Perception of Angular Acceleration About the Yaw Axis of a Flight Simulator - Thresholds and Reaction Latency for Research Pilots
Perception of angular acceleration by a pilot in a flight simulator
ANGULAR ACCELERATION↗
SEARCH · Engineering Papers
Results for “accelerator simulation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Neutrinos from the Galactic Center Hosting a Hypernova Remnant
Similar to star-forming galaxies or starburst galaxies, star-forming regions in our Galaxy can host cosmic-ray (CR) accelerators and rich gas as targets of hadronuclear interaction. By our estimations, the IceCube neutrino observatory might detect muon neutrinos from a CR accelerator associated with a molecular cloud complex in our Galaxy. The associated high-energy gamma-ray emission might be observed by the Cherenkov Telescope Array (CTA), High-Altitude Water Cherenkov Gamma-Ray Observatory (HAWC), and Large High Altitude Air Shower Observatory (LHAASO). Furthermore, taking the Galactic Center (GC) region as an example, we assume that a hypernova exploded in the past in the GC. We simulate the acceleration of CRs in the hypernova remnant (HNR) as well as their confinement and escape. The high-energy protons escape from the HNR, diffuse around the GC, interact with molecular clouds, and then produce gamma-rays and neutrinos. In the optimal cases, the GC would be a promising 100 TeV gamma-ray source for LHAASO’s one-month observation. We propose that neutrino-induced searching for starting track-like and high-energy starting events (HESEs) observed by IceCube, from the GC region with a radius of 1°.8, would help us discover the particle accelerator in the GC or constrain our models. Under the constraint from high-energy gamma-ray observations by the H.E.S.S. telescope, we estimate the exposure time needed to make a significant discovery for the optimal cases. The analysis combining observations of IceCube and ANTARES, starting track-like events and HESEs, future observations by neutrino detectors IceCube-Gen2 and KM3net, and gamma-ray telescopes CTA, HAWC, and LHAASO would help to constrain our models.
79 ASTRONOMY AND ASTROPHYSICS↗
Analysis and simulation for a model of electron impact excitation/deexcitation and ionization/recombination
This paper describes a kinetic model and a corresponding Monte Carlo simulation method for excitation/deexcitation and ionization/recombination by electron impact in a plasma free of external fields. The atoms and ions in the plasma are represented by continuum densities and the electrons by a particle distribution. A Boltzmann-type equation is formulated and a corresponding H-theorem is formally derived. An efficient Monte Carlo method is developed for an idealized analytic model of the excitation and ionization collision cross sections. To accelerate the simulation, the reduced rejection method and binary search method are used to overcome the singular rate in the recombination process. Numerical results are presented to demonstrate the efficiency of the method on spatially homogeneous problems. The evolution of the electron distribution function and atomic states is studied, revealing the possibility under certain circumstances of system relaxation towards stationary states that are not the equilibrium states, a potential non-ergodic behavior.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Particle-in-cell simulation of x-ray wakefield acceleration and betatron radiation in nanotubes
Though wakefield acceleration in crystal channels has been previously proposed, x-ray wakefield acceleration has only recently become a realistic possibility since the invention of the single-cycled optical laser compression technique. We investigate the acceleration due to a wakefield induced by a coherent, ultrashort x-ray pulse guided by a nanoscale channel inside a solid material. By two-dimensional particle-in-cell computer simulations, we show that an acceleration gradient of TeV/cm is attainable. This is about 3 orders of magnitude stronger than that of the conventional plasma-based wakefield accelerations, which implies the possibility of an extremely compact scheme to attain ultrahigh energies. In addition to particle acceleration, this scheme can also induce the emission of high energy photons at ∼O(10–100) MeV. Our simulations confirm such high energy photon emissions, which is in contrast with that induced by the optical laser driven wakefield scheme. In addition to this, the significantly improved emittance of the energetic electrons has been discussed.
