Simulation Of Configuration Research Articles

An Evaluation of Self-Adaptive Mechanisms for Misconfigurations in Small Uncrewed Aerial Systems

Small uncrewed aerial systems, sUAS, provide an invaluable resource for performing a variety of surveillance, search, and delivery tasks in remote or hostile terrains which may not be accessible by other means. Due to the critical role sUAS play in these situations, it is vital that they are well configured in order to ensure a safe and stable flight. However, it is not uncommon for mistakes to occur in configuration and calibration, leading to failures or incomplete missions. To address this problem, we propose a set of self-adaptive mechanisms and implement them into a self-adaptive framework, CICADA , for Controller Instability-preventing Configuration Aware Drone Adaptation. CICADA dynamically detects unstable drone behavior during flight and adapts to mitigate this threat. We have built a prototype of CICADA using a popular open source sUAS flight control software and experimented with a large number of different configurations in simulation. We then performed a case study with physical drones to determine if our framework will work in practice. Experimental results show that CICADA’s adaptations reduce controller instability and enable the sUAS to recover from up to 33.8% of poor configurations. In cases where we cannot complete the intended mission, invoking alternative adaptations may still help by allowing the vehicle to loiter or land safely in place, avoiding potentially catastrophic crashes. These safety-focused adaptations can mitigate unsafe behavior in 52.9% to 64.7% of dangerous configurations. We further show that rule-based approaches can be leveraged to automatically select an appropriate adaptation strategy based on the severity of instability encountered, with up to a 14.2% improvement over direct adaptation. Finally, we introduce a variation of our primary adaptation strategy designed to allow more cautious adaptation with limited configuration information, which gets within 6.7% of our primary adaptation strategy despite not requiring an optimal knowledge base.

Predicting Blockchain Application Performance with Machine Learning Techniques

Blockchain technology is a distributed ledger designed to record all transactions within its network, characterized by its decentralized nature, resistance to tampering, and attributes such as consistency, anonymity, and traceability. However, evaluating blockchain applications' performance can be complex due to their intricate and distributed infrastructure. This research employs machine learning model-based methods to predict blockchain systems' performance using predetermined configuration parameters. The data used in this study is derived from a blockchain simulator, generating blockchain data to facilitate performance predictions. The simulation process involves using simulated data and configuration settings for each run, including parameters such as the number of nodes, the number of miners, consensus algorithm, maximum block size, and transaction quantities, among others. Output metrics such as the total number of blocks, transaction rate, block propagation time, and latency are utilized to assess network performance. The simulator was run 184 times with various configurations. Our findings indicate that the Random Forest model outperformed other models used in the experiments, achieving the highest R² scores for multiple metrics, such as 0.987 for total number of transactions and 0.765 for average block propagation time, while also demonstrating lower RMSE values, indicating more accurate predictions.

Preventing Workload Interference with Intelligent Routing and Flexible Job Placement Strategy on Dragonfly System

Dragonfly is an indispensable interconnect topology for exascale HPC systems. To link tens of thousands of compute nodes at a reasonable cost, Dragonfly shares network resources with the entire system such that network bandwidth is not exclusive to any single application. Since HPC systems are usually shared among multiple co-running applications at the same time, network competition between co-existing workloads is inevitable. This network contention manifests as workload interference, where a job’s network communication can be severely delayed by other jobs. This study presents a comprehensive examination of leveraging intelligent routing and flexible job placement to mitigate workload interference on Dragonfly systems. Specifically, we leverage the parallel discrete event simulation toolkit, SST, to investigate workload interference on Dragonfly with three contributions. We first present Q-adaptive routing, a multi-agent reinforcement learning routing scheme, and a flexible job placement strategy that, together, can mitigate workload interference based on workload communication characteristics. Next, we enhance SST with Q-adaptive routing and develop an automatic module that serves as the bridge between SST and HPC job scheduler for automatic simulation configuration and automated simulation launching. Finally, we extensively examine the workload interference under various job placement and routing configurations.

