scholarly journals Rare Event Sampling Improves Mercury Instability Statistics

2021 ◽  
Vol 923 (2) ◽  
pp. 236
Author(s):  
Dorian S. Abbot ◽  
Robert J. Webber ◽  
Sam Hadden ◽  
Darryl Seligman ◽  
Jonathan Weare
Keyword(s):  
Numerical Simulation ◽  
Computational Cost ◽  
Rare Event ◽  
Sampling Technique ◽  
Direct Numerical Simulations ◽  
Diffusion Monte Carlo ◽  
Probability Estimates ◽  
Planetary Dynamics ◽  
The Future ◽  
Chaotic Nature

Abstract Due to the chaotic nature of planetary dynamics, there is a non-zero probability that Mercury’s orbit will become unstable in the future. Previous efforts have estimated the probability of this happening between 3 and 5 billion years in the future using a large number of direct numerical simulations with an N-body code, but were not able to obtain accurate estimates before 3 billion years in the future because Mercury instability events are too rare. In this paper we use a new rare-event sampling technique, Quantile Diffusion Monte Carlo (QDMC), to estimate that the probability of a Mercury instability event in the next 2 billion years is approximately 10−4 in the REBOUND N-body code. We show that QDMC provides unbiased probability estimates at a computational cost of up to 100 times less than direct numerical simulation. QDMC is easy to implement and could be applied to many problems in planetary dynamics in which it is necessary to estimate the probability of a rare event.

The Development and Status of the Stab-Resistant Body Armor

2011 ◽  
Vol 331 ◽  
pp. 73-76 ◽  
Author(s):  
Zhi Zhong Ding ◽  
Yue Qing Zhang ◽  
Xiao Ming Qian
Keyword(s):  
Numerical Simulation ◽  
The State ◽  
Body Armor ◽  
Performance Tests ◽  
Future Trends ◽  
Important Study ◽  
The Future

This paper summarized the state of the stab-resistant body armor, and summed up the main developing trends and important study ports of the area. Then, we discussed the frequently-used fibers, structures, performance tests and numerical simulation. Predictably, the future trends of the stab-resistant body armor is the balance of functionality and comfort.

scholarly journals Modelling of textile composite reinforcements on the micro-scale

2014 ◽  
Vol 14 (1) ◽  
pp. 28-33 ◽  
Author(s):  
Oliver Döbrich ◽  
Thomas Gereke ◽  
Chokri Cherif
Keyword(s):  
Numerical Simulation ◽  
Finite Element Analysis ◽  
Computational Cost ◽  
Textile Composite ◽  
Element Analysis ◽  
Simulation Tools ◽  
Micro Scale ◽  
Generating Method ◽  
Geometrical Effects ◽  
Composite Reinforcements

Abstract Numerical simulation tools are increasingly used for developing novel composites and composite reinforcements. The aim of this paper is the application of digital elements for the simulation of the mechanical behaviour of textile reinforcement structures by means of a finite element analysis. The beneficial computational cost of these elements makes them applicable for the use in large models with a solution on near micro-scale. The representation of multifilament yarn models by a large number of element-chains is highly suitable for the analysis of structural and geometrical effects. In this paper, a unit cell generating method for technical reinforcement textiles, using digital elements for the discretization, is introduced.

Sequential Sampling Based Reliability Analysis for High Dimensional Rare Events With Confidence Intervals

2020 ◽  
Author(s):  
Yanwen Xu ◽  
Pingfeng Wang
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Computational Efficiency ◽  
Rare Events ◽  
Computational Cost ◽  
Rare Event ◽  
Probability Analysis ◽  
High Dimensional ◽  
Dimensional System ◽  
Study Results

