Search Results (239)

Particle swarm optimization is a metaheuristic optimization algorithm that boasts advantages such as fast convergence speed, fewer tunable parameters, and a simple search mechanism. However, it suffers from premature convergence and insufficient later-stage exploitation, limiting its performance on multimodal and high-dimensional problems. In light of this, this paper proposes a Chaos-based Differential Mutation Particle Swarm Optimization (CDMPSO) algorithm to address these limitations. The algorithm employs four synergistic strategies: cubic chaotic mapping with inverse learning for population initialization; adaptive inertia weight to balance exploration and exploitation; convex lens imaging inverse learning to escape local optima; and random differential mutation to maintain population diversity. Ablation experiments validate the contribution of each strategy, with adaptive weight being the most significant. Comparative experiments demonstrate that CDMPSO achieves an average ranking of 1.00, outperforming standard PSO, CPSO (Constriction Particle Swarm Optimization), ACPSO (Adaptive Chaotic Particle Swarm Optimization), and HPSOALS (Hybrid Particle Swarm Optimization with Adaptive Learning Strategy). On the unimodal function f₁, it attains ultra-high precision of 7.07 × 10⁻²⁴⁸, and on the multimodal function f₉, it uniquely converges to the theoretical optimum of zero. The results demonstrate that CDMPSO possesses excellent convergence precision, global search capability, and robustness, providing an effective solution for complex engineering optimization problems. Full article

This paper presents an analysis of the exhaust gas concentration of a compression ignition engine powered by diesel fuel and rapeseed oil under dynamic conditions. The measurement cycle consisted of a 100 s segment of the WLTC cycle. An attempt was then made to estimate the exhaust gas concentration using predictive algorithms based on parameters recorded using the OBD-II diagnostic interface. The model was validated based on previously unobserved measurements of the measurement cycle, and the procedure was repeated several times with random parameter changes. Due to the dynamic nature of the combustion process (taking into account its non-linearity and inertia), a delayed feature design was used. A consistent time horizon of input information was selected for the tabular and sequential models used. The results obtained indicated that Gradient-Boosted Regression Trees class algorithms achieved the highest quality of fit and were characterised by the greatest stability. Full article

Push–pull exhaust systems are widely applied for controlling industry-processing fumes, and their performance is fundamentally governed by the coupling interaction among the air-curtain jet (“push”), the buoyant thermal plume generated by the heat source, and the converging flow induced by the exhaust hood (“pull”). However, the dynamic characteristics and design criteria of this coupled flow field under large temperature differences remain insufficiently explored. Here, a series of scaled experiments combined with numerical simulations is conducted to systematically investigate the coupling behavior of the air-curtain jet and the thermal plume, and two quantitative performance indicators, namely plume deflection height and flow rate along the plume deflection path, are proposed to evaluate flow control effectiveness and energy dissipation. An orthogonal experimental design is further employed to analyze the sensitivity of heat-source and air-curtain parameters with respect to these indicators. The results demonstrate that the air temperature reaches its maximum at approximately 0.8 m downstream of the air-curtain outlet, and that both the supply velocity and outlet width of the air curtain are dominant parameters exerting statistically significant influences on plume deflection height and flow rate along the path (p < 0.01). Furthermore, the Archimedes number effectively characterizes the competition between jet inertia and plume buoyancy in the coupled flow field, with its appropriate value preliminarily recommended to be controlled below 40. This study provides quantitative insights for the engineering design of push–pull exhaust systems operating under high thermal load conditions. Full article

The increasing integration of inverter-based distributed energy resources (DERs) significantly reduces power system inertia, posing critical challenges to transient stability. Traditional fault ride-through strategies, relying on passive and localized rules, often fail to provide effective coordinated support in low-inertia grids. To address these limitations, this paper proposes a Transient Dynamic Features Adaptation Distributed Cooperative Control (TDA-DCC) framework. This approach integrates a dynamic context-aware policy network based on multi-head attention mechanisms to extract temporal features from local observations, allowing agents to anticipate transient dynamics rather than merely reacting to instantaneous states. A multi-agent deep deterministic policy gradient algorithm is employed to optimize a global multi-dimensional objective function encompassing frequency, voltage, and rotor angle stability. Furthermore, to ensure engineering reliability, a hybrid execution architecture is introduced, which embeds a deterministic safety monitor to switch between the intelligent policy and a robust backup controller during extreme anomalies. Case studies on a modified IEEE 39-bus system demonstrate that the proposed method significantly enhances transient stability margins and robustness against sensor failures compared to conventional baselines. Full article

