This study investigates the instability characteristics of dynamic disasters resulting from disruption caused by extracting resources underground. Utilizing the split Hopkinson pressure bar (SHPB) system, the dynamic ...This study investigates the instability characteristics of dynamic disasters resulting from disruption caused by extracting resources underground. Utilizing the split Hopkinson pressure bar (SHPB) system, the dynamic response mechanism of coal energy evolution is examined, and the energy instability criterion is established. The validity of the instability criterion is explored from the standpoint of damage progression. The results demonstrate that the energy conversion mechanism undergoes a fundamental alternation under impact disturbance. Moreover, the energy release rate as well as the energy dissipation rate undergo comparable changes across distinct levels of impact disturbance. The distinction between the energy release rate and the energy dissipation rate (DRD) increases as coal mass deformation grows. Prior to coal facing instability and failure, the DRD experienced an inflection point followed by a sharp decrease. In conjunction with the discussion on the damage evolution, the physical and mechanical significance of DRD remains clear, which can essentially describe the whole impact loading process. The phenomenon that the inflection point appears and DRD subsequently suddenly decreases can be employed as the energy criterion prior to the failure of instability. Furthermore, this paper provides significant reference for the prediction of dynamic instability of coal under dynamic disturbance.展开更多
While analytical solutions of critical(phase)transitions in dynamical systems are abundant for simple nonlinear systems,such analysis remains intractable for real-life dynamical systems.A key example is thermoacoustic...While analytical solutions of critical(phase)transitions in dynamical systems are abundant for simple nonlinear systems,such analysis remains intractable for real-life dynamical systems.A key example is thermoacoustic insta-bility in combustion,where prediction or early detection of the onset of instability is a hard technical challenge,which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries.The instabilities arising in combustion chambers of engines are mathematically too complex to model.To address this issue in a data-driven manner instead,we propose a novel deep learning architecture called 3D convolutional selective autoencoder(3D-CSAE)to detect the evolution of self-excited oscillations using spatiotemporal data,i.e.,hi-speed videos taken from a swirl-stabilized combustor(laboratory surrogate of gas turbine engine combustor).3D-CSAE consists of filters to learn,in a hierarchical fashion,the complex visual and dynamic features related to combustion instability from the training videos(i.e.,two spatial dimensions for the image frames and the third dimension for time).We train the 3D-CSAE on frames of videos obtained from a limited set of operating conditions.We select the 3D-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video.The proposed model clearly shows performance improvement in detecting the precursors and the onset of instability.The machine learning-driven results are verified with physics-based off-line measures.Advanced active control mechanisms can directly leverage the proposed online detection capability of 3D-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions.展开更多
基金Projects(51934007,12072363,52004268) supported by the National Natural Science Foundation of ChinaProject(22KJD440002) supported by the Natural Science Fund for Colleges and Universities in Jiangsu Province,China。
文摘This study investigates the instability characteristics of dynamic disasters resulting from disruption caused by extracting resources underground. Utilizing the split Hopkinson pressure bar (SHPB) system, the dynamic response mechanism of coal energy evolution is examined, and the energy instability criterion is established. The validity of the instability criterion is explored from the standpoint of damage progression. The results demonstrate that the energy conversion mechanism undergoes a fundamental alternation under impact disturbance. Moreover, the energy release rate as well as the energy dissipation rate undergo comparable changes across distinct levels of impact disturbance. The distinction between the energy release rate and the energy dissipation rate (DRD) increases as coal mass deformation grows. Prior to coal facing instability and failure, the DRD experienced an inflection point followed by a sharp decrease. In conjunction with the discussion on the damage evolution, the physical and mechanical significance of DRD remains clear, which can essentially describe the whole impact loading process. The phenomenon that the inflection point appears and DRD subsequently suddenly decreases can be employed as the energy criterion prior to the failure of instability. Furthermore, this paper provides significant reference for the prediction of dynamic instability of coal under dynamic disturbance.
文摘While analytical solutions of critical(phase)transitions in dynamical systems are abundant for simple nonlinear systems,such analysis remains intractable for real-life dynamical systems.A key example is thermoacoustic insta-bility in combustion,where prediction or early detection of the onset of instability is a hard technical challenge,which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries.The instabilities arising in combustion chambers of engines are mathematically too complex to model.To address this issue in a data-driven manner instead,we propose a novel deep learning architecture called 3D convolutional selective autoencoder(3D-CSAE)to detect the evolution of self-excited oscillations using spatiotemporal data,i.e.,hi-speed videos taken from a swirl-stabilized combustor(laboratory surrogate of gas turbine engine combustor).3D-CSAE consists of filters to learn,in a hierarchical fashion,the complex visual and dynamic features related to combustion instability from the training videos(i.e.,two spatial dimensions for the image frames and the third dimension for time).We train the 3D-CSAE on frames of videos obtained from a limited set of operating conditions.We select the 3D-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video.The proposed model clearly shows performance improvement in detecting the precursors and the onset of instability.The machine learning-driven results are verified with physics-based off-line measures.Advanced active control mechanisms can directly leverage the proposed online detection capability of 3D-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions.
