Computer Calculation Time Research Articles

Segmented Two-Dimensional Progressive Polynomial Calibration Method for Nonlinear Sensors.

Nonlinearity in sensor measurements reduces the sensor's accuracy. Therefore, accurate calibration is necessary for reliable sensor operation. This study proposes a segmented calibration method that divides the input range into multiple sections and calculates the optimized calibration functions for each one. This approach reduces the overall error rate and improves the calibration accuracy by isolating distinctive regions. The modified progressive polynomial calibration technique is used to calculate the calibration function. This algorithm addresses the computational complexity, allowing for reduced polynomial degrees and improving the accuracy. The segmented calibration method achieves a significantly lower error rate of 0.000006% compared to the original single calibration method, which has an error rate of 0.0823%, when using the same six calibration points and a fifth-degree polynomial function. This method maintains improved accuracy with fewer calibration points, and its ability to reduce the computational complexity and calculation time while using lower polynomial degrees is confirmed. Additionally, it can be extended to two dimensions to reduce the errors caused by cross-sensitivity. The results from a two-dimensional simulation show a reduction in the error rate ranging from 15.84% to 2.07% in an 8-bit signed fixed-point system. These results indicate that the segmented calibration method is an effective and scalable solution for various typical sensors.

Surrogate-Based Uncertainty Analysis for Groundwater Contaminant Transport in a Chromium Residue Site Located in Southern China

Numerical modeling is widely acknowledged as a highly precise method for understanding the dynamics of contaminant transport in groundwater. However, due to the intricate characteristics of environmental systems and the lack of accurate information, the results are susceptible to a significant degree of uncertainty. Numerical models must explicitly consider related uncertainties in parameters to facilitate robust decision-making. In a Chromium Residue Site located in southern China (the study area), this study employed Monte Carlo simulation to assess the impact of variability in key parameters uncertainty on the simulation outcomes. Variogram analysis of response surface (VARS), global sensitivity analysis, and an XGBoost (version 2.0.0)-based surrogate model was employed to overcome the substantial computational cost of Monte Carlo simulation. The results of numerical simulation indicate that the contaminant is spreading downstream towards the northern boundary of contaminated site near Lianshui River, threatening water quality. Furthermore, migration patterns are complex due to both downstream convection and upstream diffusion. Sensitivity analysis identified hydraulic conductivity, recharge rate, and porosity as the most influential model parameters, selected as key parameters. Moreover, uncertainty analysis indicated that the variability in key parameters has a minimal impact on the simulation outcomes at monitoring wells near the contaminant source. In contrast, at wells positioned a considerable distance from the contaminant source, the variability in key parameters significantly influences the simulation outcomes. The surrogate model markedly mitigated computational workload and calculation time, while demonstrating superior precision and effectively capture the non-linear correlations between input and output of the simulation model.

Machine Learning K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Accurate and Efficient Cubic-Scaling Exact Exchange Plus Random Phase Approximation within Plane Waves.

The exact-exchange plus random-phase approximation (EXX+RPA) method has emerged as a crucial tool for precisely characterizing electronic structures in molecular and solid systems. We present an accurate and efficient implementation of EXX+RPA calculations that scale cubically and are conducted within plane waves. Our approach incorporates the interpolative separable density fitting (ISDF) algorithm, effectively mitigating the computational challenges associated with the plane wave basis set. To overcome the constraints of the conventional ISDF algorithm, characterized by the exceptionally high prefactor in QR factorization for interpolation point selection, we introduce an enhanced machine learning K-means method. This method incorporates a novel empirical weight function called "SSM+" for more precise interpolation point selection, capturing physical information more accurately across diverse systems. Our machine learning approach offers a quasiquadratic scaling alternative, effectively replacing the computationally demanding cubic-scaling QRCP algorithm in plane-wave-based EXX+RPA calculations. Furthermore, we enhance the method's capabilities by optimizing GPU acceleration using MATLAB's integrated GPU toolkit. In particular, our approach reduces the computational scaling of χ0 from 3.80 to 2.13 and the overall computational scaling of EXX from 2.74 to 2.10. We achieve a remarkable GPU acceleration speedup of up to 35×. Regarding CPU computation time, the standard quartic-scaling method requires 22 h to compute Si128, while QRCP completes the calculation in only around 1 h, achieving a speedup up to 20×. However, the utilization of the K-means algorithm reduces the time to 800 s, a substantial improvement of 100× compared to the standard algorithm. By employing the K-means algorithm, the computational time for interpolative point calculation using QRCP decreases from 1 h to 1 min, resulting in a 55× speed increase. With this improved algorithm, we successfully computed the dissociation curve of H2 and the equilibrium polyynic geometry of C18 molecules.

