To address the dual requirements of dynamic topology optimization and static strength in the design of excavator working devices, this paper proposes a dynamic topology optimization method based on equivalent static loads. First, a rigid-flexible coupling dynamic model of the working device is constructed under combined excavation conditions to analyze the dynamic response of the arm in terms of stress and deformation. Then, local element stress constraints are converted into global constraints using the P-norm method. A topology optimization model is established with the minimization of the arm's maximum flexibility as the objective function, constrained by the P-norm stress and volume fraction. Finally, dynamic topology optimization of the arm is performed using the equivalent static loads method, and the topological structure of the arm is redesigned with consideration for manufacturability. Finite element simulation results demonstrate that the proposed method achieves a 24.63% reduction in the mass of the arm, while the maximum stress increases by only 5.26% and remains below the material's allowable limit, thereby fulfilling the design requirements.

The development of renewable energy in rural areas presents significant potential. Integrating renewable energy sources, such as wind power and photovoltaics, into microgrids as distributed generation systems offers a viable approach for local energy utilization. In recent years, the rapid advancement of agriculture, forestry, animal husbandry, and fisheries has led to an increasing demand for electricity in these regions. However, the existing power infrastructure remains underdeveloped, resulting in a pronounced imbalance between supply and demand. This paper investigates the optimization of rural microgrid configurations by incorporating demand response strategies and the synergistic interactions among wind turbines, photovoltaic systems, batteries, and loads. A multi-objective optimization model is developed to maximize annual profits and environmental externality (namely, the proposed microgrid achieves equivalent carbon dioxide emissions reductions by replacing thermal power generation through either selling green electricity to the main grid or meeting rural load demands), which is subsequently transformed into a single-objective formulation using the Shapley value method and solved via a global harmonic search algorithm. Simulation results validate the applicability of the proposed solution method and demonstrate the economic performance, development potential, and environmental benefits of the optimized microgrid configurations.

To enhance the accommodation capability and operational flexibility of renewable energy systems, address the insufficient architectural integration of existing ammonia-based energy systems, and overcome the limitations of current optimization algorithms in tackling complex nonlinear multi-objective problems, this paper proposes a synergistic integrated energy system with liquid ammonia as the central hub. The system integrates multi-energy flows encompassing electricity, hydrogen, ammonia, and heat, leveraging ammonia fuel cell power generation, ammonia cracking, and ammonia-blended gas turbines for both electricity and heat production. A bi-level optimization model is formulated, coupling upper-layer multi-objective capacity planning with lower-layer stochastic chance-constrained scheduling. To solve this model, a hybrid algorithm, designated as LSDBO-WOA, is developed by integrating an improved dung beetle optimizer (LSDBO) with the whale optimization algorithm (WOA). Case study results demonstrate that the proposed algorithm achieves markedly superior convergence performance compared to benchmark algorithms such as non-dominated sorting genetic algorithm II (NSGA-II), with an improvement of approximately 18.6% in comprehensive performance metrics. Furthermore, the proposed electricity-hydrogen-ammonia-heat system attains an overall energy efficiency exceeding 97.66% and reduces carbon emissions by 7.3% relative to the original system without ammonia integration.

Topology optimization and additive manufacturing (AM) have been widely applied to the lightweight design and fabrication of unmanned aerial vehicles (UAVs). However, existing topology optimization methods for UAVs typically assume isotropic materials, neglecting the anisotropy inherent in AM and the associated manufacturing precision constraints. This paper proposes a lightweight integrated method in MATLAB R2021a for the design and AM of UAV arms that simultaneously accounts for printing-induced anisotropy and minimum feature size constraints. A topology optimization model is proposed that uses nodal density and element printing angle as coupled design variables, and the corresponding sensitivity analysis is carried out. In the manufacturing phase, a contour-offset strategy is employed to generate printing paths for the optimized structures, achieving effective force transmission. The effects of manufacturing and optimization parameters on the design results are systematically investigated. The results show that, compared with the traditional optimization method, the compliance difference between the optimized structure obtained by the proposed method and the traditional method is only 0.46%. Furthermore, while ensuring manufacturability, printing efficiency is improved by approximately 69%. This approach establishes a unified design-to-manufacturing workflow, providing both a theoretical foundation and a practical pathway for the intelligent design and efficient fabrication of UAVs and other lightweight structural components.

