Search Results (300)

Wind gusts are known to significantly influence wildfire behavior, yet their direct role in ignition dynamics remains underexplored in laboratory settings. This study investigates how controlled wind gusts affect ignition behavior, combustion transitions, and heat re-lease characteristics of wildland fuels using a bench-scale wind tunnel. Three fuel types, Excelsior, wild oat (Avena), and Wheatgrass were exposed to heated stainless-steel par-ticles under varying wind speeds (1.0 and 2.0 m/s) and gust frequencies (0.03, 0.05, and 0.07 Hz). Key ignition parameters, including ignition temperature, ignition delay, smol-dering-to-flaming (StF) transition, burnout time, and heat release rate (HRR), were measured and analyzed. The results show that increasing gust frequency consistently impacted ignition behavior which reduces ignition and transition times across all fuels while raising ignition temperatures and HRR. For instance, StF transition times in Avena dropped from 58 to 42 s and flaming ignition temperatures rose from ~415 °C to ~498 °C as gust frequency increased from 0.03 Hz to 0.07 Hz at 2.0 m/s wind speed. Also, for the same set of experiments, HRR rose from 1674 J/s to 2372 J/s with increasing gusts. These findings indicate that gusty winds enhance convective heat transfer and oxygen availability, accelerating fire initiation and intensifying combustion. The results offer valuable insights for improving predictive fire spread models, ignition risk assessments, and wildfire mitigation strategies under transient wind conditions. Full article

The convective drying of agricultural materials is an energy-intensive process, and optimizing dryer design is critical for improving efficiency and product quality. This study presents a comprehensive heat and mass transfer model for the convective drying of alfalfa leaves in a rotary drum dryer. Freshly harvested leaves with an initial moisture content of approximately 70% (w.b.) were used as the test material. The proposed system features a simplified drum design aimed at enhancing process efficiency while reducing equipment complexity. The primary objective was to reduce the moisture content of alfalfa leaves to below 50% to ensure their quality during subsequent storage and transportation. To determine the optimal operating conditions, the kinematics of leaf motion inside the rotating drum and the associated heat and mass transfer phenomena were investigated through analytical modeling, numerical simulation, and experimental studies on a laboratory-scale physical model. An analytical model was developed to establish relationships between transverse kinematic characteristics (detachment condition, Froude number, drum inclination angle), average longitudinal velocity, and residence time. Numerical simulations based on the Navier–Stokes equations (continuity, momentum, and energy) provided detailed moisture content distributions within individual leaves under varying airflow orientations and drying durations. The novelty of this work lies in the integrated determination of optimized operating parameters through combined analytical, numerical, and experimental approaches. A regression model relating final moisture content to key process variables (air velocity, temperature of 60 °C, drum rotation frequency and mass of loaded material) was developed from experimental data, yielding practical recommendations for the design and operation of rotary drum dryers for alfalfa and similar agricultural materials. Full article

The “spin-up” problem, in which convection-permitting models require hours to develop realistic clouds from large-scale initial fields, critically limits short-term severe weather forecasting. Cloud analysis can serve as a feasible approach to directly assimilate hydrometeor information from remote sensing retrievals. In this study, we leverage multi-source remote sensing data, including three-dimensional mosaic radar reflectivity, hourly averaged FY-2G satellite brightness temperature (black-body temperature, TBB), and FY-2G total cloud water products, within a stepwise cloud analysis initialization scheme. The scheme is implemented in a convective-scale ensemble forecasting system (CMA-Meso, 3 km resolution) for a heavy rainfall event. For each ensemble member, three-dimensional hydrometeor increments are independently generated from these remote sensing retrievals and gradually introduced over the first ten time steps, ensuring smooth coordination with the model’s dynamic thermal framework. Quantitatively, the scheme reduces near-surface Continuous Rank Probability Score (CRPS) errors, improves the overall predictive skill by 2.6–7.9% (maximum at the 12 h spin-up period), and increases ensemble spread by 2–5.8%, mitigating under-dispersion. Probabilistic precipitation forecasts show uniform area under the relative operating characteristic curve (AROC) improvements across all thresholds, 1.16–5.77% for light rain, 3.03–8.97% for moderate rain, and 6.00–12.07% for heavy rain, with these maxima consistently occurring at the 12 h spin-up time. Although Brier scores are marginally larger, these AROC gains confirm the enhanced discrimination of convective rainfall. At 500 hPa, CRPS reductions of 7.1–15.6% emerge after 24 h (largest 15.6% for geopotential height at 24 h), zonal wind CRPS is reduced by 2.2% at 12 h, and ensemble spread increases by 3.1–7.0% for all three variables. These improvements, particularly the pronounced benefits during the initial 12 h, demonstrate that the remote sensing-driven cloud analysis effectively shortens spin-up. Mechanistically, the gains arise from physically coordinated hydrometeor-latent heat perturbations and subsequent cloud radiation feedback that continuously regulate thermal-dynamic structures. This study establishes that assimilating diverse remote sensing data via cloud analysis is an effective approach for overcoming spin-up challenges in convective-scale ensembles. Full article

