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Optimizing of selective catalytic reduction urea injection and NOx conversion analysis of diesel engine based on higher-order physical model and sequential quadratic programming algorithm

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ENERGY 2024年 313卷

作者： Liu, Wenlong Gao, Ying You, Yuelin Jiang, Changwen Hua, Taoyi Xia, Bocong Jilin Univ State Key Lab Automot Simulat & Control Changchun 130025 Peoples R China Jilin Univ Coll Automot Engn Changchun 130025 Jilin Peoples R China

In order to maximize the NOx conversion rate and minimize NH3 slip in the diesel selective catalytic reduction (SCR) system, first, the high-order SCR model was simplified and solved using the trapezoidal method and second-order backward difference formula, while the chemical reaction parameters were optimized using the Gauss-Newton method. The results of the engine bench test show that the mean absolute errors of NH3 and NOx concentrations under steady-state operating conditions were 2.67 ppm and 1.72 ppm, respectively, and 5.46 ppm and 42.50 ppm under severe operating conditions. Second, urea injection rate is optimized based on a high-order model combined with the sequential quadratic programming (SQP) algorithm. The change in NOx conversion rate at different temperatures and space velocities is analyzed, and the results show that the NOx conversion is best at 650 K and 29,000 h-1. Third, the experiment validation show that the optimal NH3/NOx ratio is 0.69 at 554 K and 54,200 h-1;0.8;and 1.37 at 746 K and 116,700 h-1. Finally, this paper simulates and analyzes the SCR emission characteristics at different temperatures and space velocities when the upstream NOx concentration is 1000 ppm.

关键词： Diesel engine Selective catalytic reduction NOx conversion rate sequential quadratic programming algorithm Upstream NH 3 /NOx

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A novel interval linear programming based on probabilistic dominance

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FUZZY SETS AND SYSTEMS 2022年 434卷 20-47页

作者： Qiu, Zhiping Xia, Haijun Beihang Univ Sch Aeronaut Sci & Engn Inst Solid Mech Beijing 100083 Peoples R China

This study investigates a novel interval linear programming based on probabilistic dominance. Firstly, the definition of interval linear programming is briefly reviewed. Then a new interval linear programming model is presented based on probabilistic domi-nance. The probabilistic dominance index treats the intervals as uniformly distributed variables and the interval inequality relation is further defined by probability. To deal with non-linearity in probabilistic dominance index, sequential quadratic programming is used to solve the problem and the performance measure approach is proposed to overcome the convergence difficulties. The deter-mination and sensitivity analysis of the target performance measure are discussed to assess the sequential quadratic programming algorithm. Meanwhile, the extension of the proposed method to fuzzy interval linear programming is discussed. Furthermore, the proposed method is applied to the design of the plane truss structure with interval parameters. Finally, the effectiveness and ratio-nality of the developed method are demonstrated by two mathematical examples and one interval parametric plane truss structure optimization example.(c) 2021 Elsevier B.V. All rights reserved.

关键词： Interval linear programming Probabilistic dominance sequential quadratic programming algorithm Performance measure approach Interval parametric plane truss structure optimization

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A Nonlinear Dynamic Optimization algorithm of a Novel Energy Efficient Pneumatic Drive System

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2022年第5期20卷 1593-1604页

作者： Du, Hongwang Xiong, Wei Liu, Wei Dalian Maritime Univ Ship Electromech Equipment Inst Dalian 116026 Peoples R China

A standard pneumatic system is usually controlled by a 5/2 directional control valve in which compressed-air utilization rate is low. To solve the problem, scholars have proposed a bridge pneumatic circuit controlled by four switch valves that uses expansion energy of compressed air to do work. The key to this bridge circuit lies in the accuracy and speed of on-off sequence of the valves. To realize this goal, we study dynamic optimization control of the circuit and establish an optimal valve opening and closing time-sequence control model with a continuous-discrete time differential algebraic equation on the base of the dynamic characteristics of the pneumatic drive system. We use the direct method in modern optimal control theory to solve the problem. Because the model has the characteristics of more equality constraints, lower freedom of variables, and a sparse structure, we use the simplified space sequential quadratic programming algorithm and interior-point methods for computation and analysis so that the process optimization and partial-differential equations are solved simultaneously. To improve the precision and the speed of solving the derivative during the solution-optimization process, the derivative information is obtained with a combination of symbol and numerical automatic differentiation. By analyzing the efficiency of the two algorithms, the interior-point method was proved to be more suitable for use in obtaining the sequence. Finally, the experimental results were realized and showed that our contribution builds a good foundation for the energy-saving research of pneumatic systems.

