Search Results (2,587)

To meet the conflicting demands of real-time simulation and high fidelity for thermal modeling of IGBT modules in digital twin applications, this paper presents a dynamic thermal network parameter updating strategy. A hybrid thermal model is constructed by combining a high-fidelity finite-element-method reference model with a 3-D compact network. Initial thermal resistance and capacitance parameters are obtained via offline calibration and validated against the transient thermal impedance curve. A dynamic identification method based on recursive least squares with precomputed sensitivity matrices is then proposed. It dynamically updates each independent thermal branch using only real-time chip junction temperature measurements. The Vincotech full-bridge IGBT module is used for simulation validation. The proposed method achieves steady-state identification errors of 3.2% for the IGBT chip thermal resistance and 4.5% for the freewheeling diode chip thermal resistance, outperforming particle swarm optimization and dual Kalman filter in both convergence speed and steady-state accuracy. Thus, it satisfies the requirements of real-time tracking and dynamic evolution for thermal models in digital twin systems. Full article

Shape-memory polymers (SMPs) are gaining significant attention for their ability to recover predefined shapes via external stimuli. Among thermally activated systems, biodegradable blends of polylactic acid (PLA) and polycaprolactone (PCL) are particularly promising for biomedical devices and soft actuators. This study develops a thermo-mechanical theoretical model to investigate the shape-memory behavior of a PLA/PCL composite blend under controlled thermal cycling. The framework integrates transient heat transfer, temperature-dependent elasticity, and viscoelastic dynamics to predict temperature evolution, deformation, and internal stress. The thermal response is computed via Newton’s law of convection, while the mechanical transition is described by a sigmoidal temperature- and crystallinity-dependent Young’s modulus. Beam bending theory is employed to evaluate the spatial distribution of strain and stress. A parametric sensitivity analysis was performed to evaluate the influence of different parameters, including the crystallinity grade, convective heat transfer coefficient, glass transition temperature, and viscoelastic recovery constant. The theoretical study accurately reproduces the shape-memory cycle, quantifying performance through fixation and recovery ratios. This model provides a robust tool for the rational design and optimization of biodegradable smart polymer structures. Full article

In casting processes, mechanical properties such as hardness are highly sensitive to both chemical composition and process parameters, making parameter design a complex and uncertain task during the embodiment stage of engineering design. Conventional trial-and-error-based approaches are often costly, time-consuming, and impractical in industrial environments. To address these challenges, this study proposes an optimized fuzzy artificial neural network (FANN)-based decision-support approach for hardness-oriented parameter design in a casting process. The developed model uses chemical composition variables, including carbon, silicon, manganese, phosphorus, sulfur, chromium, copper, and tin, together with process parameters such as casting temperature and casting time as inputs, while Brinell hardness is considered as the output. A dataset consisting of 170 experimental casting samples was employed; 128 samples were used for model development and hyperparameter selection, and 42 samples were reserved as an independent final test set. The proposed model was implemented as a scaled direct FANN weighted ensemble, in which fuzzified input variables were used to predict standardized continuous hardness values. A total of 300 FANN configurations were evaluated using five-fold cross-validation, and the five best-performing configurations were combined through RMSE-based weighted ensemble averaging. The final model was compared with Random Forest, Linear Regression, Ridge Regression, and SVR-RBF models using MSE, RMSE, MAE, R², MAPE, normalized RMSE, and ±5% prediction success rate. The results showed that the optimized FANN ensemble achieved the lowest mean RMSE in the full-data five-fold cross-validation analysis, slightly outperforming the Random Forest benchmark. In the independent final test set, Random Forest produced the lowest prediction error, whereas the proposed FANN ensemble remained competitive and achieved the same ±5% prediction success rate as Random Forest, Linear Regression, and Ridge Regression. Furthermore, a target-hardness case study demonstrated that the proposed approach could identify candidate casting conditions very close to a desired hardness level, with the nearest prediction reaching 202.985 HB for a target value of 203 HB. These findings indicate that the proposed FANN-based framework can serve not only as a hardness prediction model but also as a practical fuzzy decision-support tool for target-hardness-oriented parameter design in casting processes. Full article

