Assessment of Cavitation Erosion in a Water-Jet Pump Based on the Erosive Power Method

Cavitation affects the performance of water-jet pumps. Cavitation erosion will appear on the surface of the blade under long-duration cavitation conditions. The cavitation evolution under specific working conditions was simulated and analyzed. The erosive power method based on the theory of macroscopic cavitation was used to predict cavitation erosion. The result shows that the head of the water-jet pump calculated using the DCM-SST turbulence model is 12.48 m. The simulation error of the rated head is 3.8%. The cavitation structure of tip leakage vortex was better captured. With the decrease of the net positive suction head, the position where the severe cavitation appears in the impeller domain gradually moves from the tip to the root. The erosion region obtained by the cavitation simulation based on the erosive power method is similar to the practical erosion profile in engineering. As the net positive suction head decreases, the erodible area becomes larger, and the erosion intensity increases.

Development of a simulation model of a cargo customs complex operation as a link of a logistic supply chain

The main link in the logistics supply chain is the cargo customs complex. It provides customs and logistics services to cargo owners during the export and import of goods, complex services, placement of goods in a customs warehouse and a temporary storage warehouse. To substantiate the choice of the optimal logistics supply chain and optimize the work of the cargo customs complex, it is proposed to use simulation modeling. The model of operation of the logistics chain and the cargo customs complex is presented in a general form. The proposed model is implemented in the GPSS World simulation automation package. Testing the simulation model involved checking its adequacy. Checking the adequacy of the simulation model, which showed the maximum value of the t-statistic of 1.424 with a critical value of 1.85, proved its compliance with the work of a real object. After completing the adequacy check, the simulation error was estimated, which was 3 % with an allowable 5 %, due to the presence of pseudo-random number generators in the simulation model. Thus, the simulation error is insignificant for this study. For the cargo customs complex, an example of the simulation results is given. Based on the results of simulation modeling, it is possible to determine: the optimal type of the logistics supply chain and the optimal structure of the cargo customs complex. A wide range of tasks that the proposed simulation model can solve is presented. Thus, the developed simulation model will make it possible to analyze and improve the modes of operation of the cargo customs complex. In addition, it will allow to get an informed decision regarding the use of a certain type of logistics supply chain

Research on Thermal Characteristics of Ball Screw Feed System Considering Nut Movement

The heat generated by the ball screw feed system will produce thermal errors, which will cause the positioning accuracy to decrease. The thermal simulation modeling of the ball screw feed system is the basis for compensating thermal errors. The current thermal characteristic modeling method simplifies the reciprocating movement of the nut pair on the screw shaft to varying degrees, which leads to a decrease in simulation accuracy. In this paper, the nut is regarded as a moving heat source, and a novel method is adopted to make the moving process of the heat source closer to the actual nut movement process. The finite difference method is used to simulate the temperature field and thermal error of the ball screw feed system under different working conditions. Firstly, based on the heat transfer theory, the heat conduction differential equation of the feed system is established and discretized. The thermal error model of the ball screw feed system is established. Then, the relationship between nut heat source position and operating time is established to simulate nut reciprocating motion. Finally, the temperature and thermal error experiments of the ball screw feed system were carried out, and the temperature experiment results were compared with the simulation results of the finite difference method. The results show that the maximum simulation error of the average temperature in the operating interval is 11.4%, and the maximum simulation error of thermal error is 16.4%, which verifies the validity and correctness of the method. The thermal characteristic modeling method of the ball screw feed system proposed in this paper has a substantial application value for accurately obtaining the temperature field of the feed system.

Nearly tight Trotterization of interacting electrons

We consider simulating quantum systems on digital quantum computers. We show that the performance of quantum simulation can be improved by simultaneously exploiting commutativity of the target Hamiltonian, sparsity of interactions, and prior knowledge of the initial state. We achieve this using Trotterization for a class of interacting electrons that encompasses various physical systems, including the plane-wave-basis electronic structure and the Fermi-Hubbard model. We estimate the simulation error by taking the transition amplitude of nested commutators of the Hamiltonian terms within the η-electron manifold. We develop multiple techniques for bounding the transition amplitude and expectation of general fermionic operators, which may be of independent interest. We show that it suffices to use (n5/3η2/3+n4/3η2/3)no(1) gates to simulate electronic structure in the plane-wave basis with n spin orbitals and η electrons, improving the best previous result in second quantization up to a negligible factor while outperforming the first-quantized simulation when n=η2−o(1). We also obtain an improvement for simulating the Fermi-Hubbard model. We construct concrete examples for which our bounds are almost saturated, giving a nearly tight Trotterization of interacting electrons.

Optimization of Cold Flow Properties of Biodiesel by Addition of Cold Flow Improver

Despite the renewable and sustainable characteristics, biodiesel is poor in cold flow property (CFP) which causes a significant drawback that have limited its application. Thickening or crystallization of biodiesel in low temperature can readily result in the clogging of fuel pipes and fuel filters. The purpose of this study is to determine the optimum properties of blended biodiesel that gives the most accurate simulation results of blended biodiesel’s CFP. TmoleX18 and COSMOthermX were used to identify the viscosities and densities of pure palm oil biodiesel and pure ethanol under different temperatures. The densities, viscosities and pour points of ethanol blended biodiesel was then calculated by using Grunberg-Nissan and, Riazi and Daubert equations. The simulation results were obtained under different compositions of ethanol added from 0 to 0.2 mole fraction at temperature range of 30 °C to -5 °C. The optimum combination of viscosities and densities of blended biodiesel for the blended cold flow properties was at 10 °C and 30 °C respectively. The simulation error at 0.1 mole fraction of ethanol was 0.92 %.