This study investigates mechanism-based modeling and simulation of a single-shaft heavy-duty industrial gas turbine. Taking the PG9171E gas turbine as the case study, component-level steady-state and dynamic models are developed. The steady-state model is established using the constant mass flow (CMF) method. For dynamic modeling, both the CMF approach and the inter-component volume (ICV) approach are implemented to enable a comparative assessment of the two methods. On the basis of the steady-state model, an improved Dung Beetle Optimization (DBO) algorithm is proposed to perform model correction using measured operational data from the gas turbine. After model correction, the maximum relative error between the simulated results and the measured operating data is reduced to 1.01 × 10⁻⁵%. Following high-accuracy model correction, sensitivity analysis and a comparative dynamic study are conducted for the two dynamic modeling approaches. The results indicate that the most influential sensitivity parameter is the rotor rotational inertia, followed by the virtual volume of the combustor. Moreover, the primary discrepancy between the ICV and CMF approaches arises from differences in the operating trajectories on component characteristic maps. The ICV-based model exhibits a pronounced response lag; however, it requires less computational time than the CMF-based model, making it more suitable for rapid engineering simulation and practical applications. Full article

This study proposed an innovative assembled blast-resistant composite structure integrating ultra-high performance concrete plates and ceramic foam layers, designed to enhance blast protection for a power valve hall hole blocking system. Based on the full-scale blast test and numerical simulation, the dynamic response of the structure under blast load was revealed. The parametric studies showed that when the thickness of the UHPC ribbed plate was increased from 30 mm to 40 mm, the maximum displacement at the edge of the hole was reduced by 60.9%. However, a further increase in thickness to 50 mm led to an increase in the inertia effect due to the high stiffness, resulting in a reduction in the maximum displacement value by only 8.61%. In addition, a machine learning framework combining generative adversarial networks (GANs) and Extremely Randomized Trees (ERT) model was constructed to predict the maximum displacement of the structure under blast loading. Furthermore, interpretability analysis by the (SHapley Additive exPlanations) SHAP algorithm verified the consistency of the decision logic of the ERT model with the physical mechanism of the explosion. This study established a full-chain design framework of structural design, mechanism research and intelligent prediction, which provided theoretical support and an intelligent tool system for protection engineering. Full article

Shipyards are transitioning toward Industry 4.0 more slowly than other industrial sectors, and this inertia often limits the adoption of reliable digital workflows for reverse engineering. Within the wider research aimed at supporting the digital transition of shipbuilding operations, this study presents a dedicated methodology for evaluating 3D scan quality by combining three complementary indicators describing geometric completeness, agreement with a reference model, and measurement accuracy and variability. A purpose-designed test sample representative of shipbuilding geometrical challenges was manufactured in metal by CNC methods and in PLA through additive manufacturing. Two scanning systems, a field-oriented portable device and a metrology-oriented fixed system, were evaluated under raw surface conditions and with tracking enhancement strategies (optical markers and scanning spray). Results show that reflective surfaces represent a critical scenario, where tracking enhancement is essential to obtain continuous reconstruction and reliable dimensional correspondence. Conversely, with low-reflectivity surfaces, high-quality reconstructions can also be achieved with portable systems, with tracking enhancements mainly improving uniformity and repeatability. Overall, the proposed workflow provides a quantitative basis to support scanner selection, which involves a compromise between portability and achievable metrological performance, for shipyards reverse engineering applications. Full article

