Operating Condition Parameters Research Articles

High-temperature fretting wear behavior and microstructure stability of a laser-cladding Ti-Al-C-N composite coating meditated by variable cycle conditions

Ti-6Al-4 V titanium alloy is used for low-pressure compressor blades of aero-engine due to its high specific strength, excellent corrosion resistance and high-temperature stability. Low-pressure compressor blades are subject to fretting wear in service, which may lead to fracture failure of the blades. The preparation of wear-resistant coatings is currently an effective solution in solving the problem of fretting wear on Ti-6Al-4 V titanium alloy. However, the fretting wear resistance and microstructure stability of the coating under variable cycle fretting conditions require more attention. In this work, the fretting wear performance and microstructure stability of the Ti-Al-C-N composite coating under variable cycle fretting conditions are investigated. The results show that the parameters of variable cycle operating conditions have a notable effect on the coefficient of friction (COF). The variable frequency and variable temperature working conditions result in the alteration of the fretting operation mechanism. Whereas the variable stroke amplitude and the variable load conditions do not cause changes in the fretting operation mechanism, and both are in the gross slip region (GSR). The lowest wear rate occurs under variable stroke amplitude conditions. The wear rate is the maximum under the variable cycle temperature condition. According to the dissipated energy calculation, the difference between the two different stroke amplitudes is the biggest among all the conditions. EBSD and TEM analyses show that the subsurface of variable stroke amplitude worn scar has a more uniform strain distribution, and reveals a unique subsurface structure with a transformation from crystalline to amorphous, which contributes to improving the wear resistance. This work provides insights into coating microstructure stability and wear mechanisms under variable cycle conditions.

Coupling vibration mechanism of rotating shaft–disc–blade system with blade crack—A systematical investigation on the effect of crack, condition, and structure parameters

Rotating shaft–disc–blade (RSDB) system is one of the most important parts of turbomachinery, such as aero-engine, gas turbine, and power plant. Understating and revealing the coupling vibration mechanism of RSDB system is significant for blade health monitoring and blade crack detection. However, the multi-structure, multi-vibration source, and multi-stress coupling makes it a very tough task. This study aims to reveal the coupling vibration mechanism of RSDB system by systematically investigating the steady-state vibration responses under the influences of different blade crack, operation condition, and structure parameters. First and foremost, a coupling vibration model of RSDB system that comprehensively considers the multi-structure, multi-vibration source, and multi-stress coupling effects is presented. And then, a novel condition indicator for blade health monitoring, called blade–blade distance, is proposed to better characterize and analyse the coupling vibration of RSDB system with blade crack. At last, the comprehensive effects of blade crack, operating condition, and structure parameters on the coupling vibration mechanism of the RSDB system with blade crack are systematically studied. The comparative results demonstrate the effectiveness of blade–blade distance and super-harmonic components of shaft torsional vibration and blade bending vibration for indicating the presence of blade crack and tracking the crack severity. Another observation should be noted is that the vibration behaviour including the blade–blade distance, super-harmonic components of shaft torsional vibration and blade bending vibration, the combination frequency components of shaft bending vibration are co-affected by blade crack, operating condition, and structure parameters. It is indicated that the blade health monitoring and crack detection of rotating blade should comprehensively consider different indicators to obtain a more robust and accurate results.

Coupling vibration mechanism of multistage blisk-rotor system with blade crack

This study aims to address the issue of vibration modeling and coupling vibration mechanism analysis of multistage blisk-rotor (MBR) systems with blade cracks. First, a comprehensive coupling vibration model of the MRB system containing the flexibility of shaft and blade, the swing motion of offset blisk structure, and the nonlinear effect of blade breathing crack is developed. Then, the coupling vibration mechanism of the MRB system is systematically investigated through analytical and numerical analysis, based on which the vibration indicators for the presence of blade cracks are extracted. At last, the effects of blade crack and operating condition parameters on the coupling vibration of the MRB system are comprehensively analyzed. The comparative results indicate that the crack depth and aerodynamic force may significantly affect the crack indicators in shaft bending and torsional vibrations, while the disc unbalance mainly affects the crack indicators in shaft bending vibrations. It is also suggested that the BHM and crack detection of rotating blades should comprehensively consider different crack indicators and perform continuous condition monitoring to obtain more robust and accurate results.

