DEVELOPMENT AND TEST OF FREQUENCY SUBSECTION REGULATION SYSTEM FOR COMBINE HARVESTER HEADER CUTTER

Aiming at the problems that the cutter frequency of combine harvester is difficult to be adjusted adaptively with the forward speed, and that the missed cut or repeated cut may cause the harvesting loss to increase and the operation effect to fluctuate greatly, the system is designed to regulate the cutter frequency of combine harvester by sections. By constructing the cutter trajectory equation, the influence of the relationship between the forward speed of the harvester and the cutting frequency on the cutting area is analyzed, and the optimum cutting frequency range at different operating speeds is determined. The results show that the error between the actual cutting frequency and the desired frequency of the cutter is less than 0.8Hz, and the maximum relative error is less than 8.6%; the average steady-state adjustment time of the system is 1.3s when the input cutting frequency of the device changes abruptly. The research class provides technical support for the improvement of the combine harvester handling system and the increase of the machine automation level.

Low-Latency and Minor-Error Architecture for Parallel Computing XY-Like Functions with High-Precision Floating-Point Inputs

This paper proposes a novel architecture for the computation of XY-like functions based on the QH CORDIC (Quadruple-Step-Ahead Hyperbolic Coordinate Rotation Digital Computer) methodology. The proposed architecture converts direct computing of function XY to logarithm, multiplication, and exponent operations. The QH CORDIC methodology is a parallel variant of the traditional CORDIC algorithm. Traditional CORDIC suffers from long latency and large area, while the QH CORDIC has much lower latency. The computation of functions lnx and ex is accomplished with the QH CORDIC. To solve the problem of the limited range of convergence of the QH CORDIC, this paper employs two specific techniques to enlarge the range of convergence for functions lnx and ex, making it possible to deal with high-precision floating-point inputs. Hardware modeling of function XY using the QH CORDIC is plotted in this paper. Under the TSMC 65 nm standard cell library, this paper designs and synthesizes a reference circuit. The ASIC implementation results show that the proposed architecture has 30 more orders of magnitude of maximum relative error and average relative error than the state-of-the-art. On top of that, the proposed architecture is also superior to the state-of-the-art in terms of latency, word length and energy efficiency (power × latency × period /efficient bits).

Research on Energy-Saving Optimization of Rural Houses in Specific Regions Based on Internet of Things

With the expansion of social energy use, the optimization of residential energy conservation has become urgent and important. The research on rural housing in areas with hot summers and cold winters started late and is relatively backward in terms of housing energy saving, which greatly hinders the sustainable development of rural areas. The application of the Internet of Things technology helps to provide a stable technical guarantee for energy-saving optimization. Therefore, this paper takes the energy-saving optimization design of rural houses in hot summer and cold winter areas based on the Internet of Things technology as the research theme. This article first takes H rural area as the research object and analyzes the climatic conditions of the place, that is, the typical characteristics of hot summer and cold winter. Then, the intelligent temperature acquisition system is designed, and the working process and main hardware modules of the system are introduced. This text combines GPRS and Internet of Things technology, designs the temperature control system, and carries on the test to the system operation effect. The test results show that during the heating period in January, before and after the temperature control system controls the room temperature, the maximum relative error between the set temperature value and the actual temperature value is 0.49 and 0.27, respectively. It can be seen that the intelligent temperature energy-saving control system can collect the indoor temperature in real time and can well control the heating equipment.

Estimation of the Dynamic Parameters of the Bearings in a Flexible Rotor System Utilizing Electromagnetic Excitation by a Built-In Motor

