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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Obstacles to high fidelity multibody dynamics simulation

The authors take a wide ranging look at issues involved in the dynamic modeling of complex, multibodied orbiting space systems. The nature of the multibody problem is reviewed and capabilities and limitations of two major codes (DISCOS, TREETOPS) are assessed. Problem areas that limit accuracy or contribute to uncertainty in the modeling, simulation process are identified. As a specific case, the significance of including, or of not including, second-degree geometric nonlinearity in the elastic displacement field (foreshortening) is evaluated. Conclusions and recommendations are made concerning the direction future development should take to achieve higher-fidelity and more computationally efficient multibody software solutions.

Lips, K. W.↗
NASA NTRS1989

Breakdown of vertically propagating two-dimensional gravity waves forced by orography

The propagation of orographic gravity waves into an atmosphere with exponentially decreasing density is simulated with a two-dimensional, nonlinear, time-dependent numerical model. After the stationary wave is established over the mountain, the model predicts that wave breaking causes a large reduction of the vertical momentum flux in the flow, not only at levels where wave breaking is present, but also far below the lowest occurrence of overturning. More than half of the decrease in momentum flux is explained by the presence of large amplitude, downward propagating waves, which are generated in regions of wave breaking. The downward propagating waves appear almost simultaneously with overturning, and have nonzero phase speeds, suggesting a strongly nonlinear generation mechanism that depends on local wave properties. The generation of these downward propagating waves is a robust process, insensitive to mountain height, mountain width, or density scale height. These results have important implications for observational studies of orographically generated waves as well as for schemes that seek to parameterize the effects of orogoraphy in large-scale models.

Bacmeister, Julio T.↗
NASA NTRS1996

Marangoni-Benard Convection in a Evaporating Liquid Thin Layer

Marangoni-Benard convection in evaporating liquid thin layers has been investigated through flow visualization and temperature profile measurement. Twelve liquids, namely ethyl alcohol, methanol, chloroform, acetone, cyclohexane, benzine, methylene chloride, carbon tetrachloride, ethyl acetate, n-pentane, silicone oil (0.65 cSt.), and freon-113, were tested and convection patterns in thin layers of these samples were observed. Comparison among these tested samples shows that some liquids are sensitive to surface contamination from aluminum powder but some are not. The latter is excellent to be used for the investigation of surface-tension driven convection through visualization using the tracer. Two sample liquids, alcohol and freon-113 were particularly selected for systematic study. It was found that the wavelength of Benard cells would not change with thickness of the layer when it evaporates at room temperature. Special attention was focused on cases in which a liquid layer was cooled from below, and some interesting results were obtained. Convection patterns were recorded during the evaporation process and the patterns at certain time frame were compared. Benard cells were observed in thin layers with a nonlinear temperature profile and even with a zero or positive temperature gradient. Wavelength of the cells was found to increase as the evaporation progressed.

Chai, An-Ti↗
NASA NTRS2011

Method and System for Temporal Filtering in Video Compression Systems

Three related innovations combine improved non-linear motion estimation, video coding, and video compression. The first system comprises a method in which side information is generated using an adaptive, non-linear motion model. This method enables extrapolating and interpolating a visual signal, including determining the first motion vector between the first pixel position in a first image to a second pixel position in a second image; determining a second motion vector between the second pixel position in the second image and a third pixel position in a third image; determining a third motion vector between the first pixel position in the first image and the second pixel position in the second image, the second pixel position in the second image, and the third pixel position in the third image using a non-linear model; and determining a position of the fourth pixel in a fourth image based upon the third motion vector. For the video compression element, the video encoder has low computational complexity and high compression efficiency. The disclosed system comprises a video encoder and a decoder. The encoder converts the source frame into a space-frequency representation, estimates the conditional statistics of at least one vector of space-frequency coefficients with similar frequencies, and is conditioned on previously encoded data. It estimates an encoding rate based on the conditional statistics and applies a Slepian-Wolf code with the computed encoding rate. The method for decoding includes generating a side-information vector of frequency coefficients based on previously decoded source data and encoder statistics and previous reconstructions of the source frequency vector. It also performs Slepian-Wolf decoding of a source frequency vector based on the generated side-information and the Slepian-Wolf code bits. The video coding element includes receiving a first reference frame having a first pixel value at a first pixel position, a second reference frame having a second pixel value at a second pixel position, and a third reference frame having a third pixel value at a third pixel position. It determines a first motion vector between the first pixel position and the second pixel position, a second motion vector between the second pixel position and the third pixel position, and a fourth pixel value for a fourth frame based upon a linear or nonlinear combination of the first pixel value, the second pixel value, and the third pixel value. A stationary filtering process determines the estimated pixel values. The parameters of the filter may be predetermined constants.

