Search Results (1,599)

The prediction of chiller performance parameters is crucial for optimal control and fault diagnosis. Numerous efficient and accurate data-driven models have been developed and implemented. These models are normally trained on historical operational data of chiller units. However, the distribution of operational data may shift due to accumulated operating hours or changes in control strategies. Under new operating conditions, models trained on historical data often generalize poorly, leading to prediction deviations. To address this issue, this study integrates a one-dimensional convolutional neural network with a domain adaptation method that extracts features from both the source and target domains and aligns their inverse Gram matrices in terms of angle and scale. A predictive model applicable to multiple chiller performance parameters is established using limited historical data, enhancing the model’s generalization ability. Compared to the baseline model (MLP), the proposed method achieves an average reduction of 74.3% in mean absolute error (MAE) and 76.1% in root mean square error (RMSE), while the R² values exceed 0.96 (for certain scenarios). Additionally, this paper analyzes the data distribution between the source and target domains, investigates key factors affecting the model’s generalization capability, and provides insights for evaluating the quality of modeling data. Full article

This study presents a comprehensive thermal analysis, design, and optimization framework for electrothermal heating systems integrated into composite wing structures. Thermal behavior is first investigated using finite volume simulations conducted with a commercial solver. An in-house thermal solver is then developed based on the governing heat transfer equations and a second-order finite difference discretization scheme. The in-house solver is validated against the commercial solver, showing a maximum deviation of less than 1%. The validated solver is subsequently coupled with a genetic algorithm to perform multi-objective optimization of the electrothermal heating system. A novel correlation for the convection heat transfer coefficient over airfoil surfaces is developed based on extensive turbulent flow simulations and a genetic algorithm. The developed correlation equation has significantly lower percent relative error (from 34% to 6%) compared to flat plate correlations. The developed convection coefficient is incorporated into the optimization process. Key design variables, including heat generation intensity, heater strip dimensions, and the thermal conductivity of composite and surface protection materials, are included in the optimization process. An original objective function is formulated to simultaneously minimize electrical power consumption, prevent ice formation on the external surface, and limit internal temperatures to safe operating ranges for composite materials. The optimized design is evaluated under both spatially varying and constant convection heat transfer coefficients to assess the impact of convection modeling assumptions. The proposed methodology provides a unified and extensible framework for the optimal design of electrothermal ice protection systems and can be readily extended to three-dimensional composite wing configurations. Full article

Background/Objectives: Contemporary food systems face dual imperatives of ensuring nutritional adequacy while minimizing environmental resource consumption, yet conventional dietary assessment methodologies inadequately integrate these competing objectives. This simulation-based proof-of-concept study developed an artificial intelligence-driven computational framework synthesizing nutritional evaluation, environmental footprint quantification, and economic accessibility assessment. Methods: The analytical architecture integrated random forest classification, dimensionality reduction, and scenario-based optimization across a simulated population cohort of 1500 individuals. Food composition data encompassed 55 representative foods across eight categories linked with greenhouse gas emissions, water use, and price parameters. Four dietary patterns (Mediterranean, Western, Plant-based, Mixed) were characterized across nutrient adequacy, greenhouse gas emissions, water consumption, and economic cost. Results: Random forest classification achieved 39.1% accuracy, with cost, greenhouse gas emissions, and water consumption emerging as the most discriminating features. Dietary patterns exhibited convergent macronutrient profiles (protein 108.8–112.8 g per day, 4% variation) despite categorical distinctions, while calcium inadequacy pervaded all patterns (867–927.5 mg per day, 7–13% below requirements). Environmental footprints demonstrated limited differentiation (greenhouse gas 3.73–3.96 kg CO₂e per day, 6% range). Bootstrap resampling (n = 1000) confirmed narrow confidence intervals, with NHANES validation revealing substantial energy intake deviations (38–58% above observed means) attributable to adequacy-prioritized design rather than observed consumption patterns. Scenario modeling identified seasonally flexible dietary configurations maintaining micronutrient and protein adequacy while reducing water use to 87% of baseline at modest cost increases. Conclusions: This framework establishes a validated computational infrastructure for integrated dietary assessment benchmarked against sustainability thresholds and epidemiological reference data, demonstrating the feasibility of AI-driven evaluation of dietary patterns across nutritional, environmental, and economic dimensions. Full article

