Search Results (1,049)

Lumped plasticity models available in commercial software offer a limited resolution of damage distribution along structural members. This study presents an open-source workflow that combines force-based fiber elements in OpenSeesPy with automated 3D post-processing for visualizing distributed plasticity in reinforced concrete piles. A 60 cm diameter pile subjected to monotonic lateral loading is analyzed using both SAP2000’s default plastic hinges and OpenSeesPy fiber sections (Concrete02/Steel02). Although the fiber model incurs a runtime approximately 2.5 times greater, it captures the gradual spread of yielding and deterioration with improved fidelity. The presented workflow includes Python routines for interactive stress–strain visualization, facilitating the identification of critical sections and verification of strain limits. This approach offers a computationally feasible alternative for performance-based analysis with enhanced insight into member-level behavior. Because the entire workflow—from model definition through post-processing—is fully scripted in Python, any change to geometry, materials, or loading can be re-run in seconds, dramatically reducing the time taken to execute sensitivity analyses. Full article

Inverter-equipped heat pumps allow for increased energy efficiency. However, air conditioning (AC) systems often operate at low load ratios below where inverter control is effective, which reduces their energy efficiency. We developed an AC system that increases the apparent load ratio of the heat pump by using a phase-change material (PCM). Cooling and heating experiments were conducted with a PCM heat exchanger, which comprised aluminum plates and fins filled with paraffinic PCM. The result indicated a high heat transfer coefficient of >70 W/(m²·K). A simplified numerical model of the PCM heat exchanger as a lumped constant system was created based on the experiment. The calculations generally reproduced the experimental results, with root mean squared errors of 0.39 K for cooling and 0.84 K for heating, confirming their accuracy. Simulations were then conducted to evaluate the energy performance of the proposed system for the cooling season. While low load operation accounted for 39% of the total AC time for a non-PCM system, it was reduced to 2.7% for the proposed system. The proposed system demonstrated load ratios of 50–60% for most of the season, achieving an energy reduction of 11.4% owing to the improved efficiency at partial load ratios. Full article

This paper presents a numerical parametric study of a dynamic lumped-parameter model of a hoisting mechanism driven by a DC electric motor. The analysis focuses on two operating scenarios: hoisting with an initially tight rope and hoisting with an initially slack rope. The model considers the inertial, elastic, and damping characteristics of the mechanical system, as well as the motor’s dynamic behavior. Systematic simulations are used to evaluate the influence of key design parameters on the rope’s dynamic factor. Sensitivity analysis is carried out by varying each parameter within a ±20% range, and Monte Carlo simulations are employed to compute Pearson correlation coefficients and perform multiple linear regression. The results indicate that the slack rope scenario produces significantly higher peak dynamic loads than those observed in the tight rope scenario, emphasizing its importance for structural sizing and safety. The findings enhance our understanding of parameter influence and support more robust hoisting system design in transient conditions. Full article

Rotating machinery is essential in various industrial fields, and growing demands for high performance under harsh operating conditions have heightened interest in fault diagnosis and prognostic technologies. However, a major challenge in fault diagnosis research lies in the scarcity of data, primarily due to the inability to deliberately introduce faults into machines during actual operation. In this study, a physical model is proposed to realistically simulate the system behavior of a ship’s turbo-rotating machinery by coupling the torsional and lateral vibrations of the rotor. While previous studies employed simplified single-shaft models, the proposed model adopted gear mesh interactions to reflect the coupling behavior between shafts. Furthermore, the time-domain response of the system is analyzed through state-space transformation. The proposed model was applied to simulate imbalance and gear teeth damage conditions that may occur in marine turbo-rotating systems and the results were compared with those under normal operating conditions. The analysis confirmed that the model effectively reproduces fault-induced dynamic characteristics. By enabling rapid implementation of various fault conditions and efficient data acquisition data, the proposed model is expected to contribute to enhancing the reliability of fault diagnosis and prognostic research. Full article

