Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate struct...Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carded out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.展开更多
When designing large-sized complex machinery products, the design focus is always on the overall per- formance; however, there exist no design theory and method based on performance driven. In view of the defi- ciency...When designing large-sized complex machinery products, the design focus is always on the overall per- formance; however, there exist no design theory and method based on performance driven. In view of the defi- ciency of the existing design theory, according to the performance features of complex mechanical products, the performance indices are introduced into the traditional design theory of ＂Requirement-Function-Structure＂ to construct a new five-domain design theory of ＂Client Requirement-Function-Performance-Structure-Design Parameter＂. To support design practice based on this new theory, a product data model is established by using per- formance indices and the mapping relationship between them and the other four domains. When the product data model is applied to high-speed train design and combining the existing research result and relevant standards, the corresponding data model and its structure involving five domains of high-speed trains are established, which can provide technical support for studying the relationships between typical performance indices and design parame- ters and the fast achievement of a high-speed train scheme design. The five domains provide a reference for the design specification and evaluation criteria of high speed train and a new idea for the train＇s parameter design.展开更多
CONSPECTUS:In this Account,we present a comprehensive overview of recent advancements in applying data-driven combinatorial design for developing novel highenergy-density materials.Initially,we outline the progress in...CONSPECTUS:In this Account,we present a comprehensive overview of recent advancements in applying data-driven combinatorial design for developing novel highenergy-density materials.Initially,we outline the progress in energetic materials(EMs)development within the framework of the four scientific paradigms,with particular emphasis on the opportunities afforded by the evolution of computer and data science,which has propelled the theoretical design of EMs into a new era of data-driven development.We then discuss the structural features of typical EMs such as TNT,RDX,HMX,and CL-20,namely,a“scaffolds+functional groups”characteristic,underscoring the efficacy of the combinatorial design approach in constructing novel EMs.It has been discerned that those modifications to the scaffolds are the primary driving force behind the enhancement of EMs’properties.Subsequently,we introduce three distinct data-driven design strategies for EMs,each with a different approach to scaffold construction.These strategies are as follows:(1)the known scaffold strategy to identify fused cyclic scaffolds containing oxazole or oxadiazole structures from other fields via database screening and employ a high-throughput combinatorial approach with functional groups to design oxazole(and oxadiazole)-based fused cyclic EMs;(2)the semiknown scaffold strategy to construct semiknown scaffolds by integrating known scaffolds and realize the design of bridged cyclic EMs through a high-throughput combination of functional groups;(3)the unknown scaffold strategy to build caged structural models for quantitative characterization,high-throughput screening caged scaffolds from the database,construct unknown caged scaffolds by substituting atoms or substructures,and combine functional groups to design zero oxygen balance caged EMs.Employing the proposed strategies,the design capacity for EMs reaches an impressive scale of 10^(7) molecules,significantly increasing the probability of obtaining high-performance EMs.Furthermore,the incorporation of property assessment models based on machine learning and density functional theory has achieved a balance between computational accuracy and computational speed.Statistical analysis of the virtual screening has revealed the advantages of bicyclic tri-and tetrasubstituted position scaffolds in the construction of high-energy and easily synthesizable fused cyclic EMs.Additionally,the proposed strategies have been successfully applied to design multifunctional modular energetic materials,resulting in the successful synthesis of three target compounds,validating the effectiveness of data-driven combinatorial design approaches.Lastly,we discuss the current state of high-throughput combinatorial design and,in light of the multifaceted criteria required for the design of EMs,explore the feasibility of multiobjective optimization methods such as Pareto optimization.Moreover,we envision the application of generative models in the subsequent design and development of EMs.We anticipate that this Account will provide valuable insights into the theoretical design of EMs,and we envision the integration of new technologies and methodologies that could play an increasingly significant role in the future discovery of EMs.展开更多
基金Supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2015AA042101)
文摘Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carded out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.
基金Supported by National Natural Science Foundation of China(Grant Nos.51275432,51505390)Sichuan Application Foundation Projects(Grant No.2016JY0098)Independent Research Project of TPL(Grant No.TPL1501)
文摘When designing large-sized complex machinery products, the design focus is always on the overall per- formance; however, there exist no design theory and method based on performance driven. In view of the defi- ciency of the existing design theory, according to the performance features of complex mechanical products, the performance indices are introduced into the traditional design theory of ＂Requirement-Function-Structure＂ to construct a new five-domain design theory of ＂Client Requirement-Function-Performance-Structure-Design Parameter＂. To support design practice based on this new theory, a product data model is established by using per- formance indices and the mapping relationship between them and the other four domains. When the product data model is applied to high-speed train design and combining the existing research result and relevant standards, the corresponding data model and its structure involving five domains of high-speed trains are established, which can provide technical support for studying the relationships between typical performance indices and design parame- ters and the fast achievement of a high-speed train scheme design. The five domains provide a reference for the design specification and evaluation criteria of high speed train and a new idea for the train＇s parameter design.
基金support from National Natural Science Foundation of China(Nos.22275145 and 21875184)Natural Science Foundation of Shaanxi Province(Nos.2022JC-10 and 2024JC-YBQN-0112).
文摘CONSPECTUS:In this Account,we present a comprehensive overview of recent advancements in applying data-driven combinatorial design for developing novel highenergy-density materials.Initially,we outline the progress in energetic materials(EMs)development within the framework of the four scientific paradigms,with particular emphasis on the opportunities afforded by the evolution of computer and data science,which has propelled the theoretical design of EMs into a new era of data-driven development.We then discuss the structural features of typical EMs such as TNT,RDX,HMX,and CL-20,namely,a“scaffolds+functional groups”characteristic,underscoring the efficacy of the combinatorial design approach in constructing novel EMs.It has been discerned that those modifications to the scaffolds are the primary driving force behind the enhancement of EMs’properties.Subsequently,we introduce three distinct data-driven design strategies for EMs,each with a different approach to scaffold construction.These strategies are as follows:(1)the known scaffold strategy to identify fused cyclic scaffolds containing oxazole or oxadiazole structures from other fields via database screening and employ a high-throughput combinatorial approach with functional groups to design oxazole(and oxadiazole)-based fused cyclic EMs;(2)the semiknown scaffold strategy to construct semiknown scaffolds by integrating known scaffolds and realize the design of bridged cyclic EMs through a high-throughput combination of functional groups;(3)the unknown scaffold strategy to build caged structural models for quantitative characterization,high-throughput screening caged scaffolds from the database,construct unknown caged scaffolds by substituting atoms or substructures,and combine functional groups to design zero oxygen balance caged EMs.Employing the proposed strategies,the design capacity for EMs reaches an impressive scale of 10^(7) molecules,significantly increasing the probability of obtaining high-performance EMs.Furthermore,the incorporation of property assessment models based on machine learning and density functional theory has achieved a balance between computational accuracy and computational speed.Statistical analysis of the virtual screening has revealed the advantages of bicyclic tri-and tetrasubstituted position scaffolds in the construction of high-energy and easily synthesizable fused cyclic EMs.Additionally,the proposed strategies have been successfully applied to design multifunctional modular energetic materials,resulting in the successful synthesis of three target compounds,validating the effectiveness of data-driven combinatorial design approaches.Lastly,we discuss the current state of high-throughput combinatorial design and,in light of the multifaceted criteria required for the design of EMs,explore the feasibility of multiobjective optimization methods such as Pareto optimization.Moreover,we envision the application of generative models in the subsequent design and development of EMs.We anticipate that this Account will provide valuable insights into the theoretical design of EMs,and we envision the integration of new technologies and methodologies that could play an increasingly significant role in the future discovery of EMs.
