electromagnetic problem is first solved to obtain period-averaged Joule heat generation, which is then used as a source term in the energy equation. The model represents the first stage of a computational framework for submerged arc furnace modeling: at this stage, it is developed and assessed using a simplified laboratory configuration without an electric arc, while in future work it can be supplemented with an arc-channel description and extended to industrial furnace conditions. Full article

Low seawater temperatures constrain the operation of open rack vaporizers (ORVs) and intermediate fluid vaporizers (IFVs), while also increasing pumping-related emissions at LNG terminals. This study establishes a carbon-oriented framework for an expanded ORV–IFV regasification system sharing a fixed-speed seawater pump network and evaluates thermal discharge from an adjacent power plant as a supplementary heat source. Using measured LNG composition, we developed an Aspen HYSYS model based on the Peng–Robinson equation of state and steady-state energy balances, which was validated against field data. Electricity-related CO₂ emissions from seawater pumps and auxiliaries were quantified using the regional grid emission factor, while pump scheduling was formulated as a mixed-integer nonlinear programming (MINLP) problem. Model predictions differed from measurements by approximately 2%. Lower seawater temperatures increased emissions and restricted maximum regasification capacity to 80% and 57% of the design value at 3–4 °C and 2–3 °C, respectively. For LNG throughputs of 300, 500, and 700 t/h, CO₂ reduction increased with warm-seawater flow and inlet temperature; maximum reductions reached approximately 50–55% under 3–7 °C ambient seawater conditions and 40% under 6–20 °C conditions, with a 95% confidence interval of ±3.9 percentage points. Monthly discharge data indicated reductions of approximately 20% in winter and 45% in summer. Integrating power-plant waste heat with load-dependent pump scheduling can improve the carbon performance of LNG regasification. Full article

This paper investigates the constrained particle dynamics on rotational surfaces within the 4-dimensional pseudo-Euclidean space $E_2^4$, characterized by its second-order metric signature of index 2. A comprehensive Lagrangian and Hamiltonian formulation is developed to construct the specific energy and specific angular momentum as conserved Noetherian charges along timelike geodesics. By integrating Clairaut’s theorem into the geodesic flow equations, explicit analytical expressions for these fundamental physical invariants are obtained. This work explores the structural relationship between the surface’s continuous rotational symmetries and the mechanical stability of the geodesic flow. A mathematical resolution for the signature transitions manifested via the appearance of the imaginary unit i on elliptic surfaces is provided through analytic continuation and distinct coordinate charts. Furthermore, by reducing the second-order geodesic flow to a one-dimensional energy balance equation, the exact effective potentials ($V_{eff}$) are derived, and the local orbital stability zones are analytically verified via second-order radial derivatives (${\partial }_s^2{V}_{eff}>0$). These embedded geometric configurations are shown to share qualitative features with the equatorial slices of rotating relativistic spacetimes. Consequently, they can serve as potential geometric toy-models for studying the dynamics of photon spheres, ergosphere oscillations, and innermost stable circular orbits in extreme gravitational fields. Full article

Strawberries are among the most water-sensitive horticultural crops because of their shallow root systems and high transpiration rates, making them particularly vulnerable to rising temperatures, irregular rainfall, and increased vapor pressure deficits under climate change. This review paper synthesizes methods for quantifying water-use efficiency (WUE) in strawberry production, including empirical equations, crop models (AquaCrop, DSSAT, and HYDRUS), and remote sensing approaches. It examines how water use, crop productivity, and WUE respond to environmental conditions and management practices. Earlier studies show that rising temperatures, altered precipitation patterns, and increased atmospheric water demand can often reduce WUE, although responses vary depending on cultivar, management practices, and environmental conditions. In contrast, practices such as deficit irrigation, mulching, and microclimate modification may help maintain water productivity. Remote sensing tools such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Surface Energy Balance Algorithm for Land (SEBAL), and Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) are increasingly used to evaluate evapotranspiration, crop condition, and irrigation performance from field to regional scales. Collectively, these approaches improve understanding of strawberry water use and support irrigation management under changing climatic conditions. Full article