The Implications of Artificial Intelligence for Education

Throughout recent years, artificial intelligence in schooling has developed fundamentally. The first to check out the application in training. Context-oriented information from the examination is introduced in this review, including the instructive disciplines, instructive levels, research objectives, procedure, year of distribution, and ideal interest group for the simulated intelligence. Grounded coding demonstrated how affordances connected with subject substance, organization like symptomatic apparatuses, and teaching methods, for example, gaming and personalization fit into three significant topics of man-made consciousness in training. Negative mentalities, an absence of mechanical capability for understudies and educators, moral worries, and issues explicitly with the simulated intelligence device's convenience and configuration were among the difficulties faced by knowledge in schooling.

Multi-scale CLEAN for Fourier-based hard x-ray solar imaging

Abstract Multi-scale deconvolution is an ill-posed inverse problem in imaging, with applications ranging from microscopy, through medical imaging, to astronomical remote sensing. In the case of high-energy space telescopes, multi-scale deconvolution algorithms need to account for the peculiar property of native measurements, which are sparse samples of the Fourier transform of the incoming radiation. The present paper proposes a multi-scale version of CLEAN, which is the most popular iterative deconvolution method in Fourier-based astronomical imaging. Using synthetic data generated according to a simulated but realistic source configuration, we show that this multi-scale version of CLEAN performs better than the original one in terms of accuracy, photometry, and regularization. Further, the application to a data set measured by the NASA Reuven Ramaty High Energy Solar Spectroscopic Imager shows the ability of multi-scale CLEAN to reconstruct rather complex flaring topographies.

Machine Learning for Cell Design and Optimization

An essential aspect of battery research and development involves exploring various cell parameters to deliver optimal performance. Battery optimization is challenging due to the huge cost and time required to evaluate different configurations in experiments or simulations. Optimizing the cycling performance is especially costly since battery cycling is extremely time consuming. Adding to this challenge is the expansive parameter space. In this talk, I will present some of our recent works that leverage machine learning and physics-informed machine learning for cell optimization. I will first show an unsupervised machine learning approach that can reduce the battery testing time by about 75% [1]. This framework comprises two main components: a pruner and a sampler. The pruner employs the Asynchronous Successive Halving Algorithm and Hyperband to halt the cycling of unpromising batteries, thereby conserving resources for further exploration. Meanwhile, the sampler, utilizing Tree of Parzen Estimators, predicts promising configurations for future cycles based on query history. Notably, our framework accommodates categorical, discrete, and continuous parameters and can operate asynchronously in parallel to facilitate multiple simultaneous cycling cells. Next, I will show a method to embed physical laws and on-line observation into machine learning [2], so that irrelevant low-cost battery data can be utilized to identify battery parameters by machine learning without knowledge of their ground truth as the training data. We take diffusivity as an example and show that it can be obtained from easily measured sequence of battery voltage over time. Our results show that this method accurately quantifies not only the diffusivities of both positive and negative electrodes, but also as complex non-linear functions of lithium concentration, purely based on the cell voltage data requiring neither diffusivity nor concentration measurement. Notably, it can accurately predict non-monotonic, many-to-one relations such as “w” shape functions. Moreover, this method is immune to measurement noise and capable of simultaneously estimating multiple parameters. Finally, I will report a new approach of Self-directed Online Learning Optimization (SOLO) [3] for cell optimization, which integrates Deep Neural Network (DNN) with Finite Element electrochemical calculations. A DNN learns and substitutes the objective as a function of design variables. A small number of training data is generated dynamically based on the DNN’s prediction of the optimum. The DNN adapts to the new training data and gives better prediction in the region of interest until convergence. The optimum predicted by the DNN is proved to converge to the true global optimum through iterations. Our algorithm reduced the computational time by up to 5 orders of magnitude compared with directly using heuristic methods, and outperformed all state-of-the-art algorithms tested in our experiments. Broadly, we would like to emphasize that integrating knowledge into machine learning [4] can lead to significant advances in cell design. REFERENCES:[1] C. Deng, A. Kim, and W. Lu, “A generic battery-cycling optimization framework with learned sampling and early stopping strategies,” Patterns, 3, 100531 (2022). https://doi.org/10.1016/j.patter.2022.100531[2] B. Wu, B. Zhang, C. Deng, and W. Lu, “Physics-encoded deep learning in identifying battery parameters without direct knowledge of ground truth,” Applied Energy, 321, 119390 (2022). https://doi.org/10.1016/j.apenergy.2022.119390[3] C. Deng, Y. Wang, C. Qin, Y. Fu, and W. Lu, “Self-directed online machine learning for topology optimization,” Nature Communications, 13, 388 (2022). https://doi.org/10.1038/s41467-021-27713-7[4] C. Deng, X. Ji, C. Rainey, J. Zhang, and W. Lu, “Integrating machine learning with human knowledge,” iScience, 23, 101656 (2020). https://doi.org/10.1016/j.isci.2020.101656

