Search Results (675)

Urban stormwater poses a threat to urban safety and development. The systematic spatial planning of low-impact development (LID) facilities is increasingly recognized as a sustainable approach to enhancing urban flood resilience. Previous studies have largely focused on empirically-based design approaches or have employed uncoupled computational methods, resulting in a lack of accuracy in flood simulation results. In this study, a novel framework was proposed. The Non-dominated Sorting Genetic Algorithm II was employed to perform multi-objective optimization of the spatial layout of LID facilities, and a coupled model of SWMM and TELEMAC was developed to simulate surface flooding based on the optimized schemes. Under three rainfall scenarios, three schemes on the Pareto Front—representing the lowest cost, the optimal compromise and the least overflow—were selected to investigate how scheme parameters and overflow are influenced by rainfall intensity and design preferences. The results indicate that scheme parameters and node overflow show greater variation under the influence of different design preferences than under different rainfall conditions. Taking the schemes selected in this study as examples, under the lowest-cost scheme, LID coverage was less than 10%, resulting in a limited reduction in node overflow; but when ‘minimum overflow’ was set as the design preference, node overflow was virtually eliminated, with peak water levels at flood-prone locations reduced to 0.05 m, 0.08 m and 0.10 m under 50-year, 100-year and 200-year storm conditions, respectively. The framework for urban flooding simulation and flood control scheme design proposed in this study is potentially applicable to comparable settings, subject to similar data availability and physical conditions, serving as a reference for enhancing urban resilience to flooding. Full article

Petroleum engineering projects face complex risk environments. Existing risk control systems often only provide overall risk safety thresholds. They lack effective quantitative interval estimation methods. Moreover, translating quantitative analysis findings into actionable on-site management instructions remains challenging. Symmetry serves as a core analytical perspective for cutting-edge research in control theory and system engineering. Feedforward and feedback controls are functionally complementary and sequentially cascaded, featuring intrinsic complementary symmetry. From the perspective of feedforward–feedback symmetry, this study constructs a GA-LLM hybrid framework combining genetic algorithm (GA) and large language models (LLMs) to address the above shortcomings. This framework is jointly composed of four collaboratively functioning modules, comprising the risk status input module, GA feedforward control module, retrieval-augmented generation (RAG) knowledge retrieval module, and the LLM feedback control strategy-generation module. In this framework, the GA serves as the feedforward controller, which computes the joint inscribed control box for each risk factor offline based on the risk relationship model established by the Back Propagation (BP) neural network. The RAG-enhanced LLM serves as the feedback controller, dynamically generating structured risk control instructions based on deviations. Case validation results demonstrate that the BP neural network achieved excellent performance with an R² of 0.99575 under leave-one-out cross-validation. The GA successfully solved the joint control box for the 14 risk factors, achieving a 100% joint constraint satisfaction rate for any combination within the box. The RAG retrieval module achieved a Recall@5 of 0.8933, MRR@5 of 0.7367, nDCG@5 of 0.7505, and Success@5 of 1.000. Ablation experiments show that the RAG-LLM scheme outperformed both the LLM without RAG scheme and the rule-based template scheme across four dimensions, with an inter-rater reliability ICC(2,1) of 0.719, reaching a moderate reliability level. This study integrates the quantitative optimization capability of GA with the semantic generation capability of LLM, enabling the transformation from the joint control box to executable management instructions. It not only provides a practical tool for petroleum engineering risk management but also offers new insights for the design of intelligent control systems from the perspective of feedforward–feedback symmetry. Full article

In the new-type power system, the traditional generation planning paradigm has shifted to a new paradigm of source–storage collaborative planning. However, source–storage coordinated planning is facing deep-seated structural challenges of unbalanced multi-objective coordination and insufficient adaptability to market mechanisms. This paper first designs a source–storage coordinated planning framework with a two-layer structure of planning decision-making and operation verification, which takes into account multiple low-carbon, economic, and security planning objectives, and considers the dual market driving of energy storage participating in active-power and reactive-power regulations. Secondly, a two-layer optimal planning model is constructed: the upper-layer aims at minimizing the investment cost of new source–storage and minimizing annual carbon emissions, while the lower-layer aims at minimizing the comprehensive operation cost and maximizing the revenue of the energy storage market. The feature of this model is that it can simultaneously consider the coupling effect of the active-power market and the reactive-power market. Thirdly, a two-layer closed-loop iterative solution method based on Non-dominated Sorting Genetic Algorithm II is adopted to generate the source–storage coordinated planning scheme. Finally, simulation calculations are performed on the modified New England 39-bus system. The results show that, when considering the market driving of energy storage in both active-power and reactive-power regulations, the installed capacity of new energy reaches 465 MW, which is 55% higher than that in the no-market scenario, while the renewable energy curtailment rate is only 1.7%. The correctness and effectiveness of the proposed two-layer source–storage coordinated planning method in this paper are verified. Full article

