Search Results (7,865)

This research assesses the practical synthesis of Building Information Modeling (BIM), Autodesk Forma and Dalux to support LEED-oriented sustainable design for a higher education building. Autodesk Revit 2025 functioned as the central BIM platform, while Autodesk Forma enabled early-stage simulations of solar exposure, daylight potential, wind conditions, microclimate, noise and solar-energy potential. Dalux supported model coordination and information management in accordance with ISO 19650 principles. The workflow links simulation outputs to BIM elements through project-defined parameters, allowing performance evidence to inform design refinement. Quantitative indicators were consolidated for daylight exposure, wind comfort, outdoor thermal stress, acoustic exposure and photovoltaic potential. The solar-energy analysis considered an area of approximately 970 m², an annual potential of 1030 kWh/m² and a theoretical yield of approximately 999,100 kWh/year. Assuming 70% roof coverage and 18% panel efficiency, the estimated photovoltaic the expected output is approximately 125,113 kWh/year. These findings demonstrate the value of combining BIM, cloud-based analysis and CDE-based coordination for early-stage sustainable design, while LEED certification, operational energy modelling and lifecycle assessment require additional specialist validation. The proposed workflow provides a consistent approach for aligning design development with sustainability objectives and can be applied to similar building types. Full article

In the thermal insulation winding process of solid rocket motors, the roller-tape contact pressure is a critical factor determining bonding quality. However, accurately predicting this pressure is challenging due to the complex three-dimensional contact involving a thin, nearly incompressible rubber tape and an elliptical concave press roller. This paper proposes a theoretical model extending the classical elastic foundation model by incorporating correction strategies to account for material incompressibility and geometric confinement. A finite element (FE) model was developed to simulate the contact and verified against Hertz theory. Two key parameters of the theoretical model were calibrated using the FE results and justified through a parametric study on Poisson’s ratio and a theoretical analysis of the contact half-width. The theoretical predictions of deformation, contact pressure distribution, and pressing force agree well with the FE results under different applied displacements and mandrel radii without parameter recalibration, demonstrating the model’s generality. Experimental validation employing hybrid inverse analysis confirms the model’s global accuracy, yielding <11% relative error between the predicted and measured pressing forces. This study establishes a theoretical foundation for pressure control in the winding process and provides insights into contact problems for thin elastic layers with high Poisson’s ratios (≥0.45). Full article

Double-skin façades (DSFs) can moderate heat transfer and airflow between the outdoor environment and the building interior; however, their performance in hot-arid climates is highly dependent on cavity geometry, ventilation arrangement, and the interaction between the airflow and any active cooling surfaces. The objective of this study is to establish, under a single idealised extreme hot-arid design point, how sealed, ventilated and actively cooled double-skin façade cavities differ in their predicted temperature, velocity and turbulent kinetic energy fields, and which arrangements merit controlled follow-up study. The four configurations are treated as an idealised comparative case study rather than as validated building-performance predictions. This exploratory study uses computational fluid dynamics (CFD) to compare the aerothermal behaviour of four DSF cavity configurations under prescribed external air and outer-wall temperatures of 50 °C, an inner-wall temperature of 24 °C, and an external inlet velocity of 3.06 m/s. The configurations comprise a sealed 0.4 m cavity (M1), a wind-driven ventilated 0.4 m cavity (M2), the same ventilated cavity with six 25 mm cooling pipes at 10 °C (M3), and a concept-stage lateral-flow arrangement combining a 0.10 m cavity, a 0.025 m slit and four 80 mm cooling pipes at 10 °C (M4). The simulations employ the standard k-ε turbulence model with fixed thermal boundary conditions. Along the reported sampling lines, M1 exhibited a nearly uniform air temperature of approximately 45.7 °C, whereas M2 remained close to the imposed 50 °C external-air temperature. M3 produced lower temperatures in the immediate vicinity of the cooling pipes, but most of the sampled profile remained near ambient conditions. M4 exhibited a broader spanwise temperature range of approximately 26.9–50 °C, with local pipe-adjacent air temperatures approaching 24 °C and cooler regions developing along parts of the lateral flow path. The findings provide preliminary concept-screening evidence and support further controlled parametric analysis, higher-fidelity modelling, and experimental validation. Full article