Artificial intelligence (AI) weather models achieve forecast skill comparable to numerical weather prediction at far lower computational cost, yet their reliability for high-impact extremes remains largely uncharacterized. We present an event-based diagnostic evaluation of Aurora, a deterministic AI model, across 16 case studies chosen for physical diversity rather than statistical representativeness, spanning tropical cyclones (TCs), freezes, heatwaves, atmospheric rivers (ARs), and extreme precipitation at lead times from 1 to 21 days. Aurora showed strong short-range (1–7 day) skill: TC track and landfall positions were accurate for well-behaved systems, temperature extremes achieved high spatial agreement, and the atmospheric river structure was reproduced faithfully. This study’s central finding is a pattern–amplitude divergence: beyond 7 days, large-scale circulation patterns remained moderately skillful even as surface amplitudes weakened toward climatological values. Event-specific failures include a severe recurvature forecast failure for Hinnamnor, TC intensity biases, and a pronounced in-sample versus out-of-sample precipitation skill gap that is substantially confounded by event-type differences (large-scale monsoon vs. mesoscale-convective/cutoff-low regimes), so the gap cannot be attributed to training-period recency alone. Across the events examined here, Aurora provides reliable deterministic guidance within 7 days. We recommend deploying Aurora as a rapid ensemble generation and regime-identification tool alongside physics-based numerical weather prediction at short-to-medium range, with its directional intensity and amplitude biases corrected through post-processing before standalone use in operational warnings. Full article

A computational model of anisotropic biomass particle pyrolysis was developed to evaluate the influence of particle properties and operating conditions during fast pyrolysis. The model integrates multicomponent CRECK kinetics with intraparticle heat and mass transfer. Simulations were performed at reactor temperatures between 400 and 700 °C and convective heat transfer coefficients from 50 to 1500 W·m⁻²·K⁻¹, representing conditions from slow to fast pyrolysis. Results showed that particle size and biomass composition strongly affect conversion time and product distribution, while aspect ratio becomes significant for larger particles. Increasing particle diameter from 3 to 8 mm more than doubled conversion times and increased char yield from approximately 16% to 23%. Lignin-rich biomass such as palm shell generated higher char yields and lower volatile production than cellulose-rich feedstocks such as oak or sugarcane bagasse. Higher reactor temperatures and heating rates reduced conversion times by up to 75%, with moderate effects on product yields. Analysis of Biot and pyrolysis numbers indicated that millimeter-scale particles operate in a transition regime where conduction, convection, and chemical kinetics occur on similar timescales. Therefore, accurate reactor design models must simultaneously consider coupled transport and reaction phenomena. Full article

