Flue gas recirculation (FGR) is an effective technique for reducing thermal nitrogen oxide (NOx) emissions of gas turbines. However, variations in inlet oxygen concentration significantly alter the internal combustion characteristics of staged swirl combustors and induce combustion instability. To clarify the coupling mechanism between oxygen content and combustion stability under diverse operating conditions, three-dimensional numerical simulations are performed on a staged swirl combustor. The effects of oxygen mass fraction ranging from 11% to 23%, together with multiple operating parameters including inlet temperature, inlet velocity and operating pressure, on flame morphology, velocity fluctuation, heat release fluctuation and pressure fluctuation, are systematically investigated. The results show that increasing the inlet temperature optimises the uniformity of heat release, compensates for the combustion inhibition under low-oxygen conditions, and effectively improves combustion stability. Oxygen content exhibits a non-monotonic regulatory effect on combustion pulsation characteristics. Appropriate reduction of oxygen content narrows the high-temperature reaction zone and suppresses pressure fluctuations, thereby improving combustion stability, whereas a moderate low-oxygen condition of 17% aggravates velocity fluctuations and deteriorates combustion stability. Although elevated oxygen content enhances the overall heat release intensity, it increases the amplitude and dominant frequency of heat release fluctuations, which triggers combustion instability. Furthermore, high inlet velocity and high operating pressure amplify the disturbance of low-oxygen environments on the flame field and further degrade combustion stability. This study clarifies the competitive and coupling relationships among oxygen concentration, operating parameters and combustion dynamic characteristics, providing a theoretical basis for the stability optimisation and low-oxygen combustion regulation of gas turbine combustors with flue gas recirculation.
The fire response of cross-laminated timber (CLT) sandwich wall assemblies depends on interactions among the structural core, lining, cavities and secondary timber members. This study examines the thermal roles of these layers under medium-scale radiant exposure. The thermogravimetric mass change, mass-loss rate and heat-flow response were obtained for CLT, Fermacell® gypsum–fiber board and timber stud specimens by simultaneous thermal analysis. One complete, artifact-free record per material group was selected for mechanistic comparison; the data were not used to characterize material variability statistically. The degradation intervals were compared qualitatively with temperatures, visual observations and post-test damage recorded during a 90 min exposure of the wall assembly at 20 kW/m2. Fermacell® retained 77.8% of its initial mass; CLT and stud specimens retained 22.5% and 20.8%, respectively. The largest mass loss of both wood-based materials occurred at 280–430 °C, with mass-loss-rate peaks at 372.1 °C for the CLT and 359.4 °C for the stud. The lining initially delayed heat transfer. After board cracking, cavity heating was followed by the degradation and glowing of the timber stud. Layer-specific thermal analysis supports the mechanistic interpretation of the tested assembly, but it neither predicts event timing nor replaces standardized fire resistance testing or classification.
Early and reliable fire and smoke detection is essential for forest-fire warning and emergency response, especially in UAV-view and complex-background imagery, where small fire spots and diffuse smoke are easily affected by illumination variations and visually similar high-brightness or cloud- and fog-like backgrounds. To address these challenges, this paper formulates early fire and smoke recognition as a bounding-box detection task and proposes a Detail–Scale–Context Detection Network, named DSC-Det. DSC-Det is designed as a lightweight one-stage detection network and introduces three task-oriented components: a Detail–Context Downsampling Module (DCDM) for reducing information loss during early feature compression, a Dynamic Dual-Branch Fusion Module (DDFM) for adaptive multi-scale feature interaction under complex backgrounds, and a Shared-Regression Asymmetric Classification Head (SACH) for improving classification adaptation across feature layers while maintaining shared regression. Experiments on a constructed forest-fire-oriented fire and smoke dataset for UAV-view and complex-background monitoring scenes show that DSC-Det achieves 90.1% mAP@0.5 and 66.9% mAP@0.5:0.95, outperforming the lightweight reference detector by 2.3% and 4.4%, respectively. The results demonstrate that DSC-Det improves early forest-fire and smoke detection with controlled model complexity.
