10 results about "Downburst" patented technology

The invention discloses an angle-adjustable multi-fan array type downburst three-dimensional wind field physical simulation device. The angle-adjustable multi-fan array type downburst three-dimensional wind field physical simulation device comprises a modularized detachable frame and a multi-fan array installed in the frame. The fan array is composed of a plurality of fan rows, and each row comprises a plurality of independent fan units. Each fan unit is provided with an independent pitching and yawing adjusting mechanism which is driven by a servo motor, and accurate control over the outflow angle is achieved. A vertical guide rail is arranged on the frame, each row of fans is matched with the guide rail through a sliding block mechanism, and accurate positioning of the height of the whole row is achieved through an independent height adjusting mechanism. By independently controlling the rotating speed, the pitch angle, the yaw angle and the height of each row of each fan unit and utilizing the space vector synthesis technology, the three-dimensional flow field characteristic diffused after the downburst impacts the ground is effectively reproduced, and the problems that a traditional simulation technology depends on a wind tunnel, and a three-dimensional wind field structure is difficult to truly restore are solved.

The invention discloses a method for determining the most unfavorable working condition of downburst wind load of a power transmission line. The method comprises the following steps: 1) obtaining the line wind load of the power transmission line under downburst; 2) performing quasi-static linear analysis on the power transmission line-insulator system under the action of downburst wind to obtain wind vibration response of the multi-span power transmission line-insulator system; 3) with seeking maximum transverse and longitudinal wind loads of wires of a tangent tower and a strain tower as an optimization target, constructing a worst working condition parameter combination optimization model when the counterforce transmitted to the tower by the multi-span power transmission line-insulator system is maximum; 4) solving the model to obtain the most unfavorable working conditions of the tangent tower and the strain tower; and 5) selecting power transmission lines of different conductor types as research objects, and optimizing the maximum values of the transverse and longitudinal wind loads of the conductors of the tangent tower and the strain tower to obtain the most unfavorable wind effect parameter configuration under different working conditions. The method provided by the invention remarkably reduces the time cost and the calculation overhead required by numerical simulation.

The invention discloses an air line wind field risk automatic detection method and system based on meteorological grid data. The method comprises the steps of obtaining starting and ending point coordinates of a target area; geometrically segmenting the route based on a preset interval to generate a route point sequence; selecting a representative point set for wind field analysis from the grid wind field data based on the route point sequence; based on the representative point set, constructing a geometric analysis structure for wind field analysis; and based on a geometric analysis structure and the grid wind field data, calculating and integrating a wind field divergence index, a wind gain index and a wind loss index, carrying out alarm arbitration, and generating alarm information for micro downburst and / or wind shear. According to the method, meteorological grid data are processed by combining geometric segmentation of the air line and wind field divergence analysis, and micro-downburst and wind shear influencing the air line are automatically identified and positioned. And the continuity, the positioning precision and the anti-interference capability of low-altitude flight route wind field risk monitoring are remarkably improved.

The invention discloses a downburst symmetric and asymmetric structure analysis method facing meteorological space field data and based on Fourier wave number decomposition, a medium and a program product, and belongs to the technical field of meteorological data processing and diagnosis. According to the method, for the attributes of small temporal-spatial scale and rapid evolution of downburst, a downburst disaster point is determined by using a ground observation maximum wind speed threshold criterion, an event constraint area is determined, and a target variable field Var is collected; and calculating the horizontal center of the weather system based on Var amplitude weighting. A radius and azimuth angle sampling frame is constructed by taking a horizontal center as an original point, interpolation and polar coordinate resampling are realized by adopting natural adjacent point interpolation, and a regularized polar coordinate variable field Var (r, theta) is obtained. Fourier series expansion is executed along an azimuth angle, a zero wave number symmetric component and a multi-order non-zero wave number asymmetric mode are separated, an amplitude-phase parameter set is output, structural feature extraction and quantitative characterization are achieved, and support is provided for disaster falling area analysis and business evaluation.

This invention discloses a low-altitude wind shear recognition system and method based on convolutional neural networks, belonging to the field of meteorological monitoring technology. The system includes: a low-altitude wind shear dataset construction module, which generates a wind shear image dataset containing crosswind shear, low-altitude jet stream, headwind and tailwind shear, and micro-downbursts; a data preprocessing module, which performs preprocessing on the images; a convolutional neural network module, which incorporates a lightweight convolutional neural network with a progressive channel design, trained using a two-stage transfer learning strategy, and utilizes an improved loss function obtained by weighted summation of cross-entropy loss, flow direction consistency constraint loss, and global vortex intensity constraint loss to obtain a wind shear recognition model; and a wind shear intelligent classification module, used to identify the type of wind shear in low-altitude wind field images in real time. This invention has the advantages of high recognition accuracy, low computational load, good real-time performance, and low hardware cost, and can effectively ensure the flight safety of UAVs in complex low-altitude wind field environments.

The present application relates to the technical field of meteorological disaster monitoring and early warning and numerical prediction, and more particularly to an extreme gale weather prediction method based on a meteorological prediction large model, which comprises the following steps: obtaining a numerical weather prediction three-dimensional physical field and weather radar, meteorological satellite and ground meteorological observation and assimilating them into a consistent field; time advancing and obtaining sinking potential energy and cold pool diagnosis through a micro-downburst calculation program, forming a coarse resolution gust through boundary layer similar mapping and hysteresis kernel, generating a fine scale base map under the restriction of a cold pool front by using an optimal transport of advection constraint, outputting an image element gust distribution and an over-threshold probability by a conditional diffusion probability generation model and extracting a wind damage polygon; fusing radar, satellite, lightning and ground gust, using monotonic normalization flow calibration and intersection and union ratio and conformal coverage verification, updating parameters with limited online amplitude, and improving the reliability of spatial positioning, occurrence time and probability description.

The invention relates to the technical field of wind engineering and artificial intelligence, and discloses a downburst average wind speed prediction method and system based on a regression random forest, and the method comprises the steps: building a CFD numerical simulation model of mobile downburst based on an impact jet theory; secondly, performing uniform sampling in a two-dimensional parameter space formed by an environment wind speed and a storm center moving speed by using a Haarton sequence to generate a parameter combination sample; thirdly, constructing a training data set containing multiple groups of input features and target output through batch CFD calculation; then, constructing a regression random forest model, and optimizing the number and the maximum depth of the decision trees by using parameter sensitivity analysis; and finally, using the trained model to rapidly predict any to-be-predicted working condition. According to the method, the wind speed double-peak characteristics of the mobile downburst can be accurately captured, the calculation cost is greatly reduced while the high prediction precision is ensured, and real-time rapid prediction of the downburst wind field is realized.

The invention provides a navigation risk weather identification method based on laser wind-finding radars, and the method comprises the steps: carrying out the cooperative PPI scanning through arranging the laser wind-finding radars at two ends of an airport runway, and carrying out the horizontal wind field data meshing through employing the PPI scanning data obtained by the two laser wind-finding radars in real time as basic data; and identifying a wind shear region based on the wind shear characteristics in the horizontal wind field. In a wind shear area, a judgment method based on navigation risk weather characteristics is combined, monitoring and early warning of medium and small-scale severe convection weather (micro downburst, gust front, turbulence, strong wind areas and the like) are achieved, reliable data support is provided for airport wind shear early warning, and the coping capacity of an aircraft to sudden wind shear in the take-off and landing stage is improved.