ST40 electromagnetic predictive studies supported by machine learning applied to experimental database

Nuclear fusion is entering the era of power plant-scale devices, which are now undergoing extensive studies to support the design phase. Plasma disruptions pose a high risk to these classes of devices because of the large stored thermal and magnetic energy which might jeopardize machine integrity and availability. Therefore, disruptions within these devices must be virtually eliminated, and any disruptions which do happen must be highly mitigated. However, the characterisation, prediction and technology used to mitigate disruptions is still an area of active development. In this paper, the authors investigate the disruptions within ST40, with particular attention at the identification of causes and effects associated with disruptions, both from a physics basis and an engineering standpoint. This paper aims at presenting preliminary predictive analyses of ST40 plasma scenarios by exploiting Machine Learning techniques applied to an experimental database populated by plasma pulses executed during the ST40 2021–2022 experimental campaign. The database contains both disrupted and non-disrupted pulses. Using Machine Learning, common features within disruptions are automatically classified and identified, mapping the controllable operational space in terms of plasma displacement and variation of specific plasma internal parameters. The classification was validated by benchmarking the numerical reconstruction of the plasma dynamics with experimental data recovered from the plasma diagnostics. Subsequent Machine Learning analyses allowed the extrapolation of new disrupted plasma configurations for preliminary predictive simulations of the plasma column displacement. Thanks to the numerical simulations performed in MAXFEA environment, it is possible to investigate the plasma vertical displacement both during disrupted and regularly terminated plasma scenarios and to provide lessons to be learnt for the next ST40 experimental campaign and for the design of future ST devices.

Numerical prediction of the fluid damping of a standing disc with a variable axial distance from a rigid wall

Abstract Near-resonant flow-induced vibrations are a significant cause of fatigue damage in hydraulic impellers. The amplitude of these vibrations is limited mainly by the fluid damping, making its quantification paramount for the safety of hydraulic turbines. To study the fluid damping for low-specific-speed radial impellers, a submerged disc in still water with a variable vertical distance from a rigid wall is investigated. This setup serves as an abstraction of the axial gap between impeller hub and head cover, and of the modal behavior of impellers for the case of diametrical nodal lines. The fluid damping is computed numerically for the 3-nodal-diameters mode using the imposed modal motion approach for various axial gap sizes and vibration amplitudes. The results are contrasted with the experimental damping measured with the same setup. First, a simulation configuration that accurately replicates the experimentally measured damping in value and tendency is presented. The factors that would normally impede a valid numerical prediction are exposed and solutions are suggested. Second, the valid simulation results are thoroughly studied, and the flow field is visualized to gain insight into the physical mechanisms that cause the fluid mass and damping. As a result, the interaction between fluid and structure in this system is understood.

Broad spectrum quality assessment of flow simulations in Pelton turbine runners

Abstract The presented work lists quantities of interest that are expected from Computer Fluid Dynamics (CFD) simulations over the course of a Pelton runner design. It covers a broad spectrum of heterogeneous quantities, from simple scalar values such as hydraulic efficiency of the runner to time-dependent multi-dimensional data such as the evolution of the water distribution at the outlet of the runner over a jet passing cycle. A method is proposed to perform comparison between simulation results and a set of reference experimental results. The approaches selected to quantify agreement between experimental data and simulation results are explained. Finally, the result of the presented work offers a tool evaluating key performance indicators to quantify the discrepancy between simulation results and actual physical behaviour of the turbine. It aims at helping users of numerical simulations to select the most appropriate simulation configuration either for a given application addressing a limited amount of quantities of interest or when they are interested in the best compromise to predict several characteristics of the flow in a Pelton turbine.