Triply periodic minimal surface (TPMS) heat exchangers offer high surface-area-to-volume ratios and interconnected flow pathways, making them attractive for compact thermal management. However, accurately predicting nanofluid heat transfer over a wide range of nanoparticle concentrations and operating conditions in complex TPMS geometries remains computationally challenging because of the coupled effects of porous architecture, flow dynamics, and concentration-dependent thermophysical properties. In this study, a hybrid physics-informed neural network (PINN) framework was developed to reconstruct concentration-dependent ${Al}_2{O}_3–water$ nanofluid temperature fields in TPMS gyroid heat exchangers. The originality of the proposed approach lies in integrating sparse thermocouple measurements, a steady-state convection–diffusion equation, boundary condition residuals, concentration-dependent nanofluid property models, and a physics-based concentration scaling procedure within a unified framework. The proposed framework was applied to aluminum and silver TPMS heat exchangers over a wide range of nanofluid volume fractions and flow conditions. The trained PINN accurately reconstructed the experimentally measured temperature field, demonstrating excellent agreement with the reference experimental data. Predictions at concentrations beyond the experimentally measured reference condition were obtained using the physics-based concentration scaling model. The effective heat transfer coefficient and Nusselt number were subsequently evaluated from the predicted mean TPMS temperature through an energy balance formulation. Increasing nanoparticle concentration reduced the predicted TPMS temperatures by approximately 17.5–18.5%, while the combined increase in concentration and flow rate produced an overall temperature reduction of about 33.5%. Relative to the selected baseline condition, the combined variation in concentration and flow rate was associated with calculated increases of 62.08% in $h_{eff}$, 58.33% in Nu, and 59.32% in Re. These results demonstrate the potential of the proposed hybrid PINN framework as a computationally efficient surrogate for evaluating nanofluid-enhanced TPMS heat exchangers, while acknowledging that predictions away from the training concentration depend on the validity of the concentration scaling model. Full article

Converter-dominated power systems increasingly require flexible resources that can support frequency while preserving a physically interpretable energy trajectory. Thermal-energy-storage (TES)-based virtual energy storage systems (VESSs) can shift electrical demand within thermal-energy and comfort constraints, and have therefore attracted extensive interest. However, existing studies commonly coordinate requested or normalized power without fully connecting it to actuator execution and the electrical-to-thermal energy path. Command-level sharing cannot be directly equated with executed physical power, and controller storage cannot be combined with joule-valued hardware energy without dimensional separation. Therefore, this paper proposes a physically coupled and energy-traceable virtual asynchronous machine (VAM) control method for TES-based VESSs. First, a loss-resolved averaged model establishes the point-of-common-coupling (PCC)–converter–DC-link–actuator–TES physical chain and separates the hardware Hamiltonian from the dimensionless control Lyapunov function. Second, a neighbor-coupled marginal controller is embedded in a command–projection–execution chain so that frequency regulation and weighted sharing are evaluated using the executed service. Third, a constraint-handling mechanism combines directional headroom gating, actuator saturation and ramp limits, thermal comfort bounds, and request-inactive state reset to maintain executable trajectories under the declared constraints. Simulations under a sustained $120\mathrm{kW}$ disturbance show that primary-only control retains a $-0.08883\mathrm{Hz}$ steady-state offset, whereas the proposed nominal case restores frequency. In the constrained case, the 30 s terminal trend remains above the prescribed limit, while both terminal windows of the 60 s run satisfy the restoration criterion; the final-window mean error and dimensionless eligible-unit sharing spread are $-6.317\times {10}^{-5}\mathrm{Hz}$ and $6.564\times {10}^{-5}$, respectively. The model-internal electrical–thermal balance achieves a dimensionless relative RMS residual of $6.2361\times {10}^{-8}$. Because the PCC voltage/current pair is reconstructed from the same power source, this residual quantifies model-internal consistency rather than independent measured closure. These results demonstrate constrained frequency restoration, executed-power coordination, and energy traceability within the averaged-model scope. Full article

Permanent magnet synchronous motors (PMSMs) are widely used in new energy vehicles, electric drive systems, and industrial servo applications. Excessive permanent magnet temperature may lead to magnetic performance degradation or even irreversible demagnetization; therefore, accurate estimation of permanent magnet temperature is of considerable importance. However, existing data-driven methods generally rely heavily on high-frequency measurements, and their prediction accuracy tends to deteriorate under low-frequency sampling conditions. Moreover, purely data-driven models lack explicit physical constraints, which limits their interpretability and generalization capability. To address these issues, this study proposes a physics-informed long short-term memory model for permanent magnet temperature prediction. A physics-based loss function is formulated using the PMSM d–q-axis voltage balance equations, while the d- and q-axis inductances are treated as trainable parameters during network optimization. This design enables the temperature prediction task and the electromagnetic constraints to be optimized jointly. Multi-operating-condition experiments are conducted using a publicly available electric motor temperature dataset, and the proposed model is compared with CNN, GRU, MLP-PINN and TNN models. In addition, experiments involving different downsampling ratios, errors in the high-temperature region, parameter sensitivity, physical parameter identification, and input-feature effects are performed to comprehensively evaluate the proposed model. The results show that the PINN-LSTM model achieves the best overall prediction performance, with an MAE of 1.6048 °C, an RMSE of 2.1890 °C, and an R² of 0.9861, outperforming all comparison models. The model also maintains high prediction accuracy in the high-temperature region, with an MAE of 1.363 °C and an RMSE of 1.896 °C. Furthermore, the parameters learned by the model can effectively reconstruct the variation trends of the d- and q-axis voltages under the test operating conditions. Sensitivity analysis of the temperature coefficients further demonstrates that the model is robust to deviations in key physical parameters. These results indicate that the proposed method can achieve accurate and robust permanent magnet temperature prediction under low-frequency sampling conditions, providing an effective solution for motor thermal-state monitoring and health management. Full article