Lu, Ligang↗
NASA NTRS2019

Efficient Near-Field to Mid-Field Sonic Boom Propagation Using a High-Order Space Marching Method

An efficient strategy for propagating sonic boom signatures from a near-field Computational Fluid Dynamics (CFD) solution to the mid-field is presented. The method is based on a high-order accurate finite-difference discretization of the 3D Euler equations on a specially designed curvilinear grid and a single sweep space marching solution algorithm. The new approach leads to more than a factor of two reduction in overall computational resources compared to the current method used to propagate near-field sonic booms to the ground. Accuracy and efficiency of the near-field to mid-field process is demonstrated using a selection of test cases from the AIAA Sonic Boom Prediction Workshops. Azimuthal dependence of nonlinear wave propagation from the near-field to mid-field is analyzed along with its effects on the ground level noise.

Housman, Jeffrey A.↗
DOE OSTI2020

Neural networks for modeling and control of particle accelerators

Charged particle accelerators support a wide variety of scientific, industrial, and medical applications. They range in scale and complexity from systems with just a few components for beam acceleration and manipulation, to large scientific user facilities that span many kilometers and have hundreds-to-thousands of individually-controllable components. Specific operational requirements must be met by adjusting the many controllable variables of the accelerator. Meeting these requirements can be challenging, both in terms of the ability to achieve specific beam quality metrics in a reliable fashion and in terms of the time needed to set up and maintain the optimal operating conditions. One avenue toward addressing this challenge is to incorporate techniques from the fields of machine learning (ML) and artificial intelligence (AI) into the way particle accelerators are modeled and controlled. While many promising approaches within AI/ML could be used for particle accelerators, this dissertation focuses on approaches based on neural networks. Neural networks are particularly well-suited to modeling, control, and diagnostic analysis of nonlinear systems, as well as systems with large parameter spaces. They are also very appealing for their ability to process high-dimensional data types, such as images and time series (both of which are ubiquitous in particle accelerators). In this work, key studies that demonstrated the potential utility of modern neural network-based approaches to modeling and control of particle accelerators are presented. The context for this work is important: at the start of this work in 2012, there was little interest in AI/ML in the particle accelerator community, and many of the advances in neural networks and deep learning that enabled its present success had not yet been made at that time. As such, this work was both an exploration of possible application areas and a generator of proof-of-concept demonstrations in these areas.

43 PARTICLE ACCELERATORS↗
NASA NTRS2004

Learning to Control Advanced Life Support Systems

Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for advanced life support.

Subramanian, Devika↗
DOE OSTI2023

Adaptive Online Multivariate Signal Extraction With Locally Weighted Robust Polynomial Regression

High-frequency, multivariate data collected in real-time and used to control or make decisions regarding a process’ operation often contain some noise and outliers. Thus, a method to extract the signal is needed in order to reduce the number and magnitude of control-based adjustments that are implemented. Such a method must be (i) online, depending only on past and current observations; (ii) fast, producing a smooth value more quickly than the measurement frequency; (iii) robust, ignoring brief bursts of erroneously measured values; (iv) multivariate, ignoring observations that are jointly unusual; (v) adaptive, adjusting to periods of rapid fluctuation in the signal versus periods of stability; and (vi) purely data-driven, not incorporating any information about the process from which the data are collected. Most existing methods are only able to address a subset of these six features. Furthermore, we also require the method to be nonlinear, providing a local nonlinear estimate of the signal. In this work, we propose a novel, real-time signal extraction method based on a local, robust polynomial fit. We demonstrate the performance of our method compared to a state-of-the-art competitor through simulation. For illustration, the methodology is applied to data collected from a reverse osmosis water treatment process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Entanglement Enabled Intensity Interferometry of different wavelengths of light

Highlights: • We use quantum entanglement to erase the color of photons for a new imaging method. • Can achieve higher resolution imaging by accessing hidden phase information. • We detail practical experimental designs to implement our theoretical protocols. We propose methods to perform intensity interferometry of photons having two different wavelengths. Distinguishable particles typically cannot interfere with each other, but we overcome that obstacle by processing the particles via entanglement and projection so that they lead to the same final state at the detection apparatus. Specifically, we discuss how quasi-phase-matched nonlinear crystals can be used to convert a quantum superposition of light of different wavelengths onto a common wavelength, while preserving the phase information essential for their meaningful interference. We thereby gain access to a host of new observables, which can probe subtle frequency correlations and entanglement. Further, we generalize the van Cittert–Zernike formula for the intensity interferometry of extended sources, demonstrate how our proposal supports enhanced resolution of sources with different spectral character, and suggest potential applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1996

Aerodynamic Influence Coefficient Computations Using Euler/Navier-Stokes Equations on Parallel Computers