To enhance the smoothness, quietness, and reliability of traction gear transmissions in high-speed electric multiple units operating under uncertain conditions, this study introduces an innovative arc cylindrical gear system along with a robust optimization strategy for gear modification. Center distance error and material elastic modulus are selected as key disturbance factors, and their uncertainty distributions are modeled using fuzzy interval theory and the optimal level-cut method. A robust optimization model is developed with the maximum profile modification amount, modification length, and arc tooth line radius as design variables to analyze the correlation between modification parameters and noise generation. The model aims to minimize radiated noise while satisfying noise reliability constraints, and the optimal robust modification scheme is obtained using a genetic algorithm. The results indicate that the optimized gear design achieves a 63.7% reduction in transmission error, a 48.4% decrease in maximum contact stress at the meshing-out position, and a 16.3% drop in radiated noise, while maintaining a robust reliability index R s of 98.02% across the full range of disturbance parameters. This study offers theoretical guidance and practical strategies for achieving robust and reliable design of traction gears in next-generation high-speed trains.

Abstract During the preliminary design of high-temperature superconducting generators (HTSGs), the structural complexity of HTS field windings is commonly reduced by adopting a traditional circular arc current sheet approximation. The calculated field distribution is subsequently integrated into 2D finite element method (FEM) simulations for further refinement and optimization. However, due to the strong field dependence of the current density in HTS tapes, large-scale field windings exhibit highly non-uniform current density distributions. As a result, conventional analytical models introduce significant errors in calculating the maximum flux density of HTS windings, increasing the time required for iterative fine-tuning of FEM models. Therefore, we propose an improved numerical magnetic field analysis model. This model simplifies the control equations of traditional numerical methods using a homogenization strategy, and employs a self-consistent formulation to derive the current density distribution within HTS field windings. Experiments demonstrate that the proposed model significantly enhances the efficiency and accuracy of magnetic field calculations for HTS windings. As an application of this model, we investigated the influence of different field windings geometries at the armature windings. We employed a genetic algorithm to optimize the magnetic field design, resulting in a more sinusoidal air-gap magnetic field and suppression of higher-order harmonics. Therefore, the proposed analytical model provides an effective tool for evaluating and optimizing the overall electromagnetic performance of large-scale HTSGs.

Abstract We consider a two-stage distributionally robust optimization (DRO) model with multimodal uncertainty, where both the mode probabilities and uncertainty distributions could be affected by the first-stage decisions. To address this setting, we propose a generic framework by introducing a $$\phi $$ ϕ -divergence based ambiguity set to characterize the decision-dependent mode probabilities and further consider both moment-based and Wasserstein distance-based ambiguity sets to characterize the uncertainty distribution under each mode. We identify two special $$\phi $$ ϕ -divergence examples (variation distance and $$\chi ^2$$ χ 2 -distance) and provide specific forms of decision dependence relationships under which we can derive tractable reformulations. Furthermore, we investigate the benefits of considering multimodality in a DRO model compared to a single-modal counterpart through an analytical analysis. Additionally, we develop a separation-based decomposition algorithm to solve the resulting multimodal decision-dependent DRO models with finite convergence and optimality guarantee under certain settings. We provide a detailed computational study over two example problem settings, the facility location problem and shipment planning problem with pricing, to illustrate our results, which demonstrate that omission of multimodality or decision-dependent uncertainties within DRO frameworks result in inadequately performing solutions with worse in-sample and out-of-sample performances under various settings. We further demonstrate the speed-ups obtained by the solution algorithm against the off-the-shelf solver over various instances.

