In this work, a simulated aircraft fuel tank inerting system has been successfully estab- lished based on a model tank. Experiments were conducted to investigate the influences of different operating parameters on the...In this work, a simulated aircraft fuel tank inerting system has been successfully estab- lished based on a model tank. Experiments were conducted to investigate the influences of different operating parameters on the inerting effectiveness of the system, including flow rate of the inert gas （nitrogen-enriched air）, inert gas concentration, fuel load of the tank and different inerting approaches. The experimental results show that under the same operating conditions, the time span of a complete inerting process decreased as the flow rate of inert gas was increased; the time span using the inert gas with 5% oxygen concentration was much longer than that using pure nitrogen; when the fuel tank was inerted using the ullage washing approach, the time span increased as the fuel load was decreased; the ullage washing approach showed the best inerting performance when the time span of a complete inerting process was the evaluation criterion, but when the decrease of dissolved oxygen concentration in the fuel was also considered to characterize the inerting effective- ness, the approach of ullage washing and fuel scrubbing at the same time was the most effective.展开更多
In this study,a real-time optimal control approach is proposed using an interactive deep reinforcement learning algorithm for the Moon fuel-optimal landing problem.Considering the remote communication restrictions and...In this study,a real-time optimal control approach is proposed using an interactive deep reinforcement learning algorithm for the Moon fuel-optimal landing problem.Considering the remote communication restrictions and environmental uncertainties,advanced landing control techniques are demanded to meet the high requirements of real-time performance and autonomy in the Moon landing missions.Deep reinforcement learning(DRL)algorithms have been recently developed for real-time optimal control but suffer the obstacles of slow convergence and difficult reward function design.To address these problems,a DRL algorithm is developed using an actor-indirect method architecture to achieve the optimal control of the Moon landing mission.In this DRL algorithm,an indirect method is employed to generate the optimal control actions for the deep neural network(DNN)learning,while the trained DNNs provide good initial guesses for the indirect method to promote the efficiency of training data generation.Through sufficient learning of the state-action relationship,the trained DNNs can approximate the optimal actions and steer the spacecraft to the target in real time.Additionally,a nonlinear feedback controller is developed to improve the terminal landing accuracy.Numerical simulations are given to verify the effectiveness of the proposed DRL algorithm and demonstrate the performance of the developed optimal landing controller.展开更多
Characteristic spectra in the 0.5-2.5 terahertz (THz) range of three commercial derv fuel oils have been obtained using THz time-domain spectroscopy and calculated using density functional theory.The simulated results...Characteristic spectra in the 0.5-2.5 terahertz (THz) range of three commercial derv fuel oils have been obtained using THz time-domain spectroscopy and calculated using density functional theory.The simulated results and experimental absorption curves suggest that the skeleton vibration is predominant in the THz region,and the absorption bumps of diesels are a superposition of various components.The investigation demonstrates that different diesels can be distinguished using THz time-domain spectroscopy and THz technology is a promising method to detect the composition and properties of diesels via chemical analysis.展开更多
In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell(PEMFC),a fusion prediction method(CKDG)based on adaptive noise complete ensemble empirical mode decomposition(C...In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell(PEMFC),a fusion prediction method(CKDG)based on adaptive noise complete ensemble empirical mode decomposition(CEEMDAN),kernel principal component analysis(KPCA)and dual attention mechanism gated recurrent unit neural network(DA-GRU)was proposed.CEEMDAN and KPCA were used to extract the input feature data sequence,reduce the influence of random factors,and capture essential feature components to reduce the model complexity.The DA-GRU network helps to learn the feature mapping relationship of data in long time series and predict the changing trend of performance degradation data more accurately.The actual aging experimental data verify the performance of the CKDG method.The results show that under the steady-state condition of 20%training data prediction,the CKDA method can reduce the root mean square error(RMSE)by 52.7%and 34.6%,respectively,compared with the traditional LSTM and GRU neural networks.Compared with the simple DA-GRU network,RMSE is reduced by 15%,and the degree of over-fitting is reduced,which has higher accuracy.It also shows excellent prediction performance under the dynamic condition data set and has good universality.展开更多
