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At least 793 records · Page 44

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Reduced basis approximations of parameterized dynamical partial differential equations via neural networks

Projection-based reduced order models are effective at approximating parameter-dependent differential equations that are parametrically separable. When parametric separability is not satisfied, which occurs in both linear and nonlinear problems, projection-based methods fail to adequately reduce the computational complexity. Devising alternative reduced order models is crucial for obtaining efficient and accurate approximations to expensive high-fidelity models. In this work, we develop a timestepping procedure for dynamical parameter-dependent problems, in which a neural-network is trained to propagate the coefficients of a reduced basis expansion. This results in an online stage with a computational cost independent of the size of the underlying problem. Here, we demonstrate our method on several parabolic partial differential equations, including a problem that is not parametrically separable.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2010

Reduced-Order Models for the Aeroelastic Analysis of Ares Launch Vehicles

This document presents the development and application of unsteady aerodynamic, structural dynamic, and aeroelastic reduced-order models (ROMs) for the ascent aeroelastic analysis of the Ares I-X flight test and Ares I crew launch vehicles using the unstructured-grid, aeroelastic FUN3D computational fluid dynamics (CFD) code. The purpose of this work is to perform computationally-efficient aeroelastic response calculations that would be prohibitively expensive via computation of multiple full-order aeroelastic FUN3D solutions. These efficient aeroelastic ROM solutions provide valuable insight regarding the aeroelastic sensitivity of the vehicles to various parameters over a range of dynamic pressures.

Silva, Walter A.↗

DOE OSTI2024

Novel transition dynamics of topological solitons

Continuous phase transitions can be classified into ones characterized by local-order parameters and others that need additional topological constraints. The critical dynamics near the former transitions have been extensively studied, but the latter is less understood. We fill this gap in knowledge by studying the transition dynamics to a parity-breaking topological ground state called the chiral soliton lattice in quantum chromodynamics at finite temperature, baryon chemical potential, and an external magnetic field. We find a slowing down of the soliton’s translational motion as the critical magnetic field approaches, while the local relaxation rate to the solitonic state remains finite. The time required to converge to the stationary state strongly depends on the symmetry of the initial configuration, determining whether translational motion occurs during the dynamic process. Published by the American Physical Society 2024

Nishimura, Kentaro↗

NASA NTRS1978

Identification of the stability parameters of an aeroelastic airplane

Phase variable transformations are used to construct the mathematical model of an aeroelastic aircraft in a form that is amenable to partial or piecemeal acceptance of parameters estimated from flight data. The problem is one of parameter identification of large scale dynamic systems involving a system matrix characterized by about 200 elements. A mathematical model of the USAF Total In-Flight Simulator was computed using the FLEXSTAB program. As data became available during the progress of the flight test program, it was processed and substituted in the mathematical model for parameters obtained from the FLEXSTAB program. Results tend to show a progressive and orderly transition from an analytically defined mathematical model to one obtained from the flight tests of the actual aircraft.

Rynaski, E. G.↗

NASA NTRS1988

Vector excitation coding with dynamic bit allocation

Vector excitation coding (VXC) has shown promise for digital transmission of fairly high communications quality speech at low bit rates, but current version of the algorithm still suffer from audible degradations, particularly at 4.8 kb/s. The authors examine the technique of dynamic bit allocation (DBA) to improve the performance of VXC for a given bit rate. The approach is based on the observation that the minimum bit rate needed to code adequately both the long-term and short-term predictors in VXC varies dynamically with time. Therefore, in frames where fewer bits suffice for the predictors, the unneeded bits can be reallocated to other coder parameter sets such as excitation vectors. By dynamically distributing available bits among the different coder parameter sets while keeping the total number of bits in each frame fixed, it is possible to improve overall coder performance without increasing bit rate.