With the rapid penetration of electric vehicles (EVs) and renewable energy generation in distribution networks, the coordinated scheduling of flexible EV loads and uncertain renewable resources has become a critical research focus in modern power systems. This study investigates the collaborative optimal dispatch of thermal units, EVs, and renewable power generation. Different from conventional closed-loop game-based bi-level optimization, this paper constructs a transmission–distribution integrated scheduling framework and proposes a sequential hierarchical progressive optimization strategy for EV charging and discharging dispatch to fully tap the cross-level coordination potential of power grids. The upper transmission layer optimizes the joint operation of thermal units, wind power, and photovoltaic units to minimize the overall power supply cost, where the inequality power balance constraint is reasonably adopted to reserve power regulation margin for renewable fluctuation and meet practical engineering operation requirements. To effectively address the severe uncertainty of renewable power output without relying on accurate probability distribution information, information gap decision theory (IGDT) is employed to realize robust scheduling with risk-averse and opportunity-seeking decision adaptability. In the lower distribution layer, a theoretically grounded nodal electricity price (NEP) model integrating node loss sensitivity (NLS) and node load rate (NLR) is applied to substitute iterative power flow calculation, which realizes the spatial optimal allocation of EV charging and discharging nodes while significantly improving computational efficiency. The proposed framework comprehensively minimizes network power loss and user charging cost. Finally, extensive simulations based on the IEEE 33-node distribution system verify the effectiveness, computational superiority, and robustness of the proposed sequential hierarchical coordinated scheduling strategy. Full article

In this work, Cu₂CoSnS₄ (CCTS) thin films were deposited on glass substrates by thermal evaporation and investigated for photovoltaic applications. The influence of substrate temperature, varied from 25 °C to 200 °C, on the structural, morphological, and optical properties of the films was experimentally studied using X-ray diffraction (XRD), scanning electron microscopy (SEM), and photoluminescence (PL) measurements. XRD analysis confirmed the formation of crystalline CCTS with a stannite structure and a preferential orientation along the (112) plane. SEM observations revealed rough and non-uniform surfaces accompanied by an increase in grain size with increasing substrate temperature. Room-temperature PL measurements indicated a band-gap energy of approximately 1.3 eV, suitable for photovoltaic applications, and confirmed the presence of secondary phases in the p-type stannite CCTS films. Despite the promising photovoltaic properties of CCTS, numerical studies on CCTS-based solar cells remain scarce in the literature. In this context, a numerical study of the CCTS-based solar structure grown on glass was also performed using SCAPS-1D, showing good agreement with experimental photovoltaic results and validating the simulation model. Replacing the glass substrate with silicon improved the device efficiency to 5.77%. Further optimization of the series and shunt resistances significantly enhanced the photovoltaic performance, achieving a power conversion efficiency of 16.77%, with FF = 52.94%, V_oc = 0.89 V and J_sc = 35.19 mA/cm². Full article

Large mineral flotation workshops in severe cold regions represent special industrial indoor environments characterized by the coexistence of limited ventilation and intense heat release. Such conditions generate pronounced spatial thermal stratification and localized heat accumulation within the workshop, leading to uneven worker thermal exposure and increased thermal discomfort and heat stress risk. However, conventional thermal comfort models were primarily developed for ordinary buildings with relatively stable thermal environments. Their applicability to large industrial workshops remains insufficiently validated. Nine representative monitoring points were arranged in the summer operating areas of the workshop, and thermal comfort surveys were conducted among 35 workers who had adapted to the local climate and working environment. The predicted mean vote (PMV) model was used as the baseline assessment framework, while an adaptive predicted mean vote (aPMV) model was further calibrated using field-based thermal sensation information. Monte Carlo simulation was employed to evaluate uncertainty propagation under field-data constraints, and Sobol sensitivity analysis was conducted to identify the dominant factors affecting thermal comfort predictions. The results demonstrated that the conventional PMV model exhibited a clear warm prediction bias under the investigated industrial conditions. After adaptive correction, the deviation from the field-based TSV was reduced by 82.93%, indicating improved agreement with workers’ actual thermal perception. Sensitivity analysis identified metabolic rate as the dominant contributor to aPMV output variance, with first-order and total-effect Sobol indices of 0.530 and 0.535. The proposed framework provides a scenario-specific approach for thermal comfort assessment in the investigated flotation workshop and offers preliminary methodological references for similar large-scale flotation workshops. Full article