Methanol compression-ignition engines are vital for transport carbon neutrality, yet methanol’s low cetane number, corrosivity, low viscosity, and cavitation tendency compromised piezoelectric injector reliability. This study proposed systematic optimization strategies tailored to methanol’s fuel properties. A sealed thin-walled metal encapsulation, fabricated from precipitation-hardening martensitic stainless steel, was designed to isolate corrosive methanol media. The geometry of the tubular spring was optimized to meet the stiffness requirements for high-frequency injections. A monolithic nozzle without side pin holes, also upgraded to the same precipitation-hardening martensitic stainless steel, effectively suppressed stress corrosion cracking by leveraging the material’s combined high strength and excellent corrosion resistance. A dedicated return-line backpressure valve compensated for hydraulic leakage and improved fuel replenishment, and nozzle hole taper and inlet fillet radius were optimized to mitigate cavitation. Cold-motoring reliability tests showed the optimized injector maintained flow deviation within 3% after 100 million cycles, whereas the unoptimized prototype reached 8% deviation at 60 million cycles. The single-cycle injected fuel quantity coefficient of variation dropped from 4% to 1.3%. Spray characteristic comparison tests further confirmed that the optimized injector maintained stable flow consistency and atomization quality after prolonged cyclic operation. These optimizations effectively resolved corrosion, wear, and hydraulic instability caused by methanol, significantly enhancing flow consistency and durability over the service life. The results provided critical component-level technical support for advancing methanol compression-ignition engines from laboratory research to industrial application, addressing key reliability barriers that previously hindered engineering deployment of methanol-fueled powertrains.
High-temperature smoke remains a primary threat in tunnel fire safety. The curved walls of small-radius Urban Traffic Link Tunnels (UTLTs) significantly alter smoke flow patterns and temperature distribution. Furthermore, no quantitative model exists to assess the impact of transverse fire location variation on temperature distribution in small-radius UTLTs. To address this gap, this study integrates experimental and numerical methods to specifically investigate the influence of curvature radius and transverse fire location on ceiling temperature distribution. Key findings demonstrate: (1) Wall-adjacent fires exhibit substantially higher temperatures than non-adjacent scenarios, resulting from restricted air entrainment (increasing flame height) combined with wall thermal constraint effects. (2) Competition between centrifugal and inertial forces consistently produces a higher maximum temperature rise beneath the convex ceiling versus the concave side in curved sections. (3) A novel dimensionless parameter Rcs is derived from smoke control volume force analysis. This parameter quantifies the coupled effect of curvature radius and ventilation velocity on convex-concave ceiling temperature difference, enabling a predictive regression equation. (4) Through dimensional analysis, key governing dimensionless parameters are identified. Incorporating the Richardson number (Ri), which characterizes inertial-to-buoyant force competition, a predictive model for maximum ceiling temperature rise in small-radius UTLTs is ultimately established.
Water-mist curtains act as thermal barriers to longitudinal smoke propagation in confined-space fires, but their downstream cooling remains difficult to predict with reduced-order models. This study develops a calibrated semi-analytical model that uses the incident temperature at the curtain’s upstream face and combines a one-dimensional droplet residence-time solution with a Stefan-flow heat-transfer reduction. A lumped closure coefficient, k = Aeq/A0, collectively accounts for the simplified initial velocity and trajectory, spray nonuniformity, ensemble shielding, representative properties, and boundary inputs. The coefficient is inferred from 5 MW FDS cases with D32 = 400–700 μm and is not interpreted as breakup or coalescence, which were absent from the monodisperse simulations. Cases at 2, 4, and 6 MW provide within-domain blind tests, whereas 1, 3, and 7 MW provide supplementary assessment; the maximum reconstructed relative deviation in exit temperature is 13.4%. A 1:5 experiment supplies a cross-scale trend comparison, but its geometry differs from the full-scale FDS domain, and only the 3 MW-equivalent fire has an archived mass-loss calibration. The model is therefore limited to the present calibration domain and should not be transferred directly across geometries, nozzles, or ventilation conditions.
Electric vehicle (EV) fires can rapidly affect smoke and thermal conditions in underground parking garages. In this study, fifteen PyroSim/FDS cases were screened, but quantitative analysis was restricted to four prescribed-extraction cases and one baseline-matched two-device spray pair. The 0.30 m production mesh was selected using characteristic-fire-diameter, geometric-resolution, and computational-cost criteria. A matched 0.20/0.30/0.50 m check yielded non-monotonic fixed-point responses; mesh independence was not established. Extraction cases were compared using 270–300 s means and the first downward crossing of a 10 m visibility reference. At the same nominal outflow of 10 m3/s, two 5 m/s surfaces produced lower M1 gas temperature and CO and higher visibility than one 10 m/s surface. Relocating the second spray device beneath the vehicle reduced the τ = 120–150 s mean M4 underside-region gas temperature from 776.5 to 103.4 °C, while M1 visibility remained about 0.22 m. Because the model lacks a physical make-up-air path and corresponding experiments were not reproduced, these findings are limited to local prescribed-boundary comparisons. Relevant experiments support the represented mechanisms but the results do not validate the absolute point values. The simulations do not demonstrate battery extinguishment, maintained tenability, or code compliance.