Yong, Mei↗

NASA NTRS1993

Active stabilization to prevent surge in centrifugal compression systems

This report documents an experimental and analytical study of the active stabilization of surge in a centrifugal engine. The aims of the research were to extend the operating range of a compressor as far as possible and to establish the theoretical framework for the active stabilization of surge from both an aerodynamic stability and a control theoretic perspective. In particular, much attention was paid to understanding the physical limitations of active stabilization and how they are influenced by control system design parameters. Previously developed linear models of actively stabilized compressors were extended to include such nonlinear phenomena as bounded actuation, bandwidth limits, and robustness criteria. This model was then used to systematically quantify the influence of sensor-actuator selection on system performance. Five different actuation schemes were considered along with four different sensors. Sensor-actuator choice was shown to have a profound effect on the performance of the stabilized compressor. The optimum choice was not unique, but rather shown to be a strong function of some of the non-dimensional parameters which characterize the compression system dynamics. Specifically, the utility of the concepts were shown to depend on the system compliance to inertia ratio ('B' parameter) and the local slope of the compressor speedline. In general, the most effective arrangements are ones in which the actuator is most closely coupled to the compressor, such as a close-coupled bleed valve inlet jet, rather than elsewhere in the flow train, such as a fuel flow modulator. The analytical model was used to explore the influence of control system bandwidth on control effectiveness. The relevant reference frequency was shown to be the compression system's Helmholtz frequency rather than the surge frequency. The analysis shows that control bandwidths of three to ten times the Helmholtz frequency are required for larger increases in the compressor flow range. This has important implications for implementation in gas turbine engines since the Helmholtz frequencies can be over 100 Hz, making actuator design extremely challenging.

Epstein, Alan H.↗

NASA NTRS1996

Computational Study of Chaotic and Ordered Solutions of the Kuramoto-Sivashinsky Equation

We report the results of extensive numerical experiments on the Kuramoto-Sivashinsky equation in the strongly chaotic regime as the viscosity parameter is decreased and increasingly more linearly unstable modes enter the dynamics. General initial conditions are used and evolving states do not assume odd-parity. A large number of numerical experiments are employed in order to obtain quantitative characteristics of the dynamics. We report on different routes to chaos and provide numerical evidence and construction of strange attractors with self-similar characteristics. As the 'viscosity' parameter decreases the dynamics becomes increasingly more complicated and chaotic. In particular it is found that regular behavior in the form of steady state or steady state traveling waves is supported amidst the time-dependent and irregular motions. We show that multimodal steady states emerge and are supported on decreasing windows in parameter space. In addition we invoke a self-similarity property of the equation, to show that these profiles are obtainable from global fixed point attractors of the Kuramoto-Sivashinsky equation at much larger values of the viscosity.

Smyrlis, Yiorgos S.↗

NASA NTRS1994

On the dynamics of some grid adaption schemes

The dynamics of a one-parameter family of mesh equidistribution schemes coupled with finite difference discretisations of linear and nonlinear convection-diffusion model equations is studied numerically. It is shown that, when time marched to steady state, the grid adaption not only influences the stability and convergence rate of the overall scheme, but can also introduce spurious dynamics to the numerical solution procedure.

Sweby, Peter K.↗

NASA NTRS2008

Virtual Shaker Testing: Simulation Technology Improves Vibration Test Performance

In the field of vibration testing, the interaction between the structure being tested and the instrumentation hardware used to perform the test is a critical issue. This is particularly true when testing massive structures (e.g. satellites), because due to physical design and manufacturing limits, the dynamics of the testing facility often couples with the test specimen one in the frequency range of interest. A further issue in this field is the standard use of a closed loop real-time vibration control scheme, which could potentially shift poles and change damping of the aforementioned coupled system. Virtual shaker testing is a novel approach to deal with these issues. It means performing a simulation which closely represents the real vibration test on the specific facility by taking into account all parameters which might impact the dynamic behavior of the specimen. In this paper, such a virtual shaker testing approach is developed. It consists of the following components: (1) Either a physical-based or an equation-based coupled electro-mechanical lumped parameter shaker model is created. The model parameters are obtained from manufacturer's specifications or by carrying out some dedicated experiments; (2) Existing real-time vibration control algorithm are ported to the virtual simulation environment; and (3) A structural model of the test object is created and after defining proper interface conditions structural modes are computed by means of the well-established Craig-Bampton CMS technique. At this stage, a virtual shaker test has been run, by coupling the three described models (shaker, control loop, structure) in a co-simulation routine. Numerical results have eventually been correlated with experimental ones in order to assess the robustness of the proposed methodology.

