Yushuo Tan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yushuo Tan has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Lingfei Xiao
College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2 shared publications - Robert R. Sattarov
Ufa State Petroleum Technological University
1 shared publication - Xiaole Zhang
College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
1 shared publication - Ye Wei
College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
1 shared publication - Zirui Han
College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
1 shared publication
Latest Publications
- Open Access
Constrained Model Predictive Control for Generation Power Distribution on Aircraft Engines
Aiming at the increasing demand for electric energy in aircraft in the future, a multi-objective optimization aircraft engine constrained model predictive control method based on generation power distribution is proposed. Firstly, based on the aircraft engine component level model and the equilibrium manifold theory, the aircraft engine equilibrium manifold expansion model is established. Secondly, the influence of the power generation is modeled, and the influence of the low- and high-pressure shaft generators on the normal operation of the aircraft engine is studied and compared. The control variables such as fuel flow and total generation power are taken as the constraint conditions to design the constraint model predictive controller. Furthermore, the multi-objective grey wolf optimization algorithm is introduced to intelligently optimize the parameters of the designed controller. At last, the simulation based on the component level model shows that the high-pressure shaft generator has less influence on the state quantity, including engine thrust, than the low-pressure shaft generator. The proposed control method using the multi-objective gray wolf optimization (MOGWO) algorithm has rapid response and no steady-state error.
Open AccessConstrained Model Predictive Control for Generation Power Distribution on Aircraft Engines
Aiming at the increasing demand for electric energy in aircraft in the future, a multi-objective optimization aircraft engine constrained model predictive control method based on generation power distribution is proposed. Firstly, based on the aircraft engine component level model and the equilibrium manifold theory, the aircraft engine equilibrium manifold expansion model is established. Secondly, the influence of the power generation is modeled, and the influence of the low- and high-pressure shaft generators on the normal operation of the aircraft engine is studied and compared. The control variables such as fuel flow and total generation power are taken as the constraint conditions to design the constraint model predictive controller. Furthermore, the multi-objective grey wolf optimization algorithm is introduced to intelligently optimize the parameters of the designed controller. At last, the simulation based on the component level model shows that the high-pressure shaft generator has less influence on the state quantity, including engine thrust, than the low-pressure shaft generator. The proposed control method using the multi-objective gray wolf optimization (MOGWO) algorithm has rapid response and no steady-state error.
- Open Access
Distributed Hybrid Electric Propulsion Aircraft Design Based on Convex Optimized Power Allocation Management
In order to ensure that aircraft have medium and long-range flights, enhanced aerodynamic performance, and reduced fuel consumption, this paper presents an original Distributed Hybrid Electric Propulsion Aircraft (DHEPA) design scheme and proposes a novel power allocation management method based on convex optimization. Firstly, by taking the Tecnam P2006T general-purpose aircraft as a reference, key components of DHEPA are selected and modeled. Then, a power allocation management method for DHEPA is proposed on the basis of convex optimization, which takes the minimum fuel consumption as the performance index to realize the reasonable power allocation of the battery and engine, while avoiding sliding into the local optimum of allocation. Finally, momentum theory and numerical simulation methods are used to analyze the aerodynamic enhancement effect of the propeller on the wing in the DHEPA, and a dynamics method is utilized to calculate the dynamics performance of the aircraft at several important stages. The results show that, compared with the reference aircraft, the lift of the DHEPA is increased by 46%. Under typical sectors, the DHEPA has a higher rate of climb and maximum leveling off speed at cruise, and a significantly lower fuel consumption.
Open AccessDistributed Hybrid Electric Propulsion Aircraft Design Based on Convex Optimized Power Allocation Management
In order to ensure that aircraft have medium and long-range flights, enhanced aerodynamic performance, and reduced fuel consumption, this paper presents an original Distributed Hybrid Electric Propulsion Aircraft (DHEPA) design scheme and proposes a novel power allocation management method based on convex optimization. Firstly, by taking the Tecnam P2006T general-purpose aircraft as a reference, key components of DHEPA are selected and modeled. Then, a power allocation management method for DHEPA is proposed on the basis of convex optimization, which takes the minimum fuel consumption as the performance index to realize the reasonable power allocation of the battery and engine, while avoiding sliding into the local optimum of allocation. Finally, momentum theory and numerical simulation methods are used to analyze the aerodynamic enhancement effect of the propeller on the wing in the DHEPA, and a dynamics method is utilized to calculate the dynamics performance of the aircraft at several important stages. The results show that, compared with the reference aircraft, the lift of the DHEPA is increased by 46%. Under typical sectors, the DHEPA has a higher rate of climb and maximum leveling off speed at cruise, and a significantly lower fuel consumption.