"Spacecraft encounter" refers to a close flyby or fast proximity between two or more spacecraft in space. It involves the approach of one spacecraft to another, whether for observation, communication, captur...
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"Spacecraft encounter" refers to a close flyby or fast proximity between two or more spacecraft in space. It involves the approach of one spacecraft to another, whether for observation, communication, capture, or interception purposes. This paper presents a novel approach to planning orbital encounter missions for spacecraft within a specified time constraint. The proposed method takes advantage of a unique encounter process where the vehicle performs only two in-plane impulses and matches the target location at the encounter position using multirevolution phasing. To ensure the minimum maneuvering fuel consumption from the selection of the encounter position and analyze the optimality of the proposed encounter maneuver, the determination method of the minimal orbital intersection distance is first given by constructing constraint equations in polynomial form. Then, the algorithm framework for determining the optimal encounter maneuvers is established. This scheme allows us to derive the optimal maneuver position, direction, and impulse magnitude, thereby creating a detailed two-layer solution framework for an arbitrary target. Concurrently, the strategy accommodates orbital phase deviations by proffering a rapid correction method that factors in the perturbation environment. To expand this optimal encounter maneuver planning method to multitarget missions, we develop a reduced-order allocation procedure for optimal pairing between vehicles and targets. Through the use of numerical simulations exemplified by one single-target and two multitarget missions, it is evident that the proposed method is both highly efficient and effective for encountering any given target.
The reversible pump-turbine plays an important role in hydropower stations, but pressure pulsation during their operation affects their performance and lifespan. Accurate prediction of pressure pulsation signals can p...
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The reversible pump-turbine plays an important role in hydropower stations, but pressure pulsation during their operation affects their performance and lifespan. Accurate prediction of pressure pulsation signals can provide an important basis for energy planning and stable operation of pumped storage units, thereby promoting sustainable development of the environment. This study introduces an optimization method that combines long short-term memory (LSTM) and variable mode decomposition (VMD) to enhance the prediction accuracy of pressure pulsation signals. First, by decomposing the pressure pulsation signal into multiple relatively stable subsequence components using VMD, the characteristics of the original signal become more distinct. Subsequently, individual LSTM-based time series prediction models were constructed for each modal function, and the hyperparameters related to subsequence were optimized using the sparrow search algorithm. To validate the efficacy of the proposed approach, this paper conducted experiments using pressure pulsation signals of a pump-turbine obtained through numerical simulation. The experimental data was divided into training and testing sets, with the former used to train the LSTM model and the latter used for validation. The experimental results show that the optimized VMD with an optimized LSTM method can effectively improve the prediction accuracy of pressure pulsation signals in reversible pump-turbine. A dual optimization prediction model based on a sparrow search algorithm, variable mode decomposition, and long short-term memory is proposed, and the results are compared with traditional prediction models. The pressure pulsation signals at different monitoring points of the pump-turbine are predicted, and the prediction accuracy and error of different prediction models are analyzed, verifying the superiority of the proposed prediction model in this ***
The imposition of carbon taxes on goods imported by the European Union can considerably affect economic development. Computer numerical control (CNC) machine tools are indispensable in manufacturing, and reducing thei...
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The imposition of carbon taxes on goods imported by the European Union can considerably affect economic development. Computer numerical control (CNC) machine tools are indispensable in manufacturing, and reducing their environmental impact is crucial. Most machining pollution is produced during the operation of these tools. In this study, the carbon emissions of machine tools during energy consumption (EC) and machining processes were examined. Next, EC models were curve-fit using the least-squares method, and the leave-one-out method was employed to enhance the fairness and stability of the training process. In this manner, second-order power consumption models were obtained. On the basis of these models, this study conducted multiobjective optimization for effectively reducing carbon emissions, minimizing processing times, and maximizing surface quality. Three optimization algorithms-particle swarm optimization (PSO), the genetic algorithm (GA), and gray wolf optimization (GWO)-were used for the multiobjective optimization, and the GWO algorithm was found to yield the best results. Implementation of the GWO-optimized parameters in an actual machining process resulted in reductions of 54.4%, 16.3%, and 14.7% in carbon emissions, processing time, and surface roughness, respectively. Thus, the method proposed in this article can achieve efficient green manufacturing without sacrificing machining quality, thereby contributing to sustainable machining operations.