Ricci, Stefano↗

NASA NTRS2016

On the Calculation of Uncertainty Statistics with Error Bounds for CFD Calculations Containing Random Parameters and Fields

This chapter discusses the ongoing development of combined uncertainty and error bound estimates for computational fluid dynamics (CFD) calculations subject to imposed random parameters and random fields. An objective of this work is the construction of computable error bound formulas for output uncertainty statistics that guide CFD practitioners in systematically determining how accurately CFD realizations should be approximated and how accurately uncertainty statistics should be approximated for output quantities of interest. Formal error bounds formulas for moment statistics that properly account for the presence of numerical errors in CFD calculations and numerical quadrature errors in the calculation of moment statistics have been previously presented in [8]. In this past work, hierarchical node-nested dense and sparse tensor product quadratures are used to calculate moment statistics integrals. In the present work, a framework has been developed that exploits the hierarchical structure of these quadratures in order to simplify the calculation of an estimate of the quadrature error needed in error bound formulas. When signed estimates of realization error are available, this signed error may also be used to estimate output quantity of interest probability densities as a means to assess the impact of realization error on these density estimates. Numerical results are presented for CFD problems with uncertainty to demonstrate the capabilities of this framework.

Uncertainty↗

NASA NTRS2013

Unification of X-ray Winds in Seyfert Galaxies: From Ultra-fast Outflows to Warm Absorbers

The existence of ionized X-ray absorbing layers of gas along the line of sight to the nuclei of Seyfert galaxies is a well established observational fact. This material is systematically outflowing and shows a large range in parameters. However, its actual nature and dynamics are still not clear. In order to gain insights into these important issues we performed a literature search for papers reporting the parameters of the soft X-ray warm absorbers (WAs) in 35 type 1 Seyferts and compared their properties to those of the ultra-fast outflows (UFOs) detected in the same sample. The fraction of sources with WAs is >60 per cent, consistent with previous studies. The fraction of sources with UFOs is >34 per cent, >67 per cent of which also show WAs. The large dynamic range obtained when considering all the absorbers together, spanning several orders of magnitude in ionization, column, velocity and distance allows us, for the first time, to investigate general relations among them. In particular, we find significant correlations indicating that the closer the absorber is to the central black hole, the higher the ionization, column, outflow velocity and consequently the mechanical power. In all the cases, the absorbers continuously populate the whole parameter space, with the WAs and the UFOs lying always at the two ends of the distribution. These evidence strongly suggest that these absorbers, often considered of different types, could actually represent parts of a single large-scale stratified outflow observed at different locations from the black hole. The UFOs are likely launched from the inner accretion disc and the WAs at larger distances, such as the outer disc and/or torus. We argue that the observed parameters and correlations are, to date, consistent with both radiation pressure through Compton scattering and magnetohydrodynamic processes contributing to the outflow acceleration, the latter playing a major role. Most of the absorbers, especially the UFOs, show a sufficiently high mechanical power (at least approx 0.5 per cent of the bolometric luminosity) to provide a significant contribution to active galactic nuclei (AGN) feedback and thus to the evolution of the host galaxy. In this regard, we find possible evidence for the interaction of the AGN wind with the surrounding environment on large scales.

Unification↗

DOE OSTI2025

A consensus mathematical model of vaccine-induced antibody dynamics for multiple vaccine platforms and pathogens

Introduction: Vaccine platforms used in successful, licensed vaccines have varied among pathogens. However, antibody level is still the main clinical correlate of protection in most approved vaccines. Decisions as to the best vaccine platform to pursue for a given pathogen may be informed through improved understanding of the process of antibody generation and its temporal dynamics, as well as the relationship between these processes and the type of vaccine. Methods: We have analyzed the dynamics of antibody generation for different vaccine platforms against diverse pathogens, and developed a consensus mathematical model that captures antibody dynamics across these diverse systems. Initially, the model was fitted to a rich dataset of antibody and immune cell concentrations in a SARS-CoV-2 vaccine experiment. We then used concepts from machine learning, such as transfer learning, to apply the same model to a variety of systems, involving different pathogens, vaccine platforms, and booster dose use/timing, fixing most parameter values relating to the dynamics of the immune system. Results: The model includes B cell proliferation and differentiation, as well as the generation of plasma cells, which secrete large amounts of antibody, and memory B cells. Overall, the model describes antibody generation in all systems tested well and shows that the main differences across platforms are related to the dynamics of antigen presentation. Discussion: This model can be used to predict antibody generation in pairs of vaccine platform/pathogen, allowing for the use of in silico results to narrow down experimental burden in vaccine development.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Machine Learning–Guided Boolean Matrix Inference for Real-Time O-RAN Conflict Detection

