Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling

Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients.

A Spectrum Correction Method Based on Optimizing Turbulence Intensity

Based on the classical spectral representation method of simulating turbulent wind speed fluctuation, a harmonic superposition algorithm was introduced in detail to calculate the homogeneous turbulence wind field simulation in space. From the view of the validity of the numerical simulation results in MATLAB and the simulation efficiency, this paper discussed the reason for the bias existing between three types of turbulence intensity involved in the whole simulation process: simulated turbulence intensity, setting reference turbulence intensity, and theoretical turbulence intensity. Therefore, a novel spectral correction method of a standard deviation compensation coefficient was proposed. The simulation verification of the correction method was carried out based on the Kaimal spectrum recommended by IEC61400-1 by simulating the uniform turbulent wind field in one-dimensional space at the height of the hub of a 15 MW wind turbine and in two-dimensional space in the rotor swept area. The results showed that the spectral correction method proposed in this paper can effectively optimize the turbulence intensity of the simulated wind field, generate more effective simulation points, and significantly improve the simulation efficiency.

Improved Simulation Efficiency for Optimising Inflow Control Device Completions Using Suitably Upscaled Models and Parallel Global Search Optimisation Techniques

Inflow Control Device (ICD) completions can improve well performance by adjusting the inflow profile along the well and reducing the influx of unwanted fluids. The ultimate aim of using ICD completions is to provide maximum oil recovery and/or Net Present Value (NPV) over the life of the field. Proactive ICD optimisation studies use complex reservoir models and high-dimensional nonlinear objective functions to find the optimum ICD configurations over the life of the field. These complex models are generated from fine scale detailed geological models to accurately capture fluid flow behaviour in the reservoir. Although these high-resolution geological models can provide better performance predictions, their simulation runtimes can be computationally expensive and time consuming for performing proactive ICD optimisation studies that often require thousands of simulation runs. We propose a new workflow where we use upscaled and locally refined models coupled with parallelised global search optimisation techniques to improve the simulation efficiency when performing ICD optimisation and decision-making studies. Our approach preserves the flow behaviour in the reservoir and maintains the interaction between the reservoir and the well in the near wellbore region. Moreover, when coupled with parallel optimisation techniques, the simulation time is significantly reduced. We present an in-house code that couples global search optimisation algorithms (Genetic Algorithm and Surrogate Algorithm) with a commercial reservoir simulator to drive the ICD configurations. We evaluate the NPV as the objective function to determine the optimum ICD configurations. We apply and benchmark our approach to two different reservoir models to compare and analyse its efficiency and the optimisation results. Our analysis shows that our proposed approach reduces the run time by more than 80% when using the upscaled models and the parallel optimisation techniques. These results were based on a standard dual-core parallel desktop configuration. Additional results also showed further reduction in run time is possible when employing more processors. Additionally, when testing different ICD completion strategies (ICDs in producers only, ICDs in injectors only, and ICDs in both producers and injectors), the NPV can be increased by 9.6% for the optimised ICD completions. The novelty of our work is rooted in the much-improved simulation efficiency and better performance predictions that supports ICD optimisation and decision-making studies during field development planning to maximize profit and minimize risk over the life of the field.

Variance-Reduction Methods for Monte Carlo Simulation of Radiation Transport

After a brief description of the essentials of Monte Carlo simulation methods and the definition of simulation efficiency, the rationale for variance-reduction techniques is presented. Popular variance-reduction techniques applicable to Monte Carlo simulations of radiation transport are described and motivated. The focus is on those techniques that can be used with any transport code, irrespective of the strategies used to track charged particles; they operate by manipulating either the number and weights of the transported particles or the mean free paths of the various interaction mechanisms. The considered techniques are 1) splitting and Russian roulette, with the ant colony method as builder of importance maps, 2) exponential transform and interaction-forcing biasing, 3) Woodcock or delta-scattering method, 4) interaction forcing, and 5) proper use of symmetries and combinations of different techniques. Illustrative results from analog simulations (without recourse to variance-reduction) and from variance-reduced simulations of various transport problems are presented.