Battery electric vehicle (BEV) fires in road tunnels can intensify thermal, smoke transport, visibility, and CO exposure hazards under confined ventilation. This study evaluated the combined mitigation performance of water mist and semi-transverse mechanical ventilation. A three-dimensional PyroSim/FDS model of a 200 m × 10 m × 5 m tunnel was established with a 7 MW BEV design fire at the midpoint. The prescribed-source model was assessed against a reduced-scale lithium-ion battery tunnel experiment; at the representative monitoring location, the simulated temperature history reproduced the main trend, with deviations of approximately 7% and 10% at the first and second peaks. Thirty-six coupled cases examined ventilation mode, nominal opening velocity, nozzle arrangement and spacing, flow rate input, droplet diameter, and spray cone angle. Supply ventilation improved hot-smoke-layer cooling and visibility, whereas exhaust ventilation more effectively reduced the local CO volume fraction. Under the baseline weighting scheme, the highest-ranked case reduced the peak local ceiling-region and near-fire gas temperatures by 77.8% and 82.2%, increased average visibility during 200–500 s by 42.9%, and achieved a comprehensive relative mitigation index (CRMI) of 56.6%. Two supplementary nominal 10 MW simulations showed that this case retained substantial thermal control, reducing the two peak temperatures by 65.7% and 74.1%, but did not improve local visibility or CO. Thus, the thermal-mitigation trend persisted at the higher nominal input, whereas the full multi-hazard ranking was not transferable across fire sizes.
The growing demand for renewable biomass energy has driven in-depth research into pyrolysis, in which particle size has emerged as a key factor influencing reaction kinetics and gas release. In this study, beech wood with four different sizes were prepared. A thermogravimetric analyzer (TGA 4000) and a Fourier transform infrared spectrometer (FTIR) were used to analyze the thermal behavior of the biomass under a high-purity N2 atmosphere at heating rates of 10, 20, and 40 K/min. Conversion rates and activation energies were calculated from the thermogravimetric data using two model-free methods, while infrared spectroscopy was employed to analyze gas composition and release characteristics. The experimental results indicate that changes in particle size significantly affect the DTG curves: as particle size increases, the maximum rate of weight loss gradually rises. In terms of pyrolysis kinetic parameters, the activation energy of the biomass samples increased from 166.42 kJ/mol to 176.07 kJ/mol. Gas release peaks also exhibited a trend of shifting toward higher temperature regions. The primary gaseous products were classified into six functional group/gas categories, with their yields ranked in descending order as follows: CO2 > CH2O > CH3OH > H2O > CH4 > CO. Except for CO2, the yields of all other components increased with increasing particle size. These research findings provide data and guidance for the recovery and reuse of biomass resources, as well as for the modeling of biomass pyrolysis reactors, and the classification, pretreatment, and process optimization of biomass materials, thereby accelerating their practical application.
This study addresses the critical need for accurate and efficient large-scale urban fire spread path prediction in dense urban areas by proposing a new gravitational framework-based theory. Its core innovation is the “characteristic attractive force” model, which mechanistically quantifies fire spread as a dynamic interaction between buildings, integrating factors like spacing, height, area and density effects to predict trajectories from the initially ignited building. This study adopts a GIS-based rapid prediction framework that circumvents the dependence on complex physical parameters. It utilizes high-precision spatial data and optimized algorithms to streamline prediction processes while retaining favorable prediction accuracy. Validated on two real-world clusters, the proposed approach enables effective visualization of dynamic propagation trajectories and pathway spectra that characterize the detailed propagation routes and ignition sequences. Notably, the framework achieves exceptional efficiency, completing predictions for large clusters in tens of seconds per scenario, making it suitable for real-time risk assessment. Overall, this work advances urban fire modeling with an innovative, efficient, and practical tool to support fire safety engineering and emergency management decision-making.
