Method For Determination Of Parameters Research Articles

Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural networks

A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed using the constrained molecular dynamics model simulations, focusing on the heavy-ion collisions at the low-intermediate incident energies from several ten to one hundred MeV/nucleon in which the emissions of heavy fragments with the charge numbers larger than 3 become crucial. To make the CNN applicable in the task of the impact parameter determination at the present energy range, specific improvements are made in the input selection, the CNN construction and the CNN training. It is demonstrated from the comparisons of the deep CNN method and the conventional methods with the impact parameter-sensitive observables, that the deep CNN method shows better performance for determining the impact parameters, especially leading to the capability of providing better recognition of the central collision events. With a proper consideration of the experimental filter effect in both training and testing processes to keep consistency with the actual experiments, the good performance of the deep CNN method holds, and shows significantly better in terms of predicting the impact parameters and recognizing the central collision events, compared to that of the conventional methods, demonstrating the superiority of the present deep CNN method. The deep CNN method with the consideration of the filter effect is applied in the deduction of nuclear stopping power. Higher accuracy for the stopping power deduction is achieved benefitting from the better impact parameter determination using the deep CNN method, compared to using the the conventional methods. This result reveals the importance to select a reliable impact parameter determination method in the experimental deduction of the nuclear stopping power as well as other observables.

Modification and Application of Limestone HJC Constitutive Model under the Impact Load

Abstract When the Holmquist-Johnson-Cook (HJC) constitutive model is used to simulate limestone under the impact load, problems of a compaction stage not being characterised and low dynamic peak stress prediction accuracy are observed. The numerical simulation and experimental results were inconsistent. In this study, we proposed a modified HJC constitutive model and parameter determination method. Based on the characteristics of the dynamic stress-strain curve of limestone and relationship between axial and volumetric strains, the linear elastic phase of the state equation of the original HJC constitutive model was modified, and a new state equation was proposed. The yield surface of the original HJC constitutive model was modified on the basis of the sensitivity analysis method and limit surface theory, and a method for determining the parameters of the modified HJC constitutive model of limestone was proposed. The modified model and parameter determination method were experimentally verified using the split Hopkinson pressure bar (SHPB) and a high-speed camera. The results showed that the state equation curve of limestone under the impact load was divided into compaction, linear elastic, and fully compacted stages. After the introduction of the pressure parameters M, P, and Q, the nonlinear change in the stress-strain curve during the compaction stage was highly consistent with the SHPB results. The strength parameters fc, A, B, and N exhibited the maximum impact on the dynamic strength of limestone. After the strength parameters A, B, and N were modified for the yield surface, the prediction accuracy of limestone dynamic peak stress was over 97%, the prediction error rate decreased by more than 10%, and the reliability of numerical simulation results improved. These results can provide a simple and feasible numerical simulation method for the dynamic analysis of rock materials.

An effective method for determination and characteristic analysis of induction motor parameters

Due to its unique and attractive characteristics, the induction motor (IM) has been widely used for years. How to design an IM with desired characteristics and how to realise high performance control for a given IM, these have always been hot research topics to many researchers. No matter what control technology is used to achieve high-performance drive of IM, it depends on in-depth understanding of motor parametric characteristics and their accurate acquisition. An effective determination method is proposed to improve the parameter accuracies of the equivalent circuit for induction motors by combining non-rotor test with dual-load test and using this method to measure and analyse the parametric characteristics of IM. Experimental validations are carried out on two induction motors with open-slot rotor and closed-slot rotor, respectively. Compared with the traditional methods, the proposed method has higher accuracy and can be an effective tool for design, control, and testing research of IM.

Advanced Computing Methods for Impedance Plethysmography Data Processing.

In this paper we are introducing innovative solutions applied in impedance plethysmography concerning improvement of the rheagraph characteristics and the efficiency increase of the developing rheograms using computer methods. The described methods have been developed in order to ensure the stability of parameters and to extend the functionality of the rheographic system based on digital signal processing, which applies to the compensation of the base resistance with a digital potentiometer, digital synthesis of quadrature excitation signals and the performance of digital synchronous detection. The emphasis was put on methods for determination of hemodynamic parameters by computer processing of the rheograms. As a result–three methods for respiratory artifacts elimination have been proposed: based on the discrete cosine transform, the discrete wavelet transform and the approximation of the zero line with spline functions. Additionally, computer methods for physiological indicators determination, including those based on wavelet decomposition, were also proposed and described in this paper. The efficiency of various rheogram compression algorithms was tested, evaluated and presented in this work.