Correlated electronic phenomena in magic-angle twisted bilayer graphene have garnered widespread research interest in two-dimensional materials science. As a powerful multiscale framework bridging atomic-scale resolution and mesoscopic structural evolution, the structural phase field crystal method has been widely adopted for graphene system studies. In this work, we develop a self-consistent XPFC model specifically for twisted bilayer graphene (tBLG) simulations. By globally optimizing the core free-energy functional parameters via a genetic algorithm, the proposed model achieves a marked improvement in consistency between the equilibrium density field and the first-principles generalized stacking fault energy surface. We further introduce a self-consistent dynamic interlayer interaction potential to replace the conventional fixed-substrate approximation, which captures the bidirectional coupling and mutual relaxation between adjacent graphene layers in a self-consistent manner. We calibrate the precise magnitude of the interlayer potential using the widths of stacking domain boundaries between distinct stacking configurations as a key metric, with the results benchmarked against atomistic simulation data. When applied to the 1.1° magic-angle tBLG system, the model uncovers spontaneous structural relaxation driven by interlayer van der Waals interactions: low-energy AB–BA stacking domains expand significantly, while high-energy AA domains shrink correspondingly. Full article

This work presents a numerical framework for the electromagnetic modeling and inverse characterization of laser-hardened steels using 3MA (Micromagnetic Multiparameter Microstructure and Stress Analysis) non-destructive testing. The proposed methodology combines a simplified two-layer eddy current model, representing the hardened case and the softer core, with the Jiles–Atherton hysteresis model. The associated inverse problem is solved by means of a genetic algorithm, enabling the identification of depth-dependent local hysteresis parameters from measured 3MA incremental permeability signals. The Jiles–Atherton hysteresis parameters are first calibrated using bulk B-H loops. Subsequently, the coupled forward model is used to establish the relationship between these parameters and the measured incremental permeability response for different hardening depths. As a proof of concept, the framework is applied to laser-hardened specimens. The identified local hysteresis and permeability characteristics show clear correlations with both case depth and excitation conditions, demonstrating the potential of the proposed approach for physics-based, non-destructive characterization of laser-hardened layers. Full article

Photovoltaic (PV) pumping systems offer a sustainable alternative to diesel solutions, yet their nonlinearity, intermittent irradiation, and complex motor-pump dynamics challenge energy extraction and reliability. Currently, these systems predominantly rely on classical Maximum Power Point Tracking (MPPT) algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC), which suffer from slow convergence, steady-state oscillations, and an inability to track Global MPP (GMPP) under uniform irradiance variation conditions. Furthermore, existing studies typically address MPPT optimization and motor control in isolation, without considering their coupled interaction, and rarely incorporate economic viability assessments. To address these limitations, this paper proposes an innovative control architecture integrating four advanced metaheuristic MPPT techniques, namely the Genetic Algorithm (GA), Gray Wolf Optimizer (GWO), Cuckoo Search (CS) algorithm, and Horse Herd Optimization Algorithm (HOA), with Model Predictive Control (MPC) for a Brushless DC (BLDC) motor-driven pumping system, supplemented by battery storage. Comprehensive simulations were conducted under both constant and variable irradiance profiles (1000 to 500 to 1000 W/m²) to evaluate dynamic performance, tracking accuracy, and system robustness. The results demonstrate that HOA and GWO significantly outperform GA and CS, achieving superior DC bus voltage stability with ripple values below 2.4 V, faster convergence times, reduced electromagnetic torque oscillations, and enhanced MPPT efficiency exceeding 99%. Under variable irradiance, HOA exhibits the fastest stabilization with minimal overshoot and superior disturbance rejection, while GA suffers from severe oscillations and CS displays sawtooth ripple patterns. A techno-economic analysis further confirms the economic viability of the proposed system, with HOA and GWO strategies yielding lower lifecycle costs, extended converter lifespans from 5 to over 12 years, and improved return on investment compared to conventional approaches. This integrated framework offers a robust, efficient, and economically sustainable solution for autonomous PV water pumping applications. Full article