Modem design requirements for an aircraft push current technologies used in the design process to their limit or sometimes require more advanced technologies to meet the requirement. New design requirements always demand to improve the operational performance. Accurate prediction of aerodynamic coefficients is essential to improve the performance. For example, in the design of an advanced subsonic civil transport, since the fluid flow at transonic regime shows strong nonlinearities, high fidelity equations, such as the Euler or Navier-Stokes equations predict flow characteristics more accurately than the linear aerodynamics, which are widely used in the current design process However, high fidelity flow equations are computationally expensive and require an order of magnitude longer time to obtain aerodynamic coefficients required in the design. Parallel computing is one possibility to cut down the computational turn-around time in using high fidelity equations so that high fidelity equations would be incorporated into the design process. By doing so, high fidelity equations would be used in the routine design process. This work will demonstrate the feasibility of using high fidelity flow equations in a design process by computing aerodynamic influence coefficients of a wing-body-empennage configuration on a multiple-instruction, multiple-data parallel computer.

Byun, Chansup↗
DOE OSTI2025

Scalable multilevel Monte Carlo methods exploiting parallel redistribution on coarse levels

Here, we study an element agglomeration coarsening strategy that requires data redistribution at coarse levels when the number of coarse elements becomes smaller than the number of MPI processes used on the finest level. The overall procedure generates coarse elements (general unstructured unions of fine grid elements) within the framework of element-based algebraic multigrid methods (or AMGe) studied previously. The AMGe-generated coarse spaces have the ability to exhibit approximation properties of the same order as the fine-level spaces since by construction they contain the piecewise polynomials of the same order as on the fine level. These approximation properties are key for the successful use of AMGe in multilevel solvers for nonlinear partial differential equations as well as for multilevel Monte Carlo (MLMC) simulations. The ability to coarsen without being constrained by the number of MPI processes, as described in the present paper, allows to improve the scalability of these solvers as well as the overall MLMC method. The paper illustrates this latter fact with detailed scalability study of MLMC simulations applied to model Darcy equations with a stochastic log-normal permeability field.

AMGe↗
NASA NTRS1995

Nonlinear Control and Discrete Event Systems

As the operation of large systems becomes ever more dependent on extensive automation, the need for an effective solution to the problem of design and validation of the underlying software becomes more critical. Large systems possesses much detailed structure, typically hierarchical, and they are hybrid. Information processing at the top of the hierarchy is by means of formal logic and sentences; on the bottom it is by means of simple scalar differential equations and functions of time; and in the middle it is by an interacting mix of nonlinear multi-axis differential equations and automata, and functions of time and discrete events. The lecture will address the overall problem as it relates to flight vehicle management, describe the middle level, and offer a design approach that is based on Differential Geometry and Discrete Event Dynamic Systems Theory.

Meyer, George↗
NASA NTRS1978

Adaptation of a program for nonlinear finite element analysis to the CDC STAR 100 computer

The conversion of a nonlinear finite element program to the CDC STAR 100 pipeline computer is discussed. The program called DYCAST was developed for the crash simulation of structures. Initial results with the STAR 100 computer indicated that significant gains in computation time are possible for operations on gloval arrays. However, for element level computations that do not lend themselves easily to long vector processing, the STAR 100 was slower than comparable scalar computers. On this basis it is concluded that in order for pipeline computers to impact the economic feasibility of large nonlinear analyses it is absolutely essential that algorithms be devised to improve the efficiency of element level computations.

Pifko, A. B.↗
DOE OSTI2024

Heterogeneous Cluster Energetics and Nonlinear Thermodynamic Response in Supercritical Fluids

Microstructural heterogeneities arising from molecular clusters directly affect the nonlinear thermodynamic properties of supercritical fluids. We present a physical model to elucidate the relation between energy exchange and heterogeneous cluster dynamics during the transition from liquidlike to gaslike conditions. By analyzing molecular-dynamics data and employing physical principles, the model considers contributions from three key processes, namely, changing cluster density, cluster separation, and transfer of molecules between clusters. We show that the proposed model is consistent with the energetics at subcritical conditions and can be used to explain the nonlinear behavior of thermodynamic response functions, including the peak in the isobaric heat capacity.

42 ENGINEERING↗
DOE OSTI2026

High-resolution mountain topography can inform global snow vulnerability estimates