Establishment and Experimental Verification of Stress-Temperature Coupled Damage Model of Warm Frozen Soil

Under the background of rising environmental temperature, the state of warm frozen soil near the phase transition is extremely unstable. In order to explore the relationship between temperature and mechanical properties of warm frozen soil, a damage model of warm frozen soil structure under the coupling of stress and temperature is established based on the strain equivalent theory of damage mechanics. Based on the Mohr-Coulomb criterion, the nominal stress is used to represent the stress damage of frozen soil elements, the initial elastic modulus is used to represent the temperature damage, and a composite damage factor is introduced to describe their coupled relationship. Through a triaxial compression experiment of frozen soil, the experimental data and stress-strain curve are obtained. The full-fitting method based on the experimental data (method 1) and the semitheoretical semifitting method based on the characteristic points of the stress-strain curve (method 2) are used to obtain the shape parameters and scale parameters of the stress-temperature coupled damage model corresponding to different fitting methods. Based on the triaxial compression tests of frozen sand and frozen silty clay, the reliability of the stress-temperature coupled damage model results obtained by the two parameter determination methods under the conditions of strain softening and strain hardening is verified. The results show that both methods are applicable under the condition of strain softening and strain hardening and method 2 is better than method 1 under the condition of strain softening. Compared with the prediction results of the single stress damage model, the stress-temperature coupled damage model can effectively reduce the influence of the parameter estimation error on the results and improve the overall stability of the model.

An intelligent process parameters determination method based on multi-algorithm fusion: a case study in five-axis milling

• An intelligent parameters determination method is proposed based on multi-algorithm. • An improved GRNN with high accuracy is proposed for small batch of experiments. • An improved NSGA-II is proposed to generate the Pareto frontier with good uniformity. Process parameters have a significant effect on surface integrity, which determines the service performance of the parts. To improve surface integrity, the process parameters are determined: 1) by experienced engineers directly, 2) based on the Pareto frontier automatically constructed by swarm intelligence algorithms. However, as the Pareto frontier contains many non-dominated solutions, the final parameters are still determined by experienced engineers, which reduces the intelligence level. Therefore, an intelligent process parameters determination method based on multi-algorithm fusion is proposed towards minimal surface residual stress in feed or transverse direction ( Rs f , Rs t ) and surface roughness ( Ra ) in five-axis milling. Firstly, the Improved Generalized Regression Neural Network ( IGRNN ), which enhances the nonlinear mapping capability even in dealing with a small batch of experiments, is proposed to predict the Rs f , Rs t , and Ra with certain inputs (including lead angle, tilt angle, cutting depth, feed speed, and spindle speed). Then based on the proposed model, the Improved Non-dominated Sorted Genetic Algorithm-II ( INSGA-II ), which improves the uniformity of the Pareto frontier, is used to obtain a series of non-dominated process parameters. Finally, the optimal parameters are determined by the Principal Component Analysis ( PCA ) without manual weight assignment for Rs and Ra . By comparing with the second-best one, although the Rs f decreases by 0.33%, which is still able to obtain negative residual stress, the Rs t and Ra are greatly improved by 9.3% and 47.94%, respectively. The proposed method could improve the intelligent level of process parameters determination and the service performance of the parts. Furthermore, it lays a foundation for the realization of intelligent manufacturing.

A Novel Cutting Method for Face-Hobbed Spiral Bevel Gears Based on Tool Path Modification

A novel tooth surface design method of face-hobbed spiral bevel gears is proposed, which utilizes a solid cutter without axis tilting through tool path control combining with optimized cutter blade profile to control tooth contact patterns and realize tooth lengthwise chamfering. Firstly, a gear tooth surface generating plan of tool path controlling actively is developed, the mathematical model of the tool path is built, and the equations are given in detail, the determination method of all control parameters is also described. Next, the tooth surface functions of generating gear and machined gear are established based on a mathematic model of the traditional cradle machine tool. Then, the machine setting is also given in concise equations. Finally, the tooth contact analysis is also conducted for a specific gear pair to validate the validity and the reasonability of the new tooth modification method.