Recently, near-net-shape rolling has emerged as a key manufacturing technology for producing high-precision, fatigue-resistant bearing rings with irregular cross-sections, particularly for the production of Wind Turbine Shaft Bearings (WTSBs). The deformation behavior of the material during this rolling process is governed by temperature and rolling speed. Therefore, based on a thermomechanical coupled analysis, a simulation model for the deformation process of GCr15SiMn profiled rings during variable-speed rolling was developed in this study. The model was experimentally validated, confirming a dimensional error of less than 3‰. And then, the distribution of strain and temperature were analyzed during the rolling of the profiled ring. As the initial temperature increased from 1040 °C to 1160 °C, the standard deviation of strain (SDP) decreased from 6.12 to 4.05. Correspondingly, the standard deviation of temperature (SDT) was raised from 4.32 to 4.74. When the drive roll speed was increased from 2.5 rad/s to 4.0 rad/s, the SDP was reduced from 4.24 to 3.42. In addition, the SDT decreased from 4.42 to 3.21. The research indicates that SDP is primarily affected by initial temperature, whereas SDT is significantly influenced by drive roller speed. On the one hand, this study provides a clearly defined optimization framework, parameter ranges for achieving optimal uniformity in GCr15SiMn material (temperature of 1100–1130 °C, speed of 3.0–3.5 rad/s), and their anticipated benefits (an SDP reduction of 32% and an SDT reduction of 15%). On the other hand, it also establishes quantifiable industrial control targets, defining key quality assurance values (SDP ≤ 5.0, SDT ≤ 4.5). The rolling stability and precision can be improved through the selection of optimized rolling temperatures and speeds. This finding provides a theoretical foundation and technical framework for improving the rolling process stability of profiled cross-section bearing rings. Furthermore, this study is of positive significance for reducing the manufacturing costs of high-performance WTSBs. Full article

This study proposes Enhanced Beaver Behavior Optimizer (EBBO) to overcome the original BBO algorithm’s limitations in handling complex optimization problems. EBBO integrates a three-phase cooperative framework, incorporating adaptive mutation, dynamic opposition-based learning, and an risk-aware decision strategy inspired by simulated annealing. Comprehensive evaluations on the CEC 2017 and CEC 2020 benchmark suites demonstrate that EBBO significantly outperforms nine widely used algorithms (e.g., BBO, FATA, DE) in convergence accuracy, stability, and robustness, especially for high-dimensional and multimodal functions. EBBO achieves average objective value reductions of 15–50% and standard deviation reductions of 30–70% compared to the original BBO, with Wilcoxon rank-sum tests confirming statistical significance across most functions. When applied to three classical engineering design problems—step-cone pulley, pressure vessel, three-bar truss optimization, and 3D UAV path planning—EBBO consistently achieved the best or near-optimal solutions while satisfying all nonlinear constraints. The results confirm that EBBO effectively balances exploration and exploitation, offering a reliable and efficient approach for solving complex constrained optimization challenges in both benchmark and real-world engineering contexts. Full article

Urbanization and climate change have intensified the need for advanced methods to simulate building energy performance within realistic urban environmental contexts. This study presents a microclimate-informed framework for developing representative building energy prototypes that enable the estimation of energy use for buildings sharing similar microclimatic conditions and building-level characteristics. The framework is demonstrated using Des Moines, Iowa, as a case study. The framework combines high-resolution microclimate modeling with geospatial analysis to quantify the influence of urban form and vegetation on building energy use. Localized weather files were generated using the Weather Research and Forecasting (WRF) model to capture spatial variations in microclimate across the city. Detailed three-dimensional models of buildings and trees were developed from Light Detection and Ranging (LiDAR) point cloud data and integrated with building attributes, including construction materials and heating and cooling systems, to generate representative building typologies use them to build a similarity-based lookup table. Urban energy simulations were conducted using the Urban Modeling Interface (UMI). To demonstrate the effectiveness of the framework, simulations were conducted for two building prototypes according to the framework. Results show that monthly energy use intensity (EUI) of a representative cluster compared to randomly selected buildings differs by 10% to 19%, with both positive and negative deviations observed depending on building template and month. Thus, the proposed framework shows great promise to capture comparable energy performance trends across buildings with similar construction characteristics and urban context and minimize computational demands for doing so. While evapotranspiration effects are not explicitly modeled in the current framework, they are recognized as an important microclimatic process and will be incorporated in future work. This study demonstrates that the proposed framework provides a scalable and computationally efficient approach for urban-scale energy analysis and can support data driven decision making for climate-responsive urban planning. Full article