Multi-Physics Modeling and the Sensitivity Analysis for the Critical Factors in Solid Oxide CO2-Steam Co-Electrolysis System Performances

A multi-physics modeling framework, which includes electrochemical and chemical reactions, mass transfer, and energy balance, has been developed and validated against experiment measurements to investigate the performance of solid oxide CO2-steam co-electrolysis (SOEC) under various operating conditions and cell designs. However, multi-physics modeling for complex SOEC systems can be computationally expensive and not tractable for general system design and optimization. Reduced-order models (ROMs) have been proven as a powerful tool for reducing computational costs, closely mimic the high-fidelity SOEC stack models and identify the operational condition parameters to which the SOEC stack is sensitive and needs appropriate controls for stable operation. In this study, the deep neural networks (DNN) algorithm is employed to construct ROMs according to multi-physics simulations for SOECs to systematically investigate the SOECs’ electrochemical performance and rank the contributions of operational condition parameters for both 2 cm2 button cell and 300 cm2 planar cells. Sensitivity analysis reveals that cell voltage, fuel composition (denoted by CO2/H2O ratio), and the operating temperature impact the cell performance most. The product ratio ΔCO/ΔH2 has a stronger dependence on the CO2/H2O ratio in the fuel compositions than on other operating condition parameters. The large 300 cm2 planar cells were modeled under both adiabatic and furnace environment. The simulation and sensitivity analysis indicate that the large cell operating in adiabatic environment usually provides relatively lower performances than the one in furnace environment. Additionally, the adiabatic environment also causes the cell’s performances are more sensitive to the operation conditions, such as external voltage, fuel flow rate, and fuel compositions. The large cell in adiabatic environment also leads to higher internal temperature variation, which may impact the structure reliability.

Experimental and theoretical investigation of a miniature simple pressure pulsation attenuator lined with porous polyurethane material

A miniature simple pressure pulsation attenuator (PPA) lined with porous polyurethane material is designed and a theorical model is established for analyzing the pressure pulsation attenuation performance of PPA. The effect of internal structural parameters of PPA and external operating conditions on attenuation performance is investigated theoretically and experimentally. Spectral intensity is used as a new method to evaluate the pressure pulsation attenuation, and other common index such as pressure pulsation rate and frequency spectrum are also compared in detail. The results show that spectral intensity can more accurately reflect the overall effect of pressure pulsation and the attenuation effect at a specific frequency. The present PPA is more suitable for low static pressure and can effectively eliminate low-frequency and high-frequency pressure pulsation. In addition, the diameter and number of pores can affect the pressure pulsation attenuation. Insertion loss of PPA exhibits multiple peaks, which are mainly influenced by liner length and liner material. Furthermore, the flow rate increases as the pulsation frequency increases for the piston pump based on frequency control, which can cause the pulsation attenuation performance to basically remain unchanged. The research offers valuable insights for the design, optimization, and analysis of PPA in seawater hydraulic system.

Moisture and Temperature Profiles of Heartwood Pinus pinaster Ait. Wood Specimens during Microwave Drying

Microwave (MW) drying of wood has gained popularity in the field of wood modification. The rise in temperature during MW drying leads to increased steam pressure, enhancing wood permeability but potentially decreasing mechanical properties. Understanding temperature and moisture behaviors during MW drying is crucial for its industrial application in wood drying. Therefore, this study aimed to characterize the temperature and moisture behaviors during MW drying of small Portuguese maritime pine (Pinus pinaster Aiton.) wood samples to support a wider use of this technology. The effects on water uptake and the compressive strength parallel to the grain were also investigated. The results indicated three distinct phases in the MW drying rates, with an average of 0.085% of water removed per second. Moreover, the temperature underwent three distinct stages: an initial rapid increase, a period of constant temperature, and a slight decrease until drying was complete. At the beginning of MW drying, the temperatures were below 100 °C, with average temperatures ranging from 126 to 145 °C. Specimens with lower initial moisture content had higher temperatures, and a positive correlation was found between initial moisture content and drying time. In contrast, negative correlations were found between the initial moisture content and average temperature, as well as average temperature and MW drying time. Additionally, the operating condition parameters used in MW drying of pine samples enhanced water impregnability by 65%, generating a slight reduction of 11% in compressive strength. It was also noticed that the initial moisture content did not impact MW-dried samples’ water uptake or compressive strength. Finally, although small clear wood samples of maritime pine were utilized, the temperature and moisture patterns observed closely matched real-scale specimens. Thus, the findings corroborate a wide utilization of MW technology for wood drying, mainly demonstrating positive possibilities for structural-sized wood specimens.

