Trajectory optimization of an oscillating industrial two-stage evaporator utilizing a Python-Aspen Plus Dynamics toolchain

This paper presents a novel and robust two-stage pursuit strategy for the incomplete-information impulsive space pursuit-evasion missions considering the J2 perturbation. The strategy firstly models the impulsive pursuit-evasion game problem into a far-distance rendezvous stage and a close-distance game stage according to the perception range of the evader. For the far-distance rendezvous stage, it is transformed into a rendezvous trajectory optimization problem and a new objective function is proposed to obtain the pursuit trajectory with the optimal terminal pursuit capability. For the close-distance game stage, a closed-loop pursuit approach is proposed using one of the reinforcement learning algorithms, i.e., the deep deterministic policy gradient algorithm, to solve and update the pursuit trajectory for the incomplete-information impulsive pursuit-evasion missions. The feasibility of this novel strategy and its robustness to different initial states of the pursuer and evader and to the evasion strategies are demonstrated for the sun-synchronous orbit pursuit-evasion game scenarios. The results of the Monte Carlo tests show that the successful pursuit ratio of the proposed method is over 91% for all the given scenarios.

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Optimal Conceptual Design of Two-Stage Reusable Rocket Vehicles Including Trajectory Optimization

Journal of Spacecraft and Rockets ◽

10.2514/1.1082 ◽

2004 ◽

Vol 41 (5) ◽

pp. 770-778 ◽

Cited By ~ 28

Author(s):

Takeshi Tsuchiya ◽

Takashige Mori

Keyword(s):

Conceptual Design ◽

Trajectory Optimization ◽

Two Stage

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Simulation of two-stage automotive shredder residue pyrolysis and gasification process using the Aspen Plus model

BioResources ◽

10.15376/biores.16.3.5964-5984 ◽

2021 ◽

Vol 16 (3) ◽

pp. 5964-5984

Author(s):

Bin Yang ◽

Ming Chen

Keyword(s):

Process Parameters ◽

Aspen Plus ◽

Product Distribution ◽

Recycling Rate ◽

Main Process ◽

Optimal Process ◽

Two Stage ◽

Shredder Residue ◽

Gasification Process ◽

Automotive Shredder Residue

The disposal of automotive shredder residue (ASR) directly affects China’s goal of achieving a 95% recycling rate for end-of-life vehicles. Pyrolysis and gasification have gradually become the most commonly used thermochemical technologies for ASR recycling. To obtain more hydrogen-rich syngas, it is necessary to determine the optimal process parameters of the ASR pyrolysis and gasification process. The main process parameters of the two-stage ASR pyrolysis and gasification process were studied using the established Aspen Plus model. Through analyzing the effects of process parameters, such as the temperature, equivalence ratio, and mass ratio of steam to ASR feedstock, on the product distribution and product characteristics of ASR pyrolysis and gasification, the optimal process parameters were determined. A series of comparative experiments under different conditions were conducted. The experimental results verified the accuracy and reliability of the Aspen Plus simulation model for the ASR pyrolysis and gasification processes and verified the practical feasibility of the process parameters obtained from the simulation analysis.

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Two-stage airbreathing launcher trajectory optimization including first-stage return

10.2514/6.1992-5007 ◽

1992 ◽

Cited By ~ 1

Author(s):

B. CHRISTOPHE

Keyword(s):

Trajectory Optimization ◽

Two Stage

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Rapid trajectory optimization for hypersonic entry using convex optimization and pseudospectral method

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-06-2018-0159 ◽

2019 ◽

Vol 91 (4) ◽

pp. 669-679 ◽

Cited By ~ 3

Author(s):

Jinbo Wang ◽

Naigang Cui ◽

Changzhu Wei

Keyword(s):

Convex Optimization ◽

Computational Efficiency ◽

Trajectory Optimization ◽

Approximation Error ◽

Optimal Solution ◽

Initial Guess ◽

Two Stage ◽

Content Type ◽

Highly Nonlinear ◽

Solution Accuracy

Purpose This paper aims to develop a novel trajectory optimization algorithm which is capable of producing high accuracy optimal solution with superior computational efficiency for the hypersonic entry problem. Design/methodology/approach A two-stage trajectory optimization framework is constructed by combining a convex-optimization-based algorithm and the pseudospectral-nonlinear programming (NLP) method. With a warm-start strategy, the initial-guess-sensitive issue of the general NLP method is significantly alleviated, and an accurate optimal solution can be obtained rapidly. Specifically, a successive convexification algorithm is developed, and it serves as an initial trajectory generator in the first stage. This algorithm is initial-guess-insensitive and efficient. However, approximation error would be brought by the convexification procedure as the hypersonic entry problem is highly nonlinear. Then, the classic pseudospectral-NLP solver is adopted in the second stage to obtain an accurate solution. Provided with high-quality initial guesses, the NLP solver would converge efficiently. Findings Numerical experiments show that the overall computation time of the two-stage algorithm is much less than that of the single pseudospectral-NLP algorithm; meanwhile, the solution accuracy is satisfactory. Practical implications Due to its high computational efficiency and solution accuracy, the algorithm developed in this paper provides an option for rapid trajectory designing, and it has the potential to evolve into an online algorithm. Originality/value The paper provides a novel strategy for rapid hypersonic entry trajectory optimization applications.

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