Resilient UAV (Unmanned Aerial Vehicle) swarm operations are a complex research topic where the dynamic environments in which they work significantly increase the chance of systemic failure due to disruptions. Most existing SAR (Search and Rescue) frameworks for UAV swarms are application-specific, focusing on rescuing external non-swarm agents, but if an agent in the swarm is lost, there is inadequate research to account for the resiliency of the UAV swarm itself. This study describes the design and deployment of a Swarm Specific SAR (SS-SAR) framework focused on UAV swarm agents. This framework functions as a resilient mechanism by locating and attempting to reconnect communications with lost UAV swarm agents. The developed framework was assessed over a series of performance tests and environments, both real-world hardware and simulation experiments. Experimental results showed successful recovery rates in the range of 40%–60% of all total flights conducted, indicating that UAV swarms can be made more resilient by including methods to recover distressed agents. Decision-based modular frameworks such as the one proposed here lay the groundwork for future development in attempts to consider the swarm agents in the search and rescue process.
{"title":"Increasing Operational Resiliency of UAV Swarms: An Agent-Focused Search and Rescue Framework","authors":"A. Phadke, F. A. Medrano","doi":"10.3389/arc.2023.12420","DOIUrl":"https://doi.org/10.3389/arc.2023.12420","url":null,"abstract":"Resilient UAV (Unmanned Aerial Vehicle) swarm operations are a complex research topic where the dynamic environments in which they work significantly increase the chance of systemic failure due to disruptions. Most existing SAR (Search and Rescue) frameworks for UAV swarms are application-specific, focusing on rescuing external non-swarm agents, but if an agent in the swarm is lost, there is inadequate research to account for the resiliency of the UAV swarm itself. This study describes the design and deployment of a Swarm Specific SAR (SS-SAR) framework focused on UAV swarm agents. This framework functions as a resilient mechanism by locating and attempting to reconnect communications with lost UAV swarm agents. The developed framework was assessed over a series of performance tests and environments, both real-world hardware and simulation experiments. Experimental results showed successful recovery rates in the range of 40%–60% of all total flights conducted, indicating that UAV swarms can be made more resilient by including methods to recover distressed agents. Decision-based modular frameworks such as the one proposed here lay the groundwork for future development in attempts to consider the swarm agents in the search and rescue process.","PeriodicalId":431764,"journal":{"name":"Aerospace Research Communications","volume":"64 51","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139385492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaowei Gao, Wei-Wu Jiang, Xiang-Bo Xu, Hua‐Yu Liu, Kai Yang, J. Lv, M. Cui
In this article, the progress of frequently used advanced numerical methods is presented. According to the discretisation manner and manipulation dimensionality, these methods can be classified into four categories: volume-, surface-, line-, and point-operations–based methods. The volume-operation–based methods described in this article include the finite element method and element differential method; the surface-operation–based methods consist of the boundary element method and finite volume method; the line-operation–based methods cover the finite difference method and finite line method; and the point-operation–based methods mainly include the mesh free method and free element method. These methods have their own distinctive advantages in some specific disciplines. For example, the finite element method is the dominant method in solid mechanics, the finite volume method is extensively used in fluid mechanics, the boundary element method is more accurate and easier to use than other methods in fracture mechanics and infinite media, the mesh free method is more flexible for simulating varying and distorted geometries, and the newly developed free element and finite line methods are suitable for solving multi-physics coupling problems. This article provides a detailed conceptual description and typical applications of these promising methods, focusing on developments in recent years.
{"title":"Overview of advanced numerical methods classified by operation dimensions","authors":"Xiaowei Gao, Wei-Wu Jiang, Xiang-Bo Xu, Hua‐Yu Liu, Kai Yang, J. Lv, M. Cui","doi":"10.3389/arc.2023.11522","DOIUrl":"https://doi.org/10.3389/arc.2023.11522","url":null,"abstract":"In this article, the progress of frequently used advanced numerical methods is presented. According to the discretisation manner and manipulation dimensionality, these methods can be classified into four categories: volume-, surface-, line-, and point-operations–based methods. The volume-operation–based methods described in this article include the finite element method and element differential method; the surface-operation–based methods consist of the boundary element method and finite volume method; the line-operation–based methods cover the finite difference method and finite line method; and the point-operation–based methods mainly include the mesh free method and free element method. These methods have their own distinctive advantages in some specific disciplines. For example, the finite element method is the dominant method in solid mechanics, the finite volume method is extensively used in fluid mechanics, the boundary element method is more accurate and easier to use than other methods in fracture mechanics and infinite media, the mesh free method is more flexible for simulating varying and distorted geometries, and the newly developed free element and finite line methods are suitable for solving multi-physics coupling problems. This article provides a detailed conceptual description and typical applications of these promising methods, focusing on developments in recent years.","PeriodicalId":431764,"journal":{"name":"Aerospace Research Communications","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114151413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, gas turbines intake jet air at high temperatures to improve the power output as much as possible. However, the excessive temperature typically puts the blade in the face of unpredictable damage. Film cooling is one of the prevailing methods applied in engineering scenarios, with the advantages of a simple structure and high cooling efficiency. This study aims to assess the uncertain effect that the three major film cooling parameters exert on the global and fixed-cord-averaged film cooling effectiveness under low, medium, and high blowing ratios br. The three input parameters include coolant hole diameter d, coolant tube inclination angle θ, and density ratio dr. The training dataset is obtained by Computational Fluid Dynamics (CFD). Moreover, a seven-layer artificial neural network (ANN) algorithm is applied to explore the complex non-linear mapping between the input flat film cooling parameters and the output fixed-cord-averaged film cooling effectiveness on the external turbine blade surface. The sensitivity experiment conducted using Monte Carlo (MC) simulation shows that the d and θ are the two most sensitive parameters in the low-blowing-ratio cases. The θ comes to be the only leading factor of sensitivity in larger blowing ratio cases. As the blowing ratio rises, the uncertainty of the three parameters d, θ, and dr all decrease. The combined effect of the three parameters is also dissected and shows that it has a more significant influence on the general cooling effectiveness than any single effect. The d has the widest variation of uncertainty interval at three blowing ratios, while the θ has the largest uncertain influence on the general cooling effectiveness. With the aforementioned results, the cooling effectiveness of the gas turbine can be furthermore enhanced.