关键词： Dynamic optimization interior-point method pneumatic bridge pneumatic circuit sequential quadratic programming algorithm

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Truss Structure Optimization Design Based on FE-PSO-SQP algorithm 3rd

Truss Structure Optimization Design Based on FE-PSO-SQP Algo...

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International Workshop on New Approaches for Multidimensional Signal Processing (NAMSP)

作者： Xu, Zhiqiang Cui, Yunxian Li, Baoliang Dalian Jiaotong Univ Sch Mech Engn Dalian 116028 Liaoning Peoples R China Eastern Liaoning Univ Sch Chem & Mech Engn Dandong 118001 Liaoning Peoples R China

ISBN: (纸本)9789811978449;9789811978425;9789811978418

Compared with other structural optimization design algorithms, particle swarm optimization (PSO) gains many superiorities, like being easy to understand the principle and fewer parameters in the calculation model. When we use the PSO to deal with truss structure optimization problems, this algorithm usually has low computational accuracy, slow rates of convergence, and poor population varieties in the further model calculation. To overcome these shortcomings and better solve the truss structure optimization problem, FE-PSO-SQP algorithm, a new structure optimization method, is proposed herein by combining the PSO algorithm with the sequential quadratic programming (SQP) algorithm and finite element method (FE). In addition, a set of calculation program is developed by ANSYS software. When the self-made program is used to conduct simulation calculation on the truss structure optimization problem, the calculation results show that FE-PSO-SQP algorithm has faster convergence speed and higher calculation accuracy than FE-PSO algorithm, and can be used for structure optimization design.

关键词： Particle Swarm Optimization Truss Finite Element Method Structure Optimization sequential quadratic programming algorithm

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Priority-based resource allocation in wireless powered UAV-assisted networks

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IET NETWORKS 2022年第5期11卷 156-168页

作者： Basharat, Mehak Naeem, Muhammad Anpalagan, Alagan Ryerson Univ Dept Elect & Comp Engn 350 Victoria St Toronto ON M5B 2K3 Canada COMSATS Univ Dept Elect & Comp Engn Wah Cantt Pakistan

Unmanned aerial vehicles (UAVs) can be deployed to combine terrestrial and aerial networks to provide flexible connectivity to a large number of devices in three-dimensional (3D) space. Powering wireless devices through energy harvesting and wireless power transfer have been investigated to provide uninterrupted operations. Resource allocation is critical to improving the overall performance of the network. This paper focusses on a downlink network in which UAVs serve as base stations to provide connectivity to ground users. A priority-based charging of UAVs from the ground charging stations needs to be efficiently designed for the sustainable operation of the overall network. A binary linear integer programming (BLIP) problem is formulated to minimise the charging cost and maximise the number of UAVs that can be charged by charging stations. First, the BLIP is transformed into a quadratic programming problem to solve it in polynomial time. A sequential quadratic programming algorithm is developed to solve the optimisation problem. Simulation results demonstrate the effectiveness of the proposed work compared to existing solutions.

关键词： charging cost terrestrial networks UAVs priority-based resource allocation binary linear integer programming problem aerial networks electric vehicle charging base stations three-dimensional space energy harvesting powering wireless devices integer programming optimisation ground charging stations autonomous aerial vehicles quadratic programming quadratic programming problem wireless power transfer ground users sustainable operation downlink network resource allocation unmanned aerial vehicles sequential quadratic programming algorithm flexible connectivity wireless powered UAV-assisted networks uninterrupted operations

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Real-Time Optimization of Energy Management Strategy for Fuel Cell Vehicles Using Inflated 3D Inception Long Short-Term Memory Network-Based Speed Prediction