Temperature variability and weather-related fluctuations significantly affect the energy, agricultural, and industrial sectors that are highly sensitive to meteorological changes. These conditions may lead to financial losses caused by demand fluctuations and operational disruptions. This study aims to develop a fractional weather-derivative pricing model based on temperature dynamics by integrating the Ornstein–Uhlenbeck (OU) process, the classical Black–Scholes model (BSM), and the fractional Black–Scholes model (fBSM). Daily temperature data from 2016 to 2025 obtained from the Bandung Geophysical Station, West Java, Indonesia, were used as the basis of analysis. Temperature dynamics were modeled using an OU process, and parameter estimation was conducted using Ordinary Least Squares (OLS). The strike price was determined using Historical Burn Analysis (HBA), whereas weather-derivative pricing was performed using call and put option approaches under both the BSM and fBSM frameworks, incorporating the Hurst parameter to capture long-term memory effects. The results indicate that the fractional Black–Scholes model analytical solution is obtained using the Daftardar–Gejji Aboodh method. Furthermore, the OU process successfully captured daily temperature dynamics, yielding a Mean Absolute Percentage Error (MAPE) of 4.344% and a Root Mean Square Error (RMSE) of 1.396 C, indicating high predictive accuracy across both relative and absolute error measures. In addition, the fBSM consistently generated higher option values than the classical BSM, particularly under higher observed temperatures during the study period and at higher strike prices. These findings demonstrate that long-term memory significantly influences effective volatility and option valuation. This study is expected to contribute to the development of weather derivative models that more realistically represent temperature dynamics and to serve as a reference for weather derivative pricing, hedging, and decision-making, as well as for more measurable, systematic, and sustainable climate-related financial analysis using derivative pricing frameworks. Full article

Essential oils are natural products frequently subject to economically motivated adulteration with cheaper substances like vegetable oils, mineral oils, or organic solvents. This study developed and validated a rapid high-temperature gas chromatography with flame ionization detection (HTGC-FID) method for the simultaneous determination of high-boiling adulterants: triacylglycerides (vegetable oils) and medicinal white oil (mineral oil) in essential oils. The method utilizes on-column injection onto a DB-5 capillary column (30 m × 0.53 mm, 0.88 μm) with a temperature program from 60 to 380 °C and hydrogen carrier gas. Validation parameters demonstrated excellent linearity (R² = 0.9957–0.9978), high repeatability (content RSD < 3%), and sufficient sensitivity (LOQ of 0.03% for triacylglycerides, and 0.63% for medicinal white oil). The method was successfully applied to 20 commercial essential oils. While medicinal white oil was undetected, several samples contained triacylglycerides (up to 3.79%) and other adulterants (up to 52%). Significantly reduced response factors confirmed extensive adulteration in some products. The proposed HTGC-FID method represents a simple, cost-effective, and efficient tool for routine quality control, enabling direct quantification of high-boiling adulterants without tedious sample preparation. Full article

Excessive concentrations of carbon dioxide (CO₂) in the atmosphere lead to adverse environmental effects. Biologically assisted processes that rely on organisms such as microalgae (i.e., Chlorella vulgaris) are common in capturing CO₂ from the atmosphere. Microalgae are rich in proteins, vitamins, minerals, and omega-3 fatty acids. Thus, microalgae production serves both health and environmental sectors. Varying light intensity and temperature are shown to influence algae growth. To quantify algae production under different light intensity and temperature conditions, and monitoring or scaling-up of biological reactors, reliable mathematical models are required. In this work, mathematical models that incorporate light intensity and temperature effects on algae growth in batch and continuous bioreactors are developed. Based on the modeling, the growth rate is maximum at T_opt = 25 °C, reaching the value of μ_max = 0.14 day⁻¹. The growth rate exponentially increases until light intensity (I) reaches around 150 ${\displaystyle \frac{\mathsf{\mu }\mathrm{m}\mathrm{o}\mathrm{l}}{{\mathrm{m}}^2\mathrm{s}}}$, which is approximately the optimal light intensity for Chlorella vulgaris. The effect of T on growth rate is found to be more sensitive than light intensity (I) in both batch and continuous reactor systems. When there are too many parameters in models, uncertainties exist and parameter estimation and model predictions become cumbersome. For these reasons analytical solutions to the models are presented in simplified forms and these models are more practical and easier to implement. The novelty of the work is also the presentation of the models in analytical forms. Analytical solutions to the two reactor models (batch and continuous) will help quantify biomass production as a function of time under the varying light intensity and temperature conditions encountered. Full article

