Simulation Solver Research Articles

Analysis of Mining Equipment Procurement Plan and Logistics Path Optimization Based on QUBO Model

By analyzing the configuration and operation plan of mining equipment, this paper establishes a linear programming model and converts it into the corresponding quadratic unconstrained binary optimization (QUBO) form. Based on this, the simulated annealing solver built-in in Kaiwu SDK and CIM simulator are used to study the configuration and operation plan of mining equipment. For work one: it is required to provide a procurement plan that maximizes profits within the budget range, that is, to determine the required excavator models and corresponding quantities. This problem is based on the premise of not considering the service life of excavators, with the maximum value of the product of the four excavator models and their corresponding long-term profits and quantities as the objective function, and with the constraints of start-up capital budget and excavator type, a 0-1 integer programming model is established. By using the decision variable of the model as a 0-1 variable and the constraint as an inequality constraint, the constraint is then transformed into the corresponding Hamiltonian operator, and the penalty term coefficient P is introduced and added to the objective function, thereby transforming it into QUBO form. Due to the low complexity and linearity of the model, the Kaiwu SDK's built-in simulated annealing solver and CIM simulator were used to solve the problem and obtain the final maximum profit and procurement plan. For work two: compared to work one, the condition of equipment lifespan of 5 years has been added, and it is required to plan the model and quantity of excavators that need to be purchased, while providing the matching relationship between excavators and mining trucks. This problem requires consideration of various cost conditions, with the difference between revenue and cost within five years as the objective function, and matching constraints, quantity constraints, and budget constraints as constraints, ultimately constructing a general linear programming model. By using inequality constraint relationships, the relaxation variable s in binary expression is introduced to express the constraint term, thereby transforming it into QUBO form. Afterwards, Kaiwu SDK was used to solve the procurement plan and obtain the matching relationship between excavators and mining trucks. For work three: Based on work two, the number of devices has been increased. For the constructed linear programming model, it can be transformed into a QUBO model or even a subQUBO model. Firstly, preprocess the data and use Spearman correlation analysis to determine the degree of correlation between the data, visualizing the data. For the constructed linear programming model, compared to problem two, only parameter variables were added, and the constraint conditions were consistent with problem two. A relaxation variable s was introduced to construct a constraint term, which was then transformed into a QUBO model. Combined with classical calculations, the subQUBO method was used. Finally, the procurement plan was obtained through Kaiwu SDK and a new matching relationship between excavators and mining trucks was provided. **************** ACKNOWLEDGEMENTS**************** This work is supported by ministry of education industry-university cooperative education project (Grant No.: 231106441092432), the research and practice of integrating "curriculum thought and politics" into the whole process of graduation design of Mechanical engineering major: (Grant. No.: 30120300100-23-yb-jgkt03), research on the integration mechanism of "course-training-competition-creation-production" for innovation and entrepreneurship of mechanical engineering majors in applied local universities (Grant. No.: CXKT202405), Mechanical manufacturing equipment design school-level "gold class" construction project (Grant. No.: 30120324001).

A Python-based flow solver for numerical simulations using an immersed boundary method on single GPUs

A Python-based flow solver for numerical simulations using an immersed boundary method on single GPUs

A numerical study on the mixing time prediction of miscible liquids with high viscosity ratios in turbulently stirred vessels

A numerical study on the mixing time prediction of miscible liquids with high viscosity ratios in turbulently stirred vessels

A Comparison of FPGA Implementation of Latency-Based Solvers for Power Electronic System Real-Time Simulation

A Comparison of FPGA Implementation of Latency-Based Solvers for Power Electronic System Real-Time Simulation

SWEMniCS: a software toolbox for modeling coastal ocean circulation, storm surges, inland, and compound flooding

Flooding from storm surges, rainfall-runoff, and their interaction into compounding events are major natural hazards in coastal regions. To assess risks of damages to life and properties alike, numerical models are needed to guide emergency responses and future assessments. Numerical models, such as ADCIRC have over many decades shown their usefulness in such assessments. However, these models have a high threshold in terms of new user engagement as development and compilation is not trivial for users trained in compiled programming languages. Here, we develop a new open-source finite element solver for the numerical simulation of flooding. The numerical solution of the underlying PDEs is developed using the finite element framework FEniCSx. The goal is a framework where new methods can be rapidly tested before time-consuming development into codes like ADCIRC. We validate the framework on several test cases, including large-scale computations in the Gulf of Mexico for Hurricane Ike (2008).