Improvements in the land surface configuration to better simulate seasonal snow cover in the European Alps with the CNRM-AROME (cycle 46) convection-permitting regional climate model

Abstract. Snow cover modeling remains a major challenge in climate and numerical weather prediction (NWP) models even in recent versions of high-resolution coupled surface–atmosphere (i.e., at kilometer scale) regional models. Evaluation of recent climate simulations, carried out as part of the WCRP-CORDEX Flagship Pilot Study on Convection (FPSCONV) with the CNRM-AROME convection-permitting regional climate model at 2.5 km horizontal resolution, has highlighted significant snow cover biases, severely limiting its potential in mountain regions. These biases, which are also found in AROME numerical weather prediction (NWP) model results, have multiple causes, involving atmospheric processes and their influence on input data to the land surface models in addition to deficiencies of the land surface model itself. Here we present improved configurations of the SURFEX-ISBA land surface model used in CNRM-AROME. We thoroughly evaluated these configurations on their ability to represent seasonal snow cover across the European Alps. Our evaluation was based on coupled simulations spanning the winters of 2018–2019 and 2019–2020, which were compared against remote sensing data and in situ observations. More specifically, the study tests the influence of various changes in the land surface configuration, such as the use of multi-layer soil and snow schemes, the division of the energy balance calculation by surface type within a grid cell (multiple patches), and new physiographic databases and parameter adjustments. Our findings indicate that using only more detailed individual components in the surface model did not improve the representation of snow cover due to limitations in the approach used to account for partial snow cover within a grid cell. These limitations are addressed in further configurations that highlight the importance, even at kilometer resolution, of taking into account the main subgrid surface heterogeneities and improving representations of interactions between fractional snow cover and vegetation. Ultimately, we introduce a land surface configuration, which substantially improves the representation of seasonal snow cover in the European Alps in coupled CNRM-AROME simulations. This holds promising potential for the use of such model configurations in climate simulations and numerical weather prediction both for AROME and other high-resolution climate models.

Microscopic study on the memory effect of methane hydrate in the presence of silica nanoparticles: Ⅱ. Residual structure hypothesis & impurity imprinting hypothesis

Microscopic study on the memory effect of methane hydrate in the presence of silica nanoparticles: Ⅱ. Residual structure hypothesis & impurity imprinting hypothesis

Validation evidence with experimental and clinical data to establish credibility of TAVI patient-specific simulations

The objective of this study is to validate a novel workflow for implementing patient-specific finite element (FE) simulations to virtually replicate the Transcatheter Aortic Valve Implantation (TAVI) procedure. Seven patients undergoing TAVI were enrolled. Patient-specific anatomical models were reconstructed from pre-operative computed tomography (CT) scans and subsequentially discretized, considering the native aortic leaflets and calcifications. Moreover, high-fidelity models of CoreValve Evolut R and Acurate Neo2 valves were built. To determine the most suitable material properties for the two stents, an accurate calibration process was undertaken. This involved conducting crimping simulations and fine-tuning Nitinol parameters to fit experimental force-diameter curves. Subsequently, FE simulations of TAVI procedures were conducted. To validate the reliability of the implemented implantation simulations, qualitative and quantitative comparisons with post-operative clinical data, such as angiographies and CT scans, were performed. For both devices, the simulation curves closely matched the experimental data, indicating successful validation of the valves mechanical behaviour. An accurate qualitative superimposition with both angiographies and CTs was evident, proving the reliability of the simulated implantation. Furthermore, a mean percentage difference of 1,79±0,93 % and 3,67±2,73 % between the simulated and segmented final configurations of the stents was calculated in terms of orifice area and eccentricity, respectively. This study shows the successful validation of TAVI simulations in patient-specific anatomies, offering a valuable tool to optimize patients care through personalized pre-operative planning. A systematic approach for the validation is presented, laying the groundwork for enhanced predictive modeling in clinical practice.

Particle‐based MR modeling with diffusion, microstructure, and enzymatic reactions

AbstractPurposeTo develop a novel particle‐based in silico MR model and demonstrate applications of this model to signal mechanisms which are affected by the spatial organization of particles, including metabolic reaction kinetics, microstructural effects on diffusion, and radiofrequency (RF) refocusing effects in gradient‐echo sequences.MethodsThe model was developed by integrating a forward solution of the Bloch equations with a Brownian dynamics simulator. Simulation configurations were then designed to model MR signal dynamics of interest, with a primary focus on hyperpolarized 13C MRI methods. Phantom scans and spectrophotometric assays were conducted to validate model results in vitro.ResultsThe model accurately reproduced the reaction kinetics of enzyme‐mediated conversion of pyruvate to lactate. When varying proportions of restrictive structure were added to the reaction volume, nonlinear changes in the reaction rate measured in vitro were replicated in silico. Modeling of RF refocusing effects characterized the degree of diffusion‐weighted contribution from preserved residual magnetization in nonspoiled gradient‐echo sequences.ConclusionsThese results show accurate reproduction of a range of MR signal mechanisms, establishing the model's capability to investigate the multifactorial signal dynamics such as those underlying hyperpolarized 13C MRI data.