This study addresses the challenges of coordinating spatio-temporal water allocation to optimize water productivity and reduce carbon emissions in water resource management, particularly the lack of high-resolution, integrated optimization frameworks capable of simultaneously tackling water scarcity and greenhouse gas (GHG) emissions. We propose a modeling approach for large-scale regional rice irrigation that explicitly represents the physical-process-based relationships among irrigation water, yield, and methane (CH4) emissions. Using GIS, a grid-based simulation domain was constructed at a 500 m × 500 m resolution, and the GIS-DSSAT and GIS-DNDC models were employed to simulate yield and CH4 emissions under varying irrigation amounts. The Random Forest algorithm—selected for its ability to capture complex nonlinear interactions—was used to establish the response surfaces linking irrigation water, yield, and CH4 emissions. A spatio-temporal irrigation optimization model was then developed to simultaneously reduce CH4 emissions and enhance water productivity. This methodology was applied to the Sanjiang Plain in Heilongjiang Province, where the NSGA-II algorithm was used to derive optimal irrigation schemes for rice cultivation across 408,264 grid cells. The results revealed quadratic nonlinear relationships between irrigation water amount, yield, and CH4 emissions. Compared to the conventional irrigation practice in the region, which typically involves 15–20 flood irrigation events per season, the optimized irrigation schedule comprised 7–14 events—with 12 events accounting for 42% of the cases—and an irrigation duration ranging from day 137 to 256. This led to a 10.3% reduction in total irrigation volume, a 9.6% decrease in CH4 emissions per unit yield, and a 21.8% increase in water productivity. This study provides valuable decision support for optimizing regional water allocation and developing rice cultivation strategies that improve productivity while reducing emissions.

To enhance the sustainable operation of electricity–hydrogen coupling multi-microgrids (EHCMMG), this study proposes a multi-objective dispatch optimization framework driven by electricity price prediction. Although EHCMMG plays a vital role in renewable energy integration and multi-energy synergy, three major sustainability-related research gaps remain: insufficient consideration of cross-regional, multi-market, and multi-stakeholder interests; inadequate electricity–hydrogen demand response mechanisms; and limited investigation of uncertainty modeling that balances economy and security. To address these issues, this study first designs an EHCMMG architecture that supports electric-hydrogen interactions both within and outside the cluster. An electricity price prediction-driven multi-objective dispatch optimization model oriented toward multiple stakeholders is then proposed. This model incorporates incentive-based electricity–hydrogen demand response and constraints on carbon emissions. Moreover, operational uncertainties arising from renewable energy generation are addressed through the coordinated integration of spinning reserve capacity constraint and chance-constrained programming. The results show that the cluster cost, the market integrated operator (MIO) net revenue, user energy cost, and total carbon emissions are CNY 17.502 million, CNY 12.684 million, CNY 5.556 million, and 8168.126 tons in baseline scenario, respectively. The proposed model effectively balances economic efficiency, operational reliability, and low-carbon performance, thereby enhancing the overall sustainability of the EHCMMG.

Abstract Choosing the cross-section of conductors is an essential task in planning distribution grids. The cross-sectional area of conductors significantly influences the investment cost as well as the energy loss of the electricity grid. However, previous studies have mainly used the maximum load value and the constant power load model to select the conductor cross-section. This paper presents an optimization framework that takes into consideration the influence of the load curve shape and the dependence of the load’s power consumption on voltage to determine the optimal wire cross-section. The proposed optimization framework aims at minimizing the total life-cycle expenditure of the power grid while complying with constraints such as load flow equations, node voltage limits, along with current limits on branches. The suggested optimization formulation is modeled as second-order cone programming with integer variables (MISOCP), guaranteeing a globally optimal solution using commercial optimizers such as GUROBI. The proposed MISOCP model is transformed from a nonlinearly constrained optimization model with integer variables (MINLP) by constructing a conic quadratic model of the load flow equations, an equivalent ZP formulation of voltage-dependent loads (ZIP load model), and a precise linearization process of the bilinear products involving binary and continuous variables. The suggested optimization framework is evaluated on the 16-node system and the real Vietnamese 54- and 102-node medium-voltage power grids. These systems utilize different time resolutions: the 16-node system is analyzed using a 24-time-period profile, while the Vietnamese grids are assessed using a day-night load profile with 96 time periods. The calculation results highlight the need to consider load curves and load power dependence on voltage when selecting conductor sizes for distribution grids. Ultimately, the proposed framework is positioned as a high-precision, strategic offline planning tool, prioritizing global optimality and investment accuracy over real-time computational speed.