Under ground-based observation geometry, infrared radiant-intensity sequences of space objects are jointly influenced by object micromotion, thermal radiation, time-varying viewing conditions, and atmospheric propagation. Existing simulation studies often prescribe the line of sight or simplify the coupling between viewing geometry and atmospheric attenuation, which limits long-duration ground-based sequence analysis. This study develops a physics-informed framework for generating atmosphere-attenuated infrared radiant-intensity sequences of space objects undergoing precession or tumbling. The framework reconstructs observation geometry from azimuth–elevation–range trajectories, updates facet normals through a unified micromotion attitude model, computes visible projected area and transient facet temperature, and incorporates MODTRAN-derived elevation-dependent atmospheric transmittance. Using this framework, we construct IRPeriodic, an eight-class simulated dataset for long-duration univariate time-series classification. We further propose LPD-Net, which integrates large-kernel residual feature extraction, prototype-guided dynamic temporal alignment, and differential periodic representation to capture long-range waveform morphology, sample-dependent temporal correspondence, and segment-level local variation. On IRPeriodic, LPD-Net achieves an accuracy of 0.8618 ± 0.0057, a macro-F1 of 0.8615 ± 0.0061, and a Matthews correlation coefficient of 0.8426 ± 0.0065, outperforming the evaluated neural-network and ROCKET-type baselines. Ablation and synthetic-noise sensitivity analyses indicate that the performance gain is mainly associated with long-context feature extraction, with additional improvements from dynamic alignment and differential periodic statistics. Auxiliary experiments on selected public UCR datasets suggest that the representation is also competitive for univariate time-series classification. These results demonstrate the effectiveness of LPD-Net on the proposed physics-informed benchmark for long-duration ground-based infrared radiant-intensity sequence classification. Full article

The coordinated operation of interconnected multi-carrier energy hubs is a key enabler of cost-efficient and flexible energy management in modern smart cities. This paper develops a comprehensive optimization framework for the day-ahead scheduling of interconnected energy hubs in residential and commercial sectors. The problem is formulated as a mixed-integer linear programming (MILP) model that jointly manages electricity, natural gas, and thermal energy flows. To enhance operational flexibility, the proposed model incorporates demand response programs for both electrical and thermal loads, multiple energy storage technologies, and electric vehicles with vehicle-to-grid (V2G) capability. Six operating scenarios are defined to assess the impact of different resources and coordination levels, ranging from independent hub operation to fully integrated interconnected scheduling. Simulation results show that coordinated operation of the energy hubs, supported by flexible loads, storage systems, and electric vehicles, can significantly reduce total daily operating costs compared with conventional standalone configurations. The findings confirm that energy exchange among hubs, combined with demand-side flexibility and EV participation, improves both economic performance and system efficiency. The proposed framework offers a scalable scheduling approach for future integrated multi-energy systems. Full article

The increasing penetration of renewable energy has significantly intensified the demand for flexible operation of thermal power plants, making dynamic simulation an essential tool for understanding transient behaviors and developing advanced operational strategies for circulating fluidized bed (CFB) power plants. This review critically examines the existing dynamic modeling approaches for industrial-scale CFB power plants, with particular emphasis on their applicability to flexibility studies. Existing CFB flue-gas side models are systematically classified into three categories: 3D physics-based CFD models, behavioral/data-driven models, and semi-empirical mechanistic models. Their characteristics are critically compared in terms of spatial and temporal scales, empirical dependence, model generality, computational and implementation burden, and applicability to CFB flexibility studies. Dynamic modeling of the steam–water cycle is also reviewed, showing that it has reached a relatively mature stage owing to well-established thermo-hydraulic theories and standardized modeling platforms. The current research bottleneck is therefore identified as the dynamic coupling between the flue-gas side and the steam–water cycle for integrated CFB whole-plant simulation. Based on the comparative analysis, semi-empirical mechanistic models are identified as a particularly suitable framework for industrial-scale CFB flexibility studies requiring minute-to-hour transient simulation, physical interpretability, and whole-plant coupling. Finally, future research directions are discussed, highlighting how integrated dynamic models can support CFB flexibility-enhancement technologies and the development of new-generation coal-fired power plants. Full article