Abstract Analysis of rare failure events accurately is often challenging with an affordable computational cost in many engineering applications, and this is especially true for problems with high dimensional system inputs. The extremely low probabilities of occurrences for those rare events often lead to large probability estimation errors and low computational efficiency. Thus, it is vital to develop advanced probability analysis methods that are capable of providing robust estimations of rare event probabilities with narrow confidence bounds. Generally, confidence intervals of an estimator can be established based on the central limit theorem, but one of the critical obstacles is the low computational efficiency, since the widely used Monte Carlo method often requires a large number of simulation samples to derive a reasonably narrow confidence interval. This paper develops a new probability analysis approach that can be used to derive the estimates of rare event probabilities efficiently with narrow estimation bounds simultaneously for high dimensional problems. The asymptotic behaviors of the developed estimator has also been proved theoretically without imposing strong assumptions. Further, an asymptotic confidence interval is established for the developed estimator. The presented study offers important insights into the robust estimations of the probability of occurrences for rare events. The accuracy and computational efficiency of the developed technique is assessed with numerical and engineering case studies. Case study results have demonstrated that narrow bounds can be built efficiently using the developed approach, and the true values have always been located within the estimation bounds, indicating that good estimation accuracy along with a significantly improved efficiency.

Dynamic Compensation of Intelligent Sensors

2003 ◽  
Author(s):  
Marco P. Schoen
Keyword(s):  
Dynamic Characteristics ◽  
Dynamic Performance ◽  
Information Matrix ◽  
Computational Cost ◽  
Feedback Gain ◽  
Measured Quantity ◽  
Identification Algorithm ◽  
Optimal Constant ◽  
Intelligent Sensors ◽  
The Future

Commercial sensors have generally, due to their own characteristics, some undesirable influences on the measured quantity and its precision. In particular, the dynamic characteristics can be reflected on to the measured quantity and lead to false or delayed interpretation of the underlying physical process. The quality and therefore the cost of the sensor is often tied with the dynamic performance of these instruments. Intelligent sensors are able to adapt to changing environments, calibrate themselves, and predict the pattern of the future signal. This paper presents algorithms to improve the dynamic performance of sensors, identify the dynamic characteristics of the sensor, and to predict the future pattern of the measured quantity. In particular, two inverse filters are proposed for the improvement of the sensors dynamic performance. One filter incorporates an optimal constant feedback gain that reduces the computational cost and increases the accuracy. A system identification method is used to identify the sensor’s dynamic properties and allows for adaptation of the inverse filter’s parameters. This identification algorithm computes the optimum input to the system i.e. the sensor. The optimization is based on the inverse correlation matrix of the information matrix. A genetic algorithm is used to perform both optimizations, for the computation of the optimal input, and for the optimal constant feedback gain. In addition, a predictive filter formulation is given that is based on the identified system. Simulation results indicate that both inverse filters are capable of recovering the original or true signal. The second filter shows superiority in terms of convergence, lower computational cost, and lower error due to its optimized parameters. The predictive filter indicates good working accuracy for the signal prediction.

scholarly journals Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

2020 ◽  
Vol 10 (6) ◽  
pp. 20200007 ◽  
Author(s):  
Shunzhou Wan ◽  
Agastya P. Bhati ◽  
Stefan J. Zasada ◽  
Peter V. Coveney
Keyword(s):  
Free Energy ◽  
Initial Conditions ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Free Energy Calculations ◽  
Enhanced Sampling ◽  
Energy Prediction ◽  
Massive Scale ◽  
Chaotic Nature ◽  
High Level

A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

scholarly journals Air sampling of smallpox virus

1974 ◽  
Vol 73 (1) ◽  
pp. 1-8 ◽  
Author(s):  
G. Thomas
Keyword(s):  
Air Sampling ◽  
Sampling Technique ◽  
Surface Sampling ◽  
Smallpox Virus ◽  
Future Design ◽  
Adhesive Surface ◽  
Successful Recovery ◽  
The Future

SUMMARYAirborne smallpox virus has been recovered in an isolation hospital using an adhesive surface sampling technique in the presence of very low aerosol concentrations. Previous work in this field is reviewed. Successful recovery of airborne virus depends on sampling large volumes of air with a suitable sampler and thorough investigation of the whole sample taken for the presence of viable virus. More information on the characteristics and behaviour of airborne smallpox virus is needed in particular with regard to the future design and siting of smallpox isolation units.

