Disclosed is a wind turbine layout optimization method combining with a dispatching strategy for the wind farm. In the wind farm micro-siting stage, the installed wind turbines number and the arrangement positions are optimized. In this method, the dispatching strategy of wind turbines is considered during the layout optimization of wind turbines, and the axial induction factor of each wind turbine is introduced into the layout optimization variables. The dispatching strategy of maximizing the wind farm power generation is combined with the layout optimization of wind turbines in the construction stage of the wind farm, so that the wake effect is effectively reduced and the capacity cost is reduced, which meet the requirement of actual wind farm. A hybrid optimization algorithm is proposed in this method, with a greedy algorithm to optimize the turbine number and a particle swarm optimization (PSO) algorithm to refine the turbine layout scheme.

The present invention discloses a hoisting container pose control method of a double-rope winding type ultra-deep vertical shaft hoisting system. The method comprises the following steps of step 1, building a mathematical model of a double-rope winding type ultra-deep vertical shaft hoisting subsystem; step 2, building a position closed-loop mathematical model of an electrohydraulic servo subsystem; step 3, outputting a flatness characteristics of a nonlinear system; step 4, designing a pose leveling flatness controller of a double-rope winding type ultra-deep vertical shaft hoisting subsystem; and step 5, designing a position closed-loop flatness controller of the electrohydraulic servo subsystem.

The disclosure relates to a computer-implemented method for performing a deformation-based physics simulation described by a partial differential equation. The method comprises providing a geometrical model representing a portion of the real world. The method comprises performing a hybrid discretization of the model. The performing of the hybrid discretization comprises discretizing one or more first objects in the portion each with a mesh and one or more second objects in the portion each with a point cloud. The method comprises one or more iterations. Each iteration comprises performing a simulation run based on a discretization of the partial differential equation and on the hybrid discretization. The iteration comprises assessing a deformation as a result of the simulation run. The deformation corresponds to a shape deformation of the one or more second objects. The iteration comprises updating the hybrid discretization to model the deformation by moving points of a point cloud.

Disclosed are techniques for determining shroud size of a fan. The techniques receive by a computer processing system digital data of a three-dimensional representation of a shroud of an axial fan, partition the received data into a first partition corresponding to a shroud segment and a second partition corresponding to a fan segment. determine a shroud boundary ring for the shroud segment and a viewing angle of the shroud boundary ring, apply to an image of the first partition a beam shooting process to determine the shroud diameter, determine if there are pixels in the image, which have values that produce signals indicating that the pixels are coincident with portions of the shroud and when signal is detected, calculate the shroud diameter. One aspect includes using the determined should size opening for performing a flow simulation.

A computer-implemented method for generating a prediction model for predicting rotor blade damages of a wind turbine is provided, wherein the method provides data including data sets for wind turbines, where each data set includes respective values of turbine variables(s), weather variable(s) and damage variable(s) wherein the method includes: a) discretizing the values, resulting in modified data sets; b) structure learning of a plurality of Bayesian networks based on the modified data sets, where each Bayesian network is learned by another learning method; c) determining an optimum Bayesian network based on a performance measure reflecting the prediction quality of a respective Bayesian network, where the optimum Bayesian network has the best performance measure; d) parameter learning of the optimum Bayesian network based on the modified data sets, resulting in conditional probabilities, where the optimum Bayesian network combination with the conditional probabilities is the prediction model.

An apparatus and method for forming an engine component for a turbine engine, the engine component comprising a wall bounding an interior; a panel portion defining a portion of the wall, the panel portion comprising: an outer wall; an inner wall spaced from the outer wall to define a wall gap; and a structural segment formed within the wall gap comprising at least one structural element. The method including calculating a factor and adjusting a variable until the factor is between a given range.

Disclosed is a wind turbine layout optimization method combining with a dispatching strategy for the wind farm. In the wind farm micro-siting stage, the installed wind turbines number and the arrangement positions are optimized. In this method, the dispatching strategy of wind turbines is considered during the layout optimization of wind turbines, and the axial induction factor of each wind turbine is introduced into the layout optimization variables. The dispatching strategy of maximizing the wind farm power generation is combined with the layout optimization of wind turbines in the construction stage of the wind farm, so that the wake effect is effectively reduced and the capacity cost is reduced, which meet the requirement of actual wind farm. A hybrid optimization algorithm is proposed in this method, with a greedy algorithm to optimize the turbine number and a particle swarm optimization (PSO) algorithm to refine the turbine layout scheme.

Methods, apparatus, systems, and articles of manufacture are disclosed to improve fluid flow simulations. An example apparatus includes a property identifier to, prior to execution of the computer-based model, identify donors and recipients representative of one or more model regions of the computer-based model to simulate for a plurality of time steps including a first time step and a second time step, the donors having donor properties, in response to computing a flow field for the first time step of the computer-based model, a property extractor to extract the donor properties from extraction surfaces of the donors, and a property assignor to assign the donor properties to boundary surfaces of respective ones of the recipients, and a flow field generator to generate an output flow field for the second time step based on the recipients having the assigned donor properties.

Disclosed is a method for analyzing wind turbine blade coating fatigue due to rain erosion. According to the method, a stochastic rain field model is established, and the coating fatigue life of the wind turbine blades is calculated based on a crack propagation theory. The present patent innovatively develops a stochastic rain field model considering the shape, size, impact angle, and impact speed of raindrops to simulate the raindrop impact process, analyzes the impact stress of raindrops on the blade coating by using a smooth particle hydrodynamics method and a finite element analysis method, calculates the impact stress of all raindrops in the random rainfall process by using a stress interpolation method, and carries out fatigue analysis for the blade coating based on the impact stress.

A system for generating power includes an environmental engine that determines performance metrics for a plurality of wind turbines deployed at a plurality of windfarms, such that each windfarm includes a corresponding subset of the plurality of windfarms. The performance metrics for a given wind turbine of the plurality of wind turbines characterizes wind flowing over blades of the given wind turbine. The system includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a set of models for each wind turbine of the plurality of wind turbines, wherein each model of each set of models is generated with a different machine learning algorithm and selects, for each respective set of models, a model with a highest efficiency metric. The AI engine provides edge computing systems operating at the plurality of windfarms with a selected model and corresponding recommended operating parameters.