Computational Elements Research Articles

The Deep Space Radiation Probe: Development of a first lunar science payload for space environment studies and capacity building

Regions outside of Low Earth Orbit (LEO, altitudes above approximately 1000 km) are classified as “deep space”, including Medium Earth Orbit (MEO), geostationary orbit (GEO), as well as cislunar and lunar space. The deep space environment poses many challenges for human and robotic exploration, including stronger ionizing radiation fluxes, more extreme temperature variations, as well as limited data downlink volume. With the growth of the rideshare and hosted payload model aboard government and commercial lunar missions, developing the capacity to design and implement payloads and other space avionics for this environment is of increased importance this decade. Utilizing one of the growing number of rideshare opportunities offered by commercial lunar mission providers, National Central University (NCU) has been working on the rapid development of Taiwan’s first scientific payload for lunar lander use, with launch aboard the HAKUTO-R Mission 2 (M2) lander from ispace, inc. scheduled not earlier than Q4 2024. This Deep Space Radiation Probe (DSRP) will provide measurements of radiation dose, dose rate, and single event upset (SEU) rate during the Earth-Moon transit, in lunar orbit, as well as on the lunar surface. DSRP utilizes elements of the on-board computer (OBC) developed and flight qualified aboard the NCU-developed IDEASSat 3U CubeSat mission in 2021, and was developed by a student team, in consultation with experienced engineers from the lunar lander team. In this paper, we will report on the objectives, concept of operation, design, and implementation of the DSRP project. We will also describe the steps taken to facilitate parallel development of the DSRP payload and the HAKUTO-R M2 lander, as well as lessons learned during the design, implementation, and qualification process. The radiation data provided by DSRP will be beneficial for the development of future deep space spacecraft avionics, as well as crewed missions, and will also serve to build the capacity for deep space spacecraft and payload development at NCU.

Modelling of aero-mechanical response of wind turbine blade with damages by computational fluid dynamics, finite element analysis and Bayesian network

Computational Fluid Dynamics and Finite Element Analysis structural models are developed for generation of large datasets of high-fidelity aerodynamic loading, displacement and stress for the NREL 5 MW wind turbine blade. Based on the datasets, analysis of structural responses of the turbine blade and their possible shifts with respect to a change in the operating conditions, e.g. rotor plane tilting and yawing, are performed. A Bayesian Network model is trained to produce ranges of values and likelihoods of occurrence of abnormal structural response or the most likely damage in the blade from inputs of operating conditions. The numerical analysis showed that yawing and tilting of rotor plane has very strong influence on the performance of a wind turbine. The out-of-plane bending is the dominant mode of the rotor blade while twisting and other complex modes of displacement could be more significant during some operating conditions such as at high tilt and yaw angles. The Bayesian Network is capable of computing probability mass functions of mechanical stress or displacement of the blade given a rotor rotation speed and a type of damage or vice versa, identifying a type of damage given a measured stress distribution level.

Ferroelectric capacitors and field-effect transistors as in-memory computing elements for machine learning workloads

This study discusses the feasibility of Ferroelectric Capacitors (FeCaps) and Ferroelectric Field-Effect Transistors (FeFETs) as In-Memory Computing (IMC) elements to accelerate machine learning (ML) workloads. We conducted an exploration of device fabrication and proposed system-algorithm co-design to boost performance. A novel FeCap device, incorporating an interfacial layer (IL) and Hf0.5Zr0.5O2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {Hf}_{0.5}\ ext {Zr}_{0.5}\ ext {O}_2$$\\end{document} (HZO), ensures a reduction in operating voltage and enhances HZO scaling while being compatible with CMOS circuits. The IL also enriches ferroelectricity and retention properties. When integrated into crossbar arrays, FeCaps and FeFETs demonstrate their effectiveness as IMC components, eliminating sneak paths and enabling selector-less operation, leading to notable improvements in energy efficiency and area utilization. However, it is worth noting that limited capacitance ratios in FeCaps introduced errors in multiply-and-accumulate (MAC) computations. The proposed co-design approach helps in mitigating these errors and achieves high accuracy in classifying the CIFAR-10 dataset, elevating it from a baseline of 10% to 81.7%. FeFETs in crossbars, with a higher on-off ratio, outperform FeCaps, and our proposed charge-based sensing scheme achieved at least an order of magnitude reduction in power consumption, compared to prevalent current-based methods.