Numerical Simulation of Free-Surface Turbulence

1994 ◽  
Vol 47 (6S) ◽  
pp. S163-S165
Author(s):  
Douglas G. Dommermuth ◽  
Rebecca C. Y. Mui
Keyword(s):  
Numerical Simulation ◽  
Numerical Modeling ◽  
Free Surface ◽  
Numerical Simulations ◽  
Free Surface Flows ◽  
Direct Numerical Simulations ◽  
Large Eddy Simulations ◽  
Surface Flows ◽  
Large Eddy ◽  
Free Surface Turbulence

Direct numerical simulations and large-eddy simulations of turbulent free-surface flows are currently being performed to investigate the roughening of the surface, and the scattering, radiation, and dissipation of waves by turbulence. The numerical simulation of turbulent free-surface flows is briefly reviewed. The numerical, modeling, and hardware issues are discussed.

Analysis of Aerodynamic Characteristics in the Bottom of a Car

2013 ◽  
Vol 373-375 ◽  
pp. 20-23
Author(s):  
Hai Tao Bao ◽  
Cheng Wang
Keyword(s):  
Numerical Simulation ◽  
Velocity Field ◽  
Pressure Field ◽  
Final Analysis ◽  
Aerodynamic Characteristics ◽  
Specific Reference ◽  
Computational Results ◽  
The Future ◽  
Cfd Method

The aerodynamic characteristics of ordinary vehicle have been studied by lots of scholars, while few people pay enough attention to the aerodynamic characteristics in the bottom of the car. As the requirements of regulations for the performance of the car continues to increase, Analysis of Aerodynamic Characteristics of the car is much more necessary now. In this paper , by using CFD method and getting benefit from the established CFD software, we gave external velocity field and pressure field of the car. The data was analyzed and summarized and the computational results are obtained. In the final analysis for the flatness in the bottom of the car influence on aerodynamic characteristics.This paper has done some useful attempts, and it will provide specific reference significance to the numerical simulation on design of the car in the future.

scholarly journals A fast direct numerical simulation method for characterising hydraulic roughness

2015 ◽  
Vol 773 ◽  
pp. 418-431 ◽  
Author(s):  
D. Chung ◽  
L. Chan ◽  
M. MacDonald ◽  
N. Hutchins ◽  
A. Ooi
Keyword(s):  
Numerical Simulation ◽  
Direct Numerical Simulation ◽  
Computational Cost ◽  
Simulation Method ◽  
Reynolds Numbers ◽  
Proof Of Concept ◽  
Hydraulic Roughness ◽  
Velocity Shift ◽  
High Reynolds ◽  
Reynolds Number Flows

We describe a fast direct numerical simulation (DNS) method that promises to directly characterise the hydraulic roughness of any given rough surface, from the hydraulically smooth to the fully rough regime. The method circumvents the unfavourable computational cost associated with simulating high-Reynolds-number flows by employing minimal-span channels (Jiménez & Moin, J. Fluid Mech., vol. 225, 1991, pp. 213–240). Proof-of-concept simulations demonstrate that flows in minimal-span channels are sufficient for capturing the downward velocity shift, that is, the Hama roughness function, predicted by flows in full-span channels. We consider two sets of simulations, first with modelled roughness imposed by body forces, and second with explicit roughness described by roughness-conforming grids. Owing to the minimal cost, we are able to conduct direct numerical simulations with increasing roughness Reynolds numbers while maintaining a fixed blockage ratio, as is typical in full-scale applications. The present method promises a practical, fast and accurate tool for characterising hydraulic resistance directly from profilometry data of rough surfaces.

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