Open Radio Access Networks (O-RAN) are emerging, software-driven cellular architectures that promote flexibility by enabling components from different vendors to interoperate. Multiple control applications called xApps can independently adjust network parameters in near real time, often without awareness of each other's actions. This creates a system highly prone to unintended conflicts and performance degradation due to the inherent complexity of such openness. To model such systems and ultimately prevent or mitigate xApp conflicts, it is essential to understand the dynamic relationships between xApps (A), the control parameters they adjust (P), and the resulting KPI responses (K). While the mappings from A to P and from K to A can often be derived from xApp specifications, the relationship from P to K is typically hidden within the system’s dynamics and must be inferred from observed data. We propose a novel data-driven Boolean inference framework that uncovers the hidden P?K dependencies using machine learning and interpretable rule induction. Continuous parameters and KPIs are first binarized using decision tree classifiers, and a binary influence matrix L is then inferred by solving Boolean matrix equations over time. This compact representation improves interpretability and enables real-time tracking of dynamically evolving parameter-KPI dependencies. We demonstrate the effectiveness of our method in a realistic mobile handover scenario, where it accurately recovers the underlying logic and enables proactive conflict detection.

42 - ENGINEERING↗

DOE OSTI2021

Computational fluid dynamics modeling and analysis of silica nanoparticle synthesis in a flame spray pyrolysis reactor

Flame Spray Pyrolysis (FSP) is a method for large-scale production of nanoparticles and nanoscale powders employed in a wide range of industrial applications. Particle size and morphology are complex functions of the physicochemical phenomena occurring in the FSP reactor. An extensive study of FSP-related phenomena can be utilized to develop effective strategies for achieving desired particle size/morphology and scaling up the overall yield of an FSP system. In this work, a computational fluid dynamics (CFD) model of an FSP reactor is developed to simulate the coupling of key phenomena involved in the particle synthesis process: liquid spray breakup and evaporation, mixing, combustion, and particle formation/growth of silica nanoparticles. Herein, the particle sizes and their distributions from the CFD simulations are validated against experimental data. Subsequently, the simulations are utilized to investigate the impact of process parameters on the resultant flame dynamics and particle growth. Firstly, the CFD results show that the particle sizes are strongly correlated with the precursor concentration in the solvent. At lower precursor concentrations, the spread of the distribution is relatively insensitive to the value of the concentration. At higher concentrations, the spread is higher as the collision probability between particles is higher. Secondly, increasing the pilot flow rate increases the length of the pilot flames impacting the local ignition location of the spray flame. Lastly, it is shown that the dispersion gas flow rate strongly influences the spray flame shape. This shape can be used for control of particle growth as it helps determine the regions of high temperature and the residence time of the particles in the high temperature region enabling the design and process optimization of the FSP reactor.

42 ENGINEERING↗

NASA NTRS2021

Modeling the Effects of Liquid-to-Gas Density Ratio on Slosh Dynamics

Mechanical models are commonly used in Guidance, Navigation and Controls (GN&C) system-level models to represent propellant slosh forces on launch vehicles and spacecraft. The slosh model parameters are typically predicted using semi-empirical analytical methods that are based on experimental data for high liquid-to-gas density ratios (such as water and air at standard sea level conditions). Model parameter calculations typically neglect any contribution to slosh dynamics from the gas phase. However, some cryogenic propellant systems operate in conditions where the density ratio can be orders of magnitude smaller. Analytical and computational modeling was used in this effort to investigate the effects of liquid-to-gas density ratio on slosh dynamics. No experimental data nor previous studies are available at this time that quantify the effect of liquid-to-gas density ratio on slosh dynamics. However, in a recent CFD study, results showed that density ratio can have a significant effect on slosh model parameters. In this study, a dual pendulum slosh model was created that distinctly represents both liquid and gas phase dynamics. Solution of the model, which invokes the Euler-Lagrange equations of motion, demonstrates that slosh frequency is a combination of liquid slosh frequency and gas phase slosh frequency. Additionally, it shows that slosh mass is reduced due to the opposing motion of the gas phase with respect to that of the liquid phase. The slosh model parameter trends were verified using Computational Fluid Dynamics (CFD) analysis results for various liquid-to-gas density ratios, tanks, and fill levels.