Snow is changing globally. Computationally intensive snow reanalysis products and downscaled climate model projections allow for the estimation of historical and projected changes in snow over ∼4–10 km resolutions, but these resolutions are coarse relative to the scales needed for water supply and flood planning. Fine-scale digital elevation models (DEMs) are widely available but are underutilized to make first-order assessments of snow vulnerability. Here, we leverage DEMs at a 7.5 arc s (∼250 m) resolution, combining these with historical freezing level height estimates from ERA-5 to derive estimates of changes in the snow-receiving area (SRA) and its variability across global mountain ranges. Results show estimated SRA declines in 29% (1.9 million km2) of the global mountain area from 1982–2020; 66% of the mountainous areas had no change over the historical period. At +1.5 °C of warming relative to the pre-industrial control, global mountain SRA would decline by 9.5% (1.0 million km2) relative to recent conditions. This loss would be approximately doubled with +2 °C of warming. In a +4 °C warming scenario, an additional 34% (3.6 million km2) of SRA would be lost beyond the +2 °C case. Across individual mountain ranges, SRA losses can occur nonlinearly with warming, with some locations that have historically had relatively minor SRA losses at risk of substantially larger losses in warmer climates. Analysis using coarser-resolution DEMs can underestimate or overestimate SRA and its rate of loss, with the largest impacts in relatively warm, low-elevation mountain ranges. Results of this work provide estimates of projected loss in SRA at policy-relevant warming levels; inform the resolutions needed for process-based snow modeling; identify snow vulnerability hotspots; and provide a new integrated approach to snow vulnerability assessment that is achievable at global scales and highlights potential nonlinearities from recent trends to a variety of future warming scenarios.

climate, mountains↗
DOE OSTI2023

Tidal energy extraction modifies tidal asymmetry and transport in a shallow, well-mixed estuary

Tidal energy extraction is increasingly being studied as a potential renewable energy resource in estuaries worldwide. Although it is understood that energy extraction via tidal stream turbines can modify currents and transport within estuaries, it is not clear how the underlying nonlinear physical mechanisms dictating tidal hydrodynamics are modulated. This research investigates the influence of a hypothetical tidal stream turbine array on barotropic tidal processes in a shallow, well-mixed system: the Piscataqua River – Great Bay (PRGB) estuary, using a numerical model. The modeled turbine farm includes 180 turbines which would extract an estimated 44.7 GWh of energy, annually. The tidal hydrodynamic model for the existing condition is validated with in-situ observations of currents and water level before analyzing tidal asymmetry and transport with and without tidal turbines. Results indicate that the tidal turbine array will decrease tidal elevation and current magnitudes system-wide, but generally reduce ebb currents and transport more than flood over most of the estuary footprint, thereby diminishing tidal asymmetry. The smaller asymmetric distortion compared to the no-turbine case is attributed to reductions in the storage volume of water over the estuary’s extensive tidal flat regions between low and high waters which decreases the associated nonlinear intertidal storage mechanism up to 25%. This leads to weakened ebb dominance over estuary sections from the mouth to mid-reaches, where depths are deep enough to keep the combined nonlinear shallow water and frictional effects from asserting control over the storage mechanism. Even in upstream shallow regions where depth-dependent friction controls asymmetry in both cases, the frictional mechanism is reduced only by 10% with turbines. Some environmental considerations of this work are discussed, with focus on sediment transport, water quality, and ecology.

16 TIDAL AND WAVE POWER↗
NASA NTRS2000

Monsoon-Enso Relationships: A New Paradigm

This article is partly a review and partly a new research paper on monsoon-ENSO relationship. The paper begins with a discussion of the basic relationship between the Indian monsoon and ENSO dating back to the work of Sir Gilbert Walker up to research results in more recent years. Various factors that may affect the monsoon-ENSO, relationship, including regional coupled ocean-atmosphere processes, Eurasian snow cover, land-atmosphere hydrologic feedback, intraseasonal oscillation, biennial variability and inter-decadal variations, are discussed. The extreme complex and highly nonlinear nature of the monsoon-ENSO relationship is stressed. We find that for regional impacts on the monsoon, El Nino and La Nina are far from simply mirror images of each other. These two polarities of ENSO can have strong or no impacts on monsoon anomalies depending on the strength of the intraseasonal oscillations and the phases of the inter-decadal variations. For the Asian-Australian monsoon (AAM) as a whole, the ENSO impact is effected through a east-west shift in the Walker Circulation. For rainfall anomalies over specific monsoon areas, regional processes play important roles in addition to the shift in the Walker Circulation. One of the key regional processes identified for the boreal summer monsoon is the anomalous West Pacific Anticyclone (WPA). This regional feature has similar signatures in interannual and intraseasonal time scales and appears to determine whether the monsoon-ENSO relationship is strong or weak in a given year. Another important regional feature includes a rainfall and SST dipole across the Indian Ocean, which may have strong impact on the austral summer monsoon. Results are shown indicating that monsoon surface wind forcings may induce a strong biennial signal in ENSO and that strong monsoon-ENSO coupling may translate into pronounced biennial variability in ENSO. Finally, a new paradigm is proposed for the study of monsoon variability. This paradigm provides a unified framework in which monsoon predictability, the role of regional vs. basin-scale processes, its relationship with different climate subsystems, and causes of secular changes in monsoon-ENSO relationship can be investigated.

Lau, K. M.↗