Predictive Refined Computational Modeling of ACL Tear Injury Patterns.

Anterior cruciate ligament (ACL) ruptures are prevalent knee injuries, with approximately 200,000 ruptures annually, and treatment costs exceed USD two billion in the United States alone. Typically, the initial detection of ACL tears and anterior tibial laxity (ATL) involves manual assessments like the Lachman test, which examines anterior knee laxity. Partial ACL tears can go unnoticed if they minimally affect knee laxity; however, they will progress to a complete ACL tear requiring surgical treatment. In this study, a computational finite element model (FEM) of the knee joint was generated to investigate the effect of partial ACL tears under the Lachman test (GNRB® testing system) boundary conditions. The ACL was modeled as a hyperelastic composite structure with a refined representation of collagen bundles. Five different tear types (I-V), classified by location and size, were modeled to predict the relationship between tear size, location, and anterior tibial translation (ATT). The results demonstrated different levels of ATT that could not be manually detected. Type I tears demonstrated an almost linear increase in ATT, with the growth in tear size ranging from 3.7 mm to 4.2 mm, from 25% to 85%, respectively. Type II partial tears showed a less linear incline in ATT (3.85, 4.1, and 4.75 mm for 25%, 55%, and 85% partial tears, respectively). Types III, IV, and V maintained a nonlinear trend, with ATTs of 3.85 mm, 4.2 mm, and 4.95 mm for Type III, 3.85 mm, 4.25 mm, and 5.1 mm for Type IV, and 3.6 mm, 4.25 mm, and 5.3 mm for Type V, for 25%, 55%, and 85% partial tears, respectively. Therefore, for small tears (25%), knee stability was most affected when the tears were located around the center of the ligament. For moderate tears (55%), the effect on knee stability was the greatest for tears at the proximal half of the ACL. However, severe tears (85%) demonstrated considerable growth in knee instability from the distal to the proximal ends of the tissue, with a substantial increase in knee instability around the insertion sites. The proposed model can enhance the characterization of partial ACL tears, leading to more accurate preliminary diagnoses. It can aid in developing new techniques for repairing partially torn ACLs, potentially preventing more severe injuries.

Research on the Grinding Process of Superhard Particles in the Fluidized Bed Opposed Jet Mill Based on the CFD-DEM Methodology

The process of superhard particle breakage in the grinding zone of the fluidized bed opposed jet mill is investigated using the CFD-DEM (computational fluid dynamics-discrete element method) coupling method with the Tavares UFRJ Breakage Model in the present study. The effects of structural and operational parameters, such as target plate structure, nozzle position, air inlet velocity, and feed rate, on the equipment stress distribution, airflow velocity, pressure field, particle velocity, and cumulative particle size distribution are thoroughly studied to determine the optimal structural and operational parameters. Experimental validation is conducted, including scanning electron microscope (SEM) observation of particle morphology and analysis of particle size distribution of ground product particles. The simulation results indicate that the wear rate of the structure without a target plate is lower than that of the structure with a target plate in the grinding central zone. Therefore, the structure without a target plate is chosen for further investigation. The cumulative particle size distribution after grinding is influenced by nozzle position, air inlet velocity, and feed rate. The particle D50 is positively correlated with nozzle spacing and feed rate, while it is negatively correlated with air inlet velocity. The optimal grinding effect is achieved when the distance between the nozzle and the center of the grinding zone ranges from 52.5 mm to 72.55 mm, the air inlet velocity is 950 m/s, and the feed rate is 10.5 g/s. Through experimental investigation, it has been observed that when the feed rate is 10 g/s, the particle size distribution becomes more uniform. Furthermore, consistent trends in the cumulative particle size distribution in the experiment and simulation results can be found, which validates the present numerical model. It was observed that particles at low feed rates retain certain angular edges, while particle roundness increases at high feed rates.

