Computational Simulation Time Research Articles

Generation of initial debris cloud distributions for breakup events based on CARDC-SBM

With the dramatic increase in the number of resident space objects, the risk of collision between space objects and debris is growing. To protect spacecraft from such collisions, it is crucial to determine the generation patterns of space debris and analyze the evolution of debris clouds. In this paper, a continuous distribution formula is derived for each parameter of the breakup debris based on the China Aerodynamics Research and Development Center spacecraft breakup model (CARDC-SBM). Using CARDC-SBM, we theoretically establish a normal distribution for the separation velocity of the breakup debris in relation to the breakup mass center. Based on this foundation, the mean and standard deviation of the debris separation velocity distribution are empirically modeled using polynomial and exponential functions. Consequently, an empirical formula with a broad range of applicability is derived, which significantly reduces the computation time of debris cloud simulations. The initial velocity distribution given by this empirical formula is employed as the initial condition for the evolution of debris clouds from the perspective of a boundary value problem. Simulations using CARDC-SBM and subsequent analysis of the debris cloud evolution resulting from a breakup event reveal that the distribution of each parameter is determined solely by the relative collision velocity and the spacecraft's nominal density in the impact breakup scenario. Our results show that the empirical formula for the debris cloud distribution provides good agreement with Monte Carlo simulations.

Study on ammonia/methanol blends with ammonia cracking for low-carbon combustion and NO reduction

This work aims to study the ammonia/methanol blended fuel with ammonia cracking in lean-burn condition. Ammonia on-line cracking to produce hydrogen production has the potential to realize the high-efficiency combustion. A new ammonia/methanol/hydrogen mechanism was updated and simplified using several reduction methods to optimize the computation time in 3D simulation. Visualization measurements in constant volume combustion chamber (CVCC) for flame propagation and NO generation were conducted, and 3D computational fluid dynamics (CFD) modeling was also established with the simplified mechanism simultaneously to investigate the impact of methanol addition and ammonia cracking in the lean-burn mode. The findings showed that methanol addition and ammonia cracking can significantly improve the combustion rate, and 60%ammonia/40%methanol with ammonia cracking ratio of 90% (N60R90) at equivalence ratio (ER) of 0.7 yields the similar LBV as pure methanol at ER of 1.0 in laminar burning condition. Its ER can be extended to 0.4 to obtain the similar level of LBV as pure ammonia at 1.0. The experiments and 3D simulations revealed that N60R90 at 0.4 yields the equivalent level of combustion rate as N60R0 at 0.8, while obtaining much lower NO emission. In addition, N60R100 at 0.4 without fuel-NO formation during the combustion can get ultra-low NO emission. This study provides the insights on low-carbon combustion strategy for ammonia cracking with low NO.

Calibration and Experimentation of Discrete Elemental Model Parameters for Wheat Seeds with Different Filled Particle Radii

A Gas–solid two-phase flow coupling simulation is widely used to study the working process of pneumatic seed dischargers. Due to the demand for deterministic particle orbit numerical calculation models, seeds are mostly modeled using the particle aggregation method, where the seed model is formed through particle aggregation bonding without overlapping. The smaller the radius and the more filled ball particles used in this method, the closer they resemble the real morphology of the seed. However, this results in the over-consumption of simulation computational resources and simulation time growth. In this study, we used wheat seeds as the research object, studied the effect of seed models with different filled ball radii on the kinetic response characteristics between the particles, and searched for the optimal number of filled ball particles for the seed model. With the help of three-dimensional scanning and inverse fitting methods to obtain the seed profile, we used different radii (0.2 mm, 0.24 mm, 0.28 mm, 0.32 mm, 0.36 mm, and 0.4 mm) to fill the ball particles, and formed a wheat particle bonding model for a gas–solid coupling simulation. We used a combination of real tests and simulation measurements of bottomless cylinder-lifting and slip-stacking. The interspecies static and dynamic friction factors in seed models with different radii of filled spherical particles were first calibrated using the angle of repose as an index. Then, the parameters were verified using bottomless cylinder lifting and slip stacking tests, which used the coefficient of variation for the simulation test’s angle of repose as an index. Our results show that the smaller the radius of the filled ball, the closer the simulation results were to the real value. Validation was conducted using a gas–solid coupling simulation of an air-blown wheat seed discharger, with the seed filling rate as an index. Our results showed that the simulation length and simulation accuracy were optimal when the radius of the filling particle was 0.32 mm.