This study compares the two (2D)- and three-dimensional (3D) accuracy of tooth reduction depths in porcelain laminate veneer prepared using conventional and 3D-printed guide techniques. Forty 3D-printed maxillary casts were divided into four groups: freehand (FH) (n = 10), silicone guide (SG) (n = 10), cross-shaped 3D-printed guide (3D_C) (n = 10), and stackable 3D-printed guides (3D_S) (n = 10). Butt-joint veneer preparation was performed on the left central incisor. Two-dimensional analysis was performed to assess trueness using mean absolute differences (MADs) from the planned depth at eight designated points, while precision was compared within groups. Three-dimensional analysis evaluated trueness by superimposing post-preparation scans with reference casts and precision via intra-group superimposition, with deviation errors measured using the Root Mean Square (RMS) method. One-way ANOVA and Bonferroni post hoc tests were used (α = 0.05). In 2D analysis, 3D_S exhibited a significantly lower MAD than FH at most of the measured points (p < 0.05), more accurate incisal reduction at mesial and distal points compared to 3D_C (p < 0.001), and more accurate mesial (p = 0.011) and distal (p = 0.001) cervical margin preparation than SG. In the 3D trueness assessment, 3D_S exhibited significantly lower deviation errors than FH (p < 0.001) and SG (p = 0.012) while also achieving the highest overall 3D precision with the lowest RMS (0.067 ± 0.013), followed by 3D_C (0.086 ± 0.019). Veneer preparation guided by a stackable 3D-printed guide resulted in more accurate tooth reduction depths compared to the other three techniques. Full article

This work investigates the effect of ambient humidity on the Electromechanical Impedance (EMI) signatures of steel-reinforced concrete (RC) for structural health monitoring (SHM). The influence of varying relative humidity (%RH) is quantified using three RC blocks containing piezoelectric sensors bonded to the steel reinforcements of the RC blocks. We show that the the Root Mean Squared Deviation (RMSD) score is strongly affected by humidity, highlighting the need to address humidity effects to achieve robust damage detection using EMI. Using the reactive component of the EMI (X) in the range of 20 kHz and 120 kHz, a three-layer one-dimensional convolution neural network (1D-CNN) was able to estimate ambient %RH between 20% and 80%, with a Mean Absolute Error (MAE) of 2.14%RH. The results highlight the significant impact of humidity on EMI-based SHM and suggests that the imaginary part of the EMI signature can be used to detect the effect of humidity. This work provides a foundation for more robust SHM systems in humidity-varying environments applicable to a wide range of concrete infrastructure. Full article

In this study, thermoplastic polyurethane (TPU) parts were fabricated using fused filament fabrication (FFF) by varying key process parameters, namely extruder temperature (210–230 °C), layer thickness (200–400 µm), and printing speed (30–50 mm/s). A Box–Behnken experimental design was used to systematically evaluate the combined influence of these parameters on surface roughness (R_a), dimensional deviation (DD), and ultimate tensile strength (UTS). After fabrication, all specimens were subjected to a Tetrahydrofuran (THF)-based chemical smoothing process to modify surface characteristics. Surface roughness measurements showed a substantial reduction after chemical smoothing, with values decreasing from an initial range of 13.17 ± 0.21–15.87 ± 0.23 µm to 4.01 ± 0.18–7.35 ± 0.16 µm, corresponding to an average decrease of approximately 50–72%. Dimensional deviation improved moderately, from 260–420 µm in the as-printed condition to 160–310 µm after post-processing, representing a reduction of about 20–38%. Mechanical testing revealed a consistent increase in UTS following chemical smoothing, with values improving from 30.24–40.30 ± 0.52 MPa to 33.97–47.94 ± 0.36 MPa, yielding an average increase of approximately 10–24%. Then, the experimental data were used for multi-objective optimization of the FFF process parameters, using a non-dominated sorting genetic algorithm (NSGA-II) implemented in Python 3.11, to identify best parameter combinations that provide a balanced surface quality, dimensional accuracy, and mechanical performance. Full article