The optimization of aircraft is a typical multidisciplinary and multi-objective problem. To solve this problem, the difficulty lies not only in the high cost of discipline performance evaluation but also in the comple...
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The optimization of aircraft is a typical multidisciplinary and multi-objective problem. To solve this problem, the difficulty lies not only in the high cost of discipline performance evaluation but also in the complex coupling relationship between different disciplines. To improve the optimization efficiency, a new optimization method is proposed, including two new algorithms: conditional generative adversarial nets with vector similarity (VS-CGAN) and distributed single-step deep reinforcement learning with transfer learning (TL-DSDRL). For low-cost disciplines, VS-CGAN learns the relationship between variables and objectives through presampling to compress the variable domains. The cosine function is used to evaluate the similarity between the random noise and generated variables to avoid mode collapse. For high-cost disciplines, TL-DSDRL improves optimization efficiency through pretraining. The newly designed reward function and multi-agent cooperation mechanism enhance the multi-objective search ability of reinforcement learning.
In this study, we present a novel strategy for dynamically optimizing polynomial multigrid cycles to accelerate convergence within the dual-time-stepping formulation of the artificial compressibility method. To accomp...
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In this study, we present a novel strategy for dynamically optimizing polynomial multigrid cycles to accelerate convergence within the dual-time-stepping formulation of the artificial compressibility method. To accomplish this, a Gaussian process model is developed using Bayesian optimization to efficiently sample possible cycles to minimize run-time. To allow the use of conventional optimization methods, we developed fractional smoothing steps, moving the optimization from a discrete space to a continuous space. Initially, a static, offline, approach was developed, and optimal cycles were found for two flow past cylinder test cases with Re=200 and Re=500;however, when exchanging optimal cycles between the different test cases, there was significant degradation in speedup. Toward this, a dynamic, online, approach was developed where cycles are optimized during a simulation. The performance of the resulting optimal cycles gave a similar speedup to the offline approach while achieving a net reduction in run-time. Again testing the optimization strategy on the flow past a cylinder, this yielded candidates with mean speedups of similar to 3.0x and similar to 2.1x, respectively. Finally, testing online optimization on a turbulent flow past a cylinder at Re=3900 resulted in an overall speedup of similar to 1.9x.
Climate change and urbanization contribute to the increased frequency of short-duration intense rainstorms. Traditional solutions often involve multiple scenarios for cost-effectiveness comparison, neglecting the rati...
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Climate change and urbanization contribute to the increased frequency of short-duration intense rainstorms. Traditional solutions often involve multiple scenarios for cost-effectiveness comparison, neglecting the rationality of placement conditions. The effective coupling and coordination of the location, number, size, and cost of storage tanks are crucial to addressing this issue. A three-phase approach is proposed to enhance the dynamic link between drainage pipeline and storage tanks in urban high-density built-up areas, integrating Python language, SWMM, the Elitist Non-Dominated Sorting Genetic algorithm (NSGA-III), and the Analytic Hierarchy Process (AHP) methods. In the first stage, each node within the pipeline network is considered as a potential storage tank location. In the second stage, factors such as the length and diameter of the upstream connecting pipeline, as well as the suitability of the storage tank location, are assessed. In the third stage, the length and diameter of the downstream connecting pipeline node are evaluated. The results show that the 90 overflow nodes (overflow time >0.5h) have been cleared using the three-phase approach with a 50a (duration = 3h) return period as the rainfall scenario, which meets the flooding limitations. After the completion of the three-phase method configuration, the total overflow and SS loads were reduced by 96.45% and 49.30%, respectively, compared to the status quo conditions. These two indicators have decreased by 48.16 and 9.05%, respectively, compared to the first phase (the traditional method of only replacing all overflow nodes with storage tanks). The proposed framework enables decision-makers to evaluate the acceptability and reliability of the optimal management plan, taking into account their preferences and uncertainties.
Based on Kirchhoff Law about arbitrary sinusoidal steady-state circuit network, optimization principle of dynamic design variables is adopted. Making real parts and imaginary parts of sub-circuit current and node pote...