We study a Camassa–Holm-type equation with a prescribed, time-independent bottom profile, $m_t+(u+h\left(x\right))m_x+2u_{x}m+{\displaystyle \frac{1}{2}}{h}_x\left(x\right)m=0,m=(1-{\partial }_x^2)u.$ The model is considered here as a mathematically motivated bottom-modified Camassa–Holm equation. The bottom modifies the transport velocity and the lower-order term is chosen so that the basic momentum balance keeps the same cancellation structure as in the flat-bottom case. We clarify the meaning of a gentle bottom in terms of bounded multiplier norms of the prescribed profile and do not claim a complete asymptotic derivation from the Euler equations. Under suitable regularity assumptions on h, we prove local well-posedness in Sobolev spaces by verifying the hypotheses of Kato’s quasilinear semigroup theorem. We also derive an $L^2$ momentum identity and a continuation criterion based on the integrability of $\parallel u_x{\parallel }_{L^{\infty }}$. The proof of the continuation criterion is strengthened by combining the momentum bound with a high-order $H^s$ energy estimate. Full article

Rapid urban-industrial development has intensified surface warming in global cities, including India, posing critical challenges for sustainable urban environments. While advanced AI and remote sensing methods have mapped urban heat patterns, a fundamental thermodynamic understanding of how cities generate, absorb, store, and dissipate heat with the urban land transformation remains underexplored. This study conceptualizes the urban geo-thermodynamics mechanism as a comprehensive framework to quantify urban surface energy exchanges, heat flux dynamics, and thermal responses in the Haldia urban-industrial region (103.84 km²) of eastern India. The analysis employs Landsat-derived impervious surface expansion, land surface temperature (LST), and normalized difference vegetation index (NDVI), NASA POWER radiation fluxes, world settlement footprint 3D structural (2023) and material stock (2024) data, and census-based population records (1991–2021). The integrated mathematical formulations were developed after the remote sensing-GIS-based statistical analysis for the urban energy balance through the Urban Thermodynamic Index (UTI), Urban Heat Retention Efficiency (UHRE), and Urban Cooling Potential (UCP) indices, which were developed from energy balance equations linking net radiation (Q*), anthropogenic flux (${\mathrm{Q}}_{\mathrm{F}}$), sensible and ground heat (${\mathrm{Q}}_{\mathrm{H}}$, ${\mathrm{Q}}_{\mathrm{G}}$), and latent heat flux (${\mathrm{Q}}_{\mathrm{E}}$). The results reveal a 36% increase in UTI and a 28% rise in UHRE between 1991 and 2021, indicating enhanced surface heat accumulation and anthropogenic energy input associated with built-up area and population growth (22.87–53.37 km²) and (1452–2375 person/km²). In contrast, UCP declined by 22%, reflecting reduced evaporative cooling due to vegetation loss, with the regression-based calibration (R² = 0.89; RMSE = 0.74 °C) validating strong correspondence with observed LST. These findings demonstrate a quantifiable link between thermodynamic processes and the transformation of the urban morphological landscape. The proposed mathematical-thermodynamic structure provides a scientific, GIS-based statistical method for urban heat risk assessment, energy-efficient planning, and geo-thermal environmental management, supporting global initiatives toward climate-resilient and sustainable urban development. Full article

This study experimentally investigated the wave transmission and setup characteristics of biomimetic submerged structures as Nature-based Solutions (NbSs) for coastal protection. A non-porous monolithic pillar and a highly porous, multi-branched staghorn coral replica were tested in a 2D flume featuring a 1:20 foreshore slope representative of Kuta Beach, Bali. The results revealed a highly divergent, period-dependent hydrodynamic response. Under short-period waves ($T=0.8 \mathrm{s}$), attenuation was density dependent; the porous replica gradually dissipates energy through canopy micro-turbulence, yielding transmission coefficients ($K_t$) ranging from 0.25 to 1.18. Conversely, under long-period waves ($T=1.6 \mathrm{s}$), the attenuation mechanism shifted to density-independent, depth-induced breaking. This establishes a critical hydrodynamic trade-off: higher wave attenuation (lower $K_t$) inherently generates a higher coastal wave setup due to momentum transfer. Crucially, while both structures reduced transmission, the internal porosity of the multi-branched replica facilitated sub-surface return flow, effectively capping the maximum normalized wave setup at 0.09. This represents an 18% reduction in setup-induced coastal hazards compared to the monolithic baseline. To facilitate practical engineering design, new empirical equations ($R^2\approx 0.80$) for predicting $K_t$ were derived, integrating the frontal area index (${\lambda }_f$). Ultimately, these findings demonstrate that multi-branched biomimetic structures provide an optimal NbS design, balancing effective wave energy attenuation with the mitigation of secondary setup hazards for vulnerable coastal regions. Full article