Investigation of CMP Process of Silica Substrate by Ceria Abrasive: Insights from Molecular Dynamics Simulation with Neural Network Potential

Chemical mechanical polishing (CMP) is a process of surface planarization by chemical and mechanical driving forces. CMP is expected to solve various problems, including the surface roughness on semiconductor devices. An aqueous slurry, including abrasive grains and chemical components, is used in the CMP process. Development of slurries with high polishing rate, excellent planarity, and superior processability is not only a key challenge, but also a significant opportunity for the future of our industry. In order to achieve the development, it is necessary to understand the detailed mechanism of CMP, which involves complex phenomena accompanied by interfacial chemical reactions and mechanical friction. Computational scientific approaches, such as density functional theory (DFT) and molecular dynamics (MD) simulations, are powerful tools for understanding complex tribochemical phenomena at the atomic scale. However, these methods require much computation time and present difficulties for large-scale simulations. To overcome this limitation, a computational method called Neural Network Potential (NNP) is a powerful tool because it achieves a good balance between accuracy and computational efficiency by constructing potential energy surfaces based on a neural network-based method. We performed NNP-MD simulations of the ceria/silica interface with the development of CMP slurry including ceria abrasive grains, which is typically used on the surfaces of silicon layered devices. As a result, we succeeded in observing polishing phenomena at the interface at the atomic scale, namely Ce-O-Si bond formation between ceria abrasive grains and silica substrate, hydrolysis reaction of Si-O bond, and associated mono-silica dissociation from the substrate. We also performed simulations of systems containing chemical compounds added to the slurry for the purpose of controlling the polishing rate and observed the role of additives on the CMP process. In this presentation, we will show simulation results and discuss the mechanism of CMP of silica substrates with ceria abrasive grains in terms of dependence on surface morphology at the atomic scale and the role of additives. Reference [1] Tran*, R., Lan*, J., Shuaibi*, M., Wood*, B., Goyal*, S., Das, A., Heras-Domingo, J., Kolluru, A., Rizvi, A., Shoghi, N., Sriram, A., Ulissi, Z., & Zitnick, C. (2022). The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis. arXiv preprint arXiv:2206.08917.

A GPU-Accelerated Method for 3D Nonlinear Kelvin Ship Wake Patterns Simulation

The study of ship waves is important for ship detection, coastal erosion and wave drag. This paper proposed a highly paralleled numerical computation method for efficiently simulating three-dimensional nonlinear kelvin waves. First, a numerical model for nonlinear ship waves is established based on potential flow theory, the Jacobian-free Newton–Krylov (JFNK) method and the boundary integral method. To reduce the amount of data stored in the JFNK method and improve the computational efficiency, a banded preconditioner method is then developed by formulating the optimal bandwidth selection rule. After that, a Graphics Process Unit (GPU)-based parallel computing framework is designed, and we used the Compute Unified Device Architecture (CUDA) language to develop a GPU solution. Finally, numerical simulations of 3D nonlinear ship waves under multiple scales are performed by using the GPU and CPU solvers. Simulation results show that the proposed GPU solver is more efficient than the CPU solver with the same accuracy. More than 66% GPU memory can be saved, and the computational speed can be accelerated up to 20 times. Hence, the computation time for Kelvin ship waves simulation can be significantly reduced by applying the GPU parallel numerical scheme, which lays a solid foundation for practical ocean engineering.