Quantitative analysis of energy loss and performance prediction of regenerative compressor

The intricate internal flow structure within the regenerative compressor leads to substantial energy loss, presenting a long-standing challenge in accurately predicting its performance under various operating conditions. This paper establishes a performance prediction model that considers the impact of blade side exit angle and generalizes energy loss coefficient to accurately predict the performance of regenerative compressor under different operating conditions. Through numerical simulation analysis, the internal flow structure of the regenerative compressor is visualized, and the distribution and variation patterns of internal energy loss are studied. The magnitudes of loss rates for various components are quantitatively analyzed, revealing the underlying energy loss mechanism. A link between loss coefficients and the operating condition parameters of the regenerative compressor is established. Finally, the prediction model is applied to various similar compressor models for performance prediction, and compared with numerical simulation results. The results indicate that the prediction model can effectively predict the performance of various regenerative compressor models under different operating conditions, with maximum errors in total pressure and efficiency of 3.8% and 3.4%, and at rated conditions of 1.13% and 1.78%, respectively.

Well blowout Flame's thermal radiation prediction under environmental wind based on multi-point heat sources and inverse analysis

This study addresses the inadequacy of traditional multi-point source thermal radiation models in predicting the thermal radiation of gas well blowout fires under windy conditions. Initially, a Computational Fluid Dynamics (CFD) approach for predicting thermal radiation from well blowout jet flames was developed and validated against existing literature. Subsequently, the impact of wind on the thermal radiation of well blowout flames was thoroughly analyzed based on CFD model. Next, a novel predictive model for the center trajectory of well blowout flames under wind conditions was introduced based on CFD numerical simulation results and non-reactive jet trajectory theory. Building upon this, a novel prediction method of well blowout flame's thermal radiation under wind condition was proposed based on weighted multi-point sources model. Finally, the weights within proposed model were refined using an inverse optimization technique, specifically the Conjugate Gradient Global Optimization Algorithm, while also establishing a correlation between the optimal weight and the operating condition parameters of well blowout fires. Results indicated that the average difference between the thermal radiation values computed by the proposed method and those derived from numerical simulations was less than 10%. And the proposed model demonstrates clear superiority, particularly in terms of accuracy and convenience compared with traditional multi-point source model and single point source model. This research offers a practical and effective method for predicting thermal radiation from well blowout flames under wind conditions, which was of great significance for well blowout fire's disposal and rescue.

Chaos identification and parameter optimization for vibration suppression in composite bistable structures

An innovative composite bistable system (CBs) coupled of prestressed linear and buckled beams with fixed ends has been designed to enhance the damping and suppress the dynamic response. The dynamic equations of the system are derived by applying Hamilton's principle. When the amplitude of the harmonic excitation exceeds the critical value, the buckling beam of the bistable system enters a state of inter- and in-well combined oscillations. Chaotic motion is present. This motion disperses the vibrational energy over a wide frequency band. The vibration in the high-frequency range significantly enhances the dissipation of the viscoelastic layer, leading to an effective suppression of the system's vibration. Approximate analytical solutions and numerical validation of the chaotic and non-chaotic boundaries of the steady state vibration of the bistable system are carried out based on the harmonic balance method (HBM) and the Melnikov method. The Particle Swarm Optimisation (PSO) algorithm is used to optimise system parameters for specific operating conditions. This optimisation process provides guidance for the design and improvement of CBs systems.