43 PARTICLE ACCELERATORS↗
Application of the Mixed-Mode Model for Numerical Simulation of Pearlitic Transformation
The multiscale model of pearlitic transformation, including the finite element solution of the second Fick’s equation with moving boundary in the microscale and Fourier equation in the macroscale, is presented in this paper. According to the mixed-mode approach, both the volume diffusion and the interface mobility were considered. Model describes sidewise and frontal growth of cementite and ferrite plates in a single grain of the austenite. Assessment of the possibility of the developed model application to determine the key parameters in terms of strength properties of pearlitic steel, i.e., pearlite grain size and colony size as well as interlamellar spacing for various cooling conditions, was the main aim of this work. The model was validated and verified on the basis of experimental tests performed for two eutectoid steels. In practice, developed model can support design process for a technology of high-strength rods and long products manufacturing. Rails were selected as a case study in this work, and therefore, numerical simulations of accelerated cooling of the rail head were performed and the relationship between the parameters of heat treatment, parameters of the structure and properties of the finished product was determined on the basis of obtained results.
36 MATERIALS SCIENCE↗
A Pilot Opinion Study of Lateral Control Requirements for Fighter-Type Aircraft
As part of a continuing NASA program of research on airplane handling qualities, a pilot opinion investigation has been made on the lateral control requirements of fighter aircraft flying in their combat speed range. The investigation was carried out using a stationary flight simulator and a moving flight simulator, and the flight simulator results were supplemented by research tests in actual flight. The flight simulator study was based on the presumption that the pilot rates the roll control of an airplane primarily on a single-degree-of-freedom basis; that is, control of angle of roll about the aircraft body axis being of first importance. From the assumption of a single degree of freedom system it follows that there are two fundamental parameters which govern the airplane roll response, namely the roll damping expressed as a time constant and roll control power in terms of roll acceleration. The simulator study resulted in a criterion in terms of these two parameters which defines satisfactory, unsatisfactory, and unacceptable roll performance from a pilot opinion standpoint. The moving simulator results were substantiated by the in-flight investigation. The derived criterion was compared with the roll performance criterion based upon wing tip helix angle and also with other roll performance concepts which currently influence the roll performance design of military fighter aircraft flying in their combat speed range.
Creer, Brent Y.↗
The effect of mix on capsule yields as a function of shell thickness and gas fill
An investigation of direct drive capsules with different shell thicknesses and gas fills was conducted to examine the amount of shock induced (Richtmyer-Meshkov) mix versus Rayleigh-Taylor mix from deceleration of the implosion. The RAGE (Eulerian) code with a turbulent mix model was used to model these capsules for neutron yields along with time-dependent mix amounts. The amount of Richtmyer-Meshkov induced mix from the shock breaking out of the shell is about 0.1 μg (0.15 μm of shell material), while the Rayleigh-Taylor mix is of order 1 μg and determines the mixed simulation yield. The simulations were able to calculate a yield over mix (YOM) ratio (experiment/mix simulation) between 0.5 and 1.0 for capsules with shell thicknesses ranging from 7.5 to 20 μm and with gas fills between 3.8 and 20 atm of D{sub 2} or DT. The simulated burn averaged T{sub ion} values typically lie with 0.5 keV of the data, which is within the measurement error. For capsules with shell thicknesses >25 μm, the YOM values drop to 0.10 ± 0.05, suggesting that some unmodeled effect needs to be accounted for in the thickest capsules.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation
Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning – especially deep learning – to molecular simulation. Furthermore, these techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.
74 ATOMIC AND MOLECULAR PHYSICS↗
Numerical simulation of a disk-shaped electron accelerating electrostatic probe
This paper presents a method utilizing the numerical plasma simulation particle-in-cell technique for studying electron accelerating probe characteristics. A probe configuration other than the commonly used plane, cylindrical, or spherical case will be studied; the probe configuration used is that of a disk. Cylindrical geometry is used in the simulation model. One of the advantages of a numerical simulation is the ease with which the details of the system can be examined. Therefore, in addition to the current-voltage characteristics of the static probe, other results are presented such as the time evolution of the plasma properties and studies of the sheath region surrounding the probe.