Accurate reconstruction of train collision accidents is essential for understanding impact conditions, assessing crashworthiness, and supporting safety improvements. This study proposes a surrogate-based optimization framework for reconstructing structural damage in train collisions from post-accident observations. The pre-impact kinematic state, expressed by a six-dimensional vector of relative offsets, rotations, and impact velocity, is formulated as an inverse problem in which a Sum of Squared Relative Deviations (SSRD) between measured and simulated residual deformations serves as the objective function. A reduced two-vehicle finite element (FE) model is developed to capture the dominant impact dynamics, an Optimal Latin Hypercube Design is used to sample the parameter space, and a Kriging surrogate model is constructed to approximate the response. A simulated annealing algorithm is applied to search for the global minimum. The framework is demonstrated on a real high-speed rear-end collision of electric multiple units. The Kriging model achieves a coefficient of determination of about 0.85, and the optimized kinematic state yields FE-predicted residual deformations that agree with field measurements at key locations to within about 5%. The results show that the method can efficiently reconstruct physically plausible collision scenarios and provide insight into parameter sensitivity and identifiability for railway safety analysis. Full article

Background: Intervertebral disc degeneration is a prevalent condition and a major risk factor for disc herniation. Mechanical overload, aging, injury, and disease contribute to the annulus fibrosus’ structural failure, which allows nucleus pulposus material to escape and reduces the capacity to absorb shock. This study builds on previous investigations by evaluating additional thermoplastic polyurethane (TPU) filaments as potential materials for additively manufactured intervertebral disc replacements. Materials and Methods: Disc-shaped specimens (Ø50 × 10 mm) were fabricated using fused deposition modeling (FDM). A gyroid infill structure was employed with unit cell sizes ranging from 4 to 10 mm³ and wall thicknesses between 0.5 and 1.0 mm. The outer wall thickness varied from 0.4 to 0.8 mm. Four TPU filaments (Extrudr FlexSemiSoft, GEEE-TECH TPU, SUNLU TPU, and OVERTURE TPU) were tested, resulting in 36 parameter combinations per filament. Printed discs were examined via stereomicroscopy. Tensile testing was conducted according to DIN EN ISO 527-1 using Type 5A specimens. Mechanical performance under physiological loading was assessed through uniaxial compression tests, in which samples were compressed to 50% of their height while force–deformation curves were recorded. Target forces were defined as 4000–7500 N to maintain comparability with prior studies. Results: Across all filaments, a maximum of three parameter combinations per material achieved forces within the target range. Microscopy confirmed the dimensional accuracy of wall thicknesses with minimal deviation. Tensile strength values for GEEE-TECH, SUNLU, and FlexSemiSoft were comparable (10–11 MPa), while OVERTURE showed significantly lower strength (approximately 9 MPa). Tensile modulus values followed a similar trend: 25–30 MPa for three filaments and 17.5 MPa for OVERTURE. Conclusions: All four TPU filaments could be used to fabricate discs that met the mechanical requirements for compression. These results confirm that both the tested TPU materials and gyroid structures are suitable for potential intervertebral disc replacement applications. Full article

In high-precision optical systems such as laser optics, astronomical observation, and semiconductor lithography, anti-reflection coatings are crucial for light transmittance, imaging quality, and stability, but traditional designs face modeling challenges in balancing ultralow reflectivity, high wavefront quality, and manufacturability amid multi-dimensional parameter coupling and multi-objective constraints. This study addresses these by proposing a unified mathematical modeling framework integrating a Symmetric five-layer high-low refractive index alternating structure (V-H-V-H-V) with dual-scale nanostructures, employing a constrained quasi-Newton optimization algorithm (L-BFGS-B) to minimize reflectivity, wavefront root-mean-square (RMS) error, and surface roughness root-mean-square (RMS) in a six-dimensional parameter space. The Sellmeier equation is adopted to calculate wavelength-dependent material refractive indices, the model uses the transfer matrix method for the Symmetric five-layer high-low refractive index alternating structure’s reflectivity, incorporates nano-surface height function gradient correction, sub-wavelength modulation, and radial optimization, applies Zernike polynomials for low-order aberration correction, quantifies surface roughness via curvature proxies, and optimizes via a weighted objective function prioritizing low reflectivity. Numerical results show the spatial average reflectivity at 632.8 nm reduced to 0.13%, the weighted average reflectivity across five representative wavelengths in the 550–720 nm range to 0.037%, the reflectivity uniformity to 10.7%, the post-correction wavefront RMS to 11.6 milliwavelengths, and the surface height standard deviation to 7.7 nm. This framework enhances design accuracy and efficiency, suits UV nanoimprinting and electron beam evaporation, and offers significant value for high-power lasers, lithography, and space-borne radars. Full article