Development of a composite model for predicting urban surface temperature distribution in the context of GIS

In this paper, we present a new model for simulating radiation heat transfer and surface temperatures in urban environments, which is a prerequisite for simulating outdoor mean radiant temperature for pedestrians. We propose a new model whose novelty lies in a new algorithm for simulating diffuse radiation by combining the Nusselt unit sphere method and Monte Carlo ray tracing algorithm. This combined use of different methods is automatically activated depending on the complexity of the scene and, in particular, the arrangement of the facets facing each other. The model is implemented in a Python-based tool, t4gpd, which combines the capabilities of geographic information science and related technologies (GIS &T) with efficient ray-casting solutions. The model is tested on a theoretical mock-up of nine virtual buildings in Nantes under summer and winter weather conditions and compared with SOLENE-Microclimat results. The view factor and solar radiation flux simulation results agree well with the reference solution, with a significant reduction in simulation computation time. However, the model has limitations due to the exclusion of vegetation evapotranspiration characteristics, wind profile model, and soil with complete stratigraphy. Overall, this new model provides an efficient tool for simulating outdoor pedestrian thermal comfort in urban environments and has potential for further development and validation.

Discrete event simulation for dynamic thermal modelling of district heating pipe

Optimizing district heating (DH) systems in a holistic manner is often impeded by the computational complexities associated with network modeling. This study introduces a novel, efficient and theoretically accurate method for dynamic thermal modelling of DH pipes. The approach is to track water frontiers traveling along the pipe using discrete event simulation (DES) paradigm. As the DES method is based on variable time steps, the computational effort is significantly reduced compared to earlier methods. The proposed model can compute outlet water temperature, temperature profile along the pipe, and heat loss based on variable inlet temperature and flow rate. The DES model was validated by comparison with real measurements of a long DH pipe. Four variants of the model with different temperature profile assumptions and interpolation methods were compared. Numerical results show that the DES model can accurately predict outlet water temperature with a maximum discrepancy of 0.52 °C. The mean error of simulated outlet temperature was −0.01 ± 0.02 °C. Average computation time for 24-h simulation was 59 μs. Overall, this study shows that the DES method is appropriate for variable time step simulation for DH pipe, potentially, for network simulation. Our study may also inspire variable time step implementation in other energy applications.

The applicability of ALARO and AROME wind-fields in multi-scale waves forecasting in open sea and coastal areas of the southern Baltic Sea

The main objective of the study was to implement the wave model in two independent systems fed respectively by wind fields with a 4 km resolution in the Baltic-wide area and a 2 km resolution in the shallow coastal regions of the southern Baltic for operational work. The study included the selection the best parameterization, which was carried out using measured data and model data from the verified SWAN-COSMO system. The chosen best parameterization of the model, including the time step, iteration, wind scaling, dispersion coefficients and swell formula, among others, allowed the identification of the optimal system that guarantees the most reliable predictions. The most reliable forecasts reproducing real conditions, both for the model powered by wind fields with a resolution of 4 km and 2 km, were obtained for the configuration characterized by the parameters: 30-min time step, 3 iterations, a wind scaling factor of 32 using DEBIAS equal to 0.89, coefficients related to local dissipation and cumulative of 2.8 × 10–6 and 3.5 × 10–5, respectively, and Ardhiun's swell formula of 1.20. The values of the correlation coefficient with both the measured data and the results of the comparative model for significant wave height and wave period, respectively, were no lower than: 0.95 and 0.73 (Petrobaltic) and 0.96 and 0.90 (Pomeranian Bay). Root mean square error (RMSE) values were <0.23 m and <0.20 s (Petrobaltic) and <0.20 m and <0.74 s (Pomeranian Bay) for significant wave height and mean wave period, respectively. Analysis of the forecast results showed some limitations in using the mesoscale model in coastal areas, and the solution was to use the high-resolution model in this zone. It was shown that the stand-alone high-resolution model (domain: southern Baltic Sea) obtained similar results to the same model system but nested in the mesoscale model (domain: Baltic Sea). In addition, this model showed shorter simulation computation time and represents an independent, less vulnerable to discontinuity in forecast generation (lack of assimilation of boundary and initial conditions from a lower resolution model). A noticeable improvement in the quality of forecasts was shown by the AROME wind-field-powered model, which, due to its high resolution of the computational grid and wind data, maps well the wave dynamics in areas with limited wave exposure (the Vistula Lagoon, the Puck Lagoon) or complex coastline (the Hel Split) and seabed (the shallows of the Pomeranian Gulf and the Słupsk Shoal. The coupled data from the ALARO and AROME models, processed internally at IMWM-NRI, provide continuity and independence from global models and indicate applicability in generating wave forecasts by other Baltic countries.