Short-term photovoltaic (PV) power forecasting is crucial for secure and economical grid operation, yet remains challenging under fast and nonstationary irradiance fluctuations. This paper presents a plant-level TA–SH–LSTM framework that integrates temporal attention into an LSTM encoder to highlight informative subsegments for improved ramp tracking and peak localization and applies budget-aware Successive Halving to jointly tune window length and key hyperparameters under a fixed training budget. To enhance PV-engineering interpretability, we establish a first-order thermal inertia surrogate that explicitly links module temperature to ambient temperature and irradiance, and evaluate robustness across irradiance-tercile regimes within the observation window. Experiments on two real PV plants from the Kaggle Solar Power Generation dataset demonstrate consistent gains over a baseline LSTM and an SH-tuned LSTM. On Plant 1, MAE/RMSE decreases from 1141.1/2066.6 kW to 223.4/424.6 kW and $R^2$ increases from 0.932 to 0.997. Without retraining, the model transfers to Plant 2 with 286.1 kW MAE, 477.1 kW RMSE, and $R^2$ = 0.993, confirming strong cross-site generalization and practical utility under varying operating conditions. Full article

Phase change materials (PCMs) have emerged as a key enabler of high-performance, low-carbon buildings through latent heat-based thermal energy storage. This paper presents a systematic and critical synthesis of advances in PCM technologies for building applications published between 2022 and 2025, analyzing over 300 peer-reviewed studies to evaluate thermal performance, economic viability, environmental impact, and climate adaptability across three integration approaches: passive, active, and hybrid systems. The studies analyzed show that passive envelope integration employing macroencapsulated or form-stable PCMs in walls, roofs, and glazing is reported to deliver 15–45% energy savings with payback periods of 8–15 years, primarily through enhanced thermal inertia and indoor temperature stabilization. Active systems, which couple PCMs with HVAC, heat pumps, or air handling units, are found to achieve 20–40% energy reductions and shorter payback periods (3–8 years) by enabling load shifting, peak shaving, and improved coefficient of performance (COP). Hybrid configurations integrating passive and active strategies with AI-driven control demonstrate, in the literature, the highest potential, with reported energy savings of up to 50%, though they entail greater complexity and capital cost. The review further highlights material-level innovations, including ternary composite PCMs, bio-based alternatives, and nano-enhanced formulations that address intrinsic limitations such as low thermal conductivity (0.1–0.3 W/m·K for organics) and cycling instability. Despite significant progress, critical gaps persist in standardized testing protocols, long-term field validation, comprehensive lifecycle assessments, and real-world scalability, particularly in tropical and cold climates. By bridging material science, building physics, and energy system engineering, this work provides a forward-looking roadmap to accelerate the deployment of PCM-based solutions in the global decarbonization of the built environment. Full article

Analytical models for energy dissipation and temperature rise in a silicone oil torsional damper are developed to investigate its thermal characteristics. An experimental test platform is established to measure the surface temperature rise in the damper, and experimental results are used to validate the proposed analytical model. Good agreement between the analytical predictions and experimental measurements demonstrates the reliability of the analytical model. The results show that the maximum temperature rise in the damper is influenced not only by the engine speed but also by the output torque. Increasing the thickness of the silicone oil layer leads to higher total energy dissipation and temperature rise, whereas increasing the kinematic viscosity of the silicone oil reduces both energy dissipation and temperature rise. In addition, decreasing either the width or the radius of the inertia ring effectively lowers the surface temperature rise in the damper. Under steady-state operating conditions, the surface temperature can be considered as the temperature of the silicone oil. It is also found that the peak temperature rise is exhibited at 2300 rpm (revolutions per minute) for the silicon oil damper used in the paper. These findings provide theoretical insight into the thermal characteristics of silicone oil dampers and offer useful guidance for designing structures with lower temperature rise. Full article

