Maximum Relative Error Research Articles

Design of a dynamic measurement system for Bl factor spatial distribution of resonant blood viscoelastic sensor

Bl factor is a key system parameter of the resonant blood viscoelastic sensor. In this paper, a dynamic measurement system for the spatial distribution of Bl factor based on velocity amplitude and motional impedance was designed. The system extracted the velocity amplitude and motional impedance of the coil under the dynamic condition of driving the sensor to generate simple harmonic oscillations using laser displacement and impedance analysis combined with in-phase/quadrature demodulation algorithm, and controlled the equilibrium position of the coil by adjusting the direct current component of the excitation current to realize the position scanning. In the position interval of [-240, 240] μm, the maximum coefficient of variation of the measurement results was 0.077 3%, and the maximum relative error to the simulation results was 2.937 9%, with a linear fitting correlation coefficient R 2 = 0.996 8. The system can be used to accurately measure the spatial distribution of Bl factor of the resonant blood viscoelastic sensor, which provides a technical support for the verification of the design of the sensor magnetic circuit.

Numerical modeling and experimental verification for anisotropic heat conduction process in velocity probe of the thermal mass flowmeter

Traditional models ignore the temperature gradients caused by anisotropic heat conduction process in the axial and radial directions of the velocity probe of a thermal mass flowmeter (TMF), essentially assuming constant wall temperature. Recently, a novel TMF with a large length–diameter ratio of the velocity probe and more evident temperature gradient was proposed; although this novel TMF is insensitive to the radial velocity profile, it leads to significant calculation errors. To reduce the error of this TMF, this study proposes a numerical model of pipeline flow heat transfer that considers the axial and radial heat conduction of the velocity probe. The velocity probe axial heat conduction numerical model is proposed, and the results of apparent thermal conductivity are verified using empirical formulas. A feedback adjustment convergence program is performed to maintain the average temperature of the velocity probe at 10 K above the temperature of the observed fluid. The simulation results showed that a temperature gradient forms in both the axial and radial directions of the velocity probe of the improved model. Moreover, the higher the flow velocity and the farther it is from its central axis, the lower the temperature. Experimental results revealed that, at a velocity rang of 1.0–7.0 m/s, the average relative error between the improved simulated results and current experimental data is 2.92 % and the maximum relative error is 10.91 %, whereas the average relative error between the traditional simulated results and original experimental data is 7.44 % and the maximum relative error is 19.45 %. Thus, the improved model better simulates the real process.

Monthly precipitation prediction based on the EMD–VMD–LSTM coupled model

Abstract Precipitation prediction is one of the important issues in meteorology and hydrology, and it is of great significance for water resources management, flood control, and disaster reduction. In this paper, a precipitation prediction model based on the empirical mode decomposition–variational mode decomposition–long short-term memory (EMD–VMD–LSTM) is proposed. This model is coupled with EMD, VMD, and LSTM to improve the accuracy and reliability of precipitation prediction by using the characteristics of EMD for noise removal, VMD for trend extraction, and LSTM for long-term memory. The monthly precipitation data from 2000 to 2019 in Luoyang City, Henan Province, China, are selected as the research object. This model is compared with the standalone LSTM model, EMD–LSTM coupled model, and VMD–LSTM coupled model. The research results show that the maximum relative error and minimum relative error of the precipitation prediction using the EMD–VMD–LSTM neural network coupled model are 9.64 and −7.52%, respectively, with a 100% prediction accuracy. This coupled model has better accuracy than the other three models in predicting precipitation in Luoyang City. In summary, the proposed EMD–VMD–LSTM precipitation prediction model combines the advantages of multiple methods and provides an effective way to predict precipitation.