The constitutive analysis model and hot processing map of the ZM6 alloy across various deformation conditions were investigated during hot compression experiments. True stress-strain curves within 300–450 °C and 0.0001–0.1 s⁻¹ were obtained from compression tests on a Gleeble-1500 platform. The results showed that higher strain rates (e.g., 0.1 s⁻¹) induced pronounced work hardening, whereas high temperatures (300–400 °C) combined with low strain rates (10⁻⁴ s⁻¹) promoted conditions conducive to dynamic recrystallization (DRX), leading to a softening tendency of steady-state flow stress. Additionally, a modified strain-compensated constitutive model was built for flow stress prediction. Material constants were plotted as fifth-order polynomial functions of strain (0.025–0.80) for precise stress predictions. The derived activation energy (Q = 182.38 kJ/mol) falls within the typical range for Mg-RE alloys. Leave-one-temperature-out cross-validation showed average AARE values of 7.2–9.8%, demonstrating the model’s interpolation capability and its sensitivity to extrapolation. Cross-validation within the training dataset showed reasonable consistency between experimental and predicted stresses (R > 0.997, AARE < 4.35%). Using the dynamic materials model, hot processing maps identified safe deformation zones and instability zones of the ZM6 alloy. Flow instability was observed at strain rates >0.01 s⁻¹, particularly at low temperatures (300–350 °C). Optimal processing windows appeared in high-energy dissipation (η > 30%) regions, e.g., 400–450 °C/10⁻⁴–10⁻³ s⁻¹. Optical microscopy confirmed that at high temperatures (≥400 °C) and low strain rates (≤0.001 s⁻¹), a uniform, fine-grained, fully recrystallized structure can be obtained, whereas low temperatures (350 °C) and high strain rates (0.1 s⁻¹) produce coarse elongated grains with limited DRX, consistent with the instability regime predicted by the processing maps. Under intermediate conditions (e.g., 400 °C, 0.01 s⁻¹), a bimodal grain distribution indicates incomplete recrystallization. Although EBSD analysis was not performed in this study, the optical microstructures directly validate the predicted safe and unstable windows. Together, all these findings provide preliminary model-based guidance for optimizing hot working parameters to balance microstructural stability and processing efficiency. Full article

The automotive panoramic roof exhibits a large-size and thin-wall geometry, with a length-to-thickness ratio approaching the thousand level. This geometric feature makes its forming quality highly sensitive to forming conditions. During the glass molding process, variations in temperature evolution, loading, and cooling parameters may lead to residual stress accumulation and springback deformation, thereby affecting dimensional accuracy and final forming quality. In this study, a full-process finite element model was established and combined with an L16(4^5) orthogonal design to investigate the effects of five key process parameters—heating temperature, holding time, quenching air velocity, quenching air pressure, and quenching time—on the mean residual stress and mean springback displacement in the glass molding process (GMP). The results showed that, within the given parameter ranges, heating temperature, holding time, and quenching time had relatively pronounced effects on the mean residual stress; the mean residual stress was relatively low when the heating temperature was 680 °C, the holding time was 3 s, and the quenching time was 12 s. Heating temperature, quenching air velocity, and quenching time had relatively pronounced effects on the mean springback displacement; the mean springback displacement was relatively low when the heating temperature was 677.5 °C, the quenching air velocity was 13 m/s, and the quenching time was 10 s. Based on the orthogonal analysis, regression models for the mean residual stress and mean springback displacement were further developed, and parameter combinations were screened using the NSGA-III method. Experimental validation showed that the relative error of the mean residual stress was controlled within 15%, indicating that the established model could, to some extent, capture the relationship between process parameters and forming quality indicators, thereby providing guidance for precision forming and process optimization of large-scale thin-walled automotive panoramic roofs. Full article

Temperature-dependent luminescence of Pr³⁺-doped materials was investigated using both conventional luminescence intensity ratio (LIR) and principal component analysis (PCA)-based thermometry. Three host matrices with distinct structural properties, LiLaP₄O₁₂, YNbO₄, and Y₂O₃, were selected to evaluate the influence of crystal structure on thermometric performance. Temperature-resolved emission spectra recorded over the 103–523 K (−170 to 250 °C) range were analyzed using both approaches, with the first principal component (PC₁) serving as a thermometric parameter in the PCA. The results show that crystal symmetry and site multiplicity strongly influence the temperature-dependent spectral evolution and, consequently, the thermometric response. LiLaP₄O₁₂ exhibits stable and well-defined spectral evolution, resulting in balanced thermometric accuracy and resolution. YNbO₄ shows enhanced sensitivity to temperature variations due to increased spectral complexity and stronger crystal-field effects, leading to improved resolution but increased calibration uncertainty. In contrast, Y₂O₃ exhibits reduced thermometric performance due to overlapping emissions from multiple crystallographically inequivalent sites with distinct thermal responses. Compared to LIR, PCA provides improved thermometric figures of merit, particularly in systems with complex and strongly overlapping emission bands, demonstrating the potential of full-spectrum analysis in luminescence thermometry. Full article