Adaptive Rigidification of Discrete Shells

We present a method to improve the computation time of thin shell simulations by using adaptive rigidification to reduce the number of degrees of freedom. Our method uses a discretization independent metric for bending rates, and we derive a membrane strain rate to curvature rate equivalence that permits the use of a common threshold. To improve accuracy, we enhance the elastification oracle by considering both membrane and bending deformation to determine when to rigidify or elastify. Furthermore, we explore different approaches that are compatible with the previous work on adaptive rigidifcation and enhance the accuracy of the elastification on new contacts without increasing the computational overhead. Additionally, we propose a scaling approach that reduces the conditioning issues that arise from mixing rigid and elastic bodies in the same model.

Inverse distance weighting to rapidly generate large simulation datasets

Obtaining large biomechanical datasets for machine learning is an ongoing challenge. Physics-based simulations offer one approach for generating large datasets, but many simulation methods, such as computed muscle control (CMC), are computationally costly. In contrast, interpolation methods, such as inverse distance weighting (IDW), are computationally fast. We examined whether IDW is a low-cost and accurate approach for interpolating muscle activations from CMC.IDW was evaluated using lateral pinch simulations in OpenSim. Simulated pinch data were organized into grids of varying sparsity (high, medium, and low density), where each grid point represented the muscle activations associated with a unique combination of mass and height of a young adult. For each grid, muscle activations were calculated via CMC and IDW for 108 random mass-height pairs that were not coincident with simulation grid vertices. We evaluated the interpolation errors from IDW for each grid, as well as the sensitivity of lateral pinch force to these errors. The root mean square error (RMSE) associated with interpolated muscle activations decreased with increasing grid density and never exceeded 4%. While CMC received a target thumb-tip force of 40 N, errors from the interpolated muscle activations never impacted the simulated force magnitude by more than 0.1 N. Furthermore, the computation time for CMC simulations averaged 4.22 core-minutes, while IDW averaged 0.95 core-seconds per mass-height pair.These results indicate IDW is a practical approach for rapidly estimating muscle activations from sparse CMC datasets. Future works could adapt our IDW approach to evaluate other tasks, biomechanical features, and/or populations.

Development of a new MC Fast Dose Engine for small compact radiation fields generated by the CyberKnifeⓇ Iris medical linac