Towards the prediction of flame transfer functions: Evaluation of a hybrid LES-CAA with compressible LES

The prediction of flame transfer functions, particularly in practically relevant systems, remains challenging and computationally demanding. Numerical approaches are a valuable addition to experimental acoustic characterizations of industrial configurations. Conventionally, fully compressible numerical simulations are used that naturally include acoustic fluctuations in their computations, but can be computationally expensive depending on the configuration. Therefore, a convenient approach to use tailored numerics for the underlying physics is considered in this work. In this work, this is realized by applying a runtime-coupled method of computational fluid dynamics and computational aeroacoustics to a single-sector aero-engine combustor. This hybrid computational fluid dynamics and computational aeroacoustics method captures fluid flow behavior and combustion dynamics in a low-Mach computational fluid dynamics domain while allowing for acoustic perturbations in the computational aeroacoustics. Runtime exchange of hydrodynamic and acoustic quantities between the two solvers allows for a bidirectional coupling and, by extension, a complete description of the combustion system. In this work, the hybrid computational fluid dynamics and computational aeroacoustics is applied in a high-fidelity large eddy simulation configuration. The flame transfer function is evaluated for both compressible and hybrid simulations. The results for both numerical approaches are validated with each other and compared to the experimentally obtained flame transfer function. Finally, the computational effort for the numerical approaches is considered. This article presents the first application of a high-fidelity computational fluid dynamics framework using large eddy simulation with bidirectional coupling with the acoustic solver to an industry-relevant configuration. The aim is to provide a roadmap towards investigating thermoacoustic instabilities in a real gas turbine engine at reduced computational costs.

A formation pathway for terrestrial planets with moderate water content involving atmospheric-volatile recycling

Of the many recently discovered terrestrial exoplanets, some are expected to harbor moderate water mass fractions of a few percent. The formation pathways that can produce planets with these water mass fractions are not fully understood. Here, we use the code chemcomp, which consists of a semi-analytical 1D protoplanetary disk model harboring a migrating and accreting planet, to model the growth and composition of planets with moderate water mass fractions by pebble accretion in a protoplanetary disk around a TRAPPIST-1 analog star. This star is accompanied by seven terrestrial planets, of which the outer four planets likely contain water mass fractions of between 1% and 10%. We adopt a published model that considers the evaporation of pebbles in the planetary envelope, from where recycling flows can transport the volatile vapor back into the disk. We find that with this model, the planetary water content depends on the influx rate of pebbles onto the planet. A decreasing pebble influx with time reduces the envelope temperature and consequently allows the formation of planets with moderate water mass fractions as inferred for the outer TRAPPIST-1 planets for a number of different simulation configurations. This is further evidence that the recycling of vapor is an important component of planet formation needed to explain the vast and diverse population of exoplanets.

Tricortical versus bicortical anchorage in a double-screw tandem skeletal expander and a single-screw maxillary anchorage rapid palatal expander: A finite element analysis.

This study aims to employ finite element method (FEM) analysis to compare the differences between bicortical and tricortical anchorage of the posterior miniscrews in a single-screw miniscrew-assisted rapid palatal expansion (MARPE) and a double-screw tandem skeletal expander (TSE) under open and closed suture conditions. A cone beam computed tomography of the human skull of a 21.5-year-old female was utilized as a model for creating a FEM analysis. Simulations involved the insertion of four palatal miniscrews: two anterior ones with bicortical anchorage and two posterior ones (one with bicortical and another with tricortical anchorage), under open and closed suture conditions in a single-screw MARPE and double-screw TSE, resulting in a total of eight different simulation configurations. Evaluation parameters include total deformation (mm), Von Mises stress (MPa), and strain for each miniscrew body. Tricortical anchorage of the posterior miniscrews provides greater anchorage, higher stress, and deformation on the anterior miniscrews in single-screw MARPE. Tricortical anchorage combined with a double-screw TSE promotes a more even distribution of force and stress on miniscrews under open suture conditions, leading to a parallel midpalatal suture opening along its entire length and height. FEM analysis revealed favorable midpalatal suture opening with equal force distribution and less stress when posterior tricortical anchorage in conjunction with double-screw TSE is applied.