Burnable poisoning and fuel enrichment zoning are two techniques often combined in order to optimize the fuel assembly behavior during the burnup cycle. In the present work, these two techniques will be applied to the 2D optimization of the fuel assembly conceptual design for the supercritical water-cooled reactor developed in the framework of the Joint European Canadian Chinese development of Small Modular Reactor Technology project, funded within the Euratom Research and Training programme 2019–2020. The initial configuration of the fuel assembly does not include any burnable absorbers and uses a homogeneous fuel enrichment of 7.5% in ²³⁵U. The infinite multiplication factor, $k_{\infty }$, starts from approximately 1.32 and drops, almost linearly, to 1.0 after a burnup of 40.0 MWd·kg⁻¹. The uniform enrichment is, however, responsible for a pin-power peaking factor that with fresh fuel starts from 1.32 and reduces to 1.08 at the end of the burnup cycle. A simplified analytical model is developed to assess the effect of different lumped burnable absorbers on the time dependence of the assembly $k_{\infty }$. It is shown that using an adequate number of B₄C rods, positioned in the outer wall of the fuel assembly, together with a suitable distribution of six different ²³⁵U enrichments, it allows for obtaining an assembly $k_{\infty }$ factor that starts from 1.11 at the beginning of the cycle and remains quite constant over a large fraction of the burnup cycle. Moreover, the pin-power peaking factor is reduced to 1.03 at the beginning of the cycle and remains almost unchanged until the end of the burnup cycle. Full article

This work presents a one-dimensional (1D) model for simulating the behavior of an FCC riser reactor processing bio-oil. The FCC riser is modeled as a plug-flow reactor, where the bio-oil feed undergoes vaporization followed by catalytic cracking reactions. The bio-oil droplets are represented using a Lagrangian framework, which accounts for their movement and evaporation within the gas-solid flow field, enabling the assessment of droplet size impact on reactor performance. The cracking reactions are modeled using a four-lumped kinetic scheme, representing the conversion of bio-oil into gasoline, kerosene, gas, and coke. The resulting set of ordinary differential equations is solved using a stiff, second- to third-order solver. The simulation results are validated against experimental data from a full-scale FCC unit, demonstrating good agreement in terms of product yields. The findings indicate that heat exchange by radiation is negligible and that the Buchanan correlation best represents the heat transfer between the droplets and the catalyst particles/gas phase. Another significant observation is that droplet size, across a wide range, does not significantly affect conversion rates due to the bio-oil’s high vaporization heat. The proposed reduced-order model provides valuable insights into optimizing FCC riser reactors for bio-oil processing while avoiding the high computational costs of 3D CFD simulations. The model can be applied across multiple applications, provided the chemical reaction mechanism is known. Compared to full models such as CFD, this approach can reduce computational costs by thousands of computing hours. Full article

This study quantifies the uncertainty in monthly streamflow predictions under future climate scenarios in two periods (near and far future) for the Riu Mannu di Narcao basin in Sardinia, Italy. The sources of uncertainty include the hydrological model structure, model parameters, and variability in climatic inputs derived from global and regional climate models (GCM-RCM coupling) and representative concentration pathways (RCPs). Three conceptual and lumped hydrological models (GR3M, ABCD, and IHACRES) were combined with four climate models and two RCPs (RCP 4.5 and RCP 8.5) to assess future streamflow. Monte Carlo simulations were performed to evaluate parameter uncertainty, and the analysis of variance (ANOVA) method was applied to quantify the different sources of uncertainty. The results reveal that, as a single source, GCM-RCM coupling is the largest contributor, accounting for 47.32% (54.64%) of total near (far) future monthly streamflow projection uncertainties, followed by the hydrological model structure at 16.02% (21.09%), RCP scenarios at 15.35% (8.54%), and parameter uncertainty at 0.79% (1.39%). A consistent decline in median monthly streamflow is projected, especially during winter months (December to February), raising a concern about water availability in the region. Our study quantified different sources of uncertainty in monthly streamflow predictions under climate change, disentangling the roles of the hydrological model, model parameters, climate model, and climate scenario for reliable future streamflow projections. Full article

This study presents a self-powered smart wrist brace integrated with a piezoelectric sensor for real-time biomechanical monitoring during weightlifting activities. The system was designed to quantify wrist flexion across multiple loading conditions (0 kg, 0.5 kg, and 1.0 kg), leveraging mechanical strain-induced voltage generation to capture angular displacement. A flexible PVDF film was embedded within a custom-fitted wrist brace and tested on male and female participants performing controlled wrist flexion. The resulting voltage signals were analyzed to extract root-mean-square (RMS) outputs, calibration curves, and sensitivity metrics. To interpret the experimental results analytically, a lumped-parameter cantilever beam model was developed, linking wrist flexion angles to piezoelectric voltage output based on mechanical deformation theory. The model assumed a linear relationship between wrist angle and induced strain, enabling theoretical voltage prediction through simplified material and geometric parameters. Model-predicted voltage responses were compared with experimental measurements, demonstrating a good agreement and validating the mechanical-electrical coupling approach. Experimental results revealed consistent voltage increases with both wrist angle and applied load, and regression analysis demonstrated strong linear or mildly nonlinear fits with high $R^2$ values (up to 0.994) across all conditions. Furthermore, surface plots and strain sensitivity analyses highlighted the system’s responsiveness to simultaneous angular and loading changes. These findings validate the smart wrist brace as a reliable, low-power biomechanical monitoring tool, with promising applications in injury prevention, rehabilitation, and real-time athletic performance feedback. Full article