For coupling a transonic high-pressure turbine vane with a rotating detonation combustor, several integration approaches have been considered. Endwall diffusion in the vane row can facilitate coupling by enabling a higher turbine inlet Mach number operating range. Nonetheless, the introduction of diffusive axisymmetric endwalls may promote flow separation and enlarged secondary flows, leading to an overall reduction in turbine stage efficiency. To address this, the present study introduces a shape optimization framework based on computational fluid dynamics for designing diffusive non-axisymmetric endwalls in a transonic vane downstream of a rotating detonation combustor. The reference geometry is a transonic vane with diffusive axisymmetric endwalls, previously analyzed in numerical studies. Both hub and shroud endwalls are parameterized using 20 design variables, and a random sampling approach generates 1000 distinct geometrical configurations. Each design undergoes geometry generation, meshing, and steady Reynolds-averaged Navier–Stokes computation under transonic conditions using a three-dimensional commercial solver. Aerodynamic performances are assessed, and a genetic aggregation method is employed to construct a response surface. A gradient-based optimization algorithm identifies the optimal non-axisymmetric endwall configuration, which is then simulated. Comparative analysis shows that the optimized non-axisymmetric endwall significantly mitigates hub and shroud vortex effects, enhancing aerodynamic efficiency and supporting integration within turbine systems equipped with rotating detonation combustors. Full article

Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) are emerging as the technology of choice for next-generation power conversion systems, offering switching speeds, on-state resistance, and power density unattainable with silicon or even silicon carbide (SiC) devices. However, the fast switching transients that make GaN attractive also make it a demanding source of electromagnetic interference (EMI), so credible electromagnetic compatibility (EMC) analysis requires an accurate functional device model. This paper addresses the functional electrothermal modeling of a commercial 650 V GaN HEMT (GS66504B) as a prerequisite to EMC validation. The manufacturer-supplied Level 3 SPICE model is evaluated against experimental static (I-V) and dynamic (C-V) measurements. Significant discrepancies motivate an optimization methodology in which an initial manual procedure is superseded by a fully automated pipeline coupling LTspice with a Genetic Algorithm in MATLAB R2025b. A forward/reverse and dual-temperature-segment strategy reduces the mean absolute relative error to below 7% (forward I-V) and 13% (reverse I-V) over 25–100 °C, while a dedicated two-stage C–V optimization reduces the reverse-transfer capacitance error from 95.4% to 2.89%. The resulting compact, unified, and fully validated model underpins the ongoing EMC validation phase, where it will be combined with extracted parasitic and cable models in a DC-DC converter topology. Full article

This paper investigates the gear whine problem in a high-speed heavy-load reducer. A rigid-flexible coupled multibody dynamic model of the reducer is established using Romax. Transmission error, unit load, tooth root stress, and contact stress are used as optimization objectives. A comprehensive gear micro-modification method including lead crowning, lead slope, involute crowning, and involute slope is proposed, and a genetic algorithm is employed for optimization. The peak-to-peak transmission error [TE(p-p)] decreased from 0.27 μm to 0.19 μm for the first-stage gear pair and from 2.67 μm to 0.96 μm for the second-stage gear pair, corresponding to reductions of 29.63% and 64.04%, respectively. The maximum contact stresses decreased from 507 MPa to 488 MPa (3.75%) and from 627 MPa to 618 MPa (1.44%) for the first- and second-stage gear pairs, respectively. The radiated noise of the gearbox was reduced by about 10 dB on average. The proposed method provides a reference for the microgeometry design of high-speed heavy-load reducers. Full article