Development and Validation of Multiscale Coupled Thermal-Hydraulic Code Combining RELAP5 and Fluent Code

In nuclear reactor system research, the multiscale coupled thermal-hydraulic (T-H) system code and CFD code is one of the most prevalent research areas, and it could help improve simulation fidelity and optimize nuclear reactor design. Additionally, a new idea known as the function fitting method (FFM) for coupling parameter distribution has been newly proposed for exchanging data on the coupling interface, which uses math equations to present the velocity distribution characteristics at the coupling interface. This method could improve the simulation error and numerical instability. To verify and validate the abovementioned FFM, a comparison between the velocity function shape by FFM and real velocity distribution was completed. Besides, the Edwards pipe blowdown test results were used to verify the coupled code. The results showed good agreement with experiment results, and a better simulation accuracy compared to previous work. The current work will establish the ability to explore multiscale coupled thermal-hydraulic operation characteristics which permit precise local parameter distribution.

Fundamentals of Quantum Computing, Quantum Supremacy, and Quantum Machine Learning

When we aim to demonstrate that a programmable quantum device can solve complex problems which cannot be addressed by classic computers, this fundamental goal is known as quantum supremacy. This concept has changed every fundamental rule of computation. In this chapter, the detailed concept of quantum computing and quantum supremacy is explained along with various open source tools and real-time applications of this technology. The major base concepts, quantum computing, the difference between classical and quantum computer on physical level, programing quantum device, and the experiment-quantum supremacy are explained conceptually. This chapter also includes an introduction of the tools Cirq and OpenFermion plus the applications like quantum simulation, error mitigation technique, quantum machine learning, and quantum optimization, which are explained with illustrations.

Representation of simulation errors in single step methods using state dependent noise

The local error of single step methods is modelled as a function of the state derivative multiplied by bias and zero-mean white noise terms. The deterministic Taylor series expansion of the local error depends on the state derivative meaning that the local error magnitude is zero in steady state and grows with the rate of change of the state vector. The stochastic model of the local error may include a constant, “catch-all” noise term. A continuous time extension of the local error model is developed and this allows the original continuous time state differential equation to be represented by a combination of the simulation method and a stochastic term. This continuous time stochastic differential equation model can be used to study the propagation of the simulation error in Monte Carlo experiments, for step size control, or for propagating the mean and variance. This simulation error model can be embedded into continuous-discrete state estimation algorithms. Two illustrative examples are included to highlight the application of the approach.

Multi-gene Genetic Programming for Structure Selection of Polynomial NARMAX models

In the area of black-box identication, NARMAX models are of great interest. The main diculty faced when working with such models is the selection of the correct structure to represent the underlying system in the data. Orthogonal Least Squares (OLS) methods are widely used for this task, however, there are systems with a high degree of non-linearity and long term dependencies, which makes the use of traditional OLS methods computationally impracticable. In this sense, this paper studies the use of Multi-Gene Genetic Programing (MGGP) together with the traditional OLS method to increase the search space and turn the structure selection practicable for average performance computer. It is shown that, in real-life problem data, the algorithm can nd better models than previous works' models. The MGGP found a model for a hydraulic pumping system with a better one-step-ahead prediction error (0:058 mlc2 against 0:070 mlc2) using PEM technique and better free-run simulation error (0:997 mlc2 against 1:120 mlc2) using SEM technique. The MGGP found a model with such a degree of non-linearity and maximum input-output lags that totalizes 142505 candidate terms for traditional OLS analysis, which is impracticable for average performance computers.

The Significance of Sky Temperature in the Assessment of the Thermal Performance of Buildings

Energy-efficient building design needs an accurate way to estimate temperature inside the building which facilitates the calculation of heating and cooling energy requirements in order to achieve appropriate thermal comfort for occupants. Sky temperature is an important factor for any building assessment tool which needs to be precisely determined for accurate estimation of the energy requirement. Many building simulation tools have been used to calculate building thermal performance such as Autodesk Computational Fluid Dynamics (CFD) software, which can be used to calculate building internal air temperature but requires sky temperature as a key input factor for the simulation. Real data obtained from real-sized house modules located at University of Newcastle, Australia (southern hemisphere), were used to find the impact of different sky temperatures on the building’s thermal performance using CFD simulation. Various sky temperatures were considered to determine the accurate response which aligns with a real trend of buildings’ internal air temperature. It was found that the internal air temperature in a building keeps either rising or decreasing if higher or lower sky temperature is chosen. This significantly decreases the accuracy of the simulation. It was found that using the right sky temperature values for each module, Cavity Brick Module (CB) Insulated Cavity Brick Module (InsCB), Insulated Brick Veneer Module (InsBV) and Insulated Reverse Brick Veneer Module (InsRBV), will result in 6.5%, 7.1%, 6.2% and 6.4% error correspondingly compared with the real data. These errors mainly refer to the simulation error. On the other hand using higher sky temperatures by +10 °C will significantly increase the simulation error to 16.5%, 17.5%, 17.1% and 16.8% and lower sky temperature by +10 °C will also increase the error to 19.3%, 22.6%, 21.9% and 19.1% for CB, InsCB, InsBV and InsRBV modules, respectively.