The gray of two images of a same particle taken by a digital camera with different exposure times is different too. Based on the gray difference of particle images in a double-exposed photo and autocorrelation process...The gray of two images of a same particle taken by a digital camera with different exposure times is different too. Based on the gray difference of particle images in a double-exposed photo and autocorrelation processing of digital images,this paper proposes a method for measuring particle velocities and sizes simultaneously. This paper also introduces the theoretical foundation of this method,the process of particle imaging and image processing,and the simultaneous measurement of velocity and size of a low speed flow field with 35 μm and 75 μm standard particles. The graphical measurement results can really reflect the flow characteristics of the flow field. In addition,although the measured velocity and size histograms of these two kinds of standard particles are slightly wider than the theoretical ones,they are all still similar to the normal distribution,and the peak velocities and diameters of the histograms are consistent with the default values. Therefore,this measurement method is capable of providing moderate measurement accuracy,and it can be further developed for high-speed flow field measurements.展开更多
The real-time model-based control of polymer electrolyte membrane(PEM)fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various op...The real-time model-based control of polymer electrolyte membrane(PEM)fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various operational conditions,involving the pressure,temperature,humidity,and stoichiometry ratio.In this article,recent progress on the development of PEM fuel cell models that can be used for real-time control is reviewed.The major operational principles of PEM fuel cells and the associated mathematical description of the transport and electrochemical phenomena are described.The reduced-dimensional physics-based models(pseudo-twodimensional,one-dimensional numerical and zero dimensional analytical models)and the non-physics-based models(zero-dimensional empirical and data-driven models)have been systematically examined,and the comparison of these models has been performed.It is found that the current trends for the real-time control models are(i)to couple the single cell model with balance of plants to investigate the system performance,(ii)to incorporate aging effects to enable long-term performance prediction,(iii)to increase the computational speed(especially for one-dimensional numerical models),and(iv)to develop data-driven models with artificial intelligence/machine learning algorithms.This review will be beneficial for the development of physics or nonphysics based models with sufficient accuracy and computational speed to ensure the real-time control of PEM fuel cells.展开更多
文摘In this work, a simulated aircraft fuel tank inerting system has been successfully estab- lished based on a model tank. Experiments were conducted to investigate the influences of different operating parameters on the inerting effectiveness of the system, including flow rate of the inert gas （nitrogen-enriched air）, inert gas concentration, fuel load of the tank and different inerting approaches. The experimental results show that under the same operating conditions, the time span of a complete inerting process decreased as the flow rate of inert gas was increased; the time span using the inert gas with 5% oxygen concentration was much longer than that using pure nitrogen; when the fuel tank was inerted using the ullage washing approach, the time span increased as the fuel load was decreased; the ullage washing approach showed the best inerting performance when the time span of a complete inerting process was the evaluation criterion, but when the decrease of dissolved oxygen concentration in the fuel was also considered to characterize the inerting effective- ness, the approach of ullage washing and fuel scrubbing at the same time was the most effective.
基金This work is supported by the National Natural Science Foundation of China(Grants Nos.11672146 and 11432001).
文摘In this study,a real-time optimal control approach is proposed using an interactive deep reinforcement learning algorithm for the Moon fuel-optimal landing problem.Considering the remote communication restrictions and environmental uncertainties,advanced landing control techniques are demanded to meet the high requirements of real-time performance and autonomy in the Moon landing missions.Deep reinforcement learning(DRL)algorithms have been recently developed for real-time optimal control but suffer the obstacles of slow convergence and difficult reward function design.To address these problems,a DRL algorithm is developed using an actor-indirect method architecture to achieve the optimal control of the Moon landing mission.In this DRL algorithm,an indirect method is employed to generate the optimal control actions for the deep neural network(DNN)learning,while the trained DNNs provide good initial guesses for the indirect method to promote the efficiency of training data generation.Through sufficient learning of the state-action relationship,the trained DNNs can approximate the optimal actions and steer the spacecraft to the target in real time.Additionally,a nonlinear feedback controller is developed to improve the terminal landing accuracy.Numerical simulations are given to verify the effectiveness of the proposed DRL algorithm and demonstrate the performance of the developed optimal landing controller.