Nonnast, J. H.↗
Aggregate data‐driven dynamic modeling of active distribution networks with DERs for voltage stability studies
Abstract Electric distribution networks increasingly host distributed energy resources based on power electronic converter (PEC) toward active distribution networks (ADN). Despite advances in computational capabilities, electromagnetic transient models are limited in scalability because of their reliance on exact data about the distribution system and each of its components. Similarly, the use of the DER_A model, which is intended to examine the combined dynamic behavior of many DERs, is limited by the difficulty in parameterization. There is a need for improved dynamic models of DERs for use in large power system simulations for stability analysis. This paper proposes an aggregate model‐free, data‐driven approach for deriving a dynamic partitioned model (DPM) of ADNs. Detailed residential distribution feeders were first developed, including PEC‐based DERs and composite load models (CMLDs), from which the aggregated DPM was derived. The performance was evaluated through various case studies and validated against the detailed ADN model and state‐of‐the‐art DER_A model with CMLD. The data‐driven DPM achieved a of over 90%, accurately representing the aggregated dynamic behavior of ADNs. Furthermore, the DPM significantly accelerated the simulation process with a computational speedup of 68 times compared to the detailed ADN and a 3.5 times speedup compared to the DER_A CMLD model.
42 ENGINEERING↗
SU-E-T-409: Evaluation of Tissue Composition Effect On Dose Distribution in Radiotherapy with 6 MV Photon Beam of a Medical Linac
Purpose: The aim of this study is to evaluate soft tissue composition effect on dose distribution for various soft tissues and various depths in radiotherapy with 6 MV photon beam of a medical linac. Methods: A phantom and Siemens Primus linear accelerator were simulated using MCNPX Monte Carlo code. In a homogeneous cubic phantom, six types of soft tissue and three types of tissue-equivalent materials were defined separately. The soft tissues were muscle (skeletal), adipose tissue, blood (whole), breast tissue, soft tissue (9-component) and soft tissue (4-component). The tissue-equivalent materials included: water, A-150 tissue-equivalent plastic and perspex. Photon dose relative to dose in 9-component soft tissue at various depths on the beam’s central axis was determined for the 6 MV photon beam. The relative dose was also calculated and compared for various MCNPX tallies including,F8, F6 and,F4. Results: The results of the relative photon dose in various materials relative to dose in 9-component soft tissue and using different tallies are reported in the form of tabulated data. Minor differences between dose distributions in various soft tissues and tissue-equivalent materials were observed. The results from F6 and F4 were practically the same but different with,F8 tally. Conclusion: Based on the calculations performed, the differences in dose distributions in various soft tissues and tissue-equivalent materials are minor but they could be corrected in radiotherapy calculations to upgrade the accuracy of the dosimetric calculations.
60 APPLIED LIFE SCIENCES↗
AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing
The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) algorithms that enable learning from complex, large-scale data; and (3) Advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build in silico ML models that can provide transformative insights from data including: molecular dynamics simulations, next-generation sequencing, omics, imaging, and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next generation high performance computers; and (3) assessing robustness and reliability in the AI models. In this paper, we review the National Cancer Institute (NCI) -Department of Energy (DOE) collaboration, Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a multi-institution collaborative effort focused on advancing computing and data technologies to accelerate cancer research on three levels: molecular, cellular, and population. This collaboration integrates various types of generated data, pre-exascale compute resources, and advances in ML models to increase understanding of basic cancer biology, identify promising new treatment options, predict outcomes, and eventually prescribe specialized treatments for patients with cancer.
