11 results about "Flow instability" patented technology

The application discloses a hypersonic flow instability multi-field coupling prediction method and system, relates to the technical field of modern high-speed aircraft design, and comprises the following steps: acquiring the multi-physical field parameter distribution of a hypersonic flow field; based on a shock wave-boundary layer interaction mechanism, constructing an instability sensitive graph data structure by taking the multi-physical field parameters as input, setting graph nodes in the shock wave influence area, the boundary layer area and the intersection area thereof, and calculating the connection weight between the nodes; performing multi-scale decomposition on the instability sensitive graph data structure, and extracting instability modes of the shock wave scale, the vortex scale and the molecular scale; screening the instability modes by adopting a hypersonic stability criterion, and determining a dominant instability mode; and constructing an instability evolution prediction model based on the dominant instability mode, and outputting the spatiotemporal evolution law of the hypersonic flow instability. The application can solve the problem that the flow instability phenomenon under the complex multi-physical field environment of hypersonic speed is difficult to accurately predict.

This invention relates to the field of glue-filling production technology and discloses a method and system for early warning of defects in the glue-filling process. This invention extracts structural constraint features from product structural data, thereby quantifying abstract product drawings or model information into quantitative engineering parameters that can be analyzed in conjunction with glue-filling process parameters. Simultaneously, it constructs and calculates in real time values ​​for flow instability tendency, venting difficulty, process disturbance intensity, and simulates an evolution curve representing the instantaneous net propulsion capability of the filling process, among other multi-dimensional risk indicators. This allows for the keen detection of complex risks that are about to occur within traditional safety thresholds but due to the synergistic deterioration of multiple process and material parameters in unfavorable ranges. It solves the problems of low accuracy and unclear intervention direction of traditional methods, achieving a qualitative leap from post-event alarms to pre-event warnings and defect attribution, providing a clear direction and valuable time window for process early warning intervention.

The invention provides a propeller phase synchronous measurement device and method based on U-NET feature recognition, and belongs to the technical field of flow field measurement, and the device comprises a test water tank, a to-be-measured object is installed in the test water tank, the four sides of the test water tank are provided with light-transmitting windows, and optical observation and measurement are carried out; the feature capture high-speed camera is arranged on a window on any side of the test water tank, and the water prism is arranged on a window of an observation surface of the test water tank where the feature capture high-speed camera is located; the active control processing system controls synchronous triggering of the feature capturing high-speed camera and the measuring equipment through the synchronous signal controller and records collected data. According to the invention, the characteristic capturing high-speed camera after algorithm learning is used for accurately positioning and outputting required phase angle information, so that the purpose of testing and measuring a flow field at a certain specific angle is achieved; and an indispensable data basis is provided for verifying and improving related unsteady flow physical models, optimizing hydrodynamic performance of fluid machinery and inhibiting flow instability.

The application discloses a kind of aluminium profile production line's management and control system, specifically relates to metal material processing monitoring technical field, first real-time acquisition crystallizer cooling water and the multiple parameters of aluminium liquid, then according to these parameters constructs the three-dimensional flow pattern of cooling water in crystallizer. Then by calculating the mixed index and cold layer volume fraction in the atlas to determine whether the flow instability precursor appears.Once it is determined to be unstable, the required unfolding degree is calculated according to the aluminium liquid temperature and the falling height, and finally the intelligent spoiler array is executed to adjust to form controllable micro-turbulence in the cooling water channel; the application realizes early warning of instability precursor by sensing the micro-flow of cooling water, constructing three-dimensional atlas and calculating the mixed index and cold layer volume fraction; after determining instability, the intelligent spoiler array is dynamically adjusted according to the aluminium liquid temperature and the falling height to form controllable micro-turbulence to actively destroy the explosion trigger condition, and the intrinsic safety and intelligent active control level of aluminium profile production line are improved.

The invention discloses a rod bundle channel flow instability early warning method based on disturbance wave modal identification, and relates to the technical field of nuclear reactor thermal hydraulic safety. The method comprises the steps of collecting a multi-source signal of a two-phase flow in a target rod bundle channel, and performing preprocessing to obtain multi-channel time sequence data; reconstructing space-time evolution information of the disturbance wave field and the two-phase flow characteristic parameters based on the multi-channel time sequence data; modal decomposition is carried out on the spatio-temporal evolution information, and modal characteristic parameters after decomposition are calculated; based on a preset early warning criterion, the modal characteristic parameters obtained through real-time calculation are evaluated; the early warning criterion is associated with a mapping relation between different flow instability types and specific disturbance wave modal characteristic parameters; and according to an evaluation result, executing graded early warning and outputting related information. According to the method, the flow instability early warning and type distinguishing in the accident can be realized by identifying and analyzing the modal characteristics of the specific disturbance waves in the flow channel in real time.