Fast linear solvers for incompressible CFD simulations with compatible discrete operator schemes

Fast linear solvers for incompressible CFD simulations with compatible discrete operator schemes

Developing a multi-region coupled analysis method for floating offshore wind turbine based on OpenFOAM

Developing a multi-region coupled analysis method for floating offshore wind turbine based on OpenFOAM

Deep Reinforcement Learning for the Management of the Wall Regeneration Cycle in Wall-Bounded Turbulent Flows

AbstractThe wall cycle in wall-bounded turbulent flows is a complex turbulence regeneration mechanism that remains not fully understood. This study explores the potential of deep reinforcement learning (DRL) for managing the wall regeneration cycle to achieve desired flow dynamics. To create a robust framework for DRL-based flow control, we have integrated the StableBaselines3 DRL libraries with the open-source direct numerical simulation (DNS) solver CaNS. The DRL agent interacts with the DNS environment, learning policies that modify wall boundary conditions to optimise objectives such as the reduction of the skin-friction coefficient or the enhancement of certain coherent structures’ features. The implementation makes use of the message-passing-interface (MPI) wrappers for efficient communication between the Python-based DRL agent and the DNS solver, ensuring scalability on high-performance computing architectures. Initial experiments demonstrate the capability of DRL to achieve drag reduction rates comparable with those achieved via traditional methods, although limited to short time intervals. We also propose a strategy to enhance the coherence of velocity streaks, assuming that maintaining straight streaks can inhibit instability and further reduce skin-friction. Our results highlight the promise of DRL in flow-control applications and underscore the need for more advanced control laws and objective functions. Future work will focus on optimising actuation intervals and exploring new computational architectures to extend the applicability and the efficiency of DRL in turbulent flow management.

Replication advancements review in ultraviolet GaN/AlGaN quantum well light emitting diodes: engineering, simulation and validation

Abstract This paper presents a comprehensive study on the replication of ultraviolet (UV) GaN quantum well light emitting diodes (LEDs) based on Han et al.’s experimental work. The replication structures of the electroluminescence emission at 353.6 nm with a narrow 5.8 nm linewidth validated the reliability of the simulation model. However, during the simulation run, a surprising and significant peak shift was observed, resulting in an emission peak at 358.6 nm, which deviated from the reported value. This discrepancy necessitates further investigations to understand the factors responsible for this unexpected change. Nonetheless, this correlation remains crucial as a benchmark for evaluating potential quantum well and device performance enhancements by following the existing structure and composition. Ultimately, it improves learning progress in scientific studies to the quantum level. Remarkably, the optimized devices exhibited exceptional stability at high current densities and demonstrated the efficacy of Drift-diffusion Charge Control (DDCC) solver simulation, which advances UV-LED technology, tallying with the literature claims and indirectly paving the way for high-performance applications.

A numerical simulation method for ice-breaking and cavitation effects on the water-exiting vehicle

A numerical simulation method for ice-breaking and cavitation effects on the water-exiting vehicle

An open-source, adaptive solver for particle-resolved simulations with both subcycling and non-subcycling methods

We present the IAMReX (incompressible flow with adaptive mesh refinement for the eXascale), an adaptive and parallel solver for particle-resolved simulations on the multi-level grid. The fluid equations are solved using a finite-volume scheme on the block-structured semi-staggered grids with both subcycling and non-subcycling methods. The particle-fluid interaction is resolved using the multidirect forcing immersed boundary method. The associated Lagrangian markers used to resolve fluid-particle interface only exist on the finest-level grid, which greatly reduces memory usage. The volume integrals are numerically calculated to capture the free motion of particles accurately, and the repulsive potential model is also included to account for the particle–particle collision. We demonstrate the versatility, accuracy, and efficiency of the present multi-level framework by simulating fluid-particle interaction problems with various types of kinematic constraints. The cluster of monodisperse particles case is presented at the end to show the capability of the current solver in handling multiple particles. It is demonstrated that the three-level AMR (Adaptive Mesh Refinement) simulation leads to a 72.46% grid reduction compared with the single-level simulation. The source code and testing cases used in this work can be accessed at https://github.com/ruohai0925/IAMR/tree/development. Input scripts and raw postprocessing data are also available for reproducing all results.