{"title":"Deep‐Learning-Based Uncertainty Analysis of Flat Plate Film Cooling With Application to Gas Turbine","authors":"Yaning Wang, Xubin Qiu, Shuyang Qian, Yangqing Sun, Wen Wang, Jiahuan Cui","doi":"10.3389/arc.2023.11194","DOIUrl":"https://doi.org/10.3389/arc.2023.11194","url":null,"abstract":"Nowadays, gas turbines intake jet air at high temperatures to improve the power output as much as possible. However, the excessive temperature typically puts the blade in the face of unpredictable damage. Film cooling is one of the prevailing methods applied in engineering scenarios, with the advantages of a simple structure and high cooling efficiency. This study aims to assess the uncertain effect that the three major film cooling parameters exert on the global and fixed-cord-averaged film cooling effectiveness under low, medium, and high blowing ratios br. The three input parameters include coolant hole diameter d, coolant tube inclination angle θ, and density ratio dr. The training dataset is obtained by Computational Fluid Dynamics (CFD). Moreover, a seven-layer artificial neural network (ANN) algorithm is applied to explore the complex non-linear mapping between the input flat film cooling parameters and the output fixed-cord-averaged film cooling effectiveness on the external turbine blade surface. The sensitivity experiment conducted using Monte Carlo (MC) simulation shows that the d and θ are the two most sensitive parameters in the low-blowing-ratio cases. The θ comes to be the only leading factor of sensitivity in larger blowing ratio cases. As the blowing ratio rises, the uncertainty of the three parameters d, θ, and dr all decrease. The combined effect of the three parameters is also dissected and shows that it has a more significant influence on the general cooling effectiveness than any single effect. The d has the widest variation of uncertainty interval at three blowing ratios, while the θ has the largest uncertain influence on the general cooling effectiveness. With the aforementioned results, the cooling effectiveness of the gas turbine can be furthermore enhanced.","PeriodicalId":431764,"journal":{"name":"Aerospace Research Communications","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121624566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fangfang Xie, Changdong Zheng, Tingwei Ji, Xinshuai Zhang, Ran Bi, Hongjie Zhou, Yao Zheng
The ability to manipulate fluids has always been one of the focuses of scientific research and engineering application. The rapid development of machine learning technology provides a new perspective and method for active flow control. This review presents recent progress in combining reinforcement learning with high-dimensional, non-linear, and time-delay physical information. Compared with model-based closed-loop control methods, deep reinforcement learning (DRL) avoids modeling the complex flow system and effectively provides an intelligent end-to-end policy exploration paradigm. At the same time, there is no denying that obstacles still exist on the way to practical application. We have listed some challenges and corresponding advanced solutions. This review is expected to offer a deeper insight into the current state of DRL-based active flow control within fluid mechanics and inspires more non-traditional thinking for engineering.
{"title":"Deep Reinforcement Learning: A New Beacon for Intelligent Active Flow Control","authors":"Fangfang Xie, Changdong Zheng, Tingwei Ji, Xinshuai Zhang, Ran Bi, Hongjie Zhou, Yao Zheng","doi":"10.3389/arc.2023.11130","DOIUrl":"https://doi.org/10.3389/arc.2023.11130","url":null,"abstract":"The ability to manipulate fluids has always been one of the focuses of scientific research and engineering application. The rapid development of machine learning technology provides a new perspective and method for active flow control. This review presents recent progress in combining reinforcement learning with high-dimensional, non-linear, and time-delay physical information. Compared with model-based closed-loop control methods, deep reinforcement learning (DRL) avoids modeling the complex flow system and effectively provides an intelligent end-to-end policy exploration paradigm. At the same time, there is no denying that obstacles still exist on the way to practical application. We have listed some challenges and corresponding advanced solutions. This review is expected to offer a deeper insight into the current state of DRL-based active flow control within fluid mechanics and inspires more non-traditional thinking for engineering.","PeriodicalId":431764,"journal":{"name":"Aerospace Research Communications","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123069298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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