Plasma treatment: An alternative and sustainable green approach for decontamination of mycotoxin in dried food products

In the realm of food safety, dried foods have traditionally been perceived as having a lower risk profile. However, recent reports have highlighted concerning levels of mycotoxin contamination in dried food products, such as dried fruits, vegetables, and spices, exceeding permissible limits. This contamination not only compromises shelf life but also poses a significant threat to consumers, while depleting the bioactive components of these products. The food industry grapples with considerable challenges arising from the substantial risk of mycotoxin contamination in dried foods. To meet the demands of a disc; erning food market, there is an imperative need for sustainable mycotoxin-degrading technologies. These technologies must be non-thermal and chemical-free, minimizing thermal damage to treated food to preserve essential nutrients, ultimately enhancing food quality and safety. Developing non-thermal decontamination technologies for dried foods holds enormous economic and public health significance.This review centers its focus on the decontamination of dried foods, proposing a green approach through the utilization of Cold Plasma Technology (CPT). We delve into the working principles and applications of CPT in dried foods, highlight the influential factors affecting its efficacy, and acknowledge the limitations of this innovative technique. Furthermore, we pinpoint and explore emerging research opportunities in the decontamination of dried foods, particularly in conjunction with other complementary methods. CPT, an evolving and environmentally friendly technology, exhibits promising capabilities in controlling pathogens in dried food products and extending their shelf life. Critical determinants encompass the parameters of cold plasma, operational conditions, food-related variables, and microbiological factors, which collectively influence the efficacy of CPT. We advocate for future research dedicated to assessing the impact of CPT on various mycotoxins, food composition, and its interaction with food-related factors. In this vein, it is imperative to emphasize the scaling up of this technology for practical commercial applications, addressing the growing need for enhanced food safety in dried products.

Effect of uncertain operating conditions on the aerodynamic performance of high-pressure axial turbomachinery blades

The objective existence of uncertain operating conditions can cause significant variations in the aerodynamic performance of turbomachinery. This study presents a comprehensive investigation into the impact of six uncertain operating conditions on the aerodynamic performance of a turbine. Additionally, a global sensitivity analysis was performed to identify the most important operating condition parameters. To reduce the computational cost of uncertainty quantification (UQ), a new UQ framework, the Nested Sparse-grid Stochastic Collocation Method (NSSCM), is used. It has been observed that uncertain operating conditions can lead to significant variations in energy loss, which in turn directly impacts the operating efficiency of the turbine. This research finds that the suction surface and trailing edge of the blade are particularly sensitive to uncertain operating conditions. The impact of uncertainty on the static pressure coefficient decreases as the flow develops, while the uncertainty of the Mach number and energy loss coefficient increases. Inlet total pressure and outlet static pressure are the primary factors impacting turbomachinery aerodynamics, as determined by sensitivity analysis. This study provides new insights into the impact of uncertain operating conditions on turbine efficiency and can guide the development of more robust turbine designs in the future.

Combined Machine Learning and Electrochemical Impedance Spectroscopy to Diagnose and Predict the State-of-Health of Commercial SMD Solid-State Batteries

Solid-state batteries (SSBs) are considered as one alternative to conventional lithium-ion batteries as they enable safer operation. A detailed look into the reaction kinetics and the possible aging mechanisms of the solid electrolytes (SEs) and the interface of SE and active material at the composite electrodes was of major interest in recent years12. Cell scale modeling and prediction of the cell performance and the degradation however are lacking, although they would offer fast evaluation beyond material-intensive and time-consuming experiments. Besides, diagnosis with non-destructive tests is crucial for the development, design, production and application of SSBs. For example, cell with degradation need to be identified by battery management systems and balanced for the safety of the entire energy system.Herein we characterize a group of commercial solid-state batteries3 using electrochemical impedance spectroscopy (EIS) and train a machine learning model to diagnose their state-of-health (SoH) throughout cycling. The SSBs used have multilayered electrodes with a ceramic SE and need to be formed by customers. To shorten the measurements period, the experiments were conducted with distinct formation and cycling protocols, which leads to faster and various degradation modes during their lifetime. The SoH of the tested SSBs ranges from 40% to 140% SoH for the customer-defined rated capacity of 200 µAh. The EIS data was collected intermittently between the 5th cycle to the 100th cycle and was organized into 108 datasets for training and validation. Fig.1a) shows parts of the EIS experiment data. The machine learning (ML) model was then trained by using the EIS data and operating condition parameters as input values and the SoH during cycling as output property. The EIS data was preprocessed and selected with a feature selection model. Significant features, e. g., impedance data from a certain frequency range, were selected and analyzed qualitatively. The applied feature engineering transfers data from non-gaussian into gaussian distribution, which enables the usage of Bayesian ridge regression algorithms to avoid overfitting.Despite the manufacturing tolerance and inherent production deviation among the cells, the model achieves a root mean squared error of 2.5% for the SoH diagnose during validation. To predict the future SoH after 10 more cycles, it achieves a root mean squared error of 2.7%. Fig.1b) and 1c) present the diagnosed/predicted SoH vs. the observed SoH. Different from semi-empirical models using EIS data to fit equivalent circuit models (ECM), we have preprocessed EIS data in the frequency domain and applied ML directly for the SoH prediction. Our method avoids importing additional inaccuracies during the ECM fitting process. Furthermore, the feature analysis reflects the dominating changes in certain frequency ranges. The tested SSBs have stronger correlations between the imaginary part of the impedance in the high frequency range and their SoH prediction. We presume that the mechanical failures in the materials applied in the SSB might dominate its degradation in this phase, as the capacitance in bulk impedance has the strongest correlation with the SoH fading.All in all, the herein presented work highlights the promise of combining data-driven modeling with EIS characterization to predict the performance of complex electrochemical systems, and can be expanded to other cell geometries and further battery technologies.

