A coupled Computational Fluid Dynamics-Discrete Element Method(CFD-DEM)approach is used to calculate the interaction of a flexible rag transported by a fluid current with a fixed solid cylinder.More specifically a hyb...A coupled Computational Fluid Dynamics-Discrete Element Method(CFD-DEM)approach is used to calculate the interaction of a flexible rag transported by a fluid current with a fixed solid cylinder.More specifically a hybrid Eulerian-Lagrangian approach is used with the rag being modeled as a set of interconnected particles.The influence of various parameters is considered,namely the inlet velocity(1.5,2.0,and 2.5 m/s,respectively),the angle formed by the initially straight rag with the flow direction(45°,60°and 90°,respectively),and the inlet position(90,100,and 110 mm,respectively).The results show that the flow rate has a significant impact on the permeability of the rag.The higher the flow rate,the higher the permeability and the rag speed difference.The angle has a minor effect on rag permeability,with 45°being the most favorable angle for permeability.The inlet position has a small impact on rag permeability,while reducing the initial distance between the rag an the cylinder makes it easier for rags to pass through.展开更多
This exploration acquaints a momentous methodology with custom chatbot improvement that focuses on pro-ficiency close by viability.We accomplish this by joining three key innovations:LangChain,Retrieval Augmented Gene...This exploration acquaints a momentous methodology with custom chatbot improvement that focuses on pro-ficiency close by viability.We accomplish this by joining three key innovations:LangChain,Retrieval Augmented Generation(RAG),and enormous language models(LLMs)tweaked with execution proficient strategies like LoRA and QLoRA.LangChain takes into consideration fastidious fitting of chatbots to explicit purposes,guaranteeing engaged and important collaborations with clients.RAG’s web scratching capacities engage these chatbots to get to a tremendous store of data,empowering them to give exhaustive and enlightening reactions to requests.This recovered data is then decisively woven into reaction age utilizing LLMs that have been calibrated with an emphasis on execution productivity.This combination approach offers a triple advantage:further developed viability,upgraded client experience,and extended admittance to data.Chatbots become proficient at taking care of client questions precisely and productively,while instructive and logically pertinent reactions make a more regular and drawing in cooperation for clients.At last,web scratching enables chatbots to address a more extensive assortment of requests by conceding them admittance to a more extensive information base.By digging into the complexities of execution proficient LLM calibrating and underlining the basic job of web-scratched information,this examination offers a critical commitment to propelling custom chatbot plan and execution.The subsequent chatbots feature the monstrous capability of these advancements in making enlightening,easy to understand,and effective conversational specialists,eventually changing the manner in which clients cooperate with chatbots.展开更多
基金funded by the Zhejiang Provincial Natural Science Foundation of China(Grant Nos.LY21E060004,LGG22E060011)National Natural Science Foundation of China(Grant No.51976193).
文摘A coupled Computational Fluid Dynamics-Discrete Element Method(CFD-DEM)approach is used to calculate the interaction of a flexible rag transported by a fluid current with a fixed solid cylinder.More specifically a hybrid Eulerian-Lagrangian approach is used with the rag being modeled as a set of interconnected particles.The influence of various parameters is considered,namely the inlet velocity(1.5,2.0,and 2.5 m/s,respectively),the angle formed by the initially straight rag with the flow direction(45°,60°and 90°,respectively),and the inlet position(90,100,and 110 mm,respectively).The results show that the flow rate has a significant impact on the permeability of the rag.The higher the flow rate,the higher the permeability and the rag speed difference.The angle has a minor effect on rag permeability,with 45°being the most favorable angle for permeability.The inlet position has a small impact on rag permeability,while reducing the initial distance between the rag an the cylinder makes it easier for rags to pass through.
文摘This exploration acquaints a momentous methodology with custom chatbot improvement that focuses on pro-ficiency close by viability.We accomplish this by joining three key innovations:LangChain,Retrieval Augmented Generation(RAG),and enormous language models(LLMs)tweaked with execution proficient strategies like LoRA and QLoRA.LangChain takes into consideration fastidious fitting of chatbots to explicit purposes,guaranteeing engaged and important collaborations with clients.RAG’s web scratching capacities engage these chatbots to get to a tremendous store of data,empowering them to give exhaustive and enlightening reactions to requests.This recovered data is then decisively woven into reaction age utilizing LLMs that have been calibrated with an emphasis on execution productivity.This combination approach offers a triple advantage:further developed viability,upgraded client experience,and extended admittance to data.Chatbots become proficient at taking care of client questions precisely and productively,while instructive and logically pertinent reactions make a more regular and drawing in cooperation for clients.At last,web scratching enables chatbots to address a more extensive assortment of requests by conceding them admittance to a more extensive information base.By digging into the complexities of execution proficient LLM calibrating and underlining the basic job of web-scratched information,this examination offers a critical commitment to propelling custom chatbot plan and execution.The subsequent chatbots feature the monstrous capability of these advancements in making enlightening,easy to understand,and effective conversational specialists,eventually changing the manner in which clients cooperate with chatbots.