The invention relates to the technical field of aircraft engine design, and particularly provides a method for acquiring an over-stall steady-state flow field of a gas compressor. The gas compressor comprises a rotor and a stator, and the stator is arranged at the downstream of the rotor; the compressor further comprises an auxiliary body, the auxiliary body is arranged on the downstream portion of the stator, the auxiliary body is in a disc shape, and the auxiliary body generates an auxiliary force field to provide aerodynamic damping at a low flow point. The auxiliary body is introduced to the downstream of the air compressor, so that the aerodynamic damping of the compression system can be improved, and the flow instability phenomenon is inhibited.

A method can include detecting instability of multiphase fluid flow in a multiphase fluid production system using sensor measurements from the multiphase fluid production system; and, responsive to the detection of instability, increasing gas injection into the multiphase fluid production system until a variation metric of the sensor measurements decreases to a level indicative of stable multiphase fluid flow in the multiphase fluid production system.

This invention relates to the field of fluid mechanics, disclosing a method, apparatus, device, and storage medium for determining instability in rarefied gas flows. By constructing a physical control model based on gas kinetic theory and solving the baseline flow field, modal and nonmodal analyses are performed on the disturbance evolution model. On one hand, eigenvalues ​​of the disturbance growth rate are obtained to determine the absolute instability risk. On the other hand, singular value decomposition is used to obtain the maximum energy growth factor of the initial disturbance within a finite time to quantify the transient energy growth effect caused by the nonorthogonality of linear operators. Finally, the flow instability risk is output based on the combined determination result of the eigenvalues ​​and the maximum energy growth factor. This invention solves the technical problem that existing kinetic modal analysis methods cannot identify subcritical instability due to neglecting the nonorthogonality of linear operators, achieving comprehensive identification of both absolute and subcritical instability, and significantly improving the accuracy and reliability of rarefied gas flow instability prediction.

The invention relates to the technical field of mold design and manufacturing, and discloses a mold flow behavior virtual simulation design system integrating multi-field characteristics, which comprises a multi-modal data acquisition module, a multi-field coupling simulation module, a material gene spectrum library, an intelligent optimization engine module and a physical verification closed loop module, by arranging a multi-modal data acquisition module, a multi-field coupling simulation module, an intelligent optimization engine module and a physical verification closed loop module, interaction between a temperature field and a stress field of a mold can be dynamically simulated through a thermal-force coupling equation, so that error accumulation of traditional separated simulation is avoided; the coupling effect of melt flow and mold deformation is predicted through a fluid-solid coupling equation to solve the problem of flow instability in traditional single-field simulation, and millimeter-scale and micron-scale cross-scale correlation is achieved by coupling a thermal field and a flow field and coupling a force field and a thermal field. Error accumulation of traditional separated simulation is avoided through a thermal-mechanical coupling equation and a fluid-solid coupling equation.

The invention relates to the field of industrial injection molding control, and particularly discloses an in-mold glue injection speed self-adaptive adjustment method and system based on pressure fluctuation feedback, and the method comprises the steps: predicting dissipation structure evolution and flow instability risks through a meta-learner based on cross-batch historical data, actively adjusting the geometric configuration of a micro-texture, and adjusting the flow instability risk of the micro-texture; the inversion dissipation-structure coupling state tensor is fused in real time; abstracting a mold cavity into a weighted fluid network, constructing a seepage model to identify a bottleneck edge and a redundant edge, and generating a speed pulse topology modulation sequence; a dissipation-structure coupling state tensor is used as feedback, rolling optimization speed pulse is controlled based on model prediction, and the glue injection speed is controlled in real time; calculating the prediction deviation degree and the efficiency difference to drive cross-cycle evolution of the element learner; the system comprises four modules corresponding to functions. According to the method, evolution from single-cycle optimization to cross-cycle element learning is achieved through cooperation of micro-texture active regulation and control and speed self-adaptive adjustment, flow instability is effectively inhibited, and the product quality consistency is improved.

The invention discloses a method for diagnosing hydraulic instability of hydraulic machinery based on joint symbolic dynamics entropy. The method comprises the following steps: firstly, collecting a vibration signal through a vibration sensor mounted outside a system; the collected vibration signals are preprocessed, and characteristic parameters representing internal flow of the system are extracted; then establishing and training a hydraulic instability diagnosis model; and finally, collecting vibration signals on line in real time, calculating the fine composite multi-scale joint symbol dynamics entropy of unknown signals, constructing feature vectors, and inputting the feature vectors into the trained hydraulic instability diagnosis model to diagnose the internal flow state of the unit. According to the method, the problem of noise interference in traditional time domain and frequency domain analysis in the prior art is solved, the accuracy of flow instability diagnosis is improved, and meanwhile the method can be further expanded in practical engineering with the low signal-to-noise ratio.