運転記録の再現を基準とする配水池運用モデルのパラメータ決定手法

運転記録の再現を基準とする配水池運用モデルのパラメータ決定手法

Using a Mathematical Model with a Calibration Parameter Determination Method for Predicting Concentrations in Hazardous Waste Landfill Leachate, and Verifications by Column Leaching Tests

本研究では廃棄物埋立層からの浸出水濃度を予測するための数理モデル構築とそのモデル検証を行なった。特に予測が難しいと考えられる高濃度の化学物質を含有する特別管理産業廃棄物を対象に検討した。数理モデルは，シリアルバッチ溶出試験の結果を溶出モデルとして加えた移流分散方程式である。本法の課題の一つは，シリアルバッチ溶出試験が液固比 10 での評価であるのに対して，予測対象となる埋立層ではより低い液固比での溶出挙動であり，それをどのように数理モデルに反映させるのかである。本研究では，実用面に優れたモデル構築と高精度化を図るため，液固比を変えたバッチ溶出試験を行い，低液固比での実測値と予測値の差異を調べることで溶出モデルに与える補正係数を導出した。補正係数を用いた数理モデルの有効性を明らかにするためにカラム溶出試験を実施したところ，アルカリ金属とアルカリ土類金属には高精度な予測を与えることがわかった。

Analysis of digital filters used in time-series small heat flux measurement

• Four statistical assessment metrics are used to evaluate the filtering effects. • Filtering effects of six filters with different operating parameters are compared. • Intersection region method is firstly proposed to get optimal operating parameter. • Moving average filter shows a high repeatability and fidelity simultaneously. Noise fluctuation is the most common issue of small heat flux measurement and it disturbs the measurement precision. In the previous studies, how to select an appropriate filter and determine its optimal operating parameter was not given systematically. In this paper, the aim is to explore an effective filter selection and optimal operating parameter determination method for the small heat flux signal. Firstly, six filters with the large range operating parameters were chosen to remove the noise of a set of 10,000 numbers time-series heat flux in the range of 5–10 W/m 2 . Then, signal-to-noise ratio, standard deviation, mean absolute deviation and correlation coefficient were introduced to comprehensively assess the filtering results. Lastly, the intersection region method is firstly proposed to determine the optimal operating parameters based on the tradeoff between repeatability and fidelity. The numerical results indicated that the signal-to-noise ratio of the sampled heat flux was ranged from 13.310 db to 20.703 db. The standard deviation and correlation coefficient was decreased from 1.111 to 0.093–0.672 and from 1 to 0.640–0.883, while the mean absolute deviation was increased from 0 to 0.498–1.056. When the filter is fixed, the repeatability and fidelity levels of filtered data are contradictory. When comparing the filtering effects of filters, some filters simultaneously showed a high repeatability and fidelity levels. For the heat flux signal in this study, the moving average filter with 51 window width and Savitzky-Golay filters with 101 window width and three highest order are the best choice. The proposed intersection region method with four assessment metrics is effectively to find an appropriate filter with high repeatability and fidelity.

Features of research of iron ore thermal decarbonization kinetics during roasting

The work is devoted to research of chemical-metallurgical decarbonization process in iron ore pellets, taking into account thermal and technological operation features of roasting conveyor-type machines. Calcination of pelletized iron ore raw material was experimentally examined, using the samples that save their initial structure within the temperature and heating rate ranges of the existing roasting conveyor-type machines. Appearance of essential temperature gradients in the examined samples was proved experimentally; it was connected with non-stationary heating conditions and thermal effects of carbonates dissociation reaction. These conditions and effects restrict possibility of use of the existing non-isothermal kinetic methods for determination of kind and parameters of kinetic equations. The relationship among heterogeneous endothermic transformations and thermal physical calcination conditions was revealed. The research was conducted under financial support of the Russian Foundation for Basic Research (RFFI) within the scientific project No. 18-29-24094 MK and the Government Order, the project No. FSWF-2020-0019.

Hybrid two–stage СО2 transcritical mechanical compression–ejector cooling cycle: Thermodynamic analysis and optimization

In the paper, the main results of thermodynamic analysis and optimization of the hybrid two–stage carbon dioxide (СО2) transcritical mechanical compression–ejector cooling cycle are presented. The proposed hybrid cooling cycle contains a two–stage CO2 transcritical mechanical compression refrigeration machine and an ejector cooling machine using refrigerants R245ca, R601b, and R1233zd(E). The operation of the ejector cooling cycle is ensured by using some part of the heat rejected from the compressed superheated CO2 vapor of the mechanical compression refrigeration cycle. The cold obtained in the ejector cooling machine is used for subcooling the high–pressure CO2 vapor after the gas cooler. In addition, the intermediate cooling of the CO2 vapor after the low–pressure stage of the mechanical compression refrigeration cycle is realized by preheating and boiling the refrigerant of the ejector cooling cycle in the additional generator–precooler before entering the main generator–precooler. This paper presents a method for determination of the optimal design parameters and performance of the proposed hybrid cooling cycle. The results showed an increase in efficiency of the two–stage CO2 transcritical mechanical compression refrigeration machine in the proposed cooling system of up to 32.7% compared to the conventional system at transcritical operating conditions with high an environmental temperature.