General numerical method for hydraulic and thermal modelling of the steam superheaters

General numerical method for hydraulic and thermal modelling of the steam superheaters

Three-dimensional modeling of microwave discharges in a waveguide-based plasma source with experimental comparison.

This paper proposes a transient three-dimensional model to simulate microwave-induced discharges in a waveguide-based plasma source under intermediate pressures. A plane-symmetric simplification method is applied to simplify half of the microwave plasma source in the calculation domain, dramatically reducing the demand for computational resources and calculation time. Meanwhile, the numerical simulations remain in three dimensions without dimensionality reduction, which allows us to directly calculate the efficiency of power coupling from the incident microwave to the plasma. Besides, the computation decrease improves the convergence performance of the mathematical model, making it possible to model the entire discharge process from 1×10^{-9} to 1×10^{4}s. This period covers the instantaneous microwave breakdown to the formation of a stable plasma column near steady state. The results have revealed the electromagnetic waveguide structure change of the microwave plasma source during the discharge process. Several microwave power-coupling efficiencies of the waveguide-based plasma source with different thicknesses and permittivities of the glass tube are calculated and compared with the experimentally measured data. Furthermore, the effects of the glass tube on the electromagnetic modes of the traveling microwave propagating along the plasma column and the discharge properties are also investigated. The numerically obtained results generally agree with the theoretical analysis and the experimental data in our previous studies, demonstrating the validity of the proposed mathematical model and the plane-symmetric simplification method.

Measurement of inkjet droplet size based on Fraunhofer diffraction.

Inkjet printers have started to manufacture OLED/QLED pixel arrays for the display industry, and the precise measurement and control of ink droplet volume during the printing process has become important. We investigated the feasibility of Fraunhofer diffraction analysis as a volume measurement tool for fast-moving inkjet droplets. To confirm the basic idea, two Fraunhofer diffraction-based methods were used to calculate the wire diameters of well-known sized and steady-positioned metal wires. The first method was to curve-fit the whole measured diffraction intensity curve with the extensive Fraunhofer diffraction equation. The second one was to use the simple approximate diameter calculation equation with the measured position data of minimum diffraction intensity. The metal wire diameters calculated by the two methods showed less than 1.17% error. For the size measurement of fast-moving inkjet droplets, the first method showed 24.5 µm diameter and 7.7 pL volume, while the second method showed 25.4 µm diameter and 8.58 pL volume. We found that the second method was more suitable for real-time inkjet monitoring because its average computer calculation time was 33ms, and the first method took an average of 34ms, about 1000 times more CPU time. Hence, Fraunhofer diffraction analysis as an inkjet droplet volume measurement tool was feasible with a good balance of measurement time and measurement accuracy.

A novel method for noninvasive quantification of fractional flow reserve based on the custom function.

Boundary condition settings are key risk factors for the accuracy of noninvasive quantification of fractional flow reserve (FFR) based on computed tomography angiography (i.e., FFRCT). However, transient numerical simulation-based FFRCT often ignores the three-dimensional (3D) model of coronary artery and clinical statistics of hyperemia state set by boundary conditions, resulting in insufficient computational accuracy and high computational cost. Therefore, it is necessary to develop the custom function that combines the 3D model of the coronary artery and clinical statistics of hyperemia state for boundary condition setting, to accurately and quickly quantify FFRCT under steady-state numerical simulations. The 3D model of the coronary artery was reconstructed by patient computed tomography angiography (CTA), and coronary resting flow was determined from the volume and diameter of the 3D model. Then, we developed the custom function that took into account the interaction of stenotic resistance, microcirculation resistance, inlet aortic pressure, and clinical statistics of resting to hyperemia state due to the effect of adenosine on boundary condition settings, to accurately and rapidly identify coronary blood flow for quantification of FFRCT calculation (FFRU). We tested the diagnostic accuracy of FFRU calculation by comparing it with the existing methods (CTA, coronary angiography (QCA), and diameter-flow method for calculating FFR (FFRD)) based on invasive FFR of 86 vessels in 73 patients. The average computational time for FFRU calculation was greatly reduced from 1-4h for transient numerical simulations to 5min per simulation, which was 2-fold less than the FFRD method. According to the results of the Bland-Altman analysis, the consistency between FFRU and invasive FFR of 86 vessels was better than that of FFRD. The area under the receiver operating characteristic curve (AUC) for CTA, QCA, FFRD and FFRU at the lesion level were 0.62 (95% CI: 0.51-0.74), 0.67 (95% CI: 0.56-0.79), 0.85 (95% CI: 0.76-0.94), and 0.93 (95% CI: 0.87-0.98), respectively. At the patient level, the AUC was 0.61 (95% CI: 0.48-0.74) for CTA, 0.65 (95% CI: 0.53-0.77) for QCA, 0.83 (95% CI: 0.74-0.92) for FFRD, and 0.92 (95% CI: 0.89-0.96) for FFRU. The proposed novel method might accurately and rapidly identify coronary blood flow, significantly improve the accuracy of FFRCT calculation, and support its wide application as a diagnostic indicator in clinical practice.