Development and Application of an FSI Model for Floating VAWT by Coupling CFD and FEA

The emerging floating vertical axis wind turbines (VAWTs) are regarded as a preferred solution to overcome the challenges faced by the traditional horizontal type in open-sea environments. Numerous numerical models have been advanced for assessing this novel object. However, current fully coupled models predominantly rely on simplified theories, assuming a linear fluid load and a one-dimensional slender beam structure. Despite computational fluid dynamic and finite element (CFD-FEA) coupling being qualified for high precision, this technology remains limited to the fixed VAWT field. To predict the load and structural response accurately and comprehensively, this study aims to extend CFD-FEA technology to floating VAWTs. First, an aero-hydro-moor-elastic fully coupled model is developed, and this model is validated by comparing it with several model experiments. Subsequently, a full-scale floating straight-bladed VAWT is simulated with the geometry and numerical models introduced. Furthermore, load and structural responses in a typical case are analyzed in both time and frequency domains. Finally, the sensitivity analysis of each structure part in floating VAWTs to environmental parameters is conducted and discussed. The discovery highlights the intricate nature of tower structural response, which incorporates 2-node, 3-node, wind frequency, and wave frequency components. Distinct from blades or floating foundations, which are primarily influenced by a single environmental parameter, the tower response is significantly amplified by the combined effects of wind and waves.

Numerical Modeling of Foam Carrying Sand Transport in Multifactor Horizontal Wells.

The solution of wellbore multiphase flow models has an important position in oil-gas field development. However, the solution of multiphase flow models often involves a series of complicated situations such as interphase mass and energy transfer, surface problems, and so on. Foam carrying sand particles in the wellbore is a solid, liquid, and gas three-phase cylinder flow problem. To solve this problem, we developed a computational fluid dynamics-discrete element method model based on the traditional N-S equations to track the streamline of the foam fluid and sand particles in the wellbore. On this basis, we investigated the influence of three factors, i.e., foam and sand properties and wellbore parameters, on the sand carrying rate of foam. The results show that whether the sand mound at the bottom of wells that can be dispersed is mainly affected by the properties of foam. The location of sand deposition in the wellbore and the effectiveness of foam in sand transportation are mainly influenced by the wellbore parameters and sand properties.

Simulation of Transient Temperature and Clearance after Shutdown of Aeroengine Based on CFD and FEA Coupled Models

It is crucial to comprehend the heat soak phenomenon, which may result in a significant temperature increase after the shutdown followed by a gradual decrease. This could bring potential risks for the engine including oil coking. The temperature change of engine components dictates the clearance after shutdown, while startup strategies are primarily based on this. A simulation strategy, utilizing computational fluid dynamics (CFD) and finite element analysis (FEA) coupled models, is suggested to investigate the transient temperature and clearance after shutdown. The maximum temperature deviation between the simulation result and experimental data are less than 6%. Flow parameters, including velocity and mass flow rate obtained from the CFD result, were applied as boundaries of the FEA model. Based on the FEA model, transient temperature calculations were also conducted for 20 hours after shutdown. The results indicate that the FEA model demonstrates good agreement with the CFD simulation, with a maximum deviation of less than 5% and at only 0.2% of the simulation time. After the engine shuts down, the stator’s temperature change rate is faster than that of the rotor due to better cooling conditions and relatively small heat capacity. Consequently, the seal clearance increases in the initial period after shutdown and then decreases to a minimum value. The nondimensional minimum clearance can be 0.8 times the cold state value at the location of the high-pressure turbine seal.

Design of closed loop for reproduction of gas-dominated hydrate flow.

The study presents a novel setup for measuring the flow regime of hydrate particles in a gas-dominated flow, which is of interest for applications such as natural gas transportation. A closed-flow loop, driven by a novel internal fan, enables continuous observation of hydrate particle behavior in a gas flow. The experimental setup allows the production and insertion of HFC134a gas hydrate particles with diameters of 10-50μm into the gas flow loop via a bypass loop. The performance curve of the internal fan is validated, and its suitability for achieving the required flow speed (5m/s) is demonstrated. Through an observation window using camera systems, the flow regime of glass beads is successfully visualized and analyzed. To validate the experimental data, a coupled computational fluid dynamics-discrete element method model is used to simulate the particle flow density distribution. The study findings demonstrate the effectiveness of the experimental setup in characterizing the flow regime of hydrate particles in a gas-dominated flow.