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第2期70卷 1190-1199页

作者： Zhang, Caizhi Zhang, Yuanzhi Huang, Zhiyu Lv, Chen Hao, Dong Liang, Chen Deng, Chenghao Chen, Jinrui Chongqing Univ Sch Automot Engn State Key Lab Mech Transmiss Chongqing Automot Collaborat Innovat Ctr Chongqing 400044 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore China Automot Technol & Res Ctr Co Ltd Fuel Cell Test Tech Platform Tianjin 300300 Peoples R China Natl New Energy Vehicle Technol Innovat Ctr Fuel Cell Dept Beijing 100176 Peoples R China Chongqing Changan New Energy Vehicle Technol Co L Prop Res Inst Chongqing 400000 Peoples R China

The performance of speed prediction-based energy management strategy (EMS) for fuel cell vehicles (FCVs) highly relies on the accuracy of predicted speed sequences. Therefore, the future speed sequences are estimated by Inflated 3D Inception long short-term memory (LSTM) network, which can use the historical speed and image information to improve the accuracy of speed prediction. Meanwhile, the energy economy and powertrain system durability are the objectives of real-time optimization. For optimizing energy economy and powertrain system durability of FCVs, the real-time optimization of EMS using the Inflated 3D Inception LSTM network-based speed prediction is proposed. To do this, the mathematical models including energy economy and powertrain system durability of FCVs are developed at the beginning. Then, based on the predicted speed sequences, a real-time optimization method with sequential quadratic programming (SQP) algorithm is proposed to minimize the energy consumption and take into consideration powertrain system degradation in the prediction horizon. Simulation results show that the proposed EMS can significantly reduce the total cost of energy consumption and powertrain system degradation.

关键词： Energy management Optimization Mechanical power transmission Real-time systems Degradation Lithium-ion batteries State of charge Real-time optimization energy management strategy Inflated 3D Inception LSTM network sequential quadratic programming algorithm

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A search method for probabilistic critical slip surfaces with arbitrary shapes and its application in slope reliability analysis

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NATURAL HAZARDS 2021年第2期107卷 1657-1679页

作者： Liu, Yibiao Ren, Weizhong Liu, Chenchen Fu, Guijun Xu, Wenhui Cai, Simin Chinese Acad Sci Inst Rock & Soil Mech Wuhan 430071 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The Monte Carlo simulation (MCS) is generally accepted as an accurate approximation method in slope reliability analysis. However, low efficiency hinders its use. Based on the online sequential extreme learning machine (OS-ELM), the OS-ELM-MCS method is proposed, which overcomes the requirement of calculating the safety factor, respectively, when using a conventional MCS for reliability analysis. Combined with the multi-initial points sequential quadratic programming (MSQP) algorithm, a search method for probabilistic critical slip surfaces with arbitrary shapes is introduced. It is possible to conduct both a reliability analysis and deterministic analysis under the same algorithm framework. Based on the proposed method, reliability and deterministic analyses of two cases are carried out. Compared with the results of other methods, the accuracy of the reliability and deterministic analysis method is verified. This verifies that the accuracy and time performance of the proposed method are better than those of conventional algorithms. The results of these case studies reveal the differences between the conclusions of the reliability and deterministic analyses. Therefore, it is necessary to combine reliability analysis and deterministic analysis to improve the credibility of stability analysis results.

关键词： Slope reliability analysis Slip surface search algorithm Monte Carlo simulation Online sequential extreme learning machine sequential quadratic programming algorithm

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Dynamic flow optimization for a three-loop fluid heat dissipation system in spacecraft

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CASE STUDIES IN THERMAL ENGINEERING 2022年 40卷

作者： Zheng, Tong Zhao, Li -Ping Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing 100094 Peoples R China

Dynamic optimization of the fluid loop is critical for the active thermal control system (ATCS) for future spacecraft. In this paper, the dynamic heat transfer model of a three-loop fluid heat dissipation system is constructed by the transient heat current modeling method to analyze the optimal control problem of dynamic flow allocation. A sequential quadratic programming (SQP) algorithm combined with the exact external penalty function method is designed to solve the difficulty of temperature path constraints. Simulation results show that the proposed method effectively improves the optimization effect of temperature path constraints and significantly reduces the computational time. Compared with the results of mean allocation flow (unopti-mized) and steady flow optimization, the dynamic flow allocation reduced the residual heat by 6.9% and 21.5%, respectively, while meeting all the temperature constraints. In addition, the total flow rate needs to be increased at least by 48% to meet all temperature constraints and achieve similar heat dissipation capacity, when the flow allocation was designed as steady var-iables. The comparison results indicate that the dynamic flow allocation effectively improves the heat dissipation efficiency of the parallel fluid loop system and ensures the payload temperatures within the constraints.