MC-based computerized TPS is an essential tool for improving treatment quality in external radiation therapy chains. However, incorporating these systems into clinical TPS is challenging due to their long computation time. To address this problem, Phase Spaces (PhSp) can be utilized, which reduce the computation time of such simulations while maintaining accuracy levels comparable to full simulations. Since PhSps can only generate a finite number of particles, recycling the same PhSp several times during a simulation will increase statistical uncertainty. To carry out this disadvantage, the present study employs a numerical reconstruction model to develop a novel generation of MC Fast Dose Engine (MC-FDE) that relies on the Cyberknife linac parameters publicly available in the IAEA database. The MC-FDE model’s performance is evaluated based on the precision of the reconstructed parameters and the accuracy of dose calculation in the 3D BEAMSCAN water phantom and patient CT datasets. In contrast to the recently published Wasserstein Generative Adversarial Networks (WGAN), the MC-FDE reconstruction model produces significant agreement with measured data, IAEA PhSp files and TPS Accuray, as determined by marginal distributions, correlation metrics, 2-3D gamma indexes and the Dose Volume Histogram (DVH) test. Regarding calculation speed, MC-FDE has a very fast process and can generate an infinite number of particles, unlike the limited number of particles recorded in IAEA PhSp files. According to the findings of this study, the proposed MC-FDE model is considered suitable for use as part of clinical TPS.

Predictive control of reactor network model using machine learning for hydrogen-rich gas and biochar poly-generation by biomass waste gasification in supercritical water

Supercritical water gasification (SCWG) technology can convert biomass into hydrogen rich gas and biochar. Fluidized bed reactor is promising for the industrialization of this technology, and the reactor dynamic performance study is of great significance for its scaling up. However, current simulation studies mainly focus on steady-state analysis using Computational Fluid Dynamics (CFD) software, it is difficult to conduct dynamic study due to its high computational costs. To this end, a reactor network model (RNM) that accounts for the flow, heat transfer, and kinetic dynamics for fluidized bed reactor is firstly developed based on the partitioning theory, which can reduce the computation time of dynamic simulation from several days to seconds. Additionally, extensive open-loop simulations of the RNM are carried out to generate the dataset for the development of a recurrent neural network (RNN) model. Additionally, current studies mainly focus on open-loop simulation, closed loop optimization is absent. To this end, a framework for building a machine learning (ML) model and a ML-based nonlinear predictive control scheme is developed. Model predictive control (MPC) schemes based on the RNN model is used to optimize the SCWG process to achieve multiple objectives, such as maximizing hydrogen yield and carbon yield. The open loop simulation results demonstrate that H2 yield decreases from 0.00022 to 0.0002 kg s−1 with temperature reducing from 700 to 600 °C. To increase H2 yield to the setpoint, MPC increases temperature and mass flowrate to 708 °C and 417.57 kg h−1. Additionally, MPC increases carbon yield and minimizing CO2 yield by decreasing temperature to 475 °C and increasing mass flowrate to 407.38 kg h−1.

Real-time simulations of ADF STEM probe position-integrated scattering cross-sections for single element fcc crystals in zone axis orientation using a densely connected neural network

Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM) images in terms of composition or thickness often relies on probe-position integrated scattering cross sections (PPISCS). In order to compare experimental PPISCS with theoretically predicted ones, expensive simulations are needed for a given specimen, zone axis orientation, and a variety of microscope settings. The computation time of such simulations can be in the order of hours using a single GPU card. ADF STEM simulations can be efficiently parallelized using multiple GPUs, as the calculation of each pixel is independent of other pixels. However, most research groups do not have the necessary hardware, and, in the best-case scenario, the simulation time will only be reduced proportionally to the number of GPUs used. In this manuscript, we use a learning approach and present a densely connected neural network that is able to perform real-time ADF STEM PPISCS predictions as a function of atomic column thickness for most common face-centered cubic (fcc) crystals (i.e., Al, Cu, Pd, Ag, Pt, Au and Pb) along [100] and [111] zone axis orientations, root-mean-square displacements, and microscope parameters. The proposed architecture is parameter efficient and yields accurate predictions for the PPISCS values for a wide range of input parameters that are commonly used for aberration-corrected transmission electron microscopes.

Quantitative Comparison of Analytical Solution and Finite Element Method for Investigation of Near-infrared Light Propagation in Brain Tissue Model.