Low-slenderness-ratio, flat-ended autonomous underwater vehicles must balance hydrodynamic resistance against internal volume retention, yet classical slender-body criteria do not fully represent their coupled forebody wake response. A generalized Myring forebody was assessed for an AUV with L = 0.8 m, D = 0.2 m, and L/D = 4.0 using 53 steady three-dimensional Reynolds averaged Navier-Stokes simulations with the shear stress transport k-ω model. Gaussian process regression and the non-dominated sorting genetic algorithm II (NSGA-II) were used only for candidate-region screening; production grid direct CFD samples were used to determine nondominance. Strict fold-wise leave-one-out cross-validation gave ${\mathrm{Q}}^2$ = 0.173 globally and RMSE = 0.001399 and ${\mathrm{Q}}^2$ = 0.683 in the predefined 18-sample decision region, indicating local screening utility rather than global surrogate validation. The direct CFD audit identified 13 globally and seven locally nondominated samples; both previously selected test configurations were dominated after CFD back-substitution. Their three grid drag sequences were monotonic but non-asymptotic. Pressure drag comprised 75.88–79.20% of total drag. However, the reduction in the low-drag test configuration relative to the baseline arose mainly from a lower viscous contribution; axial pressure fields therefore indicate redistribution rather than exclusive drag-reduction causation. Paired CFD samples showed that the sign of the drag responded to N reversal between the two sampled ${\mathrm{L}}_{\mathrm{nose}}$ values, whereas analytical volume increased with N in both pairs. The results reveal a discrete, configuration-dependent drag volume trade-off and local N-${\mathrm{L}}_{\mathrm{nose}}$ coupling. The rectangular regions are sampling envelopes rather than validated optimum windows, and the conclusions are restricted to steady, deeply submerged, smooth-wall bare-hull conditions. Full article

An Adaptive-Parameter Maximum Correntropy Kalman Filter (APMCKF) algorithm is proposed to address state estimation degradation in semi-active suspensions caused by non-linear coupling between time-varying physical parameters and non-Gaussian noise. First, a time-varying dynamic model with non-linear damping is established via bench tests. A genetic algorithm (GA) globally optimizes key physical parameters to suppress model mismatch. Second, the APMCKF integrates an adaptive suspension parameter update mechanism. This closed-loop mechanism refreshes the system state matrix in real-time, effectively overcoming state-tracking lag. Concurrently, the maximum correntropy criterion (MCC) is embedded within the Sage–Husa recursive framework to dynamically reconstruct the observation noise covariance matrix, ensuring robust filtering under heavy-tailed noise. Simulations under ISO Class A–D random road profiles demonstrate that the APMCKF reduces the root-mean-square error (RMSE) by 62.33–81.24% compared to the adaptive Kalman filter (AKF). It also outperforms the adaptive-parameter Kalman filter (APKF), yielding a 27.49% accuracy improvement on Class D roads where non-Gaussian noise is most severe. Moreover, comparative evaluations against standard non-linear Bayesian filters demonstrate that the APMCKF successfully overcomes the truncation errors of the Extended Kalman Filter (EKF) and the tracking hysteresis of the Unscented Kalman Filter (UKF), reducing the average RMSE by up to 74.98% and 60.76%, respectively, under severe Class D non-Gaussian excitations. Furthermore, the algorithm exhibits excellent disturbance rejection under transient speed bump impacts and maintains stable error reduction across vehicle speeds of 10–25 m/s. Ultimately, the APMCKF delivers high-precision estimation and exceptional robust stability under variable speeds and non-Gaussian disturbances. Full article

To address the persistent challenges in optimizing the transmission performance of crank-rocker mechanisms—namely, the inaccuracies of local static evaluation models, the non-linear coupling constraints among multiple objectives, and the difficulties of navigating discontinuous and restricted solution spaces—this paper proposes a multi-objective collaborative design methodology grounded in kinematic and dynamic analysis. First, full-cycle mathematical models for transmission efficiency and transmission inertia are established, explicitly quantifying the impact of quick-return characteristics on inertial forces. Second, targeting the maximization of transmission efficiency alongside the minimization of transmission inertia and kinematic asymmetry, an adaptive multi-objective genetic algorithm is developed. Using a bearing life testing machine as the engineering baseline, virtual prototype simulations and multi-load physical bench tests are conducted to validate the proposed approach. Post-optimization results indicate that the full-cycle average transmission efficiency of the mechanism surges significantly from 73.6% to 91.96%, while the transmission inertial force is drastically curtailed by 72.28%. Concurrently, the advance-to-return time ratio, an indicator of kinematic asymmetry, is reduced to 1.0229. Additionally, the torque fluctuations at the output shaft are notably mitigated, and the overall operational noise level is reduced by 4 to 6 dB. This research provides a highly effective theoretical and engineering paradigm for achieving the globally collaborative optimum of planar mechanisms under complex physical constraints. Full article