Flue gas enhanced water leaching: Prediction and condition optimization of demineralization effect from coal via machine learning algorithm

A novel “Flue Gas Enhanced Water Leaching” method was previously proposed to efficiently demineralize high alkali and alkaline earth metals coals. Yet the optimized treatment conditions are unclear in consideration of the balance between removal effect and costs. Consequently, the back propagation neural network algorithm was proposed in this study to analyze the effect of characteristic parameters (particle size, ultimate analysis, proximate analysis and ash compositions) of coals and operational conditions (temperature and time) on the removal rates of Na (RNa) and Ca (RCa). Results showed that the back propagation neural network models can precisely predict RNa and RCa with respective correlation coefficient (R2) of 0.9854 and 0.9777. The coal with higher oxygen content tends to have higher RNa and RCa owing to their high organic Na/Ca contents and good hydrophilicity, and minimizing coal particles contributes to the removal of Na/Ca. Longer leaching time results in higher RNa and RCa, while higher leaching temperature contributes to RNa but shows complex effect on RCa. For practical considerations, recommended operational conditions are room temperature, within 2 h and particle size of smaller than 2 mm, and the RNa and RCa of higher than 75% and 30% can be obtained under these conditions, respectively. Therefore, this study not only provides an effective method for the prediction of RNa and RCa under “Flue Gas Enhanced Water Leaching” treatment, but also contributes to the condition optimization in its industrial applications.

Energy and Exergy Analysis on Zeotropic Refrigerants R-455A and R-463A as Alternatives for R-744 in Automotive Air-Conditioning System (AACs)

The popularity of vehicles and the increased time spent in cars with air conditioning systems has led to regulations in many countries that require the use of environmentally friendly refrigerants with minimal global warming and zero ozone depletion potential (GWP and ODP). Cars need high-performance, eco-friendly air conditioning systems to reduce their impact on the environment, lower fuel consumption, and decrease carbon emissions. The aim of the current work was to propose CO2-based blend zeotropic refrigerants, R-455A (R-744/32/1234yf) and R-463A (R-744/32/125/1234yf/134a), to improve the thermodynamic performance of pure CO2 refrigerants. The thermodynamic energy and exergy analysis and system optimization of an AAC system for the new zeotropic refrigerant blends compared to carbon dioxide (R-744), using Aspen HYSYS software, were investigated. The influence of cooler/condenser pressure, average evaporator temperature, cooler/condenser outlet temperature, and refrigerant flow rate on the cycles’ COP and exergy efficiency were conducted and are presented. The results showed that, at the same operating condition parameters, the cycle COP improved by 57.6 and 76.5% when using R455A and R463A instated of R744, respectively, with the advantage of reducing leakage problems due to the higher operating pressure of R744 (5–7 times higher than those of R455A and R463A), as well as requiring heavy equipment, but at optimal operating condition parameters, R744 and R-463A had a maximum COP of 14.58 and 14.19, respectively. The maximum COPs of R744, R455A, and R463A based on the optimal pressure of the cooler/condenser were 3.1, 4.25, and 5.4, respectively. Additionally, regarding the need for environmentally friendly air conditioning systems with acceptable performance in cars due to their impact on the environment and their contribution to global warming, the blend R455A is recommended for use as a refrigerant in AAC systems.