With the rapid development of recycling and secondary utilization of end-of-life battery materials, it is crucial to clarify the impact of full-lifecycle degradation on the thermal safety limits of lithium-ion batteries. This study focuses on a 16 Ah NCM613|graphite pouch battery. First, it analyzes the evolution of capacity decay, thickness expansion, and internal resistance during cycling at room temperature (25 °C) and high temperature (45 °C). Furthermore, an adiabatic accelerated calorimeter (ARC) is employed to investigate the influence of different states of health (SOH) levels (95% and 85%) on the battery’s thermal runaway characteristics. The findings indicate that, macroscopically, batteries in all states follow similar voltage–temperature failure pathways, with mass loss rates confined to a narrow range of approximately 16%, emphasizing the low catastrophic potential of mid-nickel chemistry. However, the microscopic kinetic mechanisms exhibit significant anisotropy: although thickness and internal resistance display no apparent abrupt increase during the late stage of room temperature aging, the capacity exhibits a highly nonlinear plunge behavior. The severe internal lithium plating side reaction triggered by this phenomenon causes the self-heating onset temperature to drop rapidly from 130.0 °C in the fresh state to 79.7 °C. Concurrently, the activation energy of the exothermic side reaction, fitted using a simplified Arrhenius equation, exhibits a non-monotonic variation with aging progress. In the early stages of aging at 95% SOH, due to high temperatures promoting more significant growth of the interfacial film or moderate film formation at room temperature enhancing interfacial thermal stability, the activation energies for both aged batteries increase, and the energy barrier at high temperatures is slightly higher than at room temperature; however, during the deep aging stage at 85% SOH, due to the degradation of active material components and the emergence of lithium plating characteristics, the energy barrier significantly decreases, with high-temperature-aged batteries exhibiting a greater reduction, highlighting the cumulative negative impact of prolonged high-temperature exposure on thermal safety. The research provides a core scientific basis for establishing a battery safety early warning and dynamic health management system covering the entire lifecycle.
Wildfires are increasingly recognized as environmental disturbances associated with socio-economic transformations in agrarian systems. This study examines the associations between reported wildfire exposure, land-market outcomes, and agrarian inequality in the Malakand Division of northern Pakistan, a region characterized by forest–agriculture interfaces and livelihood dependence on land. The study aims to analyze how different levels of wildfire exposure are associated with land values, ownership patterns, market transactions, inequality, and coping strategies among farming households. A quantitative cross-sectional design was employed using a sample of 400 households selected through multistage sampling. Data were collected through structured questionnaires and analyzed using ANOVA, chi-square tests, multiple and logistic regression, hierarchical regression, and principal component analysis. Results show that reported land values differed significantly across wildfire-exposure categories (F = 48.72, p < 0.001), with directly exposed households reporting the lowest values. Regression analysis identified direct wildfire exposure as the strongest negative statistical predictor of reported land value (β = −0.468, p < 0.001), while directly exposed households had substantially higher odds of reporting land sales (Exp(B) = 6.35). Chi-square results indicate a significant association between wildfire exposure and land transactions (χ2 = 64.82, p < 0.001). Retrospectively reported landholding data show an increase in the Gini coefficient from 0.41 before the reported fire period to 0.53 afterward. The addition of land-transaction variables increased the explained variance in agrarian inequality to 72%, which is consistent with a potential land-market pathway but does not constitute evidence of causal mediation. Coping strategies such as land sale, migration, and borrowing emerged as dominant reported responses among affected households. The study concludes that wildfire exposure is strongly associated with land devaluation, land sales, and greater agrarian inequality. Because the study is cross-sectional and lacks an independently observed pre-fire baseline or causal identification strategy, these findings should not be interpreted as definitive causal effects.
Climate change, altered ecosystems, and expanding development in fire-prone landscapes are increasing fire risk in the wildland–urban interface (WUI). This study uses Noosa, southeast Queensland, Australia, as a case study for a preliminary modeling assessment of irrigated green firebreaks (iGFBs). Using the AMICUS Vesta Mk2 fire-behavior model, fire spread rates and fireline intensity were compared across dry eucalypt control scenarios, non-irrigated green firebreak scenarios, and irrigated green firebreak scenarios receiving 1 and 2 mm m−2 day−1 of water. In line with future climate predictions, these scenarios were compared under progressively worsening fire-weather conditions. The drought-affected dry eucalypt control produced the highest predicted fire spread rates and fireline intensity, and although non-irrigated green firebreak scenarios reduced fire behavior, they may still exceed typical suppression thresholds under catastrophic conditions. In contrast, iGFB scenarios consistently reduced both fire spread rates and fireline intensity across all fire-weather classes. Sensitivity analysis indicated that the model outputs were most responsive to drought- and moisture-related assumptions, supporting the importance of fuel moisture in the performance of the iGFB concept. Although iGFBs are not a stand-alone solution suitable for all settings, the findings provide a preliminary region-specific proof of concept for iGFBs and support the need for further applied research.