Functional near-infrared spectroscopy (fNIRS) is an imaging method in which a light source and detector are installed on the head; consequently, the re-emission of light from human skin contains information about cerebral hemodynamic alteration. The spatial probability distribution profile of photons penetrating tissue at a source spot, scattering into the tissue, and being released at an appropriate detector position, represents the spatial sensitivity. Modeling light propagation in a human head is essential for quantitative near-infrared spectroscopy and optical imaging. The specific form of the distribution of light is obtained using the theory of perturbation. An analytical solution of the perturbative diffusion equation (DE) and finite element method (FEM) in a Slab media (similar to the human head) makes it possible to study light propagation due to absorption and scattering of brain tissue. The simulation result indicates that sensitivity is slowly decreasing in the deep area, and the sensitivity below the source and detector is the highest. The depth sensitivity and computation time of both analytical and FEM methods are compared. The simulation time of the analytical approach is four times larger than the FEM. In this paper, an analytical solution and the performance of FEM methods when applied to the diffusion equation for heterogeneous media with a single spherical defect are compared. The depth sensitivity along with the computation time of simulation has been investigated for both methods. For simple and Slab modes of the human brain, the analytical solution is the right candidate. Whenever the brain model is sophisticated, it is possible to use FEM methods, but it costs a higher computation time. Analytical and finite element method (FEM) depth sensitivity are almost the same.FEM requires more computation time, but can handle complicated head models.The analytical method is proposed for the first step and simple head models. The functional near-infrared spectroscopy (fNIRS) is a type of neuromonitoring that uses near-infrared light to measure brain activity indirectly and is similar to electroencephalography (EEG). A single-channel fNIRS system contains a near-infrared light source, which emits near-infrared light (NIR), and a detector is placed near the source. A light intensity change received by detectors indicates brain activity when NIR light penetrates into the gray matter. It is necessary to have a prior understanding of light penetration depth in order to measure brain activity more accurately. fNIRS can be better understood, optimized, and investigated through modeling light propagation in brain tissue. In order to study light in tissues, analytical and numerical methods can be used. In this work, we compared these two approaches quantitatively in a simple slab medium. We concluded that the numerical method takes too much time to calculate the results, but it can be applied to complicated head models. The results of these studies provide researchers with new insights into the modeling and simulation of fNIRS and diffuse optical tomography.

A method to reduce computation time in finite element simulations of deep rolling

A method to reduce computation time in finite element simulations of deep rolling

Fine-grained heterogeneous parallel direct solver for finite element problems

The improvement of computational effectiveness is a vital issue in the field of large-scale finite element analysis. The performance is fundamentally determined by the efficiency of solving sparse linear system equations using the implicit finite element method. This paper presents a direct linear solver based on heterogeneous hybrid parallel computing on CPUs and GPUs. This can efficiently utilize computing resources of multiple devices to achieve performance improvement. Initially, we partition the elimination tree into several subtrees to accomplish the task decomposition. Based on this, we build a dynamic programming mathematical model to balance the computational load of the various devices. Then, we develop a numerical decomposition strategy by combining node parallelism and tree parallelism for the CPUs. In addition, efficient numerical decomposition is achieved on the GPU through batch processing and maximizing the overlap between computations and data transfers. Numerical experiments show that, compared with MKL PARDISO, the performance of numerical factorization can be improved by up to 10 times by using CPU and dual-path GPU hybrid calculations, and the computation time of simulation can be reduced by one-third for the multicondition analysis of Body In White and by 20% for the large-scale nonlinear finite element deformation analysis.