Research on degradation mechanism of trichlorobenzene and Hg0 by nonthermal plasma catalysis

Aiming at mercury and dioxin in fire coal gas as research objects, nonthermal plasma (NTP) catalytic technology was used to investigate the degradation effect of operating condition parameters on mixed pollutants in mixed flue gas condition, and to explore the synergistic degradation of Hg0 and TCB (1, 2, 3-trichlorobenzene, TCB) under mixed flue gas conditions. The research results showed that the conversion efficiency of mercury and TCB increased with the additional output of voltage, and decreased with the increase of the gas flow rate. Under optimal reaction conditions: voltage = 17 kV, frequency = 300 Hz, gas flow rate = 2 l min−1, the conversion efficiency of Hg0 and TCB reached the highest 91.4% and 84.98%, respectively. In the NTP catalytic system, active free radicals played an important role in the synergistic conversion of mercury and TCB, which have a competitive effect, to make the conversion efficiency of mixed pollutants lower than a single substance. In the mixed flue gas condition, the mixed gas has an inhibitory effect on the synergistic conversion of mercury and TCB. Kinetic modeling of NTP catalytic synergistic reaction was established. Under three conditions of TCB, mercury and TCB, mixed simulated flue gas, the NTP catalytic technology showed a quasi-first-order kinetic reaction for the degradation of TCB. According to the synergistic effect of NTP and composites, the transformation and degradation of TCB mainly included two processes: TCB and ring opening, and Hg0 was finally oxidized to Hg2+.

Numerical Analysis on Improving the Rectangular Texture Floating Ring Gas-film Seal Characteristics with Different Bottom Shapes

Finding the right texture pattern to effectively improve the sealing performance of the floating ring gas film seal has always been a topic of interest for engineers. Herein, to reveal the effect of the rectangular textured base shape on the sealing characteristic parameters of floating ring gas film seal, four bottom shapes of rectangle, isosceles triangle, left triangle and right triangle were proposed. The governing equations were solved by the finite difference method and the correctness of the theoretical results was verified by the test bench. The effect of operating conditions parameters on the sealing performance were also analyzed. The results reveal that the trends of the experimental results is consistent well with the theoretical results, in which the relative errors are all less than 9%. It indicates that the theoretical model is scientific and valid. As the speed and inlet pressure increase, the texture bottom shape significantly changes the distribution of pressure and temperature field. And the average gas film thickness determines whether the texture produces an effect or not. Under the same operating conditions, a right-angled triangular bottom shape can obtain a good stability and cooling effect for the sealing device. With a bottom shape of the right triangle and a bottom shape of the rectangle, the maximum difference in opening force and gas film temperature rise were calculated to be 234.91 N and 0.61 oC, respectively. The research results provide theoretical support for further study of the textured floating ring gas film seal and a reference for texture optimization.

Multi-objective optimization and evaluation of PEMFC performance based on orthogonal experiment and entropy weight method

The operating condition parameters of temperature (T), anode relative humidity (RH_a), cathode relative humidity (RH_c), anode stoichiometry ratio (St_a), and cathode stoichiometry ratio (St_c) have a coupling influence on the multiple performance objectives of current density (I), pressure drop (ΔP), temperature difference (ΔT), and non-uniformity of oxygen (N) in the actual operation process of proton exchange membrane fuel cell (PEMFC). Based on the orthogonal experiment method (OEM) and entropy weight method (EWM), a new multi-objective optimization method and mathematical expression for evaluation of a PEMFC performance were proposed to clarify the influence ability of T, RH_a, RH_c, St_a, and St_c on I, ΔP, ΔT, and N and comprehensively evaluate the performance of a PEMFC in multiple performance objectives situations. The L18(37) orthogonal array was first designed by the OEM to form eighteen groups of schemes for multi-objective optimization. By analyzing the range and variance, the significance order and optimal parameter combination were individually obtained under four performance objectives. The comprehensive performance objective of output performance (OP) was then proposed, and the EWM was used to calculate the respective weight value of four performance objectives in OP. The mathematical expression of OP was calculated as OP = 0.51 I + 0.08 ΔP + 0.19 ΔT + 0.22 N, indicating that current density was the most important parameter. Eighteen groups of schemes were scored and ranked and the OP score of Scheme 6 was the highest with a value of 0.788. Finally, the analysis of range was performed again for OP scores, and the optimal combination of T = 343.15 K, St_a = 3, St_c = 3, RH_a = 20%, and RH_c = 20% was achieved, which was better than Scheme 6. This study redresses the limitation of single-performance objective optimization and maximizes the comprehensive performance of PEMFC considering multi-objectives.