Natural thermometers in concrete: Stability and reliability verification of inclusion decrepitation thermoacoustic thermometry

The fire-exposure temperature of concrete directly determines the degree of damage to its microstructure and the performance of concrete. Therefore, an accurate assessment of the fire-exposure temperature of concrete is crucial. But to date, there is not yet an accurate, practical, and effective method for measuring the internal temperature of concrete after fire. Based on the discovery that fluid inclusions in concrete act as natural thermometers, this paper investigates the scientific principles and experimental evidence of the decrepitation thermometry of inclusions. By extracting quartz sand samples from the interiors of concrete members after a fire and performing an inclusion decrepitation test on these samples, the maximum fire-exposure temperature of concrete can be determined with a high degree of accuracy. The test results reveal that the natural sand used in construction typically contains fluid inclusions, indicating that the necessary conditions for inclusion decrepitation thermometry are satisfied for concrete structures. In this study, standard quartz sand samples were configured, and inclusion decrepitation temperature measurements were conducted after subjecting the samples to different heating rates, cooling methods, thermostatic durations, and standing times after fire. The test results indicate that the impact of the aforementioned factors on the inclusion decrepitation temperature measurement is not evident. The maximum relative error between all temperature measurement results and the actual heating temperature is <10 %, and the average relative error is <2.5 %. The experimental results show that fluid inclusions are natural thermometers in concrete. After a fire, the maximum temperature experienced in the concrete can be quantitatively assessed using the initial decrepitation temperature of the inclusion. This method possesses the advantages of being a scientific test method and providing accurate temperature measurements without any indirect analysis or calculation using simple test equipment, sample preparation, and operation.

Comparative study of two models to estimate solar radiation on an inclined surface

Our work is focused on the study of the solar radiation received on three inclined surface: 32°, 60° and 90° facing south. So, we proposed two theoretical models: Brichambaut and Liu Jordan models. Brichambaut model takes into account two input parameters (local time, day number), Liu &amp; Jordan takes into account the measures of the solar irradiance (diffuse and global radiations received on horizontal plane). We tested a day 10-02-2011. We noted that the Brichambaut model gives best results, the maximum relative errors are equal to: -0.43 %, -0.60 %, 0.76 % respectively for the angles 32°, 60°, 90°. While for the Liu &amp; Jordan model we obtained the following errors: 1.55 %, 1.56 %, and 3.92 %.

Process Optimization of Polishing Titanium Alloy Material with a Pulsating Air Jet.

Titanium alloy is a widely used metal material, which can be applied in fields such as healthcare, petroleum exploration, aerospace, etc. In this paper, a new method for polishing the titanium alloy by a pulsating air jet is proposed. Compared with traditional abrasive jet polishing, this method has the advantages of simple structure, low nozzle wear, and high polishing flexibility. The working principle and material removal mechanism of the pulsating air jet polishing (PAJP) are introduced. Combined with orthogonal experiments, range analysis and variance analysis were used to find out the influence degree of each process parameter on the PAJP of titanium alloy, and the optimal level of each parameter was found. Through the experiments, a prediction model of surface roughness was established by regression analysis, and the predicted value was compared with the measured value. The maximum relative error of the prediction model was 10.3%, and the minimum relative error was 1.1%. The average relative error was 6.2%.

Effect of Interference Size on Contact Pressure Distribution of Railway Wheel Axle Press Fitting

Mechanical couplings in engineering usually use interference fits to connect the shaft and hub. A railway wheel axle is a press fit that is connected by interference and can be subjected to bending stress. In loaded press fits, a high concentration of contact stresses can be generated in the area of the axle-fillet beam, which in most cases leads to the failure of the axle due to fatigue and fretting fatigues. Therefore, it is crucial to determine the ability of the press-fitted joints to provide sufficient frictional resistance that can withstand the loads and torques by evaluating the safety factor, especially when the mechanical or structural system is loaded. In this paper, the contact pressure and stress distribution along the radius of the wheel axle are studied using the analytical calculation of Lame’s equation, and the numerical method used is by ANSYS software. It was found that interference fits have a great influence on the connection strength of interference fits, which are directly related to the contact pressure. Increasing the interference increases the contact pressure, which allows higher torque and load capacity to be transmitted. The finite element analysis showed good agreement for the highest interference value of 230 µm with a relative error of 1.4%, while this error increased to the maximum relative error of 14.33% for a minimum interference of 100 µm.