60 APPLIED LIFE SCIENCES↗
GenASiS Basics: Object-oriented utilitarian functionality for large-scale physics simulations (Version 4)
GenASiS Basics provides modern Fortran classes furnishing extensible object-oriented utilitarian functionality for large-scale physics simulations on distributed memory supercomputers. This functionality includes physical units and constants; display to the screen or standard output device; message passing; I/O to disk; and runtime parameter management and usage statistics. Herein, this revision—Version 4 of Basics—includes a name change and additions to functionality, including the facilitation of direct communication between GPUs.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices
Recent developments in machine learning interatomic potentials (MLIPs) have empowered even nonexperts in machine learning to train MLIPs for accelerating materials simulations. However, reproducibility and independent evaluation of presented MLIP results is hindered by a lack of clear standards in current literature. In this Perspective, we aim to provide guidance on best practices for documenting MLIP use while walking the reader through the development and deployment of MLIPs including hardware and software requirements, generating training data, training models, validating predictions, and MLIP inference. We also suggest useful plotting practices and analyses to validate and boost confidence in the deployed models. Finally, we provide a step-by-step checklist for practitioners to use directly before publication to standardize the information to be reported. Altogether, we hope that our work will encourage the reliable and reproducible use of these MLIPs, which will accelerate their ability to make a positive impact in various disciplines including materials science, chemistry, and biology, among others.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Integrating Learning and Physics based Computation for Fast Online Transient Analysis
In this work, a novel method that integrates learning and physics based computation is developed for greatly accelerating the simulation of full power system transient trajectories. To solve the dynamic algebraic equations, the method replaces the time-consuming dynamic computation for generator dynamics with trained predictors, while retaining the time-efficient algebraic computation of solving AC-power flow (PF) for power systems. In particular, a predictor is trained for each generator, and the system trajectories are computed by alternating steps of calling the predictors and solving AC-PF. The proposed method also allows fully parallelizable training strategies and a flexible trade-off between training time and testing accuracy. Comprehensive evaluations of the proposed method for transient/dynamic contingency analysis of the New York/New England 16-machine 68-bus power systems demonstrate excellent performance and significant acceleration of computation.
24 POWER TRANSMISSION AND DISTRIBUTION↗
GenASiS Mathematics: Object-oriented manifolds, operations, and solvers for large-scale physics simulations (version 2)
We report GenASiS Mathematics provides modern Fortran classes furnishing extensible object-oriented functionality for the solution of fields governed by selected partial differential equations. The initial release included extensible object-oriented implementations of simple meshes and the evolution of generic conserved currents thereon. This revision - Version 2 of Mathematics - includes significant reorganization and streamlining of these classes, higher-order reconstruction by a different method, a Poisson solver, coarsening to avoid Courant time step limitations near coordinate singularities, and the offloading of computational kernels to GPUs.
97 MATHEMATICS AND COMPUTING↗
Accelerating cavity fault prediction using deep learning at Jefferson Laboratory
Abstract Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model’s ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these achievements were achieved in the context of a highly imbalanced dataset, and fault predictions were made several hundred milliseconds before the onset of the fault. Anticipating faults enables preemptive measures to improve operational efficiency by preventing or mitigating their occurrence.
43 PARTICLE ACCELERATORS↗
Doses and fluxes in the proton-irradiated Big Target: A MARS15 analysis
This work is aimed at determination of secondary particle fluxes, prompt and residual doses as well as energy deposition in and around the Big Target, a 20-tonnes cylindrical target made of natural uranium with the lead core and a stainless-steel yoke, which is being considered to be irradiated by a 0.16-μA 600-MeV proton beam of the JINR Phasotron accelerator. The simulations were performed with the use of the MARS15 particle transport code employing the LAQGSM and CEM generators and based on the ENDFB-VIII neutron cross section library. The calculations for 12-hour, 100-hour, and 3-day irradiations with the aforementioned conditions indicate that temperature rise due to the beam does not require target cooling, the target can be safely handled soon after the end of irradiation, and its decommissioning is possible three years past the irradiation. Doses
43 PARTICLE ACCELERATORS↗