Estimation of the dynamic parameters of bearings is essential in order to be able to interpret the performance of rotating machinery. In this paper, we propose a method to estimate the dynamic parameters of the bearings in a flexible rotor system. By utilizing the electromagnetic excitation generated by a built-in PM motor and finite element (FE) modeling of the rotor, safe, low-cost, and real-time monitoring of the bearing dynamics can be achieved. The radial excitation force is generated by injecting an alternating d-axis current into the motor windings. The FE model of the rotor and the measured frequency responses at the motor and bearing locations are used to estimate the dynamic parameters of the bearings. To evaluate the feasibility of the proposed method, numerical simulation and experiments were carried out on a flexible rotor system combined with a bearingless motor (BELM) having both motor windings and suspension windings. The numerical simulation results show that the proposed algorithm can accurately estimate the dynamic parameters of the bearings. In the experiment, the estimates made when utilizing the excitation force generated by the motor windings are compared with the estimates made when utilizing the excitation force generated by the suspension windings. The results show that most of the stiffness and damping coefficients for the two experiments are in good agreement, within a maximum error of 8.92%. The errors for some coefficients are large because the base values of these coefficients are small in our test rig, so these coefficients are sensitive to deviations. The natural frequencies calculated from the dynamic parameters estimated from the two experiments are also in good agreement, within a maximum relative error of 3.04%. The proposed method is effective and feasible for turbomachines directly connected to motors, which is highly significant for field tests.

Comprehensive Knowledge-Driven AI System for Air Classification Process

Air classifier devices have a distinct advantage over other systems used to separate materials. They maximize the mill’s capacity and therefore constitute efficient methods of reducing the energy consumption of crushing and grinding operations. Since improvement in their performance is challenging, the development of an efficient modeling system is of great practical significance. The paper introduces a novel, knowledge-based classification (FLClass) system of bulk materials. A wide range of operating parameters are considered in the study: the mean mass and the Sauter mean diameter of the fed material, classifier rotor speed, working air pressure, and test conducting time. The output variables are the Sauter mean diameter and the cut size of the classification product, as well as the performance of the process. The model was successfully validated against experimental data. The maximum relative error between the measured and predicted data is lower than 9%. The presented fuzzy-logic-based approach allows an optimization study of the process to be conducted. For the considered range of input parameters, the highest performance of the classification process is equal to almost 362 g/min. To the best of our knowledge, this paper is the first one available in open literature dealing with the fuzzy logic approach in modeling the air classification process of bulk materials.

NUMERICAL EXPERIMENTS WITH AN ALGORITHM FOR PROCESSING EXPERIMENTAL DATA TO DETERMINE THE PARAMETERS OF A RHEOLOGICAL MODEL OF A LIQUID WITH THE EFFECT OF «SOLIDIFICATION»

Предложена методика обработки экспериментальных данных и алгоритм для ее реализации по определению параметров реологической модели вязкопластической жидкости, которая демонстрирует проявление эффекта «отвердевания». С целью проверки работоспособности алгоритма проведены численные эксперименты с наборами генерируемых случайным образом “псевдоэкспериментальных” данных с заранее заданной величиной максимальной относительной погрешности. Проведен анализ влияния максимальной относительной погрешности исходных “псевдоэкспериментальных” данных на величину относительной погрешности определяемых в ходе численных экспериментов параметров реологической модели. По итогам проведенных экспериментов показано, что относительная погрешность определения параметров реологической модели соизмерима с максимальной погрешностью генерируемых “псевдоэкспериментальных” данных. Рассмотрен пример обработки экспериментальных данных для суспензии частиц карбоната кальция на основе полиэтиленгликоля. A technique for processing experimental data and an algorithm for its implementation to determine the parameters of a rheological model of a viscoplastic fluid, which demonstrates the manifestation of the "hardening" effect, are proposed. In order to test the algorithm's operability, numerical experiments were carried out with sets of randomly generated "pseudo-experimental" data with a predetermined maximum relative error. The analysis of the influence of the maximum relative error of the initial “pseudo-experimental” data on the value of the relative error of the parameters of the rheological model determined during numerical experiments was carried out. Based on the results of the conducted experiments, it is shown that the relative error in determining the parameters of the rheological model is commensurate with the maximum error of the generated “pseudo-experimental” data. An example of processing experimental data for a suspension of calcium carbonate particles based on polyethylene glycol is considered.