Investigation of torsion angle measurement method in spatial cables of suspension bridges

Spatial cables in suspension bridges may twist between two adjacent suspension points due to transverse tension. To determine the spatial torsion angle of the cables, a scale-reduced suspender bridge model was fabricated and tested in this study. The cable torsion angles of the bridge model under different loading cases were obtained and presented. Theoretical methods (e.g., the coordinate method and the angle method) to calculate the spatial cable torsion angle in the suspender bridge were developed by analyzing the deforming characteristics of the suspender cables. The feasibility and predictive accuracy of the proposed methods were assessed using the experimental results. Results show that the proposed coordinate and angle methods produced similar torsion angles for the main cables, showing a maximum relative error of 10.4 %. Compared to the coordinate method's complex data transformation and measurement procedures, the angle method is more convenient, efficient, and easier to calculate the torsion angle of the main cable. Despite the capability of calculating the spatial cable torsion angles, the coordinate method also exhibits advanced capability in determining the geometric curves of the main cable under different spatial angles.

Evolution and prediction model of environmentally-persistent free radicals in biomass three-component pyrolytic carbon with pyrolysis temperature

To clarify the evolution mechanism of environmentally-persistent free radicals (EPFRs) in biochar as a function of pyrolysis temperature, three-component pyrolytic carbon was characterized by electron paramagnetic resonance (EPR) spectroscopy. Four types of EPFRs were distinguished by using the peak splitting method. The EPFRs in three-component pyrolytic carbon obtained at a pyrolysis temperature of 300 °C were mainly oxygen-containing free radicals (57.31–83.63 %). Upon increasing the pyrolysis temperature, simple aromatic ring radicals were formed by the dehydrogenation of biomass, and then the polymeric aromatic radicals formed, accompanied by π-electron delocalization to form phenyl-π radicals. Biomass pyrolysis was simulated by mechanically mixing the three components in different proportions. A comparison of the experimental and theoretical values indicated that interactions between the three components did not obviously affect the EPFR concentration in the range of 450–550 °C, with a maximum relative error of 12.78 %. A prediction model for the EPFRs concentration in multi-component pyrolytic carbon was proposed. This study also clarified the generation and transformation mechanisms of EPFRs in three-component pyrolytic carbon, thus providing guidance for generating and regulating biochar EPFRs.

Working process and opening characteristics of soft insulator of pulse motor

AbstractIn this paper, a simulation test system for the working process of soft insulation is designed, using equivalent simulated ablation test methods combined with a CCD measuring system and micro electron microscope scanning technology, which can better describe the thermal properties and state of insulation after ablation. At the same time, a test plan for the opening characteristics of the insulator based on different ablation times and different opening structure sizes as the main influencing factors are designed. The experimental results show that the ablation test can well simulate the state of an actual pulse engine compartment after the ablation and the maximum relative error of the ablation rate can be controlled within 5 %. From the influence of ablation time on the opening characteristics of the insulator, it can be seen that the maximum axial deformation of the insulator is different from the relative value of the opening pressure. The radial deformation of the insulator is larger than that of the non‐ablated insulator and larger than the inner diameter of the insulator shell after ablation. From the influence of the size of the opening structure of the insulator (depth of weakening grooves) on the opening characteristics of the insulator, it can be seen that as the weakening groove depth of the compartment deepens, the original pressure change pattern disappears, and the opening pressure and opening time of the insulator decrease significantly.

Non-contact voltage measurement of residential cables based on internal parameter conversion and centering probe assistance