Indoor positioning identifies location but does not directly indicate whether a route remains passable, how hazard exposure changes, or which alternative should be considered under deteriorating fire conditions. As a result, a geometrically shorter route may still be selected despite greater hazard exposure, blockage, or positioning uncertainty. This study proposes a dynamic hazard-aware route-risk assessment model based on Fire Positioning Infrastructure Theory (FPIT) for fireground decision support in building fires. The model converts BIM/IFC spatial semantics into a computable graph, maps normalized hazard scenario data onto graph edges, excludes edges exceeding scenario-specific hazard or blockage criteria, and evaluates the remaining feasible routes using an integrated route-risk score, hazard exposure, travel time, and positioning uncertainty. A normalized illustrative computational demonstration showed that the conventional shortest route had the lowest travel time but higher route-risk score, hazard exposure, and positioning uncertainty. The FPIT-based lower-route-risk-score alternative had lower values for these indicators but required longer travel time, while the intermediate detour provided a compromise. Pareto comparison retained the three feasible routes as non-dominated alternatives with different score–time–uncertainty characteristics. The computational demonstration illustrates the model’s internal calculability, traceability, comparability, and ability to represent route trade-offs; it does not constitute empirical validation or evidence of operational effectiveness in actual fireground environments.
Based on the decoupled n-dodecane skeletal mechanism and the computational fluid dynamics (CFD) numerical framework, a multilayer feedforward neural network surrogate model was developed to predict ignition delay in a constant-volume combustion vessel. The Levenberg–Marquardt optimizer with adaptive damping coefficients was used for model training, with mean squared error as the loss function and an inherent early stopping mechanism to prevent overfitting without additional weight decay regularization. To eliminate random interference from initial parameter settings, the surrogate model underwent 1000 repeated training trials, each with random weight re-initialization. The effects of hidden neurons, data partition strategy, normalization scheme, and sample size on predictive performance were systematically examined. The optimal configuration—three hidden neurons, a 70:15:15 data split, and a 105-sample training set—showed low sensitivity to data normalization. The resulting surrogate model is concise and sample-efficient, maintaining satisfactory prediction accuracy at 800 K and 1100 K while substantially reducing computational overhead. It provides a practical and reliable tool for subsequent combustion prediction and uncertainty quantification of hydrocarbon fuels. The feedforward neural network surrogate model substantially cuts the computational overhead for fuel combustion prediction to merely 15–20 min for every batch of 60 samples.
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.
To address the problems of small fire-spot scale, blurred boundaries, complex backgrounds, and insufficient feature representation of weak targets in Unmanned Aerial Vehicle (UAV)-based early forest fire detection, a YOLOv8n-based forest fire detection model, termed TriRHC-YOLO, is proposed. The model first introduces Reparameterized VGG (RepVGG)Block into the backbone network to enhance the extraction capability of shallow local features. Subsequently, a Hierarchical Feature Attention (HFA) module is designed to collaboratively model fire-spot features from three levels, namely directional structures, local textures, and global semantics, thereby enhancing the network’s capability to discriminate fire targets and suppressing interference from complex forest backgrounds. Finally, a Cross Stage Partial Feature Fusion with Cascade Star Block (C2f-CStar) module is designed to improve the representation capability of the model for local structural information and weak salient fire-spot features under complex backgrounds through cascaded spatial feature reconstruction and a star-shaped multiplicative gating mechanism. In addition, a UAV-specific early forest fire detection dataset is constructed based on the FLAME and FLAME_VISION datasets, and experimental validation is conducted on this dataset. The experimental results show that the proposed TriRHC-YOLO outperforms several classical YOLO algorithms, including YOLO11n, YOLO12, and YOLO26, as well as six advanced YOLO-based improved models. The Recall, mean Average Precision (mAP)@0.5, and mAP@0.5:0.95 reach 0.769, 0.848, and 0.608, respectively. The results of the ablation experiments further verify the effectiveness of the three designed modules. Moreover, the proposed model contains only 3.181 M parameters and achieves 168.251 Frames Per Second (FPS), demonstrating favorable real-time detection capability. Overall, the proposed method can effectively improve the detection accuracy of early weak fire targets and the background suppression capability under complex forest backgrounds, making it suitable for real-time UAV-based forest fire inspection tasks.