Large-format pouch cells enable higher pack-level energy density and simplified system architecture, yet they pose significant thermal challenges due to long internal conduction paths, pronounced spatial gradients, and limited access to core temperature. This work develops a high-fidelity electro-thermal model for large-format cells based on an equivalent circuit network that mirrors the physical assembly of tabs, welds, and electrode stacks. The model couples three-dimensional ohmic conduction in tabs, welds, and current collectors with node-level equivalent circuit models in the stack, and uses measurement-anchored parameters. The model is used to study thermally critical design factors for a 44 Ah pouch cell, including thermal management configurations, tab width, tab thickness, and tab welding. Simulation results indicate that among four active cooling options, two-sided stack surface cooling achieves the lowest temperatures and the best uniformity, lowering the average temperature by about 11 °C relative to natural convection and reducing the temperature standard deviation to 1.43 °C. It also decreases the core maximum temperature by more than 9 °C, whereas other configurations provide only 4 to 5 °C core reductions. Changes to tab geometry and welding have minor effects except under one-sided tab cooling. Full article

In the present work, three-dimensional (3D) simulations have been performed for the characterization of a rectangular column for a uniform gas distributor with µm-sized holes at a ratio of 5. The model is first validated with experimental data from the literature. Simulations are then performed for a gas distributor with identical pitch but two different hole sizes, namely 600 µm and 200 µm. Three superficial gas velocities, namely 0.002 m/s, 0.004 m/s, and 0.006 m/s, were used for each distributor type. The gas movement in the fluid is found to be a strong function of hole size. For a 600 µm hole size, the operating condition has minimal impact on gas plume movement and moves centrally in a fully aerated regime. However, for a hole size of 200 µm, for all superficial velocities, the gas plume movement is dynamic and partially aerated. The plume moves along the right wall initially and then follows vertically. These characteristics are different from the meandering plume in centrally located spargers. The liquid mixing in the bulk is a function of time. During the plume development flow, different shapes are observed. Based on the analogy with the shapes found in nature, these shapes have been termed as balloon, cap, jet or candle flame, bull horn, mushroom, tree shape, and disintegrated mushroom shapes. Quantitative insights have been obtained in the form of time-averaged radial profiles of both volume fractions and liquid axial velocities. A symmetric parabolic shape for a hole size of 600 µm and skewed asymmetric shapes for a 200 µm hole size for three different axial positions, namely 0.1, 0.25, and 0.4 m, are observed. Correlations for gas holdup and liquid velocity have been proposed for low superficial velocities, which are in good agreement with the CFD simulation data, with a deviation of 15–20%. The deviations are partly due to the use of the k-ε turbulent model. The correlations perform better than the correlations available in the reported literature for similar superficial gas velocities. Full article

Levees with double-layered foundations are characterized by a weakly permeable upper layer and a highly permeable sand layer beneath, which makes them susceptible to internal erosion, particularly backward erosion piping (BEP). Therefore, locating BEP channels before the failure of a levee is crucial for ensuring the safety of levee projects. In this study, a novel method is proposed for detecting BEP channels efficiently. This method involves applying the successive linear estimator (SLE) to fuse multipoint measured voltage to characterize the inner levee structure. Therefore, the BEP channels can be recognized from the details of the levee structure. This method is named three-dimensional geostatistical electrical resistivity tomography (3D GERT) in this study. To validate the performance of GERT, a custom-developed indoor sandbox device was used for physical BEP conductivity detection tests, and the results were analyzed via the SLE to assess the accuracy of channel engraving. The tests revealed that the surface sand was initially expelled from the piping exit, followed by the formation of a concentrated piping channel that extended upstream. The erosion depth at the piping exit was observed to be deeper than that of the main channel. This study demonstrated that 3D GERT, when the SLE was used as the inversion algorithm, detected BEP channels and achieved an internal erosion dimension deviation of less than 25.5% and a positional erosion dimension deviation within 16.5%. The accuracy of the SLE in mapping BEP channels improved with the use of a more comprehensive electrode distribution and an increased number of electrodes, thus yielding a more precise representation of the channel scale and pattern. The coefficient of determination (R²) between the acquired data and the simulated data generated by 3D GERT was greater than 0.85, demonstrating the capability of the simulated values to track and reproduce the variation trends observed in the acquired data. Thus, the SLE, when used as the inversion algorithm for 3D GERT, reliably represents BEP channels. Full article