Existing non-contact voltage measurement technology cannot be applied to residential cables containing neutral wire and earth wire in addition to live wire. A non-contact voltage measurement method of residential cables is introduced in this article. First, an equivalent circuit model of the residential cable measurement based on capacitive coupling is established. In order to solve the problem of uncertain coupling capacitance, which leading to the inability to accurately obtain the voltage amplitude, a calibration method based on internal parameter conversion and centering probe assistance is proposed. This method calibrates the cable voltage through different transfer relationships and sensor outputs, and no known reference excitation is required during calibration. Subsequently, the impact of internal parameters on measurement accuracy are analyzed and selection principles are provided. Through finite element simulation, the influence of metal spring for centering on voltage measurement is analyzed, and the measurement error of non-ideal cables (with eccentricity or ovality) is quantified. A prototype probe and internal parameter conversion circuit have been developed, and parasitic capacitance compensation have been completed. The measurement accuracy of 50 Hz voltage in a certain range (100–300 V) was tested in the laboratory, the maximum relative error of amplitude is −0.89%, and the relative error of phase is 0.68%. Four specifications of residential cables testing were completed, with a maximum relative error of −1.35%. Long- term testing was conducted on the actual field (output voltage of the distribution box), the test results showed that the output was linear, and the relative error were kept around 0.65% after primary calibration.

QES-Plume v1.0: a Lagrangian dispersion model

Abstract. Low-cost simulations providing accurate predictions of transport of airborne material in urban areas, vegetative canopies, and complex terrain are demanding because of the small-scale heterogeneity of the features influencing the mean flow and turbulence fields. Common models used to predict turbulent transport of passive scalars are based on the Lagrangian stochastic dispersion model. The Quick Environmental Simulation (QES) tool is a low-computational-cost framework developed to provide high-resolution wind and concentration fields in a variety of complex atmospheric-boundary-layer environments. Part of the framework, QES-Plume, is a Lagrangian dispersion code that uses a time-implicit integration scheme to solve the generalized Langevin equations which require mean flow and turbulence fields. Here, QES-Plume is driven by QES-Winds, a 3D fast-response model that computes mass-consistent wind fields around buildings, vegetation, and hills using empirical parameterizations, and QES-Turb, a local-mixing-length turbulence model. In this paper, the particle dispersion model is presented and validated against analytical solutions to examine QES-Plume’s performance under idealized conditions. In particular, QES-Plume is evaluated against a classical Gaussian plume model for an elevated continuous point-source release in uniform flow, the Lagrangian scaling of dispersion in isotropic turbulence, and a non-Gaussian plume model for an elevated continuous point-source release in a power-law boundary-layer flow. In these cases, QES-Plume yields a maximum relative error below 6 % when compared with analytical solutions. In addition, the model is tested against wind-tunnel data for a uniform array of cubical buildings. QES-Plume exhibits good agreement with the experiment with 99 % of matched zeros and 59 % of the predicted concentrations falling within a factor of 2 of the experimental concentrations. Furthermore, results also emphasize the importance of using high-quality turbulence models for particle dispersion in complex environments. Finally, QES-Plume demonstrates excellent computational performance.

An Enhanced Single-Particle Model Using a Physics-Informed Neural Network Considering Electrolyte Dynamics for Lithium-Ion Batteries

As power sources for electric vehicles, lithium-ion batteries (LIBs) have many advantages, such as high energy density and wide temperature range. In the algorithm design process for LIBs, various battery models with different model structures are needed, among which the electrochemical model is widely used due to its high accuracy. However, the electrochemical model is composed of multiple nonlinear partial differential equations (PDEs) that make the simulating process time-consuming. In this paper, a physics-informed neural network single-particle model (PINN SPM) is proposed to improve the accuracy of the single-particle model (SPM) under high C-rates, while ensuring high solving speed. In PINN SPM, an SPM-Net is designed to solve the distribution of lithium-ion concentration in the electrolyte. In the neural network learning process, a loss function is designed based on the physical constraints brought by the PDEs, which reduces the error of the neural network under dynamic working conditions. Finally, the PINN SPM proposed in this paper can achieve a maximum relative error of up to 1.2% compared with the high-fidelity data generated from the P2D model under various conditions. Additionally, the PINN SPM is 20.8% faster than traditional numerical solution methods with the same computational resources.