Optimization of twisted blade of pump as turbine based on high dimensional surrogate model

In order to improve the operation efficiency of the twisted blade pump as turbine (PAT), a medium specific speed PAT was selected as the research object. The variables of the twisted blade plane blade profile were defined, the twisted blade was transformed into three plane blade profiles, and the blade profiles were parameterized by MATLAB 9.7 software. MATLAB 9.7, CFturbo 2020 and Fluent 19.2 were used to build the support vector machine-high dimensional model representation (SVM-HDMR) surrogate model function for efficiency optimization of PAT. Genetic algorithm was run on MATLAB 9.7 to optimize the surrogate model function, and the optimized blade profiles were fed back. The optimization results were verified by numerical simulation and experiment. The results show that the simulation efficiency of the PAT after optimization at the design operating point is 3.51% higher than the efficiency of the PAT before optimization, and the output power is increased by 5.3%. The test efficiency of the PAT after optimization at the design operating point is 3.4% higher than the efficiency of the PAT before optimization, and the output power is increased by 5.1%.

Rapid prediction of thrust force coupling scale-span model and revised ANN in drilling CFRPs

This study develops an integrated methodology to rapidly predict the thrust force with a tapered drill-reamer (TDR) by coupling a scale-span model and revised artificial neural networks (ANN) in drilling carbon fiber reinforced polymers (CFRPs). First, the optimum mesh size of the scale-span finite element (FE) model of CFRPs was optimized to enhance simulation efficiency on the premise of ensuring accuracy in drilling. Then, an order-driven FE computation approach was first proposed to improve computing efficiency for batch samples and maximize utilization of the available computing resources. Modeling and solving of the weight indices of material property parameters (MPPs) and machining parameters for the thrust force were first carried out entirely based on a feature selection model. A multi-layer revised ANN architecture model which considers the material properties of CFRPs and the corresponding initial weight indices was first designed for the thrust force prediction in Python software. Finally, drilling experiments involving T700S-12K/YP-H26 CFRPs specimens with different machining parameters were carried out, which more than 25 prediction results of the fresh samples showed that the established ANN prediction model with a 16-18-18-18-16-1 architecture is highly prediction precision, and the maximum absolute deviation is only 4.56% with the comparisons of experiments.

Towards the Concurrent Optimization of the Server: A Case Study on Sport Health Simulation

Using computers to conduct human body simulation experiments (e.g., human sport simulation, human physiology simulation, and human clothing simulation) can benefit from both economic and security. However, the human simulation experiment usually requires vast computational resources due to the complex simulation model which combines complicated mathematical and physical principles. As a result, the simulation process is usually time-consuming and simulation efficiency is low. One solution to address the issue of simulation efficiency is to improve the computing performance of the server when the complexity of the simulation model is determined. In this paper, we proposed a concurrent optimization scheme for the server that runs simulation experiments. Specifically, we firstly propose the architecture of the server cluster for the human body simulation, and then we design the concurrent optimization scheme for the server cluster by using Nginx. The experiment results show that the proposed concurrent optimization scheme can make better use of server resources and improve the simulation efficiency in the case of human sport simulation.

Analysis and Research on Mobile Drilling Rig for Deep Seabed Shallow Strata

Drilling rigs for deep seabed shallow strata are commonly used to explore ocean cobalt-rich crust resources and other fields. This paper mainly presents the structure and mechanism of a mobile drilling rig for use in acquiring seafloor cores that are up to 1.5 m in length. The software Simcenter Amesim is used to establish the mobile drilling rig's hydraulic propulsion system model, which is the basis and a core part of the rig. Moreover, closed-loop and PID (proportion-integral-differential) control methods are separately used to control the hydraulic propulsion system for simulation analysis. Comparison of the simulation results shows that the PID control method is more convincing in verifying the design rationality of the hydraulic propulsion system. In the simulation of the PID-controlled hydraulic propulsion system, the co-simulation technology of Simcenter Amesim and MATLAB/Simulink not only establishes the hydraulic and control models but also determines the relevant simulation parameters, thereby helping improve system simulation efficiency. In its verification deployment in the South China Sea, the mobile drilling rig has been operated many times at different depths, and some cores have been successfully obtained. It was also used during the 55th Voyage of China Oceanic Scientific Expedition, which was supported by the China Ocean Mineral Resources R&D Association. Several sites were explored, and a large number of cobalt-rich crust cores were obtained. Theory and sea trials are explained to support further research on the survey of abyssal resources.