Robust stochastic low-carbon optimal dispatch of park-integrated energy system with multiple uncertainties from source and load

To realize the cascaded utilization of energy, improve the effective utilization of energy, and further reduce the carbon emissions of integrated energy systems a robust stochastic low-carbon optimal dispatch model with economy, environmental protection and reliability is developed for a park-integrated energy system wherein the multiple uncertainties brought by source and load are fully considered. First, a two-stage robust optimization algorithm is employed to handle uncertain wind power generation. A multi-case analysis method for the uncertainties of photovoltaics and load is proposed based on an improved centralized reduction algorithm. Then, considering the depreciation of the weighted average of the comprehensive operation cost, carbon emissions, and energy undersupply rate, a robust stochastic optimal dispatch model can be derived and efficiently solved by using a multi-objective fuzzy optimization algorithm with an improved membership function. Finally, by comparing the four cases, the simulation results show that the computational complexity and calculation time of the system can be reduced, the trimming result errors can be decreased, and a balance between economy, environmental protection, reliability, and robustness can be achieved.

Development of reduced and optimized mechanism for ammonia/ hydrogen mixture based on genetic algorithm

Development of reduced and optimized mechanism for ammonia/ hydrogen mixture based on genetic algorithm

Application of the Iber Two-Dimensional Model to Recover the Water Quality in the Lurín River

The Lurín River is one of the main sources of water for the city of Lima. However, the discharge of domestic wastewater, the presence of dumps, and long periods of drought cause the deterioration of the water resource. In this study, DO, BOD5, E. coli, T, EC, TSS, U, and h were monitored at 13 monitoring points spread over 20 km of river influence. This information was used to calibrate the parameters of Kdbo, Kaire, Kdos, and Kdec in the Iber two-dimensional numerical model, obtaining values of 0.55 d−1, [4.84 d−1–80.65 d−1], 10 g O2 m−2d−1, and [1.49 d−1–15.42 d−1], respectively, with efficiencies ranging from “very good” to “satisfactory”. In the hydraulic model, a discretization of the channel, banks, and plains of 3, 5, and 7 m, respectively, was considered, resulting in a computational calculation time of 4 days in each simulation. The greatest contamination occurs in July at km 5 + 400 up to the Pan-American bridge. Therefore, it is proposed to recover the river by optimizing the San Bartolo Wastewater Treatment Plant (WWTP) and a new WWTP in Pachacámac to avoid diffuse contamination, with discharge flows of 0.980 m3s−1 and 0.373 m3s−1, respectively, and 4 mg L−1, 15 mg L−1 and 1000 NMP/100 mL for DO, BOD5, and E. coli, respectively.

Novel PMU Model for Dynamic State Disturbance Analysis with Effective Data Handling System

In this paper a hybrid DFT phasor calculation method is presented. This method is used to calculate the fundamental component phasor value of the harmonic signal without any physical filter. With this method the computational time for each phasor value calculation is reduced and the calculated phasor value has the constant magnitude and rotating phase angle. This calculated phasor values are used for the disturbance or fault identification in the power system based on Total Vector Error (TVE). The %TVE-based fault identification is more effective, because the TVE value is calculated with reference phasor value. If any fault/disturbance occurs or frequency changes then %TVE value changes. This change in TVE value is reflected in the calculation of the next sample (1/f.*N sec), giving the advantage to this method as compared to the other magnitude-based fault identification systems. Normally with the phasor calculation large data is produced, which requires large memory for the storage of this data and makes the analysis difficult. To avoid large data storage system conditional-based data storage system is proposed, where the data is stored during only the disturbance conditions or at every one second. With this technique, the data to be stored is reduced and the analysis of this data also becomes simpler for the post disturbance and for future load prediction. The performance of the proposed method is evaluated in terms of accuracy of calculation and its implementation ability. The simulation results are as per the IEEE C37.118.1a2014 for the power system monitoring and fault identification.