Research on the modification of the tool influence function for robotic bonnet polishing with stiffness modeling

Robotic bonnet polishing (RBP) technology is widely used in the polishing process of optical components, but industrial robots have the characteristics of a wide processing range and low body stiffness. Therefore, in the actual polishing process, there are different polishing forces due to different stiffnesses, which leads to different tool influence functions (TIF) affecting the surface quality of optical components. To achieve a uniform surface quality of the components polished by the RBP, we modeled the stiffness of the robot and modified the TIF in conjunction with the Preston equation, finally verifying the accuracy of the modified model through experiments. The results of the fixed-point polishing experiments show that the maximum relative error before the TIF correction is 12.5% and the maximum relative error after the correction is 2.8% within the robot's 600mm*600mm machining range. The results of the plane polishing experiments show that the maximum relative error before the TIF correction is 13% and the maximum relative error after the correction is 1.7%. Therefore, a method for TIF corrections from RBP based on stiffness modeling has important engineering implications.

Lithium-Ion Battery State of Health Estimation Using Simple Regression Model Based on Incremental Capacity Analysis Features

Batteries are the core component of electric vehicles (EVs) and energy storage systems (ESSs), being crucial technologies contributing to carbon neutrality, energy security, power system reliability, economic efficiency, etc. The effective operation of batteries requires precise knowledge of the state of health (SOH) of the battery. A lack of proper knowledge of SOH may lead to the improper use of severely aged batteries, which may result in degraded system performance or even a risk of failure. This makes it important to accurately estimate battery SOH using only operational data, and this is the main topic of this study. In this study, we propose a novel method for online SOH estimation for batteries featuring simple online computation and robustness against measurement anomalies while avoiding the need for full cycle discharging and charging operation data. Our proposed method is based on incremental capacity analysis (ICA) to extract battery aging feature parameters and regression using simple piecewise linear interpolation. Our proposed method is compared with back-propagation neural network (BPNN) regression, a popular method for SOH estimation, in case studies involving actual data from battery aging experiments under realistic discharging and temperature conditions. In terms of accuracy, our method is on par with BPNN results (about 5% maximum relative error), while the simplicity of our method leads to better computation efficiency and robustness against data anomalies.

Laser Velocimetry for the In Situ Sensing of Deep-Sea Hydrothermal Flow Velocity.

Laser Doppler velocimetry (LDV) based on a differential laser Doppler system has been widely used in fluid mechanics to measure particle velocity. However, the two outgoing lights must intersect strictly at the measurement position. In cross-interface applications, due to interface effects, two beams of light become easily disjointed. To address the issue, we present a laser velocimeter in a coaxial arrangement consisting of the following components: a single-frequency laser (wavelength λ = 532 nm) and a Twyman-Green interferometer. In contrast to previous LDV systems, a laser velocimeter based on the Twyman-Green interferometer has the advantage of realizing cross-interface measurement. At the same time, the sensitive direction of the instrument can be changed according to the direction of the measured speed. We have developed a 4000 m level laser hydrothermal flow velocity measurement prototype suitable for deep-sea in situ measurement. The system underwent a withstand voltage test at the Qingdao Deep Sea Base, and the signal obtained was normal under a high pressure of 40 MPa. The velocity contrast measurement was carried out at the China Institute of Water Resources and Hydropower Research. The maximum relative error of the measurement was 8.82% when compared with the acoustic Doppler velocimeter at the low-speed range of 0.1-1 m/s. The maximum relative error of the measurement was 1.98% when compared with the nozzle standard velocity system at the high-speed range of 1-7 m/s. Finally, the prototype system was successfully evaluated in the shallow sea in Lingshui, Hainan, with it demonstrating great potential for the in situ measurement of fluid velocity at marine hydrothermal vents.

An effective modelling approach for multi-body dynamic analysis of epicyclic gear train of GTF considering both structural flexibility and mechanical interactions

