Design Methods and Strategies for Forward and Inverse Problems of Turbine Blades Based on Machine Learning

To study the feasibility of using machine learning technology to solve the forward problem(prediction of aerodynamic parameters)and the inverse problem(prediction of geometric parameters)of turbine blades,this paper built a forward problem model based on backpropagation artificial neural networks(BP-ANNs)and an inverse problem model based on radial basis function artificial neural networks(RBF-ANNs).The S2(a stream surface obtained by extending a radial curve in turbo blades)calculation program was used to generate the dataset for single-stage turbo blades,and the back propagation algorithm was used to train the model.The parameters of five blade sections in a single-stage turbine were selected as inputs of the forward problem model,including stagger angle,inlet geometric angle,outlet geometric angle,wedge angle of leading edge pressure side,wedge angle of leading edge suction side,wedge angle of trailing edge,rear bending angle,and leading edge diameter.The outputs are efficiency,power,mass flow,relative exit Mach number,absolute exit Mach number,relative exit flow angle,absolute exit flow angle and reaction degree,which are eight aerodynamic parameters.The inputs and outputs of the inverse problem model are the opposite of that of the forward problem model.The models can accurately predict the aerodynamic parameters and geometric parameters,and the mean square errors(MSEs)of the forward problem test set and the inverse problem test set are 0.001 and 0.00035,respectively.This study shows that machine learning technology based on neural networks can be flexibly applied to the design of forward and inverse problems of turbine blades,and the models built by this method have practical application value in regression prediction problems.

High performance architecture for unified forward and inverse transform of HEVC

High efficiency video coding （HEVC） transform algorithm for residual coding uses 2-dimensional （2D） 4 × 4 transforms with higher precision than H.264＇s 4 ×4 transforms, resulting in increased hardware complexity. In this paper, we present a shared architecture that can compute the 4 ~4 forward discrete cosine transform （DCT） and inverse discrete cosine transform （IDCT） of HEVC using a new mapping scheme in the video processor array structure. The architecture is implemented with only adders and shills to an area-efficient design. The proposed architecture is synthesized using ISE 14.7 and implemented using the BEE4 platform with the Virtex-6 FF1759 LX550T field programmable gate array （FPGA）. The result shows that the video processor array structure achieves a maximum operation frequency of 165.2 MHz. The architecture and its implementation are presented in this paper to demonstrate its programmable and high performance.

Uniqueness, scale, and resolution issues in groundwater model parameter identification

This paper first visits uniqueness, scale, and resolution issues in groundwater flow forward modeling problems. It then makes the point that non-unique solutions to groundwater flow inverse problems arise from a lack of information necessary to make the problems well defined. Subsequently, it presents the necessary conditions for a well-defined inverse problem. They are full specifications of （1） flux boundaries and sources/sinks, and （2） heads everywhere in the domain at at least three times （one of which is t = 0）, with head change everywhere at those times being nonzero for transient flow. Numerical experiments are presented to corroborate the fact that, once the necessary conditions are met, the inverse problem has a unique solution. We also demonstrate that measurement noise, instability, and sensitivity are issues related to solution techniques rather than the inverse problems themselves. In addition, we show that a mathematically well-defined inverse problem, based on an equivalent homogeneous or a layered conceptual model, may yield physically incorrect and scenario-dependent parameter values. These issues are attributed to inconsistency between the scale of the head observed and that implied by these models. Such issues can be reduced only if a sufficiently large number of observation wells are used in the equivalent homogeneous domain or each layer. With a large number of wells, we then show that increase in parameterization can lead to a higher-resolution depiction of heterogeneity if an appropriate inverse methodology is used. Furthermore, we illustrate that, using the same number of wells, a highly parameterized model in conjunction with hydraulic tomography can yield better characterization of the aquifer and minimize the scale and scenario-dependent problems. Lastly, benefits of the highly parameterized model and hydraulic tomography are tested according to their ability to improve predictions of aquifer responses induced by independent stresses not used in the inverse modeling efforts.

Simulation research and application on response characteristics of detecting water-filled goaf by transient electromagnetic method

Water inrush disasters poses a great threat to the safe exploitation of coal resources.To solve this problem,the transient electromagnetic method(TEM)was proposed to accurately detect the water accumulation in the goaf.The electromagnetic response characteristics of diferent water-flled goaves were studied by electromagnetic feld theory,numerical simulation and feld verifcation.Through the models of 100%water accumulation,50%water accumulation,0%water accumulation,100%water accumulation with collapsed rock,50%water accumulation with collapsed rock and 0%water accumulation with collapsed rock goaf,the characteristics of induced voltage attenuation curves were studied.Meanwhile,the relationship between the attenuation voltage value and area of the transmitting coil,the depth of the goaf,the background resistivity,and the delay time were also simulated.The results illustrate that the attenuation curve of induced voltage presented a regular exponential decay form in the 0%water accumulation model but existed abnormal exaltation for voltage in water-flled model.Through the linear ftting curve,it can be seen that the abnormal intensity of the induced voltage becomes stronger as the distance between the measuring point and the center of the target decrement.Moreover,the abnormal amplitude of the induced voltage increases with the rise of the water accumulation and collapsed rock will weakly reduce the low-resistivity anomalous efect on the water-accumulated goaf.In addition,the response value of the attenuation voltage increased as the area of the transmitting coil increases,but decreased with increasing delay time and increasing background resistivity and depth of the target body.The feld detection results of the Majiliang coal mine also confrmed the theoretical analysis and the numerical simulation.

Modeling of double ridge waveguide using ANN

The ridge waveguide is useful in various microwave applications because it can be operated at a lower frequency and has lower impedance and a wider mode separation than a simple rectangular waveguide. An accurate model is essential for the analysis and design of ridge waveguide that can be obtained using electromag- netic simulations. However, the electromagnetic simula- tion is expensive for its high computational cost. Therefore, artificial neural networks （ANNs） become very useful especially when several model evaluations are required during design and optimization. Recently, ANNs have been used for solving a wide variety of radio frequency （RF） and microwave computer-aided design （CAD） problems. Analysis and design of a double ridge waveguide has been presented in this paper using ANN forward and inverse models. For the analysis, a simple ANN forward model is used where the inputs are geometrical parameters and the outputs are electrical parameters. For the design of RF and microwave components, an inverse model is used where the inputs are electrical parameters and the outputs are geometrical parameters. This paper also presents a comparison of the direct inverse model and the proposed inverse model.