Experimental study on precise control air quantity on demand by the cooperation of multiple main fans in mine

Due to the global energy crisis worsening, numerous businesses have prioritized energy conservation and consumption reduction. However, in mines with multiple main fans, the main fans' operation efficiency is low to meet the ventilation on-demand, and the ventilation system's energy consumption is very high. This paper proposes an energy-saving control strategy for ventilation on demand with multiple fans network, which can save much energy in mines. We use the circuit method to calculate the operating condition parameters required for the joint control of multiple main fans by analyzing the influence features of multiple fans' mutual disturbance. We study the advantages and shortcomings of the traditional fan control method and create control strategy expressions for multiple fans. The two-step fan control strategy uses the adjusting wheel blades' angle method to determine the high-efficiency operating angle and the modulating fan speed method for accurate air quantity control. Furthermore, the effectiveness of the control strategy in ventilation on demand and fan energy-saving are verified on the three-dimensional complex ventilation pipe network experimental platform. The research results show that, compared to the two typical fan control strategies, our control strategy can save energy by 27.74% and 26.92%, respectively. If this strategy is applied to a mine with two main fans rated power of 500 kW, it can save energy 2,429,806 kW per year, saving USD 442.69 per day or USD 161,582.1 per year. The payback period for this strategy is approximately 14.1 months. Therefore, this research can provide a promising step towards intelligent control of air quantity on-demand while indicating enormous potential for energy savings in mine ventilation systems.

ФОРМАЛИЗАЦИЯ НЕЛИНЕЙНЫХ ХАРАКТЕРИСТИК ОПРЫСКИВАТЕЛЯ В ПРОЦЕССЕ РАСХОДОВАНИЯ РАБОЧЕЙ ЖИДКОСТИ

The diversity of natural conditions and cultivated crops, the different level of fields cultivation determines the difference in optimal design parameters and functional properties of sprayers for their effective use. Field experiment methods, calculation method and computer simulation method are used to substantiate the optimal parameters of sprayers in relation to various conditions. In computer modeling, not only the structural, but also the dynamic characteristics of the units are necessarily taken into account. One of the stages in computer modeling of the spraying units functioning is the preliminary formalization of non-stationarity and non-linearity of their static and dynamic properties. The formula for the dependence of the main moment of inertia of the sprayer at a different level of liquid in a cylindrical container was obtained by formalizing the results of a full-scale experiment. The original stand and the method of carrying out are proposed for conducting a full-scale experiment. The mathematical dependence of the change in the height of the center of mass of the spraying unit on the design parameters of the tank and the liquid level in it was obtained by the calculation method. The results obtained can be used in Simulink modeling of the functioning of the spraying unit and determining its optimal parameters for various operating conditions using a known method.

Development of an algorithm for identifying single-phase ground fault conditions in cable and overhead lines in the networks with isolated neutral

The paper addresses the issue of identification of the type of single-phase ground fault in medium voltage networks operating mainly with an isolated neutral. By analyzing the published research, the most promising methods for detection of a single-phase ground fault were selected. Further, using the selected parameters, algorithms were designed to determine the parameters, process them, and use them to identify the type of a single-phase ground fault. The algorithm developed by the authors was formalized for the application on the basis of microprocessor-based protection systems using digital signal processors. The operation process was modeled using real oscillograms of single-phase ground fault. The use of high-frequency zero-sequence current and voltage components, as well as the root-mean-square value of the zero-sequence voltage, made it possible to design an algorithm that identifies any of the possible single-phase ground fault types (bolted, arc, or contact resistance ones). The first part of the article gives an overview of the state-of-the-art of research in the field of detection and identification of single-phase-to-earth faults. In the second part, the parameters of emergency operating conditions are analyzed and the key parameters are determined based on them. The third part contains a description of the single-phase fault identification algorithm developed by the authors and the output oscillograms. The fourth part makes conclusions on the applicability of the algorithm.