To address the issues of low light absorption efficiency and limited temperature gradient distribution in conventional planar Ag₂Se-based photothermoelectric (PTE) detectors, this paper proposes a structured design strategy for the surface functional layer. Ag₂Se-based PTE detectors with periodic surface microstructure arrays were fabricated using photolithography, and the influence of surface structure on the device’s PTE response performance was systematically investigated. The results indicate that surface microstructures can enhance light absorption and localized photothermal conversion efficiency, thereby increasing the PTE output voltage. However, they also lengthen the thermal diffusion path and reduce the dynamic response speed. When the structural pitch is 6.7 um, the device exhibits optimal overall detection performance within the measured spectral range of 405–950 nm. Under irradiation at a wavelength of 950 nm and a laser power density of 120 mW/cm², the device achieved a voltage sensitivity of 0.14 mV/W. This study reveals the trade-off between enhancing the response performance and response speed of Ag₂Se-based PTE detectors through surface structural design, providing experimental evidence and design guidance for rationally optimizing device structural parameters and realizing room-temperature PTE detection. Full article

The rapid transition toward renewable energy requires transparent and spatially explicit methods for identifying suitable photovoltaic (PV) development areas. This study develops a geospatial artificial intelligence (GeoAI) decision-support framework for PV site suitability assessment in Rajasthan, India. Eleven harmonized predictors were used: global horizontal irradiance (GHI), photovoltaic power output (PVOUT), temperature, wind speed, aerosol optical depth (AOD), elevation, slope, albedo, land use/land cover (LULC), distance to roads, and distance to power lines. Reference labels were generated from an explicit rule-derived suitability index, class thresholds, and exclusion logic; therefore, the machine-learning task was to reproduce a transparent suitability framework rather than to predict observed PV yield or project-level performance. Extreme Gradient Boosting (XGBoost) was compared with simpler baseline models, evaluated using random and spatial-block validation, and interpreted using SHapley Additive exPlanations (SHAP). Independent overlays with known solar-installation records, presence-background robustness testing, and uncertainty/sensitivity analysis were used to examine spatial plausibility, spatial autocorrelation, deterministic label effects, and parameter uncertainty. The resulting outputs include pixel-level suitability zones, contiguous candidate polygons, district-level capacity-oriented summaries, and planning-priority classes. The framework is intended as a risk-aware regional screening tool: high model agreement indicates consistency with the constructed suitability labels, while final project decisions require parcel-scale land, grid, environmental, social, and economic assessment. Full article

Temperate broadleaved forests support diverse arthropod communities, but canopy-dwelling insects in European lime (Tilia cordata Mill.) stands are still poorly known. We surveyed light-attracted canopy insects in six T. cordata Genetic Conservation Units and related protected stands across Lithuania. One modified, solar-powered UV light trap was installed in the canopy (10–15 m) at each site and operated twice per month from June to August in 2023 and 2024. We used diversity metrics, similarity indices, multiple regression, and non-metric multidimensional scaling (NMDS) together with PERMANOVA to examine the structure of insect communities and assess the influence of environmental factors. In total, 6031 individuals representing 295 insect species were recorded, with higher abundance, species richness and Shannon diversity in 2024 than in 2023. Across both years and all sites, Shannon H diversity index ranged from 3.21 to 3.92. Sørensen indices indicated moderate species similarity among sites and distinct species composition at the Ukmergė genetic reserve. The 20 most abundant taxa comprised over 60% of all individuals, and dominance structure changed markedly between years: Serica brunnea dominated in 2023 but was nearly absent in 2024. Regression revealed a significant positive effect of air temperature on insect abundance (about a 31% increase per 1 °C), while precipitation had no significant effect on insect abundance. NMDS and PERMANOVA showed strong spatial structuring, with sites explaining most of the variation, and weaker but significant temporal and site-by-year effects. Overall, insect diversity metrics showed non-significant correlations with T. cordata genetic diversity parameters. Results demonstrate that mature T. cordata forest stands are important reservoirs of canopy insect diversity and highlight pronounced spatial heterogeneity, interannual dynamics, and temperature sensitivity of canopy assemblages in Lithuanian forests. Full article