Data centers are large and rapidly growing electricity consumers, and cooling systems account for a substantial share of their energy demand. A key contribution of this study is a climate-sensitive, hourly techno-economic comparison of three data-center cooling configurations under consistent operating assumptions: a conventional water-cooled centrifugal chiller baseline, a magnetic bearing chiller (MBC) system, and an MBC system integrated with aquifer thermal energy storage (ATES). The comparison is performed for Phoenix, Arizona, and Fairbanks, Alaska, which represent substantially different cooling climates in the U.S. Hourly simulations use identical information technology (IT) load profiles, identical aggregate installed chiller capacity represented by two 4058 kW chiller units, common water-side economizer controls, and site-specific weather and electricity tariffs. Results show that the MBC system reduces annual cooling-system electricity consumption from 1169.4 to 957.4 MWh in Phoenix (18.1%) and from 361.6 to 319.4 MWh in Fairbanks (11.7%). Peak cooling-system electrical demand decreases by 119.4 kW in Phoenix and 71.6 kW in Fairbanks. Relative to the centrifugal baseline, the MBC case gives a 5.8-year simple payback in Phoenix but is not economically attractive in Fairbanks under the assumed tariff. The MBC-only case gives the lowest annual cooling electricity use in both climates. The MBC+ATES case is treated only as a screening-level, discharge-assisted cold-storage scenario rather than a full techno-economic assessment of seasonal ATES, and no site-specific hydrogeological feasibility assessment is performed. Under the assumed O&M cost structure, MBC+ATES gives a higher discounted value of savings than MBC-only, but this economic result is not caused by additional cooling-electricity savings relative to MBC-only. The MBC+ATES case also has a longer payback period because of its higher capital cost. These results show that the value of advanced cooling configurations depends on climate, free-cooling availability, electricity pricing, storage assumptions, and economic assumptions within the modeling framework considered in this study. Full article

This research aims to investigate and improve the accuracy of porosity prediction in steel ingot casting by leveraging Thercast finite element simulations. In particular, the study refines the Niyama criterion through additional physical parameters and solidification modeling, aiming to reduce shrinkage porosity and enhance the overall mechanical reliability of cast components. Simulation results reveal that a lower thermal conductivity and faster cooling rates exacerbate shrinkage porosity, while a refined Niyama indicator using explicit solid-fraction weighting, with viscosity, alloy composition, and shrinkage accounted for through the underlying THERCAST material model, improves spatial localization of porosity-prone regions in the investigated case. For the investigated configuration, reducing the cooling rate to around 1.25 K/s decreased the extent of the simulated region classified as porosity-prone relative to the reference case. Furthermore, the analytical porosity–strength relation indicates a material-dependent reduction in strength when the porosity fraction exceeds 2%, underscoring the structural significance of internal voids. This study extends the practical interpretation of the classical Niyama criterion by combining solid-fraction weighting with material-dependent thermophysical inputs, addressing gaps in existing shrinkage porosity models. The approach integrates simulation findings with actual casting defects identified through ultrasonic scanning and metallographic analysis. By merging experimental insights with advanced finite element simulations, foundries can better regulate casting conditions, particularly cooling rates and thermal gradients, to minimize porosity. The refined porosity prediction framework aids in process optimization, improved material utilization, and superior quality assurance of steel ingots. Full article

Convective drying of Zingiberaceous roots, particularly ginger (Zingiber officinale), requires a uniform distribution of airflow and temperature to ensure energy efficiency and product quality. However, many drying systems exhibit aerothermal limitations that produce temperature gradients and non-uniform drying conditions. To address this issue, this study proposes a dual-swirl dryer featuring two air inlets: an upper helical inlet and a lower tangential inlet. Both inlet configurations generate swirling airflow patterns that enhance thermal uniformity and increase the residence time of hot air within the drying chamber. The internal flow behavior was investigated using Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent2025 R1 version. A three-dimensional polyhedral mesh was generated to improve computational efficiency and numerical accuracy. Turbulence and recirculation phenomena were modeled using the Realizable k–$ϵ$ turbulence model, while temperature distribution was analyzed through the energy conservation equation. Numerical predictions were experimentally validated using temperature sensors integrated into an automatic control system. The comparison between numerical and experimental results demonstrated that the dual-swirl configuration improves airflow redistribution, reduces thermal stagnation zones, and promotes a more homogeneous temperature field throughout the drying chamber. These findings confirm that the proposed system is an efficient alternative for agro-industrial drying applications. Full article