Reliable precipitation forecasts are critical for hydrological modelling and flood early warning in West African river basins, where rainfall is dominated by highly variable monsoon-driven convection. This study evaluates and improves the precipitation forecasting skill of six numerical weather prediction (NWP) models over the Ouémé River basin in Benin, with particular emphasis on lead-time dependence, basin-scale effects, and the added value of statistical bias correction. Daily precipitation forecasts, over the period 1985–2015 across lead times of one to seven days, are assessed across six sub-basins using complementary continuous and event-based verification metrics. The results indicate that precipitation forecast skill varies with model choice, forecast horizon, and spatial scale. Among the raw forecasts, the ECMWF and UK Met Office models consistently outperform the other systems with KGE values reaching 0.5. ECMWF exhibits the highest overall skill at short to medium lead times, while the UK Met Office model shows relatively low volumetric bias across most sub-basins (Pbias less than 25%). For some models, forecast performance improves with increasing basin size, reflecting the smoothing effect of spatial aggregation, although this relationship remains model-specific. Distribution-based methods outperform regression-based approaches, with empirical quantile mapping providing the most robust and consistent improvements across lead times and sub-basins. Following bias correction, Empirical quantile mapping achieved median Likelihood Ratio values of approximately 6 during validation, with upper-range values reaching 15–18 across sub-basins for both ECMWF and UK Met Office forecasts. This represents a substantial improvement over raw predictions whose distributions remained consistently bounded below 10 throughout the calibration and validation phases (more than 50% improvement). Overall, the combination of ECMWF or UK Met Office precipitation forecasts with empirical quantile mapping offers a reliable framework for improving precipitation inputs to hydrological models and flood early warning systems in the Ouémé basin. The findings highlight the importance of multi-criteria evaluation and appropriate bias correction when applying NWP precipitation forecasts in monsoon-influenced hydrological environments and flood forecasting. Full article

Polar-orbiting microwave radiometers provide indispensable all-weather measurements of the atmospheric state, yet revisit intervals of many hours leave critical gaps during rapidly evolving weather events. To address this limitation, we developed MIDAS (Microwave Inference via Diffusion Across Satellites), a probabilistic framework that estimates microwave brightness temperature (BT) fields across the geostationary full-disk domain from infrared observations at 10 min intervals. This study focuses on the five Microwave Humidity Sounder-2 (MWHS-2) humidity-sounding channels near 183 GHz, which provide vertically resolved water vapor information. MIDAS achieves relative errors below 0.5% for the majority of cases, with a channel-averaged mean absolute error of 1.15 K, outperforming a deterministic U-Net baseline (1.43 K). Beyond per-sample evaluation, MIDAS reproduces large-scale climatological patterns across the full-disk domain over a three-month summer period, consistent with Radiative Transfer for TOVS–Scattering (RTTOV-SCATT) simulations. In deep convective scenes where reconstruction is most difficult, the ensemble spread naturally tracks reconstruction difficulty, providing a built-in indicator of prediction confidence. Notably, MIDAS incorporates real-time polar-orbiting observations as physical constraints via a merge-sampling mechanism, reducing ensemble RMSE by over 20% and improving probabilistic calibration by more than 30%. Proof-of-concept assimilation experiments for two high-impact weather cases show that MIDAS-generated fields yield forecast improvements comparable to those from real satellite observations, reducing tropical cyclone track errors from approximately 110 km to 40 km and improving heavy precipitation forecasts at extreme rainfall thresholds where direct infrared assimilation shows no benefit. Overall, our framework demonstrates the potential of generative models to supplement sparse observational coverage and provide physically plausible microwave humidity fields for downstream applications. Full article