This study evaluates high-silica fiber/silica aerogel composites (HSFACs) for the passive fire protection of bridge cables. The primary objective is to reveal the high-temperature degradation mechanism of HSFAC and quantitatively determine a reliable thickness scheme for long-term hydrocarbon-fire protection of bridge cables. HSFAC specimens were heat-treated and characterized by thermal conductivity, tensile testing, SEM/TEM, FTIR, and TG analysis. A self-built furnace was used to assess an HSFAC-based cable protection system under hydrocarbon-fire exposure. Increasing heat-treatment temperature enlarged the pore and particle sizes of HSFAC and reduced its thermal-insulation performance. During 120 min of fire exposure, the cable protected by a single 5 mm HSFAC layer reached 300 °C within 45 min, whereas the cable protected by a double-layer 5 + 5 mm HSFAC system remained below 300 °C throughout the test. Finite element simulations validated against the experimental results confirmed that increasing HSFAC thickness improved thermal protection. After 90 min, the predicted cable-surface temperatures were 556 °C and 314 °C for HSFAC thicknesses of 5 mm and 10 mm, respectively. By integrating high-temperature material characterization, HC-fire testing, and thickness-dependent numerical analysis, this study links material degradation to system-level fire performance and provides a quantitative basis for HSFAC thickness design.
This study investigated microstructural, optical, and thermal changes in spruce wood (Picea abies) exposed to controlled laboratory heating to identify indicators associated with the transition from progressive thermal degradation to the initial char formation. Cubic specimens measuring 20 × 20 × 20 mm were exposed to selected temperatures between 240 and 300 °C under atmospheric conditions, with a 15 min isothermal exposure period. Microstructural changes were evaluated by scanning electron microscopy (SEM) and quantitative tracheid double cell wall measurements, supported by simultaneous thermal analysis (STA) and color and reflectance analyses. Simultaneous thermal analysis (TG/DTG/DSC) was performed on separate specimens from the same wood material to provide complementary thermal evidence. The most pronounced microstructural changes were observed between 250 and 260 °C, including substantial thinning of tracheid cell walls, degradation of bordered pits, increased brittleness, and localized structural collapse. Quantitative measurements showed reductions in double cell wall thickness exceeding 50% at 260 °C. TG/DTG analysis indicated the onset of intensive thermal degradation at 254.2 ± 1.48 °C, while optical measurements showed pronounced darkening and reduced differentiation of reflectance spectra above approximately 260 °C. The combined evaluation of complementary analytical methods indicates that the 250–260 °C interval represents a condition-dependent microstructural transition associated with accelerated thermal degradation and the early development of a charred structure under the applied experimental conditions. These findings provide complementary experimental evidence for interpreting the early stages of wood charring and may support the interpretation and future refinement of heat transfer and pyrolysis models. They complement, rather than replace, the conventional 300 °C engineering char line criterion used in structural fire design.
The aim of this study is to explore the fire resilience of traditional ancient villages in Huizhou, China, and to reveal “traditional environmental planning knowledge” as a spatial survival strategy for high-density settlements. This study adopts a qualitative interpretive paradigm, combining historical geography with a literature review, field surveys, and overlay analysis. The study found that these villages, during site selection, utilized basin topography to construct a multi-level disaster mitigation system encompassing “macro-level water systems, meso-level alleyways, micro-level firewalls, and sandwich fire-extinguishing floors.” This endogenous physical technology, based on defensive awareness and community agreements, achieves a dynamic balance of resilience between humans and the environment. The cultural interpretation based on the indicators in this study primarily reflects the disaster resilience potential of traditional planning. The conclusions should be carefully interpreted within the framework of traditional environmental design. Furthermore, commercial intervention, infrastructure renovation, and population loss are leading to the neglect of this defensive space. This lack of a holistic perspective will trigger the “resilience degradation” of ancient villages. Future research urgently needs to establish a “resilience decay model” to quantitatively assess the disaster resistance capabilities remaining after damage to the surrounding buffer space, based on traditional environmental planning knowledge.