As a driving mechanism, the electromagnetic repulsion mechanism has been widely used in mechanical switches, such as circuit breakers, current limiters, and bypass switches, owing to its high closing speed and large output force. The dynamic characteristics and structural safety of electromagnetic repulsion mechanisms are critical to the stable and reliable operation of mechanical switches. However, the dynamic characteristics of electromagnetic repulsion mechanisms are affected by multiple factors, including coil parameters, energy storage parameters, armature structural dimensions, and air gaps. Moreover, strong coupling exists among these design variables. Parameter optimization that focuses solely on operating speed or electromagnetic force may lead to local stress concentration and edge vibration of the armature, thereby compromising the operational reliability of the mechanism. To address the difficulty in synergistically optimizing dynamic characteristics and structural safety, this paper proposes a two-stage optimization method that combines the series armature equivalent method, genetic algorithm-based multi-objective optimization, structural shape optimization, and topology optimization. In the first stage, a series armature equivalent model is established, and design parameters are optimized by the genetic algorithm to obtain a parameter combination that satisfies the requirements for displacement, closing speed, and operating time. In the second stage, under the constraints of dynamic performance, armature shape optimization, topology optimization, and combined shape–topology optimization are separately conducted to reduce edge vibration and local stress concentration of the armature. The dynamic characteristics, edge vibration displacement, and stress under different optimization schemes are comparatively analyzed. The results show that the proposed two-stage optimization method can effectively improve the structural response of the armature while ensuring that the dynamic characteristics of the mechanism satisfy the design requirements. In particular, after the combined optimization, the edge vibration of the armature is reduced to 41.8% of that before optimization, and the local stress concentration is significantly alleviated. The proposed optimization framework realizes the coordination between parameter design and armature structural optimization of electromagnetic repulsion mechanisms, providing a reference for improving the dynamic characteristics and structural reliability of electromagnetic repulsion mechanisms. Full article

Temperature uniformity in thermostatic chambers is critical for material testing, biological incubation, and precision measurements, as even minor thermal gradients can compromise reliability. However, in chambers designed to avoid airflow disturbances, such as those used in semiconductor fabrication and optical experiments, forced convection and mechanical stirring are often impractical. Consequently, natural convection becomes the dominant heat transfer mechanism, introducing significant nonlinearity, large thermal inertia, and multivariable coupling among multiple heat sources. To address these issues, this study develops a multi-input–multi-output (MIMO) temperature control strategy for a rectangular chamber equipped with eight heating elements (grouped into four channels) and adjustable-angle baffles. The proposed method combines a multi-PID controller array with genetic algorithm (GA)-based parameter tuning using a transfer-function matrix model. Experiments demonstrate that baffle angles below 90° improve spatial uniformity, and the relative grouping of heaters outperforms adjacent grouping in both thermal inertia and correlation. Using GA-optimized PID parameters, the controller maintains steady-state error within ±0.5 °C and reduces settling time by approximately 140 s compared to conventional Ziegler–Nichols tuning. Validated through simulations and experiments, the proposed approach provides a reliable and cost-effective alternative to forced convection for airflow-sensitive applications, achieving superior uniformity and steady-state accuracy. Full article

Reliable nonlinear structural analysis requires hysteretic parameters that reproduce cyclic stiffness, strength, pinching, degradation, and energy dissipation. Conventional calibration is often tailored to one constitutive model and unit system, while repeated population searches become costly as dimensionality and parameter coupling increase. This study develops a unified physics-constrained deep reinforcement learning framework for OpenSees Steel02 and DowelType identification. Target responses and candidate parameters are expressed in dimensionless coordinates; bounded latent variables are decoded into admissible model parameters and mapped back to source units after calibration. A twin-delayed deep deterministic policy gradient (TD3) agent performs continuous search, with differential evolution providing local refinement when required. Validation used synthetic targets, random initial vectors, public steel records, and ten experimental hysteresis records from the authors’ research group; particle swarm optimization and a genetic algorithm served as benchmarks. The framework satisfied an NRMSE threshold of 0.02 in all 384 held-out Steel02 evaluations and achieved a mean NRMSE of 0.0166 on synthetic DowelType targets. On the ten experimental DowelType records, TD3+DE reached a mean NRMSE of 0.0654 and 30% success under the relaxed 0.05 threshold, giving accuracy comparable with tuned PSO at the same online OpenSees-call budget while retaining a reusable learned initialization step. One normalized workflow can rapidly obtain response-equivalent fits for distinct hysteretic laws and return solver-ready parameters in physical units. Full article