基金supported by the Program for New Century Excellent Talents in Universitythe National Natural Science Foundation of China+2 种基金the Research Fund for the Doctoral Program of Higher Educationthe State Key Laboratory of Heavy Oil Processing,China University of PetroleumForesight Fund Program from China University of Petroleum (Beijing)
文摘Characteristic spectra in the 0.5-2.5 terahertz (THz) range of three commercial derv fuel oils have been obtained using THz time-domain spectroscopy and calculated using density functional theory.The simulated results and experimental absorption curves suggest that the skeleton vibration is predominant in the THz region,and the absorption bumps of diesels are a superposition of various components.The investigation demonstrates that different diesels can be distinguished using THz time-domain spectroscopy and THz technology is a promising method to detect the composition and properties of diesels via chemical analysis.
基金funded by Shaanxi Province Key Industrial Chain Project(2023-ZDLGY-24)Industrialization Project of Shaanxi Provincial Education Department(21JC018)+1 种基金Shaanxi Province Key Research and Development Program(2021ZDLGY13-02)the Open Foundation of State Key Laboratory for Advanced Metals and Materials(2022-Z01).
文摘In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell(PEMFC),a fusion prediction method(CKDG)based on adaptive noise complete ensemble empirical mode decomposition(CEEMDAN),kernel principal component analysis(KPCA)and dual attention mechanism gated recurrent unit neural network(DA-GRU)was proposed.CEEMDAN and KPCA were used to extract the input feature data sequence,reduce the influence of random factors,and capture essential feature components to reduce the model complexity.The DA-GRU network helps to learn the feature mapping relationship of data in long time series and predict the changing trend of performance degradation data more accurately.The actual aging experimental data verify the performance of the CKDG method.The results show that under the steady-state condition of 20%training data prediction,the CKDA method can reduce the root mean square error(RMSE)by 52.7%and 34.6%,respectively,compared with the traditional LSTM and GRU neural networks.Compared with the simple DA-GRU network,RMSE is reduced by 15%,and the degree of over-fitting is reduced,which has higher accuracy.It also shows excellent prediction performance under the dynamic condition data set and has good universality.
基金supported by the National Natural Science Foundation of China(Grant No.50676055)sponsored by Shanghai Rising-Star Program(Grant No.07QA1405)supported by Program for New Century Ex-cellent Talents in University(NCET-10-0605)
文摘The gray of two images of a same particle taken by a digital camera with different exposure times is different too. Based on the gray difference of particle images in a double-exposed photo and autocorrelation processing of digital images,this paper proposes a method for measuring particle velocities and sizes simultaneously. This paper also introduces the theoretical foundation of this method,the process of particle imaging and image processing,and the simultaneous measurement of velocity and size of a low speed flow field with 35 μm and 75 μm standard particles. The graphical measurement results can really reflect the flow characteristics of the flow field. In addition,although the measured velocity and size histograms of these two kinds of standard particles are slightly wider than the theoretical ones,they are all still similar to the normal distribution,and the peak velocities and diameters of the histograms are consistent with the default values. Therefore,this measurement method is capable of providing moderate measurement accuracy,and it can be further developed for high-speed flow field measurements.
基金This work received financial support from Toyota Motor Engineering&Manufacturing North America,Inc.,Toyota Motor Manufacturing Canada,and Natural Sciences and Engineering Research Council of Canada through a Collaborative Research and Development Grant with the project number of CRDPJ 543945-19.
文摘The real-time model-based control of polymer electrolyte membrane(PEM)fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various operational conditions,involving the pressure,temperature,humidity,and stoichiometry ratio.In this article,recent progress on the development of PEM fuel cell models that can be used for real-time control is reviewed.The major operational principles of PEM fuel cells and the associated mathematical description of the transport and electrochemical phenomena are described.The reduced-dimensional physics-based models(pseudo-twodimensional,one-dimensional numerical and zero dimensional analytical models)and the non-physics-based models(zero-dimensional empirical and data-driven models)have been systematically examined,and the comparison of these models has been performed.It is found that the current trends for the real-time control models are(i)to couple the single cell model with balance of plants to investigate the system performance,(ii)to incorporate aging effects to enable long-term performance prediction,(iii)to increase the computational speed(especially for one-dimensional numerical models),and(iv)to develop data-driven models with artificial intelligence/machine learning algorithms.This review will be beneficial for the development of physics or nonphysics based models with sufficient accuracy and computational speed to ensure the real-time control of PEM fuel cells.