关键词： Parallel fluid loop system Heat transfer optimization sequential quadratic programming algorithm Spacecraft Dynamic flow allocation

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Dynamic optimization analyses and algorithm design for the parallel heat exchange system in spacecraft

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APPLIED THERMAL ENGINEERING 2022年 212卷

作者： Zheng, Tong Zhao, Li-Ping Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing 100094 Peoples R China

Dynamic optimization of the fluid loop is critical of the active thermal control system (ATCS) for future spacecraft. In this paper, the transient heat current model of the parallel heat exchanger system is constructed by the thermoelectric analogy method to analyze the optimal control problem of dynamic flow allocation. The continuous free time optimal flow control problem is transformed into a fixed-time nonlinear programming problem which can be solved by the sequential quadratic programming (SQP) algorithm through the control variable parameterization (CVP) and time-scaling methods. The simulation showed that the optimized flow allocation significantly reduced the process time required for system heat dissipation. Compared with the non-gradient particle swarm optimization (PSO) algorithm, the SQP method constructed in this paper decreased the optimization index by about 4%similar to 6% and the calculation time by about one order of magnitude. To diminish the sensitivity of the SQP algorithm to the initial value, this paper simplifies the model according to the system characteristics. Through the Pontryagin extremum principle, it is found and proved that, under the simplified model, the optimal flow rates are a set of time-independent variables, which significantly reduces the difficulty of the optimization problem, according to which a hybrid algorithm is designed that uses the optimal result of the simplified model as the initial value predictor. Compared with the average of using the random initial value, using the estimated initial value decreased the optimization index by more than 2% and the computation time by about 60%, under the same SQP algorithm.

关键词： Parallel heat exchange Heat transfer optimization sequential quadratic programming algorithm Spacecraft Dynamic flow allocation

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Bezier curve-based trajectory planning for autonomous vehicles with collision avoidance

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第13期14卷 1882-1891页

作者： Zheng, Ling Zeng, Pengyun Yang, Wei Li, Yinong Zhan, Zhenfei Chongqing Univ Coll Automot Engn State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Macro Net Commun Co Ltd Chongqing 400044 Peoples R China

This study proposes an effective trajectory planning algorithm based on the quartic Bezier curve and dangerous potential field for automatic vehicles. To generate collision-free trajectories, potential field functions are introduced to evaluate the collision risk of path candidates. However, many studies on artificial potential field approaches primarily focus on static and straight roads, and attach less importance to more complex driving scenarios, such as curving roads. In this study, a novel method based on the Frenet coordinate system is proposed to address such limitations. Moreover, to balance the driving comfortability and the driving safety of the path candidate, the path-planning problem is converted to an optimisation problem, and sequential quadratic programming algorithm is employed to tackle this task. Another merit of this algorithm is the curvature of the generated path is continuous even at the joints of adjacent sub-trajectories by utilising several specific properties of the Bezier curve. Furthermore, to execute the generated trajectory, a framework of velocity generation is proposed while vehicle dynamic constraints are considered. Some typical traffic scenarios, including lane-changing, lane-keeping, and collision avoidance have been designed to verify the performance of the proposed algorithm, and simulations demonstrate the validity of this method.

关键词： trajectory control road vehicles road safety collision avoidance vehicle dynamics quadratic programming mobile robots path planning road traffic path candidate path-planning problem optimisation problem sequential quadratic programming algorithm generated path adjacent sub-trajectories generated trajectory velocity generation vehicle dynamic constraints typical traffic scenarios collision avoidance Bezier curve-based trajectory autonomous vehicles effective trajectory quartic Bezier curve dangerous potential field automatic vehicles collision-free trajectories potential field functions collision risk artificial potential field static roads straight roads complex driving scenarios curving roads Frenet coordinate system driving comfortability driving safety

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