As robotic manipulators evolve toward lightweight and long-link structures, flexibility increasingly affects dynamic response and trajectory tracking accuracy. However, existing studies often lack a consistent coupling mechanism between finite element structural models and control models, and flexible effects are typically treated as disturbances, limiting the direct use of structural parameters for control prediction and optimization. This paper proposes a structure–control collaborative co-simulation framework for a six-degree-of-freedom (6-DOF) flexible-joint manipulator. ANSYS-based finite element analysis (FEA) is integrated with the MATLAB/Simulink control environment to extract joint-level equivalent stiffness, inertia, modal frequencies, and damping parameters, which are embedded into a rigid–flexible coupled dynamic model. A regression-based representation is introduced to capture unmodeled flexible residual dynamics, and a regression-compensated adaptive PID torque controller with σ-modification and a dead-zone mechanism is developed to ensure bounded adaptation and closed-loop stability. Simulation results under no-load and payload conditions demonstrate improved oscillation suppression and tracking accuracy. By establishing a unified coupling mechanism from structural parameters to the control model, the proposed method achieves consistent co-modeling of the structural and control domains and provides an engineering-feasible co-simulation approach for dynamic prediction and control optimization of multi-DOF flexible manipulators under varying operating conditions. Full article

Accurate identification of the rotational moment of inertia of a top drive system is essential for dynamic modeling, control design, and performance optimization in drilling operations. However, the strong coupling between the drive motor, transmission components, and drill string makes direct inertia measurement challenging under field conditions. To address this issue, this study proposes a moment of inertia identification method based on dynamic response analysis of the top drive system. A simplified torsional dynamic model is established by representing the top drive and drill string assembly as an equivalent lumped inertia system. By applying controlled torque excitation under no-load conditions, the system’s angular velocity response is measured and analyzed in both time and frequency domains. The relationship between applied torque and angular acceleration is utilized to identify the equivalent rotational inertia through parameter estimation. Experimental results indicate that low-frequency excitation provides more favorable conditions for reliable and accurate inertia identification, yielding improved stability and reduced estimation error compared with higher-frequency inputs. In addition, frequency response characteristics are investigated to validate the consistency and robustness of the identified inertia across different excitation frequencies. Experimental results obtained from a top drive test rig demonstrate that the proposed method can reliably estimate the equivalent moment of inertia with good repeatability under controlled experimental conditions. The identified inertia shows good agreement with theoretical calculations and exhibits stable behavior over a wide frequency range. The proposed approach avoids the need for additional sensors or structural modifications and is well suited for practical engineering applications. This study provides an effective and experimentally validated method for inertia identification of top drive systems, offering valuable support for dynamic modeling, control parameter tuning, and vibration analysis in drilling engineering. Full article

With the increasing integration of regional energy systems, the dynamic coupling characteristics of cooling, heating, and power flows have become significantly pronounced. However, traditional scheduling models often utilize steady-state assumptions that neglect the thermal transmission delay of the pipeline network, leading to spatiotemporal mismatches between energy supply and load demand. To address this issue, this paper proposes an optimal operation strategy for regional Combined Cooling, Heating, and Power (CCHP) systems that explicitly integrates thermal inertia. First, a Pipeline Fluid Micro-element Discretization Method (PFMDM) is developed based on the Lagrangian specification to accurately characterize the dynamic flow and thermal decay processes without the heavy computational burden of partial differential equations. In addition, the accuracy of PFMDM is directly benchmarked against a high-fidelity transient PDE solver (finite-volume TVD–MUSCL scheme) over a wide range of pipe lengths, flow velocities, and thermal loss coefficients, where the outlet-temperature RMSE remains below 0.2 °C. This model quantitatively reveals the “Virtual Energy Storage” (VES) mechanism of the pipeline network. Second, to overcome the “curse of dimensionality” in dynamic scheduling, a Load-Gradient-Based Adaptive Temporal Discretization (LG-ATD) method is proposed. This method maintains a fine-grained baseline for electrical settlement while dynamically aggregating thermal/cooling steps based on load fluctuations. Simulation results demonstrate that the proposed strategy corrects the significant physical deviations of the traditional steady-state model. The analysis reveals that the steady-state model underestimates the required heating and cooling supply capacities by up to 26.66% and 39.15%, respectively, due to the neglect of transmission losses and delays. By leveraging the VES mechanism, the proposed method enables a fuel-shift in the energy-supply structure, substantially decreasing the electricity purchasing cost (by 75.2% in the tested case). This reduction reflects a reallocation from grid purchases to on-site gas-fired cogeneration to maintain physical feasibility under delay and loss effects, and therefore, it is accompanied by an increase in natural gas consumption and a higher total operating cost. Furthermore, the LG-ATD method significantly alleviates the computational burden by substantially compressing the presolved model size and reducing the overall solving time by more than 80%, thereby effectively mitigating the curse of dimensionality for practical engineering applications. Full article