Heat transport in heterogeneous materials can deviate markedly from classical Fourier behavior when microstructural disorder, trapping effects, nonlinear mobility, and long-range temporal correlations interact across multiple spatial and temporal scales. These mechanisms may produce delayed relaxation, persistent thermal footprints, front deformation, and non-classical spreading patterns that are not adequately represented by conventional integer-order diffusion models. In this study, a modeling and simulation framework is developed for anomalous heat transport in heterogeneous media using a two-dimensional time-fractional porous medium equation. The model combines a Caputo fractional time derivative, which represents thermal memory, with nonlinear degenerate porous-medium diffusion, spatially heterogeneous conductivity, localized volumetric heating, and Robin-type convective boundary exchange. A conservative fully discrete numerical scheme is constructed using flux-based finite differences for the heterogeneous nonlinear diffusion operator and an $L1$ approximation for the Caputo derivative. The nonlinear algebraic system at each time level is solved using an under-relaxed Picard frozen-coefficient iteration with non-negativity enforcement and sparse direct solution of the resulting linear systems. The numerical implementation is verified through a manufactured-solution convergence study, and additional analyses are performed to examine computational cost, Picard iteration behavior, coefficient-regularization sensitivity, strong-source effects, heterogeneous conductivity structures, and long-time thermal-footprint persistence. The results show that heterogeneous conductivity mainly redirects heat through preferential pathways and enlarges the spatial footprint while producing negligible changes in global heat content. Stronger fractional memory, represented by smaller fractional order, increases the persistence and spatial reach of moderate heating, whereas larger porous-medium exponents confine heat near the source and preserve higher local peaks. Source amplitude increases the thermal burden and footprint monotonically over the tested range, including strong forcing, without producing an abrupt localization-spreading transition. Boundary exchange remains secondary in the short-time interior-heating regime considered. These findings demonstrate that the proposed two-dimensional time-fractional porous medium framework provides a verified and physically interpretable model for non-Fourier heat transport in heterogeneous materials, where local intensity, global heat retention, and spatial thermal exposure must be assessed jointly. Full article

Meeting the rigorous performance standards of modern electrified transit necessitates the deployment of high-performance outer rotor PMSMs with elevated power-to-volume ratios. However, their unique internal heat source topology inherently restricts heat dissipation. This limitation risks permanent magnet demagnetization and winding insulation failure. To address these thermal bottlenecks, this paper proposes internal bio-inspired cooling channels. These channels feature micro-scale V-shaped ribs. This design targets a 60 kW outer rotor PMSM. The motor uses a fractional-slot concentrated winding. The analytical procedure commences with the formulation of a transient 2D numerical model utilizing the Time-Stepping Finite Element approach (TS-FEM). It is coupled with the Bertotti model to compute electromagnetic losses. This approach accurately determines losses under high-frequency rated conditions. Results reveal that stator iron loss constitutes the dominant heat source. It accounts for 76.4 percent of the total electromagnetic loss. Furthermore, these losses show severe spatial concentration at the stator teeth. Subsequently, a three-dimensional fluid-solid coupled CFD model is developed. This model evaluates the proposed internal cooling channels. The design integrates bio-inspired vein networks and V-shaped ribs. These internal ribs disrupt the near-wall thermal boundary layer. This disruption enhances the local convective heat transfer. Comparative multiphysics analyses indicate improved hydraulic and thermal performance of the bio-inspired design under the same numerical boundary conditions. The bio-inspired channel achieves a more uniform static pressure distribution and reduces severe fluid stagnation zones. In the numerical model, the maximum stator and permanent magnet temperatures are reduced to 48 °C and 42 °C, respectively. This work provides a numerical design reference for thermal management in high-performance electric aviation. Full article

The northward shift in climate zones and the urban heat island effect demand passive cooling for building roofs in northern regions. Artificial turf is a lightweight candidate, but existing studies treat it as homogeneous material, overlooking blade morphology and roof-scale thermal performance. This study conducted a scaled indoor experiment using a 1 m³ building model. Three artificial turfs with different blade lengths (Type A long, Type B medium, Type C short) were compared against concrete and XPS roofs under simulated summer solar radiation. Results show that blade morphology governs thermal performance. Type A exhibited the lowest peak surface temperature (48.9 °C vs. 53.4 °C and 60.6 °C), and its interface temperature (37.0 °C) was 15.1–19.0 °C lower than Types B and C, attributed to a static air insulation layer and enhanced convection. Its cooling rate (0.98 °C/min) was 1.69–2.33 times faster. Compared to concrete and XPS, Type A had lower surface temperature, less downward heat conduction, and a 29.3 °C drop in 30 min (concrete: 22.3 °C; XPS: 21.7 °C), showing urban heat island mitigation potential. Its heat flux reduction ratio reached 42.9%, with equivalent thermal resistance of ~0.40 m²·K/W, reducing summer peak indoor temperature by 3–6 °C in aging buildings. Double-layer stacking underperformed a single long-blade layer due to heat accumulation. Optimised long-blade turf challenges the view that low albedo inevitably causes high temperature, offering dual benefits of insulation and rapid dissipation for passive cooling in urban renewal. Full article