In this paper, a new modelling approach is proposed for the dynamic investigation of epicyclic gear train, and the novelty of this work lies in consideration of both structural flexibility and mechanical interactions during the analysis procedure. The method is of capacity to directly present the dynamic results of the supporting structure for convenient practical engineering evaluation and reduce the dimension of the system reasonably with appropriate assumptions for better computational efficiency. Firstly, the mechanical interactions among components are discussed in detail, and the principle that the structural flexibility works is also explained at length. Secondly, taking the epicyclic gear train of the geared turbofan (GTF) engine; for example, the dynamic model of the system is then established based on the developed hybrid user-defined elements. For model validation, the governing equations of the system are also derived by the lumped mass method. Thirdly, with the same values of the parameters, the results of normal dynamic meshing force obtained by the proposed model are compared with the ones by the lumped mass model. It can be stated from the data that (1) the maximum relative error between the theoretical value and the average value calculated by the two models is 8.28%, (2) the gear mesh frequency obtained by the two models are sufficiently close to the theoretical value, and (3) the fluctuation trends of the dynamic force keep basically consistent with each other. In summary, the comparison presented clearly indicates that the proposed model is indeed reasonable, which provides a new way for dynamic investigation and structural redesign of a large epicyclic gear train. Finally, as a practical engineering application, the vibration result of the deformable supporting structure of the GTF gear train is also presented, which directly provides valuable reference for vibration monitoring, fault diagnosis and other engineering problems in practice.

Numerical study on thermal stress of solid oxide electrolyzer cell with various flow configurations

Development of solid oxide electrolyzer cell (SOEC) is of great significance for hydrogen energy utilization. In order to have a better understanding of the multiphysics field processes in SOEC, numerical simulation is a necessary means to be adopted. In this study, a complete three-dimensional model of a planar SOEC short stack with three flow configurations (co-flow, cross-flow, counter-flow) was built. Multiple physical fields, including electron(ion), momentum, mass, and heat transfer, and thermal stress as well, were considered. The accuracy of the model was verified by comparing with the experimental results, with the maximum relative error less than 5%. The performances of SOEC with variant flow configurations were compared in detail. Due to the difference of flow configurations, the distribution of temperature, current density, hydrogen mole fraction, and stress present different characteristics. The effects of the operating voltage and the inlet gas temperature on hydrogen production and stress concentration in SOEC were also studied. The results show that compared with co-flow and counter-flow, the hydrogen production rate of cross-flow increases by about 15%, while the stress concentration decreasing by about 70%. All results show the better performance of cross-flow configuration, which provides guidance for design and application of SOEC.

Risk Assessment Modeling of Urban Railway Investment and Financing Based on Improved SVM Model for Advanced Intelligent Systems

A risk assessment method for urban railway investment and financing based on an improved SVM model under big data is proposed. First, the inner product in the traditional SVM is replaced by a kernel function to obtain a more accurate non-linear SVM, and a classifier with high classification accuracy is achieved by finding the optimal separating hyperplane. Then, a risk index system is constructed based on the grounded theory combining with intuitionistic fuzzy sets, interval intuitionistic fuzzy sets, weighted averaging operators and the distance measure, and the selection method of assessment indexes is analyzed based on the statistical methods. Finally, the SVM model with fuzzy membership is obtained by fuzzifying the input samples of the SVM based on the given rules of fuzzy membership design. The results show that the maximum relative error between the final test results and the actual value is 0.316%, and the minimum relative error is 0.133% with three different test sets being tested in the proposed method, which can accurately assess the investment.

Explicit pipe friction factor equations: evaluation, classification, and proposal

The Colebrook equation has been used to estimate the friction factor (f) in turbulent fluids. In this regard, many equations have been proposed to eliminate the iterative process of the Colebrook equation. The goal of this article was to perform an evaluation, classification, and proposal of the friction factor for better development of hydraulic projects. In this study, Gene Expression Programming (GEP), Newton-Raphson, and Python algorithms were applied. The accuracy and model selection were performed with the Maximum Relative Error (∆f/f), Percentage Standard Deviation (PSD), Model Selection Criterion (MSC), and Akaike Information Criterion (AIC). Of the 30 equations evaluated, the Vatankhah equation was the most accurate and simplest to obtain the friction factor with a classification of very high, reaching a value of ∆f/f&lt;0.5% and 1.5&lt;PSD&lt;1.6. A new equation was formulated to obtain the explicit f with fast convergence and accuracy. It was concluded that the combination of GEP, error theory, and selection criteria provides a more reliable and strengthened model.