This paper analyzes the basic characters of optimum open-pit limit.According to them,following general rule for designing pit limit is obtained.The incremental stripping ratios is not greater than the break-even strip...This paper analyzes the basic characters of optimum open-pit limit.According to them,following general rule for designing pit limit is obtained.The incremental stripping ratios is not greater than the break-even stripping ratios,or the net incremental value is not less than zero.The rule can be used both in traditional and computer methods as direct basis to determine an optimum limit for any kinds of deposit.展开更多
Dynamic mode decomposition(DMD),as a data-driven method,has been frequently used to construct reduced-order models(ROMs)due to its good performance in time extrapolation.However,existing DMD-based ROMs suffer from hig...Dynamic mode decomposition(DMD),as a data-driven method,has been frequently used to construct reduced-order models(ROMs)due to its good performance in time extrapolation.However,existing DMD-based ROMs suffer from high storage and computational costs for high-dimensional problems.To mitigate this problem,we develop a new DMD-based ROM,i.e.,TDMD-GPR,by combining tensor train decomposition(TTD)and Gaussian process regression(GPR),where TTD is used to decompose the high-dimensional tensor into multiple factors,including parameterdependent and time-dependent factors.Parameter-dependent factor is fed into GPR to build the map between parameter value and factor vector.For any parameter value,multiplying the corresponding parameter-dependent factor vector and the timedependent factor matrix,the result describes the temporal behavior of the spatial basis for this parameter value and is then used to train the DMD model.In addition,incremental singular value decomposition is adopted to acquire a collection of important instants,which can further reduce the computational and storage costs of TDMD-GPR.The comparison TDMD and standard DMD in terms of computational and storage complexities shows that TDMD is more advantageous.The performance of the TDMD and TDMD-GPR is assessed through several cases,and the numerical results confirm the effectiveness of them.展开更多
文摘This paper analyzes the basic characters of optimum open-pit limit.According to them,following general rule for designing pit limit is obtained.The incremental stripping ratios is not greater than the break-even stripping ratios,or the net incremental value is not less than zero.The rule can be used both in traditional and computer methods as direct basis to determine an optimum limit for any kinds of deposit.
基金supported by the Taishan Scholars Program(tsqn202211059)the National Natural Science Foundation of China(12201592)+1 种基金the Shandong Provincial Natural Science Foundation(ZR2022QA006)Laoshan Laboratory(LSKJ202202302)。
文摘Dynamic mode decomposition(DMD),as a data-driven method,has been frequently used to construct reduced-order models(ROMs)due to its good performance in time extrapolation.However,existing DMD-based ROMs suffer from high storage and computational costs for high-dimensional problems.To mitigate this problem,we develop a new DMD-based ROM,i.e.,TDMD-GPR,by combining tensor train decomposition(TTD)and Gaussian process regression(GPR),where TTD is used to decompose the high-dimensional tensor into multiple factors,including parameterdependent and time-dependent factors.Parameter-dependent factor is fed into GPR to build the map between parameter value and factor vector.For any parameter value,multiplying the corresponding parameter-dependent factor vector and the timedependent factor matrix,the result describes the temporal behavior of the spatial basis for this parameter value and is then used to train the DMD model.In addition,incremental singular value decomposition is adopted to acquire a collection of important instants,which can further reduce the computational and storage costs of TDMD-GPR.The comparison TDMD and standard DMD in terms of computational and storage complexities shows that TDMD is more advantageous.The performance of the TDMD and TDMD-GPR is assessed through several cases,and the numerical results confirm the effectiveness of them.