Multi-Function Scan Circuit for Assisting the Parallel Computational Map Pattern

Some parallel computing patterns can be accelerated using appropriate networks of simple circuits. We propose a solution, based on the Beneš-Waksman permutation network, which is adapted to efficiently accelerate not only permutation, but some of the most used parallel computational patterns such as: pack, prefix operations and reductions. The structural context considered for deploying our circuit is the map parallel pattern represented by an array of computational elements. The developed network receives a vector from the map array and outputs a vector for functions such as permute, pack, prefix sum (thus closing a first global loop over the map array of cells). For reduction functions (add, min, max) the network returns a scalar (thus closing a second global loop over the map array). With these improvements, this network adds circuit support for frequently used functions, in addition to map-type functions performed in the array of computing elements. While for reduction functions the frame of the permutation network can be easily adapted, for prefix functions and for the pack function new forms of implementation are proposed. The cells of the Bene\v s-Waksman network are redesigned to support the additional functionality. Some applications are then presented to emphasize the utility of our design.

Development of Cyber Physical System Based Manufacturing System Design

Cyber Physical System (CPS) is Informatics and computer science. Cyber physical manufacturing system is a new research field. In the fields of computer science and manufacturing science and technology Promote the Fourth Industrial Revolution known as Industry 4.0. CPS is generally focused. About the integration of the physical world and cyberspace. It`s the integration of communication, Computational, control, and physical elements. Currently, CPS is Science, government and industry. Systematic literature review on cyber. The physical system of the manufacturing system is not available. The purpose of the chapters in this bookis to gain insight into manufacturing systems and develop cyber-physical systems. Physical systems for intelligent manufacturing. CPS is written using the concept of CPS. A 5-tier architecture system for manufacturing systems. Continuous Key Release. It also describes the technology of cyber physical manufacturing systems. Combined with manufacturing, CPS enables intelligent manufacturing and provides technical support for manufacturing upgrades and conversions. This can be summarized in three aspects: internet-based manufacturing, process intelligence, and product intelligence. Future manufacturing is said to be personalized manufacturing based on cyber physical systems, intelligent manufacturing, digital manufacturing, and network-based manufacturing. CPS continues to transform the manufacturing trends of the industry, creating even more amazing value for future global manufacturing scenarios

Abrasive Slurry Jet Characteristics Study

In this paper, a coupled computational fluid dynamics-discrete element (CFD-DEM) approach is used to investigate the performance of abrasive slurry jets. Firstly, the CFD method was used to simulate the VES slurry jet flow characteristics. Secondly, an orthogonal design method was used to analyse the sensitivity of the parameters of zero shear viscosity , relaxation time and flow index of the Cross fluid, in which zero shear viscosity had the most significant effect on the flow field. As the zero-shear viscosity increases, the velocity of the flow field gradually increases, but the core section of the jet becomes less concentrated and the core section of the jet decreases. Based on the VES fluid formulation used in this project, the optimum zero-shear viscosity, relaxation time and flow index were optimised to 2.5 Pa-s, -0.1s and 0.2 respectively. The results showed that the addition of VES surfactant enhanced the erosion rate of the specimens and that the erosion rate increased with increasing OTAC concentration.

Study on the mechanism of vibration signal generation in water supply pipeline leaks

This study addresses water supply network leakage by employing numerical simulations to overcome challenges related to too small orifice hindering sensor deployment. Utilizing computational fluid dynamics and finite element methods, coupled with large eddy simulation, flow field distribution within pipes and at leakage orifice is investigated. The formation of leakage flow fields is categorized into three stages: leakage formation, unstable leakage, and stable leakage. During the leakage stage, the flow field is analyzed, and four regions—bottom central, bottom vortex, upper central, and upper vortex—are identified based on spatial characteristics. Detailed variable distribution patterns (velocity, pressure, vorticity) are provided in various planes. Understanding these distribution characteristics, six excitation sources responsible for generating leakage vibration signals are identified: pulsating velocity, pulsating pressure, bubble rupture, cavity pulsation, vortex-induced vibration, and gas expansion. The relationships among these excitation sources and their intensities concerning pipeline pressure and leak orifice radius are analyzed. Notably, a positive correlation between pressure and the intensity of all excitation sources is observed, while the impact of leak orifice size on these sources is found to be intricate. This research significantly contributes to an enhanced understanding of the physical processes associated with pipeline leakage.