Aiming at the challenge of simultaneously optimizing ride comfort and wheel grounding performance for mining dump trucks under severe road conditions, this paper proposes a hydro-pneumatic suspension parameter design method based on an improved multi-objective particle swarm optimization (IMOPSO) algorithm. First, a dynamic model of the hydro-pneumatic suspension is established, incorporating the coupled nonlinear characteristics of the valve system and the gas chamber. The accuracy of the model is verified through bench tests. Subsequently, the influence of key parameters, including the damping orifice diameter, check valve seat hole diameter, and initial gas charging height, on the vertical dynamic performance of the vehicle, is systematically analyzed. On this basis, a multi-objective optimization model is constructed with the objective of minimizing the root mean square (RMS) values of both the sprung mass acceleration and the dynamic tire load. To enhance the global search capability and convergence performance of the MOPSO algorithm, adaptive inertia weighting, dynamic flight parameter update, and an enhanced mutation strategy are introduced. Simulation results demonstrate that the optimized suspension achieves significant improvements under various road conditions. On class-C roads, the RMS values of the sprung mass acceleration (SMA) and the dynamic tire load (DTL) are reduced by 37.6% and 15.8%, respectively, while the suspension rattle space (SRS) decreases by 10.2%. Under transient bump roads, the peak-to-peak (Pk-Pk) values of the same two indicators drop by 38.9% and 44.9%, respectively. Furthermore, compared to the NSGA-II algorithm, the proposed method demonstrates superior performance in terms of convergence stability and overall performance balance. These results indicate that the proposed design effectively balances ride comfort, wheel grounding performance, and driving safety. This study provides a theoretical foundation and an engineering-feasible method for the performance balancing and parameter co-design of suspension systems in heavy-duty engineering vehicles. Full article

Gravity currents constrained by bottom walls are prevalent in engineering applications such as industrial discharges and deep-sea mining, and will pose significant environmental risks. In this study, the influence of jet source parameters on the dynamics and diffusion characteristics of particle-driven bottom currents was investigated through physical experiments using Digital Image Processing (DIP). This non-invasive technology is cost-effective and exhibits broad applicability. The results demonstrated that the downstream plume front $d_{\mathrm{L}\mathrm{m}\mathrm{a}\mathrm{x}}$, the maximum lift height $h_{\mathrm{L}\mathrm{m}\mathrm{a}\mathrm{x}}$ and the average lift height $h_{\mathrm{a}\mathrm{v}\mathrm{e}}$ all exhibit a decreasing trend with increasing Richardson number (Ri) after impingement, and show a linear increase with rising Reynolds number (Re). The plume diffusion scale S follows a two-stage evolution: during the inertia-dominated stage, S evolves exponentially over time t as $S=a{e}^{bt}$, while in the equilibrium stage of negative buoyancy and turbulent dissipation, S follows a power-law relationship $S=a{t}^b$ (b < 1). The rate of change of S increases with smaller jet angles α, and the variations with dimensionless bottom clearance H/D remain within 10%. The dimensionless average longitudinal expansion rate ${\displaystyle \frac{\overline{E}}{\sqrt{g\prime /D}}}$ reaches minimum values at α = 75°, peaks at H/D = 10, and exhibits a linear decreasing trend with Ri. As Re increases, ${\displaystyle \frac{\overline{E}}{\sqrt{g\prime /D}}}$ displays a three-stage fluctuating behavior. This study provides valuable experimental data that improve the understanding of gravity current behavior under wall confinement and support the predictive modelling of gravity current. Full article