This paper proposes a network-constrained multi-step optimization approach for robust flexibility provision from distributed energy resources (DERs) under photovoltaic (PV) generation uncertainty. The proposed method optimally schedules day-ahead battery operations based on PV output predictions and confidence intervals, while strictly satisfying network constraints. To ensure operational feasibility, the AC power flow is modeled using DistFlow equations. To bridge the gap between computational tractability and exact AC feasibility, a multi-step solution strategy is introduced. First, a linearized DistFlow (LinDistFlow) model is employed within a column-and-constraint generation algorithm to efficiently identify the worst-case scenario. Subsequently, the robust day-ahead battery schedule is determined by solving a second-order cone (SOC)-relaxed DistFlow model under the identified scenario. Finally, a post-processing exact recovery step is executed by solving the original non-convex DistFlow equations under the fixed battery schedule and the worst-case scenario. This crucial step compensates for approximation errors introduced by the LinDistFlow and SOC models, significantly enhancing practical operational AC feasibility under the identified critical condition. Extensive case studies on the IEEE 33-bus test system verify the effectiveness of the proposed multi-step approach in balancing computational efficiency and robust flexibility provision. Full article

Reinforced concrete (RC) structures are susceptible to crack initiation and propagation during service, making accurate numerical simulation of crack behavior essential for assessing structural durability and safety. Current numerical approaches for simulating concrete cracking include smeared/continuum approaches, extended finite element method (XFEM), phase-field methods, and meso-mechanical models. In particular, smeared/continuum approaches (e.g., smeared crack and plastic-damage models such as CDP) indirectly reflect cracking through diffusive damage fields without providing explicit geometric information on crack locations and propagation paths. The XFEM module in commercial software is further restricted to first-order elements and encounters difficulties in simulating multi-crack propagation. These limitations indicate that further development of complementary crack-simulation frameworks is warranted. To this end, this paper presents a cracking simulation framework for RC members within the theoretical framework of the Specified Stress Method, adopting an adaptive degree-of-freedom strategy to balance computational accuracy and efficiency. The method introduces inelastic strain as an additional unknown and establishes a variational principle and the corresponding virtual work equation. Concrete cracking is described by specifying the stress on the crack plane to zero, so that the crack-surface stress remains zero after cracking, thereby avoiding the issue of damage reversibility and improving computational convergence. The method requires neither a predefined crack path nor remeshing after cracking. Unlike smeared/continuum approaches that rely on diffusive damage fields, the crack propagation paths, distribution characteristics, and evolution of multiple cracks are characterized through the spatial distribution of cracked integration points within the finite element mesh. In the present implementation, crack initiation is governed by the maximum tensile stress criterion, and a linear elastic constitutive model is adopted for concrete as a deliberate simplification to establish and verify the core computational mechanism of the framework. The proposed method was examined through three numerical examples. First, comparison with theoretical solutions confirmed the algorithm’s correctness in simulating cracking in heterogeneous RC tension members. Second, comparison with experimental results demonstrated qualitatively consistent crack propagation trends and load–displacement responses for RC beams under mixed-mode cracking; the calculated ultimate load of the plain concrete beam is lower than the experimental value, which is attributable to the use of the maximum tensile stress criterion without fracture energy considerations, and certain crack morphology deviations are observed due to the neglect of reinforcement–concrete bond-slip. Third, a multi-crack simulation of an under-reinforced RC beam showed that, whereas the XFEM module in ABAQUS captures only a single dominant crack near the mid-span, the proposed algorithm predicts multiple distributed cracking zones on both sides of the mid-span, qualitatively consistent with the typical flexural cracking behavior of under-reinforced RC beams; the algorithm also supports second-order elements (e.g., C3D20R) unavailable in the ABAQUS XFEM implementation. While the method is still in an exploratory stage, these results confirm the feasibility and potential of the Specified Stress Method as a complementary framework for RC cracking simulation, providing a basis for further development. Full article