Enhancing Urban Flood Forecasting: Integrating Weather Forecasts and Hydrological Models

Precipitation data in urban hydrological models are derived from an ideal stormwater model, which has some uncertainties and limited prediction times. Therefore, to reliably forecast urban flooding, prolong prediction time periods, and better support associated research in urban flood forecasting, a combination of weather forecasts and urban hydrology is necessary. By applying comprehensive cloud microphysical schemes in the Weather Research and Forecasting (WRF) model to the predecessor torrential rainfall associated with Typhoon Khanun (2017), this study evaluated different configurations of atmospheric-hydrological simulations based on the WRF model and InfoWorks ICM. Results showed that the microphysics scheme could significantly affect spatial and temporal distributions of the simulated torrential rainfall. Generally, the combination of WRF and NSSL schemes produced better performance. Applying the NSSL scheme to the WRF model and combining it with the InfoWorks ICM system can reproduce torrential rainfall and urban flood formations.

Dynamical investigation of NinAgm (n + m = 147, 309, 561) nanoalloys with core–shell orderings

ABSTRACT The structures and dynamical properties of core–shell bimetallic Ni-Ag nanoalloys varying with different sizes and compositions have been studied using Monte Carlo (MC) and Molecular Dynamic (MD) simulations. We have considered the compositions in which the size of the core increases while the total number of atoms is fixed. In this sense, two (Ni13Ag134, Ni55Ag92), three (Ni13Ag296, Ni55Ag254, Ni147Ag162) and four (Ni13Ag548, Ni55Ag506, Ni147Ag414 and Ni309Ag252) compositions were considered for 147, 309 and 561 atoms, respectively. Highly symmetric Mackay icosahedral structures with centred symmetric cores appear for these specific sizes and compositions. Also, smaller Ni atoms tend to occupy the core and Ag atoms prefer to segregate to the surface of the nanoalloy due to its lower surface and cohesive energy. Then, the lowest energy structures obtained by Basin Hopping MC simulations were used as initial configurations for melting simulations. The transitions between different chemical ordering patterns with increasing temperature are possible in these systems while they are still in the solid state. Although there are clear differences in the melting process of the compositions with increasing size of the core, for all cases, surface melting occurs indicating that the Ag shell melts before the inner Ni core.

Numerical simulations of the heavy rain event in the Democratic People's Republic of Korea during 9–10 August 2020

Numerical simulations of the heavy rain event in the Democratic People's Republic of Korea during 9–10 August 2020

Numerical intercomparison of PHITS and Geant4 Monte Carlo codes for fast neutron inelastic scattering applications

Fast neutron inelastic scattering is a promising non-destructive assay technique for various analytical applications. As an active neutron interrogation technique, its performance is a function of various different factors and parameters that require optimization. Monte Carlo simulation codes are indispensable for such tasks. However, the internal simulation routines implemented in such codes can rely on different physical models that can yield discrepancies in the simulation results. In this work we conduct an intercomparison of PHITS and Geant4 codes performance in application to fast neutron inelastic scattering simulations. The goal of this paper is twofold. First, we explain the differences in code configuration with respect to gamma and neutron transport, as well as internal simulation routines. Second, we conduct a performance assessment of the two codes using two different measurement configurations. One configuration consisted of a source of gamma rays in a broad energy range (100–9000 keV) and a CeBr3 detector. The other configuration consisted of a monoenergetic 2.5 MeV fast neutron source, Fe, Nd, Dy, B targets and a CeBr3 detector. Selected simulation configurations were chosen with a goal to compare the performance differences in neutron energy distribution, produced prompt gamma rays and energy deposition in CeBr3 detector between the two codes. Results of our study reveal a good coherence of both codes performance in the application of fast neutron inelastic scattering simulations. The simulation geometries and observed differences are described in detail.