Reactive polymeric membranes are emerging as promising platforms for advanced water and wastewater treatment because they combine separation with in situ contaminant transformation. Unlike conventional membranes, which mainly retain pollutants, reactive polymeric membranes can enrich, activate, and degrade micropollutants during permeation through built-in radical, redox-active, conductive, or porous catalytic domains. This review discusses the development of intrinsic reactive polymer membranes for oxidative filtration, with emphasis on the links between polymer structure, transport behavior, reactive oxygen species generation, and degradation pathways. Key membrane classes are discussed, including stable-radical polymers, redox-active polymer networks, conductive polymer membranes, and porous conjugated polymer catalytic layers. The review also highlights the importance of reactive transport kinetics, including convection–diffusion–reaction coupling, residence time, Damköhler and Péclet numbers, and adsorption-enhanced degradation. Challenges such as fouling, polymer aging, leaching, byproduct formation, and toxicity-aware benchmarking are discussed within a broader roadmap for technology translation. The review identifies the grand challenges and milestone-based priorities for developing and deploying reactive polymer membranes, including performance targets, standardized reporting, realistic water matrices, scale-up, technology readiness levels, techno-economic analysis, life cycle assessment, artificial intelligence, and digital twins. Together, these elements guide the translation of reactive polymer membrane systems from laboratory research toward full-scale water treatment applications. Full article

Hyperloop transport operates in a low-pressure environment in which convective heat transfer is strongly limited, making conventional air-based cooling ineffective. One promising thermal management approach is therefore to absorb the waste heat generated during travel in a thermal energy storage (TES) system and dissipate it during stops. In this context, latent heat storage based on water–ice systems is particularly attractive because of its high energy density and nearly constant-temperature heat absorption. However, experimental validation of such systems beyond laboratory scale is still lacking. This study therefore investigated a large-scale direct contact latent heat storage (DCLHS) system for Hyperloop thermal management, using water as heat transfer fluid and ice as phase change material. The system was evaluated for two ice morphologies, crushed ice and ice block, under both constant and time-variant cooling power profiles representative of Hyperloop operation. The objective was to assess thermal performance, exergy efficiency, and hydraulic stability at application-relevant scale, and to identify morphology-dependent trade-offs relevant for system integration. The results show that the large-scale system can operate reliably under dynamic loads and that upscaling leads to smoother thermal behavior and reduced boundary effects. Crushed ice demonstrated superior thermal responsiveness, maintaining outlet temperatures close to the phase change temperature and achieving exergy efficiencies up to 0.72 at cooling powers up to 3.8 kW while enabling stable operation at 15 °C. In contrast, the ice block configuration provided higher volumetric energy density but exhibited delayed thermal response and required substantially higher mass flow rates, which limited operation to approximately 25 °C and reduced exergy efficiency to 0.03–0.35. Overall, the results show that large-scale DCLHS is a feasible option for Hyperloop thermal management, while also revealing that system behavior at larger scale is strongly influenced by storage morphology. Full article