Circuit Modelling of Broadband Antenna Using Vector Fitting and Foster Form Approaches for IoT Applications

Computation of the broadband antenna impedance behavior requires the wideband lumped equivalent circuit. This work aims to provide a comprehensive study of broadband antenna equivalent circuit models over broad frequency ranges for Internet of Things (IoT) applications using multiple approaches. The first approach is a Foster approximation, which is based on the antenna-simulated scattering parameters and impedance data. The second concerns a vector-fitting fitting approximation, which is based on the antenna-simulated impedance/admittance data. The studied antenna is modeled based on the radiating element’s geometry and the multifractal slots, in addition to the DGS (Defected Ground Plane) effects. Applying these techniques, wideband lumped equivalent circuits of the antenna have been computed which are in good agreement with measurement data. These models could be useful for the enhancement of the performance of a broadband antenna and its systematic design as well as offering the advantage of saving computation time for full-wave simulations and facilitating the solution of problems that have both an electromagnetic part as well as a circuit part. For further analysis, the Gaussian pulse has been generated to drive a time-domain analysis of the current and power response waveforms antenna behavior under load.

Linear response theory-based theoretical approach to structural changes in a polymer induced by β-decay of substituted tritium

Polymers exposed to tritiated water undergo hydrogen defects caused by isotope substitution and subsequent β-decay of substituted tritium, causing structural changes and loss of function in the biopolymers. Here, based on linear response theory, we predict the structural change of tritium-damaged polyethylene using the equilibrium trajectory of undamaged polyethylene to reduce the computation time of molecular dynamics simulations. Specifically, the ensemble average of the change in a physical quantity, such that it represents a structural change before and after damage, was calculated numerically using the time derivative of the total potential energy difference derived analytically and the physical quantity obtained from the simulation of undamaged polyethylene on the basis of linear response theory. A comparison between theoretical and simulation results revealed that the characteristic oscillation behaviors of the structural response of polyethylene can be predicted, whereas the quantitative prediction of the steady-state values over a long period is difficult.

A Battery Cycling Optimization Framework Coupling Prediction Models and Bayesian Optimization

Energy storage is widely used in many fields, for instance, electrical grid, electric vehicles, portable devices and so forth. Rechargeable batteries such as lithium-ion, lithium-oxygen, sodium-ion, lead acid, and more broadly, supercapacitors, are highly needed to achieve high capacity and long duration while maintaining low cost. An essential task of battery research and development, in both industry and academia, and for both simulation and experiment, is to optimize the parameters for best performance. A special property of battery cycling optimization is that we evaluate the performance as a function of cycles when calculating the score of the objective function. A huge challenge is the difficulty to query the objective function due to the cost to obtain the performance, i.e., long cycling time (in both simulation and experiment) causes battery optimization to be a time-consuming task. What is worse, the space of parameter is often large and thus a great number of queries are necessary. To shorten computation time in simulation, or reduce cost in experiment, we require powerful optimization algorithms. We aim for an asynchronously parallel algorithm which considers early stopping and is flexible to incorporate prior knowledge of the batteries. Our optimization algorithm consists of two major components, a predictor and a sampler. For battery cycling, some information is revealed before finishing all cycles, thus the predictor can predict the final outcome with uncertainty based on existing information. The uncertainty of the prediction is described by a probability distribution. The sampler determines new query points based on existing observations; the observations include finished cycles and predictions from the predictor. The next searching points are those with highest expected improvement over existing query points. We compare our algorithm with current optimization methods to show significant reduction of optimization time.

Turbulence models and simulation method in the CFD simulation of 75-mm hydrocyclone

In this paper, an effective method for the classification process simulation in 75¬mm hydrocyclone is considered. The simulation results and computational time are compared using Reynolds stress model (RSM) and different large eddy simulation (LES) subgrid-scale models as turbulence models, and the volume of fluid model (VOF) as a multiphase model. The Lagrangian discrete phase model (DPM) is used to simulate the classification process of particles. As the experimental result for comparison of simulation results, Hsieh's experimental data are used. When the different LES subgrid-scale models are used, the solution converges stably by various solution convergence methods without increasing the grid numbers or reducing the size of time steps than RSM model. As a result, it is confirmed that when an appropriate simulation method is applied with the LES-WMLES S-Omega model, more accurate axial water flow velocity distribution and particle classification simulation results can be obtained at a computational cost similar to that of using the RSM model.