An improved semi-resolved computational fluid dynamics-discrete element method for simulating liquid–solid systems with wide particle size distributions

Vertical hydraulic transport of particles with wide particle size distributions is a crucial process for coal physical fluidized mining. In the present study, an improved semi-resolved computational fluid dynamics (CFD)-discrete element method was developed to simulate particle flows with wide particle size distributions. In this model, the CFD cells allocated to the particle volume and the momentum source term were defined as the dependent domain and the influential domain, respectively. On this basis, the two-way domain expansion method and the one-way domain expansion method were adopted for the liquid–solid simulation of coarse and fine particles, respectively. The dependent domain expansion coefficient and the influential domain expansion coefficient were proposed to determine the spatial range of the dependent domain and influential domain for the coarse particles, and the optimal modeling strategy for the dependent domain and influential domain expansion coefficient for the coarse particles was determined. Furthermore, a volume expansion method and a momentum source expansion method were proposed for calculating the solid volume fraction of the dependent domain and the source term of the influential domain for the coarse particles. Furthermore, the sample point method was adopted to obtain the solid volume fraction in the dependent domain for the fine particles, and the momentum source term was only updated to the particle-located cell. Subsequently, single-particle settling and binary-particle fluidizing numerical experiments were used to verify the calculation accuracy of the model. The investigation can provide a new method for numerical simulation of liquid–solid flow with wide particle size distributions.

РАСПАРАЛЛЕЛИВАНИЕ ВЫЧИСЛЕНИЙ НА ГРАФИЧЕСКОМ ПРОЦЕССОРЕ ДЛЯ УСКОРЕНИЯ ГРАНИЧНО-ЭЛЕМЕНТНЫХ РАСЧЕТОВ В МЕХАНИКЕ

In solving problems of computer modeling using various methods, accuracy and computational efficiency questions always arise. This study explores the application of two modifications of the boundary element method to solve the problem of potential distribution within a closed two-dimensional domain with a uniform potential distribution on its boundary. The first modification involves using three nonlinear shape functions instead of one. The second modification applies the Galerkin method to the boundary element approach with three nonlinear shape functions. The essence of this modification lies in the fact that the system of equations is formulated in integral form over the entire boundary element, rather than at collocation points. In addition to this, the paper describes and investigates the advantages and disadvantages of the smoothing modification applied to these approaches. Since the influence matrix consists of independently computable elements, parallelization of calculations using NVIDIA CUDA technology has been proposed to enhance computational efficiency, significantly accelerating the calculation of interaction matrix. The choice of this technology is advantageous due to the prevalence of NVIDIA graphics accelerators in almost every personal computer or laptop, as well as it is easy to use. The study presents an approach to the application of this technology and demonstrates the results, showing the acceleration of parallelized calculations which show the dependence on the number of boundary elements. A comparison of the efficiency of the selected technology when applied to two methods, collocation and Galerkin, is also presented, indicating a significant speedup of up to 22 times by computing the influence matrix of the boundary elements.

Particle deposition and clogging as an Obstacle and Opportunity for sustainable energy