Fully mechanized mining involves high energy consumption, particularly during cutting and transportation. Scraper conveyors, crucial for coal transport, face energy efficiency challenges due to the lack of accurate dynamic coal flow models, which restricts precise energy estimation and optimization. This study constructs dynamic coal flow and scraper conveyor energy efficiency models to analyze the impact of multiple variables on energy consumption and lump coal rate. A dynamic coal flow model is developed through theoretical derivation and EDEM simulations, validated for parameter settings, boundary conditions, and numerical methods. The multi-objective optimization model for energy consumption is solved using the NSGA-II-ARSBX algorithm, yielding a 33.7% reduction in energy consumption, while the lump coal area is reduced by 27.7%, indicating a trade-off between energy efficiency and coal fragmentation. The analysis shows that increasing traction speed while decreasing scraper chain and drum speeds effectively lowers energy consumption. Conversely, simultaneously increasing both chain and drum speeds helps to maintain lump coal size. The final optimization scheme demonstrates this balance—achieving improved energy efficiency at the cost of increased coal fragmentation. Additional results reveal that decreasing traction speed while increasing chain and drum speeds results in higher energy consumption, while increasing traction speed and reducing chain/drum speeds minimizes energy use but may negatively affect lump coal integrity. Full article

This paper presents a high-order model-based robust control strategy for a dual-motor road wheel actuating system in a steer-by-wire (SbW) architecture. The system consists of a belt-driven and a pinion-driven motor collaboratively actuating the road wheels through mechanically coupled dynamics. To accurately capture the interaction between actuators, structural compliance, and road disturbances, a four-degree-of-freedom (4DOF) lumped-parameter model is developed. Leveraging this high-order dynamic model, a composite control framework is proposed, integrating feedforward model inversion, pole-zero feedback compensation, and a disturbance observer (DOB) to ensure precise trajectory tracking and disturbance rejection. High-fidelity co-simulations in MATLAB/Simulink and Siemens Amesim validate the effectiveness of the proposed control under various steering scenarios, including step and sine-sweep inputs. Compared to conventional low-order control methods, the proposed approach significantly reduces tracking error and demonstrates enhanced robustness and disturbance attenuation. These results highlight the critical role of high-order modeling in the precision control of dual-motor SbW systems and suggest its applicability in real-time, safety-critical vehicle steering applications. Full article

In integrated permanent magnet synchronous motors (IPMSMs) coupled with mechanical devices such as ball screws and reducers, complex nonlinear friction characteristics often arise, leading to asymmetrical distortions such as position “flat-top” and speed “ramp-up”. These phenomena significantly degrade the system’s positioning accuracy. To address this issue, this paper introduces a symmetry-inspired nonlinear predictive speed control approach based on the Stribeck piecewise linearized friction compensation and a generalized proportional integral (GPI) observer. The proposed method leverages the inherent symmetry in the Stribeck friction model to describe the nonlinear behavior, employing online piecewise linearization via the least squares method. A GPI observer was designed to estimate the lumped disturbance, including time-varying components in the speed dynamics, friction model deviations, and external loads. By incorporating these estimates, a nonlinear predictive controller was developed, employing a quadratic cost function to derive the optimal control law. The experimental results demonstrate that, compared to traditional integral NPC and PI controllers, the proposed method effectively restores system symmetry by eliminating the “flat-top” and “ramp-up” distortions while maintaining computational efficiency. Full article

