Graphene materials like turbostratic graphene exhibit remarkable promise for an array of applications,spanning from electronic devices to aerospace technologies.It is essential to develop a fabrication method that is ...Graphene materials like turbostratic graphene exhibit remarkable promise for an array of applications,spanning from electronic devices to aerospace technologies.It is essential to develop a fabrication method that is not only economical and efficient,but also environmentally sustainable.In this study,the molten salt-assisted magnesiothermic reduction(MSAMR)method is proposed for the synthesis of few-layer turbostratic graphene.K_(2)CO_(3)serves as both the carbon source and the catalyst for graphitization,facilitating the formation of the graphene structure,while in-situ generated MgO nanoparticles exert confinement and templating effects on the growth of graphene.The molten salts used effectively prevent the aggregation and the Bernal stacking of graphene sheets,ensuring the few-layer and turbostratic structure.The synergistic effects of K 2CO 3,in-situ generated MgO,and molten salts guarantee the formation of few-layer turbostratic graphene at a relatively low temperature,characterized with 4–8 stacking layers,a mesopore-dominated microstructure,and a high degree of graphitization.展开更多
The rapid developments of artificial intelligence in the last decade are influencing aerospace engineering to a great extent and research in this context is proliferating.We share our observations on the recent develo...The rapid developments of artificial intelligence in the last decade are influencing aerospace engineering to a great extent and research in this context is proliferating.We share our observations on the recent developments in the area of spacecraft guidance dynamics and control,giving selected examples on success stories that have been motivated by mission designs.Our focus is on evolutionary optimisation,tree searches and machine learning,including deep learning and reinforcement learning as the key technologies and drivers for current and future research in the field.From a high-level perspective,we survey various scenarios for which these approaches have been successfully applied or are under strong scientific investigation.Whenever possible,we highlight the relations and synergies that can be obtained by combining different techniques and projects towards future domains for which newly emerging artificial intelligence techniques are expected to become game changers.展开更多
Convex optimization is a class of mathematical programming problems with polynomial complexity for which state-of-the-art, highly efficient numerical algorithms with predeterminable computational bounds exist. Computa...Convex optimization is a class of mathematical programming problems with polynomial complexity for which state-of-the-art, highly efficient numerical algorithms with predeterminable computational bounds exist. Computational efficiency and tractability in aerospace engineering, especially in guidance, navigation, and control (GN&C), are of paramount importance. With theoretical guarantees on solutions and computational efficiency, convex optimization lends itself as a very appealing tool. Coinciding the strong drive toward autonomous operations of aerospace vehicles, convex optimization has seen rapidly increasing utility in solving aerospace GN&C problems with the potential for onboard real-time applications. This paper attempts to provide an overview on the problems to date in aerospace guidance, path planning, and control where convex optimization has been applied. Various convexification techniques are reviewed that have been used to convexify the originally nonconvex aerospace problems. Discussions on how to ensure the validity of the convexification process are provided. Some related implementation issues will be introduced as well.展开更多
Dear authors and readers,Artificial intelligence(AI)has recently found many new applications in aerospace engineering,which range from long-term scheduling of space telescope observations to science planning for the R...Dear authors and readers,Artificial intelligence(AI)has recently found many new applications in aerospace engineering,which range from long-term scheduling of space telescope observations to science planning for the Rosetta mission,from AI-based“astronaut assistant”in International Space Station to AI instrument equipped in the Mars rover Curiosity.The current perception is that methods and techniques developed within the AI research field have the potential to revolutionize almost every aspect of space exploration.It is thus very important for aerospace engineers to monitor the advances on the state-of-the-art methods available in the AI field and be aware of their proposed applications,owing to which,this special issue is organized.展开更多
基金the funding support from the National Natural Science Foundation of China(Grant No.22278404).
文摘Graphene materials like turbostratic graphene exhibit remarkable promise for an array of applications,spanning from electronic devices to aerospace technologies.It is essential to develop a fabrication method that is not only economical and efficient,but also environmentally sustainable.In this study,the molten salt-assisted magnesiothermic reduction(MSAMR)method is proposed for the synthesis of few-layer turbostratic graphene.K_(2)CO_(3)serves as both the carbon source and the catalyst for graphitization,facilitating the formation of the graphene structure,while in-situ generated MgO nanoparticles exert confinement and templating effects on the growth of graphene.The molten salts used effectively prevent the aggregation and the Bernal stacking of graphene sheets,ensuring the few-layer and turbostratic structure.The synergistic effects of K 2CO 3,in-situ generated MgO,and molten salts guarantee the formation of few-layer turbostratic graphene at a relatively low temperature,characterized with 4–8 stacking layers,a mesopore-dominated microstructure,and a high degree of graphitization.
文摘The rapid developments of artificial intelligence in the last decade are influencing aerospace engineering to a great extent and research in this context is proliferating.We share our observations on the recent developments in the area of spacecraft guidance dynamics and control,giving selected examples on success stories that have been motivated by mission designs.Our focus is on evolutionary optimisation,tree searches and machine learning,including deep learning and reinforcement learning as the key technologies and drivers for current and future research in the field.From a high-level perspective,we survey various scenarios for which these approaches have been successfully applied or are under strong scientific investigation.Whenever possible,we highlight the relations and synergies that can be obtained by combining different techniques and projects towards future domains for which newly emerging artificial intelligence techniques are expected to become game changers.
基金the National Natural Science Foundation of China(Grant No.61603017).
文摘Convex optimization is a class of mathematical programming problems with polynomial complexity for which state-of-the-art, highly efficient numerical algorithms with predeterminable computational bounds exist. Computational efficiency and tractability in aerospace engineering, especially in guidance, navigation, and control (GN&C), are of paramount importance. With theoretical guarantees on solutions and computational efficiency, convex optimization lends itself as a very appealing tool. Coinciding the strong drive toward autonomous operations of aerospace vehicles, convex optimization has seen rapidly increasing utility in solving aerospace GN&C problems with the potential for onboard real-time applications. This paper attempts to provide an overview on the problems to date in aerospace guidance, path planning, and control where convex optimization has been applied. Various convexification techniques are reviewed that have been used to convexify the originally nonconvex aerospace problems. Discussions on how to ensure the validity of the convexification process are provided. Some related implementation issues will be introduced as well.
文摘Dear authors and readers,Artificial intelligence(AI)has recently found many new applications in aerospace engineering,which range from long-term scheduling of space telescope observations to science planning for the Rosetta mission,from AI-based“astronaut assistant”in International Space Station to AI instrument equipped in the Mars rover Curiosity.The current perception is that methods and techniques developed within the AI research field have the potential to revolutionize almost every aspect of space exploration.It is thus very important for aerospace engineers to monitor the advances on the state-of-the-art methods available in the AI field and be aware of their proposed applications,owing to which,this special issue is organized.