Productivity Prediction of Fractured Horizontal Well in Shale Gas Reservoirs with Machine Learning Algorithms

Predicting shale gas production under different geological and fracturing conditions in the fractured shale gas reservoirs is the foundation of optimizing the fracturing parameters, which is crucial to effectively exploit shale gas. We present a multi-layer perceptron (MLP) network and a long short-term memory (LSTM) network to predict shale gas production, both of which can quickly and accurately forecast gas production. The prediction performances of the networks are comprehensively evaluated and compared. The results show that the MLP network can predict shale gas production by geological and fracturing reservoir parameters. The average relative error of the MLP neural network is 2.85%, and the maximum relative error is 12.9%, which can meet the demand of engineering shale gas productivity prediction. The LSTM network can predict shale gas production through historical production under the constraints of geological and fracturing reservoir parameters. The average relative error of the LSTM neural network is 0.68%, and the maximum relative error is 3.08%, which can reliably predict shale gas production. There is a slight deviation between the predicted results of the MLP model and the true values in the first 10 days. This is because the daily production decreases rapidly during the early production stage, and the production data change greatly. The largest relative errors of LSTM in this work on the 10th, 100th, and 1000th day are 0.95%, 0.73%, and 1.85%, respectively, which are far lower than the relative errors of the MLP predictions. The research results can provide a fast and effective mean for shale gas productivity prediction.

ANN-based Fatigue Strength of Concrete Under Compression

<p>When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we predict the reduced capacity as function of a given number of cycles by means of artificial neural networks (ANN). A 203-point experimental dataset gathered from the literature was used. The proposed ANN model results in a maximum relative error of 5.1% and a mean counterpart of 1.2% for the whole dataset. It’s shown that the proposed analytical model outperforms the existing design code expressions.</p>

Numerical simulation to analyze the physical behavior of centrifugal pumps as a turbine

In this research, a methodology based on the development of numerical simulations is proposed to analyze the physical behavior of centrifugal pumps such as a turbine. Numerical simulations were carried out using OpenFOAM software. For the validation of the numerical model, the construction of an experimental test bench was carried out. The analysis carried out involves the evaluation of performance parameters of the pump as a turbine, such as head, power, and efficiency. Additionally, the effect of the rotation speed on the previous parameters is evaluated. From the results obtained, it was shown that the maximum relative error was 4%, 3.4%, and 3.8% for the head, power, and efficiency parameters, respectively. In general, it was evidenced that the proposed numerical simulation has the ability to describe the real trends of the pump as a turbine for different flow conditions. In addition, an 11% increase in rotational speed was shown to cause a 12%, 1.9%, and 3% increase in head, power, and maximum efficiency. The proposed methodology is considered an adequate tool to analyze performance and identify the best efficiency point of pump systems such as a turbine. In this way, greater energy use is guaranteed.

A new seasonal frozen soil water-thermal coupled migration model and its numerical simulation

In this paper, a mathematical model based on spherical differential unit cell is proposed as a model for studying seasonal freeze-thaw soil space infinitesimal differential unit cell. From this model, the basic equations of permafrost moisture and heat flow motion are directly derived, then the linked equations form the permafrost water-heat coupled transport model. On this basis, the one-dimensional seasonal permafrost water-heat transport equation is derived. The model reduces the original spatial three-variable coordinate system (parallel hexahedron) into a coupled equation with a single spherical radius (R) as the independent variable, so the iterations of the numerical simulation algorithm is greatly reduced and the complexity is decreased. Finally, the model is used to simulate the seasonal freeze-thaw soil in the ShiHeZi region of Xinjiang, China. The principle of the simulation is to collect the soil temperature and humidity values of the region in layers and fixed-points using a homemade freeze-thaw soil sensor, after that we solve it by numerical calculation using MATLAB. The analysis results show that the maximum relative error of the model we proposed is 4.36, the minimum error is 0.98, and the average error is 2.515. The numerical simulation results are basically consistent with the measured data, then the proposed model is consistent with the matching states of permafrost moisture content and soil temperature in the region at different times. In addition, the experiments also demonstrate the reliability and accuracy of the model.