In summary, near-surface icing risk in complex alpine terrain is jointly controlled by freezing conditions, moisture supply, freeze–thaw transitions, and topographic energy processes. Traditional approaches relying on sparse station data or single temperature thresholds fail to capture spatial heterogeneity, and frequent cloud cover together with topographic errors severely limit the application of thermal infrared remote sensing. Taking the area along the Duku Highway in the Tianshan Mountains as the study region, a daily icing risk assessment framework at 250 m resolution was constructed using multi-source remote sensing, ERA5-Land reanalysis data, topographic correction, and an energy–moisture dual-constrained model. A diurnal temperature cycle model, the CAP index, and physics-constrained machine learning were integrated to reconstruct the daily minimum land surface temperature ($T_{s,\mathrm{m}\mathrm{i}\mathrm{n}}$) at 250 m resolution under all weather conditions. A probabilistic two-tier risk assessment model was then established by incorporating moisture, topography, and freeze–thaw transitions. The results show that high-risk zones occur primarily in valleys and topographically constrained corridors rather than the coldest elevations. Validation against Landsat LST (r = 0.886) and the Bayanbulak station (bias −0.76 °C, RMSE 5.62 °C, r = 0.91) confirms spatial and seasonal accuracy. Sensitivity and Monte Carlo analyses indicate the RiskScore is mainly controlled by the low-temperature weight, while upstream parameters are less influential. The framework is best applied as a screening and early-warning product to identify sub-kilometer potential icing corridors, complementing point measurements and short-range forecasts. Full article

The evaluation of biomass boilers using partial approaches limits system understanding, because energy, exergy, dynamic, and emergy analyses describe complementary, but not equivalent, dimensions of thermo-industrial performance. In response to this gap, an integrated methodological framework is proposed to analyze two representative steam generator technologies in the sugar industry, fueled with ternary mixtures of sugarcane bagasse, Agricultural Crop Residues (ACR), and Dichrostachys cinerea, with the aim of identifying robust operating windows from a simultaneously thermal, exergetic, transient, and sustainability perspective. The methodology combines: (i) a direct and indirect steady-state model to quantify thermal losses and efficiency; (ii) an exergy model to assess conversion quality; (iii) a two-node coupled transient dynamic model capable of representing the differentiated response of the combustion zone and the water/steam system to moisture perturbations; and (iv) an emergy model to estimate the overall sustainability of the process. The results show that the effective moisture content of the mixture is the dominant control variable, since it determines the lower heating value on a wet basis, the specific fuel consumption, the main thermal loss, and the dynamic stability of the system. In the transient domain, a +5% step perturbation in moisture generates drops of 11.14–12.20 °C and 17.76–19.39 °C in furnace temperature for G1 and G2, respectively, while the steam response is damped to 1.03–1.14 °C and 2.39–2.65 °C. Likewise, moisture explains the magnitude of the response with coefficients of determination above 0.99, and the sensitivity analysis identifies the controller time constant, the thermal mass of the water/steam system, and the emissivity as the most influential parameters. Overall, the proposed framework makes it possible to go beyond isolated efficiency assessment and move toward a holistic characterization of biomass boiler performance under technically plausible ternary mixtures. Although the proposed methodological framework is transferable to other biomass combustion contexts, the numerical results—including optimal compositional zones, emergy indicators, and dynamic sensitivity coefficients—are specific to the Cuban sugar industry conditions, adopted transformities, and the biomass types evaluated herein. Full article

Accurate photovoltaic (PV) power forecasting is pivotal for facilitating the integration of renewable energy into modern power systems and supporting sustainable energy development. However, existing methods often rely on single deep learning architectures, require complex preprocessing, suffer from training instability, and lack the ability to capture long-range temporal dependencies. To address these issues, this study develops and compares two hybrid deep learning models—ConvTempNet and DilaTransNet—for hourly PV energy prediction using meteorological and temporal data from two Portuguese PV stations. Quantitative results show that the optimized ConvTempNet achieves superior hourly predictive accuracy with an hourly RMSE of 1.16 kWh and an R² of 0.95 at Tartaruga (2.66 kWh, R² = 0.95 at Zarco). Systematic evaluations were conducted, including dropout ablation (a systematic test of different dropout rates to assess model robustness and regularization effects) (0.2–0.4), performance assessment using RMSE, R², MAE, and MAPE, and sensitivity analysis to assess predictive accuracy and variable importance. Results show that the optimized ConvTempNet yields superior hourly accuracy with an hourly RMSE = 1.16 kWh and an R² = 0.95 at Tartaruga (2.66 kWh, R² = 0.95 at Zarco). The tuned DilaTransNet shows stronger robustness to moderate dropout. Solar radiation is the dominant input variable, while temperature, humidity, and hour affect the two models differently. The two models exhibit complementary strengths, supporting site-specific parameter optimization for reliable PV forecasting. Full article