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ISBN:
(纸本)9781467374439
Based on Kirchhoff Law about arbitrary sinusoidal steady-state circuit network, optimization principle of dynamic design variables is adopted. Making real parts and imaginary parts of sub-circuit current and node potential as design variables, and equilibrium relation between node potential and sub-circuit current as frame-objective function, dynamic design variables optimization algorithm analysis of arbitrary complicated sinusoidal steady-state circuit network is proposed. Universal program computing sub-circuit current and node potential is completed. Practical examples are computed. Effectiveness and feasibility is verified. A new clue is set up for computing complicated alternating-current circuit network rapidly and precisely.
This paper presents a novel gradient-based optimization algorithm for improving the accuracy of experimentally estimated modal parameters with the assistance of finite element models. Initially, we recast the discrete...
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This paper presents a novel gradient-based optimization algorithm for improving the accuracy of experimentally estimated modal parameters with the assistance of finite element models. Initially, we recast the discrete vibration response equation into a matrix form and formulate the parameter estimation problem in modal analysis as an optimization problem. Then the problem is solved with a gradient-based iterative algorithm, which explicitly exhibits the closed form of gradients used in optimization. Initial values for this iteration are parameters derived from finite element models, since every important engineering structure should be analyzed with a finite element model before it is constructed. Subsequently, the performance of this algorithm is validated by both pure numerical experiments, which simulate the physical world, and experiments using real measurement data gathered by sensors in the real physical world. The algorithm's performance is further enhanced by incorporating gradient clipping and an adaptive iteration threshold. As a comparison, a discussion on classical least-squares time-domain method for the problem is provided. For practical applications, the Shi-Tomasi corner detection and Lucas-Kanade optical flow methods are deployed to detect corner points from videos taken during the vibration of a structure and track the motion of these points in the videos.
A high-mass Mars entry, descent, and landing (EDL) mission for cargo delivery or human exploration faces the challenge of a high propellant mass fraction requirement for powered descent. This work develops a novel met...
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A high-mass Mars entry, descent, and landing (EDL) mission for cargo delivery or human exploration faces the challenge of a high propellant mass fraction requirement for powered descent. This work develops a novel method and the associated algorithm that utilize existing entry and propellant-optimal powered descent guidance algorithms for fast and robust optimization of the end-to-end EDL trajectory for achieving the overall optimized propellant efficiency. A bilevel optimization formulation aided by a predictive logic based on the optimal powered descent algorithm to determine the near-optimal transition from entry to powered descent allows the end-to-end trajectory to be optimized in a relatively simple manner. No new major software or algorithms are required other than the existing guidance algorithms. A solution to the bilevel optimization problem is shown to exist, and the convergence of the bilevel optimization algorithm is guaranteed under certain mild assumptions. The algorithm developed in this paper is able to find consistently an end-to-end near-optimal EDL trajectory in just over 10 s on a desktop computer, while general-purpose modern trajectory optimization software can take thousands of seconds. The effectiveness and robustness of the algorithm are demonstrated by successfully optimizing thousands of complete EDL trajectories efficiently and reliably from dispersed initial entry conditions.
Computational fluid dynamics simulations were utilized to investigate the steam methane reforming process with the aim to improve its efficiency. Key parameters examined for their impact on process performance include...
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Computational fluid dynamics simulations were utilized to investigate the steam methane reforming process with the aim to improve its efficiency. Key parameters examined for their impact on process performance included surface heat flux (73-108 kW/m2), tube length (1-16 m), steam-to-carbon ratio (1.4-4), and flow rate (0.22-0.38 kg/s). To analyze the simultaneous effects of these variables while reducing computational costs, Deep Neural Networks (DNN) were employed. An optimized DNN was designed to achieve acceptable performance, featuring an input layer with four neurons that represent reformer length, flow rate, heat flux, and steamto-carbon ratio. The network includes four hidden layers with 32, 16, 8, and 8 neurons respectively, and concludes with an output layer comprising seven neurons for residual methane, water vapor, produced hydrogen, carbon dioxide, carbon monoxide, wall temperature, and gas outlet temperature. The results indicated that the proposed model achieved high accuracy, exceeding 99%, in predicting both training and test data. Following the DNN modeling, an optimization algorithm based on the random search method was developed. This algorithm searches a wide range of parameters to identify the optimal conditions for simultaneously maximizing hydrogen production and minimizing reformer length.
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