High-performance difference patterns (DPs) are critical for compact and integrated microwave array systems, particularly in monopulse tracking and beam-scanning applications. However, the design of monopulse phased arrays with steep slopes, high directivity, low sidelobes, and symmetric main lobes remains challenging due to constraints imposed by the array aperture and radome structure. In this paper, a novel design method is proposed to maximize the DP directivities for monopulse linear and planar phased arrays composed of microstrip patch antennas. The DP synthesis problem is first formulated as a nonconvex optimization model for directivity maximization. By fixing the reference phase of the DP slope and applying a first-order Taylor expansion of the quadratic function, the original problem is decomposed into a sequence of convex subproblems that can be solved efficiently. The proposed method fully exploits the flexibility of the phased array feed network, enabling directivity enhancement without altering the geometric configuration of the monopulse array. Finally, three numerical examples employing a radome-enclosed linear array, a uniform planar array, and a radome-enclosed planar array are presented to demonstrate the effectiveness of the proposed method in achieving the monopulse array DP synthesis with high directivity and symmetric main lobes.

An identifiable cost-aware causal decision-making framework using counterfactual reasoning.

A correlation-based optimization model to recover lost and distorted data from scanning tunneling microscopy images based on density functional theory.

Surrogate reservoir models have emerged as efficient alternatives to approximate full-physics reservoir simulation while reducing computational costs. Even though existing studies have used synthetic models to test the feasibility of surrogate models, the application to actual reservoirs is limited. To employ the surrogate model in real-life history matching and field development optimization, key geological properties and frequent development operations need to be jointly considered. This study develops a full-scale surrogate reservoir model based on the Koopman neural operator (KNO) for actual three-dimensional (3D) oil reservoirs under waterflood operations. The model integrates static reservoir properties (permeability, net-to-gross ratio, and relative permeability) and dynamic development parameters (well placement and controls) as model inputs. A scientific sampling method that considers geological principles and monthly production operations ensures feasible and diverse training samples. The proposed 3D KNO architecture incorporates a learned grid layer to handle corner-point grids and leverages Fourier transforms to linearize nonlinear dynamics in high-dimensional space. After validating on a real oil field in China, the method demonstrates great capability in predicting pressure and saturation changes and oil production rates. By comparing the prediction performance with a baseline physics informed neural network model, the KNO model greatly outperforms the convolution neural network-based discrete mapping model. The prediction results by the KNO model align well with numerical simulations, which offers a robust and efficient tool for history matching and field development optimization.

Sequential Model-Based Optimization (SMBO) is a highly effective strategy for hyperparameter search in machine learning. It utilizes a surrogate model that fits previous trials and approximates the hyperparameter response surface (performance). This surrogate model primarily guides the decision-making process for selecting the next set of hyperparameters. Existing classic surrogates, such as Gaussian processes and random forests, focus solely on the current task of interest and cannot incorporate trials from historical tasks. This limitation hinders their efficacy in various applications. Inspired by the state-of-the-art convolutional neural process, this paper proposes a novel meta-learning-based surrogate model for efficient and effective hyperparameter optimization. Our surrogate is trained on the meta-knowledge from a range of historical tasks, enabling it to accurately predict the hyperparameter response surface even with a limited number of trials on a new task. We tested our approach on the hyperparameter selection problem for the well-known support vector machine (SVM), residual neural network (ResNet), and vision transformer (ViT) across hundreds of real-world classification datasets. The empirical results demonstrate its superiority over existing surrogate models, highlighting the effectiveness of meta-learning in hyperparameter optimization.

GNNRL-smoothing: A prior-free reinforcement learning model for mesh optimization.

Federated hyper LSTM model for storage optimization and collision prediction in an intelligent IoVT

Hyperspectral inversion of leaf nitrogen content in wheat by integrating CWT-SPA feature optimization and XGBoost-SSA model

High-performance steel wire/hybrid fiber reinforced polymer bars: Design method, performance optimization and tensile constitutive model

Intuitionistic fuzzy mathematical model for optimization of multi-objective cropland allocation problem in small scale irrigation system: A case-study in Sidama region, Ethiopia