Christopher D Moore↗

DOE OSTI2023

Role of Intra-Domain Heterogeneity on Ion and Polymer Dynamics in Block Polymer Electrolytes: An Approach for Spatially Resolving Dynamics and Ion Transport

The design of safe and high-performance, nanostructured, block polymer (BP) electrolytes for lithium-ion batteries requires a thorough understanding of the key parameters that govern local structure and dynamics. Yet, the interfaces between microphase separated domains can introduce complexities in this local behavior that can be challenging to quantify. Herein, the local polymer, cation (Li + ), and anion dynamics were described in salt-doped polystyrene block poly(oligo-oxyethylene methyl ether methacrylate) (PS-b-POEM) through a quantitative framework that considered the effects of polymer architecture, segmental mixing, chain stretching, and confinement on polymer mobility and ion transport. This framework was validated through nuclear magnetic resonance (NMR) spectroscopy measurements on solid (dry) polymer electrolyte samples. Notably, a mobility transition temperature (T mobility ) was identified through NMR spectroscopy that captured the local dynamics more accurately than the thermal glass transition temperature. Additionally, the approach quantitatively described the mobility gradient across a domain when segmental mixing effects were combined with chain stretching and confinement information, especially at higher segregation strengths – facilitating the assessment of local ion diffusion and conductivity. As a result, spatially averaged local ion diffusion predictions quantitatively matched NMR-measured ion diffusivities in the BP samples, while spatially summed ionic conductivity predictions across a domain qualitatively captured trends in the measured ionic conductivities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Reversible motions and disordered structure of soft particles in amorphous solids

In amorphous solids, soft vibrational modes derived from normal mode analysis can be utilized to identify the soft particles that are prone to irreversible rearrangements. However, the normal mode analysis approach cannot explain why the spatial distributions of clustered soft particles do not change over time. We define a softness parameter based on the vibrational density of states calculated directly from molecular dynamics simulations with both the harmonic vibrations and anharmonic relaxations being properly captured at finite temperatures. This parameter spontaneously correlates with the real space atomic motions and the dynamics heterogeneity. Using the softness parameter, we show that the softest particles are confined within rigid cages. These particles keep rearranging reversibly without long-range diffusion. The moderately soft particles rearrange irreversibly, and the hard particles mainly participate in vibrations without rearrangement. Here, we also show that the soft particles form locally disordered structures, while the hard particles present strong ordering. These findings confirm the defective nature of soft particles, and provide insights on the nature of softness as the ability to rearrange, but not necessarily irreversibly.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Weak-form inference for hybrid dynamical systems in ecology

Species subject to predation and environmental threats commonly exhibit variable periods of population boom and bust over long timescales. Understanding and predicting such behaviour, especially given the inherent heterogeneity and stochasticity of exogenous driving factors over short timescales, is an ongoing challenge. A modelling paradigm gaining popularity in the ecological sciences for such multi-scale effects is to couple short-term continuous dynamics to long-term discrete updates. We develop a data-driven method utilizing weak-form equation learning to extract such hybrid governing equations for population dynamics and to estimate the requisite parameters using sparse intermittent measurements of the discrete and continuous variables. The method produces a set of short-term continuous dynamical system equations parametrized by long-term variables, and long-term discrete equations parametrized by short-term variables, allowing direct assessment of interdependencies between the two timescales. We demonstrate the utility of the method on a variety of ecological scenarios and provide extensive tests using models previously derived for epizootics experienced by the North American spongy moth ( Lymantria dispar dispar ).

54 ENVIRONMENTAL SCIENCES↗