Rapid Analysis of Cylindrical Bypass Flow Field Based on Deep Learning Model

Deep learning models provide a novel research perspective for hydraulic machinery and fluid dynamics mechanism research. Traditional computational fluid dynamics requires a lot of computational resource and calculation time, while deep learning models can effectively solve this problem. In this paper, a deep learning model is proposed for the rapid flow field analysis of a two-dimensional cylindrical bypass flow, and the errors of the prediction results are analyzed, so as to verify the feasibility of deep learning for accelerating the numerical simulation process. On this basis, the influence of different network structures on the prediction performance of the deep learning model is explored, and the optimal structural parameters of the neural network are found, indicating that it will achieve real-time prediction of the flow field performance, and save considerable computational resource and calculation time. The research in this paper is of great significance for the application about the rapid analysis of hydraulic machinery fluid dynamics based on deep learning models.

Hybrid algorithm based on the grey wolf optimizer and direct binary search for the efficient design of a mosaic-based device

We propose a novel hybrid algorithm based on the grey wolf optimizer (GWO) and direct binary search (DBS) for the design of mosaic-based devices. The DBS algorithm leads to the local optimum structure, and the design results are changed for each trial. Thus, we need to implement the DBS design hundreds of times to find a high-performance mosaic-based device, resulting in huge computer resources and calculation time. To resolve the problem and search for a better solution, the GWO is combined with the DBS. The average and best performances of the designed devices are better by 0.5 and 1.6 points when compared with those designed by the DBS.

Backscattering X-ray imaging using Fresnel zone aperture

We demonstrate backscattering X-ray imaging using a Fresnel zone aperture (FZA), where the line width of the outermost circle is 100 μm. The method is a lensless imaging technique employing an optical aperture instead of a lens. We successfully reconstructed test objects from four images observed using a series of FZAs without an iterative procedure during the reconstruction process. We also confirmed the successful reconstruction of a series of images by changing the focal plane in a short computer calculation time, indicating that 3D information can be obtained by measuring a single set of four images.

New analytical-numerical method for modelling of tube cross-flow heat exchangers with complex flow systems

New analytical-numerical method for modelling of tube cross-flow heat exchangers with complex flow systems

HNN-based generalized predictive control for turbofan engine direct performance optimization

HNN-based generalized predictive control for turbofan engine direct performance optimization

Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, China

Abstract This study utilized the ECO Lab model calculation samples of Tai Lake, in combination with robust analysis and the GCV test, to promote a faster intelligent application of machine learning and evaluate the MARS machine learning method. The results revealed that this technique can be better trained with small-scale samples, as indicated by the R2 values of the water quality test results, which were all >0.995. In combination with the Sobol sensitivity analysis method, the contribution degree of the parameterized external conditions as well as the relationship with the water quality were examined, which indicated that TP and TN are primarily related to the external input water quality and flow, while Chl-a is related to inflow (36.42%), TP (26.65%), wind speed (25.89%), temperature (8.38%), thus demonstrating that the governance of Chl-a is more difficult. In general, the accuracy and interpretability of MARS machine learning are more in line with the actual situation, and the use of the Sobol method can save computer calculation time. The results of this research can provide a certain scientific basis for future intelligent management of lake environments.

Large-deflection analysis of prismatic and tapered beam-columns using the Differential Transform Method

Large-deflection analysis of prismatic and tapered beam-columns using the Differential Transform Method

Online Determining Heat Transfer Coefficient for Monitoring Transient Thermal Stresses

This paper proposes an effective method for determining thermal stresses in structural elements with a three-dimensional transient temperature field. This is the situation in the case of pressure elements of complex shapes. When the thermal stresses are determined by the finite element method (FEM), the temperature of the fluid and the heat transfer coefficient on the internal surface must be known. Both values are very difficult to determine under industrial conditions. In this paper, an inverse space marching method was proposed for the determination of the heat transfer coefficient on the active surface of the thick-walled plate. The temperature and heat flux on the exposed surface were obtained by measuring the unsteady temperature in a small region on the insulated external surface of a pressure component that is easily accessible. Three different procedures for the determination of the heat transfer coefficient on the water-spray surface were presented, with the division of the plate into three or four finite volumes in the normal direction to the plate surface. Calculation and experimental tests were carried out in order to validate the method. The results of the measurements and calculations agreed very well. The computer calculation time is short, so the technique can be used for online stress determination. The proposed method can be applied to monitor thermal stresses in the components of the power unit in thermal power plants, both conventional and nuclear.

Chemo-economic analysis of battery aging and capacity fade in lithium-ion battery

Chemo-economic analysis of battery aging and capacity fade in lithium-ion battery