Fine particle motions have significant implications in porous media, manifesting in various natural phenomena and industrial applications. The movement of particles can lead to channel blockage, resulting in clogging. Depending on the context, clogging can be either advantageous or detrimental. At the scale of fine particles, physiochemical interactions play a crucial role in the clogging process. To examine the influence of these interactions, we have coupled the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory into the Computational Fluid Dynamic-Discrete Element (CFD-DEM) approach. Aside from the physical properties of the fracture systems and the characteristics of fine particles, the injection rate also plays a pivotal role in clogging occurrence. Consequently, we investigated 10 different injection rates and modeled 20 simulation setups with identical particle insertion rates to concurrently analyze the effects of the DLVO theory and injection rate. Our study focused on examining the impact of these parameters on particle behavior in various regions of the domain, including particle-particle force, fluid-particle force, transitional velocity, rotational velocity, fluid velocity, and fluid-particle momentum exchange. Our findings reveal that the DLVO force significantly influences fluid and particle characteristics within the throat blockage. Our results indicated that DLVO consideration increased the particle concentrations by more than 40% before the throat. Moreover, our results demonstrate a transition between different injection rates, where a slight change in injection rate (i.e., 0.06μm/s) can change the system's behavior. We identified a critical fluid injection rate, whereby injecting fluid into the system at a rate surpassing the critical velocity can prevent clogging, while lower rates result in clogging occurrence.

Collision Energy Analysis within the Vertical Shaft Impact Crusher Based on the Computational Fluid Dynamics-Discrete Element Method.

Particles in the vertical shaft impact crusher absorb and dissipate collision energy in the impact breakage. The distribution of the collision energy determines the breakage rate of materials and breakage energy consumption of the entire system. In this paper, the gas-solid coupling method is used to explore the regional distribution of collision energy, collision frequency, and collision energy spectrum of the material particle groups. Hence, a theoretical basis is provided for the efficient and energy-saving design of the crusher. First, a coupling mathematical model of the computational fluid dynamics and discrete element method is established to describe the interaction between material and fluid in the crushing chamber. Moreover, the experiment is carried out using a PL8500 VSI crusher and compared with the simulation results to verify the model's reliability. Finally, the effects of different working conditions on the energy dissipation distribution and energy spectrum are explored. The results show that the collision energy within the crushing chamber can be accurately predicted by using the fluid-solid coupling model. Moreover, increasing the rotational speed can effectively transform low-energy collision events into high-energy collisions and increase the collision frequency with energy dissipation above the threshold energy. Thus, the probability of material breakage is increased. Last, increasing the feed rate minorly affects the material breakage rate, while the specific energy of the entire system is reduced.

A new nano-design of configurable logic module based on coplanar reversible adder and modified Fredkin gates using quantum technology

Quantum Dot Cellular Automata (QCA) and reversible logic have emerged as promising alternatives to conventional CMOS technology, offering several advantages, such as ultra-dense structures and ultra-low-power consumption. Among the crucial components of processors, the Arithmetic Logic Unit (ALU) has witnessed significant advancements in reversible computing, leading to energy-efficient and high-speed computing systems, particularly beneficial for Digital Signal Processing (DSP) applications. Conventional ALUs, reliant on irreversible logic, encounter energy inefficiencies due to information loss during computations, resulting in increased power consumption. Moreover, they may face limitations in processing speed, impacting real-time processing capabilities, especially for complex DSP tasks involving intensive arithmetic and logic operations. In response to these challenges, a research paper presents a pioneering approach, proposing a novel reversible ALU design using QCA nanotechnology. The proposed design ingeniously incorporates Modified Fredkin (MF) gates, and a coplanar reversible full adder based on the HNG gate, skillfully leveraging the unique features of QCA nanotechnology to optimize the ALU's energy-efficient and high-speed performance for DSP applications. This revolutionary QCA reversible ALU comprises 330 QCA cells arranged in a compact 0.41 μm2 area, skillfully realized through the coplanar clock-zone-based crossover approach. Its core computational elements, the three MF gates, and the innovative coplanar reversible full adder empower the ALU to execute a remarkable array of 20 distinct arithmetic and logic operations, showcasing its versatility in handling diverse DSP tasks. The proposed structure undergoes extensive simulations utilizing QCADesigner version 2.0.3 to confirm its performance. The evaluation results manifest substantial improvements compared to previous designs, boasting a 30% reduction in area occupancy, a 20% decrement in cell count, a 10% reduction in latency, and a 10% decrease in quantum cost compared to the best-known previous structure. These compelling outcomes solidify the potential of the proposed reversible ALU as a transformative advancement in energy-efficient and high-speed computing for DSP applications.