This study investigates the dynamic interaction between railway vehicles and tracks, focusing on the effects of elastic–viscous properties of spring suspensions and track inertia. This research examines vertical oscillations of a railway car moving on a non-uniformly elastic track, modeled as a system with lumped parameters. Analytical and numerical methods are employed to derive track parameters by comparing frequency characteristics of continuous and discrete models. Key findings reveal that adjacent wheelsets influence interaction forces and bending moments by approximately 10%, while rail deflections are affected by up to 20% within the speed range of 60–180 km/h and for disturbances up to 20 Hz. Experimental validation using a roller test rig confirms the theoretical predictions, demonstrating the significance of track inertia and damping in dynamic analyses. This study provides practical recommendations for improving railway vehicle design and track maintenance, emphasizing the need to account for nonlinearities and inertial effects in high-speed scenarios. Full article

A straightforward and versatile numerical approach is proposed for the nonlinear analysis of single and double-curvature masonry structures. The method is designed to broaden accessibility to both experienced and less specialized users. Masonry units are discretized with elastic quadrilateral elements, while mortar joints are modeled with a combination of elastic orthotropic plate elements or shear panels and elastic perfectly brittle trusses (cutoff bars). This method employs the simplest inelastic finite element available in any commercial software to lump nonlinearities exclusively within the mortar joints. It effectively captures the failure of curved structures under Mode 1 deformation, reproducing the typical collapse mechanism of unreinforced arches and vaults via flexural plastic hinges. The proposed method is benchmarked through three case studies drawn from the literature, each supported by experimental data and numerical results of varying complexity. A comprehensive evaluation of the global force–displacement curves, along with the analysis of the thrust line and the evolution of nonlinearities within the model, demonstrates the effectiveness, reliability, and simplicity of the approach proposed. By bridging the gap between advanced simulation and practical application, the approach provides a robust tool suitable for a wide range of users. This study contributes to a deeper understanding of the behavior of unreinforced curved masonry structures and lays a base for future advancements in the analysis and conservation of historical heritage. Full article

We propose a novel dynamic gradient descent (DGD) framework integrated with reinforcement learning (RL) for AI-enhanced indoor environmental simulation, addressing the limitations of static optimization in dynamic settings. The proposed method combines a hybrid optimizer—stochastic gradient descent with momentum and adaptive learning rates—with an RL-driven meta-controller to dynamically adjust hyperparameters in response to real-time environmental fluctuations. The core innovation lies in the time-varying optimization landscape, where a Transformer-based policy network modulates the learning process based on a reward signal that balances prediction accuracy and parameter stability. Furthermore, the system employs a multilayer perceptron predictor trained on computational fluid dynamics-augmented data to model nonlinear thermal–airflow interactions, replacing conventional lumped-parameter models. The integration of these components enables autonomous adaptation to short-term disturbances (e.g., occupancy changes) and long-term drifts (e.g., seasonal variations) without manual recalibration. Experiments demonstrate that the framework significantly improves simulation accuracy and control efficiency compared to existing methods. The contributions include a unified adaptive optimization-RL architecture, a closed-loop hyperparameter control mechanism, and scalable implementation on GPU-accelerated hardware. This work advances the state-of-the-art in intelligent building systems by enabling self-tuning simulations for real-world dynamic environments. Full article

The propulsion system is a critical component of medium–low-speed maglev trains and the single-sided linear induction motor (SLIM) has been adopted to generate thrust. However, the SLIM operates periodically in maglev trains. The temperature of the secondary plate of the SLIM rises significantly due to eddy currents when the train enters and leaves the station, where large slip occurs. Subsequently, the temperature decreases through natural cooling during the shift interval time. This periodic operating condition is rarely addressed in the existing literature and warrants attention, as the temperature accumulates over successive periods, potentially resulting in thermal damage and thrust variation. Furthermore, the conductivity of plate varies significantly in the process, which affects the losses and thrust, requiring a coupled analysis. To investigate the temperature variation patterns, this paper proposes a coupled model integrating the lumped parameter thermal network (LPTN) and the equivalent circuit (EC) of the SLIM. Given the unique structure of the F-shaped rail, the LPTN mesh is well designed to account for the skin effect. Three experiments and a finite element method (FEM)-based analysis were conducted to validate the proposed model. Finally, optimizations were performed with respect to different shift interval time, plate materials, and carriage numbers. The impact of temperature on thrust is also discussed. The results indicate that the minimum shift interval time and maximum carriage number are 70.7 s and 9, respectively, with thrust increasing by 22.0% and 22.0%. Furthermore, the use of copper as the plate material can reduce the maximum temperature by 22.01% while decreasing propulsion thrust by 26.1%. Full article