Identification of hydrodynamic coefficients of AUV in the presence of measurement biases

This paper mainly presents the parameter identification method developed from a Least Square Estimation (LSE) algorithm to estimate hydrodynamic coefficients of Autonomous Underwater Vehicle (AUV) in the presence of measurement biases. LSE based parameter determination method is developed to obtain unbiased estimated values of hydrodynamic coefficients of AUV from biased Inertial Navigation System (INS) measurements. The proposed parameter identification method consists of two phases: in the first phase, high precision INS and its auxiliary instrument including compass, pressure depth sensor, and Doppler Velocity Log (DVL) are designed as Integrated Navigational System coupled with Complementary Kalman Filter (CKF) to determine hydrodynamic coefficients of AUV by removing the INS measurement biases; in the second phase, LSE based parameter identification method is applied to the model in the first phase for obtaining unbiased estimated values of hydrodynamic coefficients of AUV. In this paper, a method for identifying the yaw and sway motion dynamic parameters of an AUV is given. Various maneuvering scenarios are verified to assess the parameter identification method employed. The simulation results indicate that using the CKF based Integrated Navigation System together with unbiased measurement conversion could produce better results for estimating the hydrodynamic coefficients of AUV.

An Efficient Mixed Finite Element Perfectly Matched Layer with Optimal Parameters Selection for Two-Dimensional Time Domain Soil-Structure Interaction Analysis

Perfectly matched layer (PML) is known as one of the best methods to simulate infinite domains in many fields such as soil-structure interaction (SSI). The performance of PML is significantly affected by PML parameters selection. However, the way to select PML parameters still remains unclear. This study proposes a method for PML parameters determination for elastic wave propagation in two-dimensional (2D) media. The scaling and attenuation functions are developed in order to increase the accuracy and effectiveness of the PML. The proposed scheme is applied for a mixed PML in time domain. The finite element method (FEM) formulations of the PML are presented so that it can be easily applied to the existing codes. ABAQUS, a popular FEM code, is used for numerical applications in this study. The proposed PML is imported into ABAQUS by using a user-defined element (UEL) written in Fortran language. Six numerical analyses of SSI are implemented to prove the efficiency of the proposed PML. The numerical analyses cover many realistic problems, including free field, surface structure, and embedded structure problems. The results demonstrate the efficiency of the proposed PML in terms of the accuracy and computational cost.

Performance analysis of the refrigerant-cooling radiant terminal: A numerical simulation

• A novel refrigerant-cooling radiant terminal system is proposed. • A numerical model is established to describe the heat transfer characteristics. • The proportions of the sensible, latent and radiant cooling capacity are obtained. • A method using determining the design parameters of this system is provided. • A characteristic equation is proposed to evaluate the cooling performance. The existing radiant cooling systems have the drawback of low energy efficiency due to its secondary heat exchange via chilled water. To overcome this dilemma, a novel refrigerant-cooling radiant terminal (RCRT) system utilizing the refrigerant as direct medium is proposed. A detailed numerical model is established to describe the heat transfer characteristics of this system and validated with the experimental data. The influences of the operating parameters and indoor ambient parameters on thermal performance of the RCRT system are analyzed based on the numerical model. Results show that the proportion of sensible, latent and radiant cooling capacity of this system is 55.5%, 17.3% and 27.2%, respectively, which indicates that natural convection is the main contributor in the heat transfer process. Additionally, a method for design parameters determination of the RCRT system is provided and a simplified characteristic equation is proposed to conveniently evaluate the cooling performance of this system in engineering application. The comparisons of cooling performance are conducted by an assessment index between the RCRT system and other cooling systems. Results show that the cooling capacity per unit projected area of the RCRT system is superior to chilled water radiant cooling systems, while inferior to forced-convection air-conditioning systems.

Robust method of determining microfacet BRDF parameters in the presence of noise via recursive optimization

Accurate bidirectional reflectance distribution function (BRDF) models are essential for computer graphics and remote sensing performance. The popular microfacet class of BRDF models is geometric-optics-based and computationally inexpensive. Fitting microfacet models to scatterometry measurements is a common yet challenging requirement that can result in a model being fit as one of several unique local minima. Final model fit accuracy is therefore largely based on the quality of the initial parameter estimate. This makes for widely varying material parameter estimates and causes inconsistent performance comparisons across microfacet models, as will be shown with synthetic data. We proposed a recursive optimization method for accurate parameter determination. This method establishes an array of local minima best fits by initializing a fixed number of parameter conditions that span the parameter space. The identified solution associated with the best fit quality is extracted from the local array and stored as the relative global best fit. This method is first applied successfully to synthetic data, then it is applied to several materials and several illumination wavelengths. This method proves to reduce manual parameter adjustments, is equally weighted across incident angles, helps define parameter stability within a model, and consistently improves fit quality over the high-error local minimum best fit from lsqcurvefit by an average of 71%.

