Short-fiber thermoplastic (SFT) composites are increasingly employed in lightweight aerospace and automotive structures owing to their favorable strength-to-weight ratio, high production rates, and recyclability. Unlike continuous-fiber systems, the mechanical response of SFTs is governed by mesoscale interactions among fiber orientation, spatial clustering, and manufacturing-induced porosity. These features exhibit significant spatial variability in manufactured components and influence stiffness, damage initiation, and nonlinear deformation. Although mesoscale finite element (FE) models can resolve such heterogeneity, their application to realistic three-dimensional microstructures remains computationally intractable. A data-driven surrogate framework is proposed to predict the mechanical behavior of additively manufactured, compression-molded (AM-CM) SFTs. Microstructures reconstructed from micro-computed tomography data were discretized into Voronoi-based cells representing distinct fiber-interaction neighborhoods. Each cell was homogenized via nonlinear FE simulations incorporating matrix damage, and the resulting stress-strain responses trained a hybrid Graph Neural Network-Long Short-Term Memory (GNN-LSTM) architecture encoding microstructural topology and history-dependent mechanical evolution. The surrogate accurately predicts stiffness and stress-strain behavior of unseen microstructures, achieving R^2≈ 0.98 relative to high-fidelity FE simulations with over two orders-of-magnitude reduction in computational cost. Coupling the framework with experimentally calibrated damage laws demonstrates that fiber orientation, clustering, and porosity collectively govern local effective stiffness. The approach provides a physics-informed, data-efficient pathway to identify mechanically weak microstructural cells and accelerate digital-twin development for SFT components.
Bio-oil obtained from biomass pyrolysis needs further hydrodeoxygenation (HDO) for its suitability in fuel applications. However, catalysts studied to date suffer from coke formation and low yields. In this study, Cu-based catalysts, prepared using the wet impregnation method, were characterized, and their performance in the twostep HDO (mild stabilization followed by severe treatment in a batch reactor) was investigated. HDO in the presence of a Cu/Al2O3 catalyst achieved a maximum 50.5 wt.% treated oil yield and 67.6% carbon conversion compared to Co-Mo/Al2O3 and Co/Al2O3 catalysts. The measured coke formation was less than 3% for the Cubased catalyst, which was attributed to controlled deoxygenation and hydrogenation. The high heating value of the treated bio-oil ranged from 31.4 to 34.6 MJ kg-1. Additionally, gas chromatography-mass spectrometry showed that the treated bio-oils were rich in alkyl phenols and hydrocarbons, with the concentration ranging from 4.6 to 55.3 and 15.9 to 32.0 wt.% of total, respectively. Direct deoxygenation, hydrogenation, and demethoxylation were the significant reactions leading to the formation of hydrocarbons. Based on the findings, Cu-based catalysts can be used for HDO of pyrolysis bio-oil to obtain valuable chemicals and fuels.
Abstract This study employs field-scale experiments and the storm water management model (SWMM) to analyze and simulate the hydrologic performance of two different infiltration swales: ALDOT infiltration swale (ALIS) and the modified infiltration swale (MIS), with a 0.3 m (12 in.) topsoil layer and a 0.15 m (6 in.) amended topsoil mix containing 20% pine bark fines, respectively, along with sand and gravel storage layers with different thicknesses. Two 1.5 m (5 ft) deep, same-size swales with 1% longitudinal slope were constructed using the two different engineered media designs. A total of 83 simulated infiltration tests were conducted over a 1-year period to validate and compare the infiltration performance of both swales under different operating conditions. The mean values of the average infiltration rates were 3.53 cm / h ( 1.39 in. / h ) for ALIS and 14.53 cm / h ( 5.72 in. / h ) for MIS, which is more than four times larger than ALIS, where topsoil limits it. The MIS consistently and statistically ( p = 0.003 ) drained faster and exhibited higher infiltration rates than ALIS, primarily due to its optimized media composition and improved vertical infiltration capacity. Both ALIS’s and MIS’s average infiltration rates increased by approximately 60% when the dry period was extended from 1 to 3 days, resulting in 15%–17% lower soil moisture. The SWMM bioretention module was used as an interpretive modeling tool for ALIS and MIS, which reproduced the average measured drawdown times well (root-mean-squared error of 0.19 h for 12 sets of experiments—52 tests) by calibrating soil saturated hydraulic conductivity and effectively simulated the swale’s surface infiltration, soil percolation dynamics, and subsurface storage behavior under different antecedent moisture and dry-period conditions.
The development of next-generation indirect slow pyrolysis rotary kilns represents a significant advancement in thermal conversion technologies, particularly in the processing of biomass waste. This study leverages advanced heat transfer strategies including an onion-shaped double-shell pyrolysis configuration and a multizone heating approach to enhance the thermal efficiency and operational performance of next-generation indirect slow pyrolysis systems based on rotary kiln technology. Utilizing an Eulerian-based Computational Fluid Dynamics (CFD) model, detailed 3D numerical simulations are performed to analyze the thermal conversion of woody biomass in various indirect slow pyrolysis plant configurations. Simulation results are validated against lab-scale pyrolysis rotary kiln data, confirming the effectiveness of the computational study for predicting the performance of theoretical design configurations. The proposed double-shell multizone design with mechanical flights demonstrates significant energy-saving potential, achieving 84% reduction in energy consumption compared to a single-shell, single-zone rotary kiln, and a 54% reduction compared to a single-shell, multi-zone pyrolysis system. The CFD model demonstrated high predictive accuracy for residence time (98.3%), average internal temperature profile (96.36%), and biochar fixed carbon ratio (83.95%). The double-shell configuration with internal mechanical flights enables more efficient thermal conversion, achieving a 70% reduction in residence time compared to the single-shell rotary kiln. Despite the effectiveness of the model in predicting thermal performance, the implementation of multistep reaction mechanisms is required to improve the predictive accuracy of biochar, syngas, and tar yields.
This study investigates employees' perceptions of artificial intelligence (AI) in the workplace, using data from 1,224 working adults across two samples. Drawing from an extended version of the Technology Acceptance Model, we examine how employees' trust in AI and their perceptions of AI's usefulness and ease-of-use at work shape their affective attitudes toward using AI, which in turn influence their intentions to adopt AI in their job. Perceived usefulness and trust in AI predicted employees' intentions to adopt it at work via affective attitudes toward using AI. The findings for perceived ease-of-use were inconsistent, suggesting potential workplace-specific implications of this pathway. None of the relationships differed by gender, education, or leadership status. The findings bridge the technology adoption and organizational science literature to offer theoretical insights, practical implications, and future research directions for facilitating employees' intentions to adopt AI at work.