Winter water allocation in regulated plain river networks requires an operational link between hydrological low water benchmarks and replenishment decisions. This study focused on the Lixiahe plain river networks; utilizing winter water level records from nine stations during 2006–2021, five hydrological methods were applied to derive candidate water level benchmarks. A one-dimensional MIKE 11 model was applied to simulate total upstream replenishment scenarios of 40–400 m³/s and to establish station-specific stage–discharge relationships under fixed downstream boundaries and structure operation rules. Within the simulated range, the candidate benchmarks corresponded to scenario-specific replenishment requirements of 153.94–268.49 m³/s. The framework supports comparison of winter water allocation strategies and identification of controlling stations, but ecological sufficiency requires independent evidence from species, habitat, thermal, dissolved oxygen, and water quality responses. Full article

The application of Machine Learning (ML) to bridge Structural Health Monitoring (SHM) is constrained by a lack of diverse and labelled datasets. Obtaining high-quality training data from operational infrastructure is inherently difficult because critical assets are typically repaired immediately upon the detection of defects, preventing the collection of data describing diverse failure modes. To address this scarcity and enable the prototyping of robust algorithms, this study presents a framework for generating synthetic modal frequencies using a calibrated Finite Element (FE) model of the S101 bridge. Aleatory uncertainties and environmental variability are incorporated through the stochastic variation of material properties and thermal loads derived from a 20-year climate record. Analysis of the generated dataset revealed that simulated thermal loads induced frequency shifts that often exceeded those caused by minor structural damage, confirming the necessity of training on environmentally representative data. The primary contribution of this work is an open-access, FAIR-compliant (Findable, Accessible, Interoperable, Reusable) synthetic dataset, intended to serve as a standardised benchmark for the SHM research community under conditions of combined structural and environmental uncertainty. To demonstrate the utility of the generated data, the performance of a supervised multi-layer perceptron and an unsupervised k-means clustering algorithm are evaluated, with the supervised approach achieving a maximum classification accuracy of 1.00. However, the framework also reveals a fundamental modelling limitation: the linear FE approach failed to replicate the physical response under pier settlement, producing frequency shifts an order of magnitude below those observed experimentally. Full article

Photovoltaic converters operate under varying climatic conditions and non-ideal grid regimes, but factor importance is often assessed either through isolated local metrics or through pooled operating data that hide regime shifts and interaction effects. This study develops a hierarchical framework for sensitivity analysis of operating profiles of grid-connected PV converters under climatic and grid uncertainty. A compact operating-profile formulation is introduced that relates solar radiation, cell and ambient temperature, grid voltage, load, and selected design/control parameters to active power, efficiency, power factor, harmonic distortion, DC bus ripple, clipping behavior, and thermal headroom. The proposed workflow combines local normalized sensitivities for fast ranking around nominal conditions, Morris screening for factor reduction, and Sobol/Saltelli variance-based indices for global prioritization under uncertainty. The framework is demonstrated on a 100 kW synthetic reduced-order benchmark representing a three-phase two-level grid-connected PV inverter with an LCL filter. To clarify the scope of validity, the reduced-order model is cross-checked against switching-level simulations for representative nominal, clipping-prone, high-temperature and grid-stress operating windows. The results show that factor importance is not universal, but depends on the selected KPI, operating regime and uncertainty scenario. In the considered benchmark, grid voltage, cell temperature and equivalent thermal resistance are the dominant total-effect contributors, while the strongest second-order contribution appears between grid voltage and filter inductance under grid-stress conditions. The proposed framework is therefore intended as a reproducible, regime-aware sensitivity workflow rather than as a universal ranking of PV converter parameters. Full article