Determination of the interfacial properties of longitudinal continuous slab track via a field test and ANN-based approaches

Due to the common existence of interlayer debonding and separation of longitudinal continuous slab track (LCST), accurately estimating the interfacial normal cohesive parameters between the concrete track slab and the cement emulsified asphalt (CA) mortar layer is an important task. This study first carried out a full-scale vertical pull test (VPT) to study the interfacial normal bond capacity of LCST. As a result, a series of load–displacement curves were measured. Then, an artificial neural network (ANN)-based approach was developed to map the relationship between the structural response and the interfacial properties under a machine learning (ML) framework. In addition, a refined macroscale finite element (FE) model that employed the exponential cohesive zone model (CZM) was established to simulate the interfacial debonding process. Two datasets of the global load–displacement and the local stress-slip responses obtained from the FE analysis were separately used to train and verify the ANN. Our results showed that the predictions given by the ANNs and the ground truth values were in close agreement. Furthermore, by feeding the well-trained ANN with the experimental load–displacement curves of the VPT, the realistic interfacial normal cohesive parameters of LCST were identified. Afterward, a comparative analysis of the experimental results and the recovered results according to the identified parameters was carried out. The results showed that the proposed parameter determination method is accurate and reliable. The developed hybrid approach that combines experimental and FE analysis with ML methods can be a promising alternative for identifying material properties in structural engineering.

A novel method to uniquely determine the parameters in Gurson–Tvergaard–Needleman model

AbstractThe Gurson–Tvergaard–Needleman (GTN) model is promising in failure prediction as it takes the micromechanical behavior of ductile metals into consideration. However, its engineering applications have scarcely progressed due to the difficulty in determining its eight strongly coupled parameters. Taking the physical background of GTN model into consideration, a set of methods was established to determine all parameters in the GTN model. The knowledge of continuum damage mechanics was introduced to experimentally determine the development of void volume fraction through an interrupted uniaxial tensile test, by which the three parameters regarding void nucleation were analytically derived. Other parameters in the GTN model were also uniquely determined through a joint use of the chemical composition analysis, the cell model analyses, and the inverse finite element method. Reliability of this novel parameter determination method was verified through failure predictions on both cracked and un‐cracked specimens of STPT410.

Constitutive modeling, computational implementation and material parameter identification for polymeric foams considering density and foaming direction

Polyurethane foam (PUF) is a porous material and significantly affected by compressive loads. Under a uniaxial compressive load, the PUF exhibits various types of material nonlinear behavior under varying conditions, such as density, foaming direction, and strain rate. This study investigates the nonlinear behavior of a PUF through phenomenological approach. A Frank–Brockman–Zairi elasto-viscoplastic constitutive model was adopted to investigate the behavior of PUFs with different densities, directions, and strain rates when subjected to compressive loads. The proposed model was discretized using the implicit time integration algorithm and implemented into a user-defined subroutine of ABAQUS. In this study, parameter determination method was also implemented to identify material parameters containing the constitutive equations. Furthermore, the detailed process of determining the material parameters containing the shape of the work-hardening rate–stress curve dependent on the type of material behavior was presented. Consequently, the macroscopic material response of the PUF, such as the stress–strain curve, can be predicted based on the proposed constitutive model. In future studies, the mechanical behavior of porous materials can be analyzed more successfully by supplementing the constitutive model. This can allow the model to simulate the complex plastic flow of porous materials after yielding that is presently difficult to implement.

Alternative Method of Assessing Factors Influencing Economic System Indices

The article deals with developing determination methods of coefficient parameters influencing economic system indices. It is necessary to assess how economic indices respond to external and internal disturbance while making reasonable managerial decisions within economic systems. The research aims to develop a tool alternative to flexibility, as the use of the latter is not comfortable when assessing economic systems performance due to the need to present influence factors variations in percentage terms. It is based on the sensitivity theory that resides in the fact that sensitivity functions are used to assess system responses following by calculating influence coefficients related. The developed theory and method can be used in different cost mana­gement tasks and break-even operations at enterprises and allow to carry out system analysis of different situations within an economic system at a micro-level. The article illustrates the suggested tool applied to the present value, which is one of the most important issues in economic theory. It should be noted that the article searches both the influence of separate factors such as annual rate, the number of years, expected value on the present value cost and the combined influence of the mentioned above on the cost, return rate and return value.