Design and analysis of a hundred-watt G-band traveling wave tube

In this paper, a hundred-watt level traveling wave tube operating in the G-band is designed and analyzed. A non-semi-arc folded waveguide (FW) slow wave structure (SWS) is adopted to achieve high output power. Compared with the conventional FW, this structure has a higher coupling impedance, which is beneficial for beam–wave interaction. To further improve the efficiency of beam–wave interaction, phase velocity taper technology is adopted in SWS. A diamond pill-box output window is designed for electromagnetic wave input and output in SWS. In addition, the electron optics including a Pierce electron gun and a periodic permanent magnet focusing system are designed for emitting and constraining the electron beam. Using CST PIC solver for simulation, the results show that under a 23 kV and 80 mA electron beam, the maximum output power of the SWS is 132.7 W and the saturated gain is 36.5 dB. The bandwidth for output power greater than 100 W is 12 GHz. A pill-box window is designed and fabricated, and experimentally measured S11 is better than −13 dB and S21 is better than −1.3 dB in 213–229.5 GHz. The electron optics generate 79 mA current at 23 kV voltage.

A high-frequency, low-power resonant radio-frequency neutron spin flipper for high-resolution spectroscopy.

We present a resonant-mode, transverse-field, radio-frequency (rf) neutron spin flipper design that uses high-temperature superconducting films to ensure sharp transitions between uniform magnetic field regions. Resonant mode allows for low-power, high-frequency operation but requires strict homogeneity of the magnetic fields inside the device. This design was found to efficiently flip neutrons at 96.6 ± 0.6% at an effective frequency of 4MHz in bootstrap configuration with a beam size of 2.4 × 2.5 cm2 and a wavelength of 0.4nm. The high frequency and efficiency enable this device to perform high-resolution neutron spectroscopy with comparable performance with currently implemented rf flipper designs. The limitation of the maximum frequency was found due to the field homogeneity of the device. We numerically analyze the maximum possible efficiency of this design using a Bloch solver simulation with magnetic fields generated from finite-element simulations. We also discuss future improvements of the efficiency and frequency to the design based on the experimental and simulation results.

A high-order finite-difference solver for direct numerical simulations of magnetohydrodynamic turbulence

A high-order finite-difference solver for direct numerical simulations of magnetohydrodynamic turbulence

Exergoeconomic optimization of a proposed novel combined solar powered electricity and high-capacity cooling load production system for economical and potent generation via utilization of low-grade waste heat source

Exergoeconomic optimization of a proposed novel combined solar powered electricity and high-capacity cooling load production system for economical and potent generation via utilization of low-grade waste heat source

Nonlinear hydrodynamic assessment of a floating solar double-hull substructure using viscous numerical wave tank

Nonlinear hydrodynamic assessment of a floating solar double-hull substructure using viscous numerical wave tank

FPGA-Based Implicit–Explicit Real-Time Simulation Solver for Railway Wireless Power Transfer With Nonlinear Magnetic Coupling Components

FPGA-Based Implicit–Explicit Real-Time Simulation Solver for Railway Wireless Power Transfer With Nonlinear Magnetic Coupling Components

A Meshless Solver for Blood Flow Simulations in Elastic Vessels Using a Physics-Informed Neural Network

A Meshless Solver for Blood Flow Simulations in Elastic Vessels Using a Physics-Informed Neural Network

Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches

A real-time capable core Ion Cyclotron Range of Frequencies (ICRF) heating model on NSTX and WEST is developed. The model is based on two nonlinear regression algorithms, the random forest ensemble of decision trees and the multilayer perceptron neural network. The algorithms are trained on TORIC ICRF spectrum solver simulations of the expected flat-top operation scenarios in NSTX and WEST assuming Maxwellian plasmas. The surrogate models are shown to successfully capture the multi-species core ICRF power absorption predicted by the original model for the high harmonic fast wave and the ion cyclotron minority heating schemes while reducing the computational time by six orders of magnitude. Although these models can be expanded, the achieved regression scoring, computational efficiency and increased model robustness suggest these strategies can be implemented into integrated modeling frameworks for real-time control applications.

Power Consumption Comparison of GPU Linear Solvers for Cellular Potts Model Simulations

Power Consumption Comparison of GPU Linear Solvers for Cellular Potts Model Simulations