Sweeping jet (SWJ) actuators are widely used in active flow control, but explicitly resolving actuator-scale unsteadiness in full-configuration computational fluid dynamics (CFD) remains prohibitively expensive because of the small geometric scales and high-frequency oscillations involved. Existing reduced-order boundary-condition models constructed from exit-plane data alone can reproduce the observed switching waveform, but they treat the actuator as an input–output black box and provide limited insight into the internal dynamics that generate the response. This work develops a causal state-space reduced-order modeling framework that links internal mixing-chamber dynamics to time-resolved exit-plane boundary conditions. Proper orthogonal decomposition (POD) is used to obtain a low-dimensional representation of the internal flow, and a data-driven linear evolution operator is identified in the reduced space by least-squares regression of successive snapshot pairs. A POD truncation rank of $r=60$ is selected from cumulative-energy and validation-error sensitivity analyses, capturing well above 99% of the fluctuation energy while lying within the converged performance regime. A corresponding reduced operator is identified for the exit plane, and spectral comparison reveals near-neutrally stable oscillatory modes in both regions. Using a $\pm 1\%$ relative frequency-matching tolerance, the dominant reduced-operator modes exhibit a 28.3% frequency overlap, providing operator-level evidence that exit-plane oscillations are dynamically linked to internal coherent structures. This correspondence is further supported by cross-spectral coherence analysis between representative internal and exit-plane probe signals, which shows strong coherence at dynamically relevant frequencies. A delayed causal output mapping is then formulated in which the internal reduced state drives the exit-plane response after an identified lag of 149 time steps, corresponding to $2.98\times {10}^{-3}$ s. This delay provides a physically interpretable convective transport timescale from the mixing chamber to the actuator exit. Over the validation interval, the model maintains a mean relative $L_2$ error below 0.02, with maximum normalized errors below 0.04 for most of the prediction horizon, and localized increases are confined to rapid jet-switching events. Field-level reconstructions of streamwise velocity and total pressure show that the model captures both phases of the jet-switching cycle, with errors concentrated primarily in high-gradient shear-layer regions. Compared with exit-only reduced-order models, the proposed internal-driven formulation improves amplitude and phase fidelity over extended prediction horizons. The resulting framework provides a compact, interpretable, operator-based representation of SWJ actuator dynamics suitable for use as a CFD-embeddable dynamic boundary condition. Full article

Tunnel kilns are widely used in ceramic manufacturing due to their continuous operation, stable performance, and relatively high thermal efficiency. However, the firing stage remains highly energy-intensive and is a major source of environmental impact, necessitating advanced strategies for performance optimization and sustainability. This study presents a comprehensive and critical review of recent developments in tunnel kiln technology, focusing on heat transfer mechanisms, thermal modeling, process optimization, airflow management, energy recovery, computational fluid dynamics (CFD), and environmental sustainability. The literature shows that kiln performance is governed by strongly coupled interactions among fluid flow, heat transfer, combustion, and material transformations. Although significant progress has been achieved through analytical modeling, experimental studies, and numerical simulations, many approaches rely on simplified assumptions or isolated subsystem analyses, limiting their applicability to real industrial conditions. Key findings emphasize the importance of optimizing airflow distribution, kiln geometry, and product arrangement to enhance convective heat transfer and temperature uniformity. Energy optimization strategies—including waste heat recovery, combustion control, and reduction in kiln car thermal mass—demonstrate considerable potential, but their effectiveness depends on integrated, system-level implementation. Environmental analyses identify the firing stage as the primary source of greenhouse gas emissions, highlighting the need for coordinated energy and emission reduction strategies. In this context, Digital Twin and Industry 4.0 technologies offer promising capabilities for real-time monitoring, predictive control, and data-driven optimization. Generally, this review underscores the need to transition from isolated optimization approaches to integrated, multi-scale frameworks that combine advanced modeling, experimental validation, and intelligent digital systems to achieve sustainable and energy-efficient ceramic manufacturing. Full article

We propose an exploratory framework based on Topological Digital Twins (TDTs) for the monitoring and short-term forecasting of spatial dynamical systems. The approach represents the system through a reduced state built from topological descriptors obtained via persistent homology. These descriptors capture features such as connected components, cycles, and large-scale structure. The framework combines three components: an observation operator mapping spatial fields to a low-dimensional state, a reduced dynamical model evolving this state in time, and a data assimilation step aimed at improving robustness. This construction maps persistence diagrams to a finite-dimensional Euclidean space. This makes the model tractable but does not preserve the full algebraic structure of the original topological objects. We provide theoretical results supporting the stability of the representation under perturbations of the input field. The method is illustrated on a bow-echo convective system observed over Corsica on 18 August 2022, where the reduced state captures the main structural organization of the system over time. A comparison with standard nowcasting methods shows complementary behavior: pixel-based approaches provide better local accuracy, while the TDT framework better preserves the global spatial structure, as reflected by Wasserstein distances and persistence-based comparisons. Additional tests also indicate that the topological observables remain stable under small perturbations of the input field. The present study is based on a single case and should be understood as a proof of concept, rather than as a definitive validation. Future work will focus on validation on larger datasets and on the use of more advanced dynamical models. Full article