Theoretical foundations for creating a quantum photon computer

Currently, significant results have emerged in the field of creation and application of quantum computers. We are talking about the Chinese quantum photonic computers Jiuzhang, Jiuzhang-2 (Jiuzhang 2.0), Jiuzhang-3 (Jiuzhang 3.0), which appeared respectively in 2020, 2021 and 2023, as well as the Canadian quantum photonic computer Borealis, which appeared in 2022. All these computers belong to the class of so-called quantum simulators – variants of quantum computers that solve one problem or a narrow class of problems. In addition to narrow specialization, quantum simulators have another important feature. In quantum simulators, controllable quantum objects simulate and effectively predict the behavior of real quantum systems. For example, in the quantum photonic computers Jiuzhang, Jiuzhang-2, Jiuzhang-3 and Borealis, the photons supplied to the inputs of these computers imitate the behavior of a “system of non-interacting identical bosons.” The Jiuzhang, Jiuzhang-2, Jiuzhang-3, and Borealis quantum simulators solve the problem of sampling bosons from a given distribution. Together they represent a significant scientific, technical and technological breakthrough in the development and creation of quantum computers. However, beyond what has been achieved, a number of important issues that are of interest both theoretically and for the field of practical applications remain unexamined. Among them are the following: about the architecture and composition of the elements of a quantum photonic computer, their number and connection diagram, as well as setting parameters that determine the required probability distribution from which boson samples are taken. The scientific problem addressed in the article is to obtain answers to the above questions by developing the theoretical foundations for creating a quantum photonic computer that solves the problem of sampling bosons from a given distribution in the most general case without restrictions on the distribution. The following results were obtained: the theoretical basis for the creation of a quantum photon computer (called in the article the Fuzuli boson sampler, briefly bsF) was developed that solves the problem of sampling bosons from a given distribution, for use as a quantum computing subsystem of a hybrid computing system to solve the problem of estimating matrix permanents; its constituent elements have been identified and studied, their numbers have been determined, and inter-element connection diagrams have been developed; an algorithm has been developed for tuning the bsF quantum photon computer to solve the problem of estimating matrix permanents; The functioning process of bsF is described. The article assumes that physically, in each cycle of operation of the bsF computer, the process of supplying photons to the inputs of the computer, their “movement” to the detectors, as well as the registration of photons by detectors is organized in such a way that the set of these photons imitates the behavior of a system of non-interacting identical bosons. Physical and engineering issues of ensuring such behavior of photons are not considered in this work. At the same time, the feasibility of such a possibility in practice (experimentally) is assumed to be undeniable. Examples confirming this position are the already created prototypes of quantum photonic computers in China (Jiuzhang, Jiuzhang-2, Jiuzhang-3) and in Canada (Borealis).

Hydraulic conveying characteristics of particles in bend based on numerical simulation and explainable stacking machine learning model

As a hydraulic lifting pipeline structure widely used in deep-sea oil, gas transportation, and sediment dredging projects, the pipeline configuration is related to the improvement of transportation efficiency and pipeline safety. Particularly, the bending section consumes the most energy and withstands severe erosion. Understanding and predicting the conveying characteristics of two-phase flow in bends is therefore crucial. In this study, CFD-DEM (computational fluid dynamics-discrete element method) simulation method is employed to calculate various cases, considering five parameters: pipeline bending radius and angle, conveying velocity, particle diameter, and concentration, to explore the influence of these parameters on pressure drop and erosion rate of pipeline and result in a data set of hundreds of cases. Based on this data set, seven machine learning models are trained to predict pressure drop and erosion rate, respectively. To enhance model accuracy, the stacking method in ensemble learning is employed to combine multiple models with good performance. Additionally, the Optuna and SHAP (SHapley Additive exPlanation) methods are utilized to optimize hyperparameters and explain the degree to which parameters impact the predictions. The result demonstrates that pressure drop is almost unaffected by bending radius, while erosion rate initially decreases and then increases with bending angle, and both increase with other parameters. Among the evaluated models, artificial neural network, XGBoost, and random forest all demonstrate high prediction accuracy. The stacking model further improves the accuracy, with mean absolute error improving by 21.7% and 32.2%, and the SHAP method demonstrated good interpretability, which is basically consistent with CFD-DEM results.