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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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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

On the Divergence of the Velocity Vector in Real-Gas Flow

A theoretical study was performed addressing the degree of applicability or inapplicability, to a real gas, of the occasionally stated belief that for an ideal gas, incompressibility is synonymous with a zero or very low Mach number. The measure of compressibility used in this study is the magnitude of the divergence of the flow velocity vector [V(bar) (raised dot) u (where u is the flow velocity)]. The study involves a mathematical derivation that begins with the governing equations of flow and involves consideration of equations of state, thermodynamics, and fluxes of heat, mass, and the affected molecular species. The derivation leads to an equation for the volume integral of (V(bar) (raised dot) u)(sup 2) that indicates contributions of several thermodynamic, hydrodynamic, and species-flux effects to compressibility and reveals differences between real and ideal gases. An analysis of the equation leads to the conclusion that for a real gas, incompressibility is not synonymous with zero or very small Mach number. Therefore, it is further concluded, the contributions to compressibility revealed by the derived equation should be taken into account in simulations of real-gas flows.

Bellan, Josette↗
DOE OSTI2025

Bulk viscosity of two-flavor color superconducting quark matter in neutron star mergers

This work investigates the bulk viscosity of warm, dense, neutrino-transparent, color-superconducting quark matter, where damping of density oscillations in the kHz frequency range arises from weak-interaction-driven direct Urca processes involving quarks. We study the two-flavor red-green paired color-superconducting (2SC) phase, while allowing for the presence of unpaired strange quarks and blue color light quarks of all flavors. Our calculations are based on the SU(3) Nambu–Jona–Lasinio model, extended to include both vector interactions and the ‘t Hooft determinant term. The primary focus is on how variations in the NJL Lagrangian parameters—specifically, the diquark and vector coupling strengths—affect both the static properties of quark matter, such as its equation of state and composition, and its dynamical behavior, including bulk viscosity and associated damping timescales. We find that the bulk viscosity and corresponding damping timescale can change by more than an order of magnitude upon varying the vector coupling by a factor of two at high densities and by a lesser degree at lower densities. This sensitivity primarily arises from the susceptibility of 2SC matter, with a smaller contribution from modifications to the weak interaction rates. In comparison, changes in the diquark coupling have a more limited impact. The damping of density oscillations in 2SC matter is similar quantitatively to nucleonic matter and can be a leading mechanism of dissipation in merging hybrid stars containing color superconducting cores.

Gravitational Waves↗
DOE OSTI2025

Knowledge graph-aided Bayesian active learning for top- K genetic interaction discovery

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompassing all possible pairwise knockdowns of over 350 host genes associated with HIV infection. We demonstrate that our framework rapidly identifies the most effective gene knockdown pairs for reducing viral load. Furthermore, we show that incorporating side information enhances performance during the early stages of active learning (low data regime), while our batch diversification strategy significantly boosts performance in later stages (high data regime). This framework is general and can be adapted to explore gene interactions in other contexts, such as synthetic lethality prediction and mapping epistatic effects across quantitative trait loci.

Computational biology and bioinformatics↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗
DOE OSTI2024

Probing bosonic overdensities with optomechanical sensing

Previous work has shown that optomechanical force sensing can be used for efficient detection of ultralight (sub-eV) dark matter candidates. We propose to extend the reach of this method to the search for ultralight dark matter in gravitationally bound configurations in the Milky Way. We consider three scenarios, each strongly motivated by previous studies: boson stars traveling in the galaxy with virial velocity; a bosonic halo centered around the Sun (a “solar halo”); and a bosonic halo centered around the Earth. For each case, we consider bound states composed of either scalar particles with a Yukawa coupling, or vector particles coupled to baryon minus lepton number charge. Accounting for all experimental constraints on coupling strength, we estimate the sensitivity reach of an optomechanical sensor search. We conclude that, although boson star encounters with Earth would be too infrequent to be detected in the relevant parameter space, current optomechanical force sensing technologies provide promising search capabilities for solar or Earth-bound halos. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Vesicular Stomatitis Virus Transmission Dynamics Within Its Endemic Range in Chiapas, Mexico

Vesicular stomatitis virus (VSV), comprising vesicular stomatitis New Jersey virus (VSNJV) and vesicular stomatitis Indiana virus (VSIV), emerges from its focus of endemic transmission in Southern Mexico to cause sporadic livestock epizootics in the Western United States. A dearth of information on the role of potential arthropod vectors in the endemic region hampers efforts to identify factors that enable endemicity and predict outbreaks. In a two-year, longitudinal study at five cattle ranches in Chiapas, Mexico, insect taxa implicated as VSV vectors (blackflies, sandflies, biting midges, and mosquitoes) were collected and screened for VSV RNA, livestock vesicular stomatitis (VS) cases were monitored, and serum samples were screened for neutralizing antibodies. VS cases were reported during the rainy (n = 20) and post-rainy (n = 2) seasons. Seroprevalence against VSNJV in adult cattle was very high (75–100% per ranch) compared with VSIV (0.6%, all ranches). All four potential vector taxa were sampled, and VSNJV RNA was detected in each of them (11% VSNJV-positive of 874 total pools), while VSIV RNA was only detected in four pools of mosquitoes. Our findings indicate that VSNJV is the dominant serotype across our sampling sites with a variety of potential insect vectors involved in its transmission throughout the year. Although no livestock cases were reported in Chiapas during the dry season, VSNJV was detected in insects during this period, suggesting that mechanisms other than transmission from livestock support VSV endemicity.

Virology↗
NASA NTRS1980

An approximate factorization solution of the Navier-Stokes equations for transonic flow using body-fitted coordinates with application to NACA 64A010 airfoils

The implementation of the approximate factorization algorithm and its ability to efficiently and accurately describe transonic flow about an NACA 64A010 airfoil section is examined. The approximate factorization algorithm is developed from the nondimensional, conservative, vectorized Navier-Stokes equations expressed in curvilinear coordinates. Equations of state and transport coefficient relations appropriate to atmospheric air are appended to close the system of partial differential equations. An algebraic turbulence model is also incorporated into the equation set. This algorithm was verified by investigating the flow about an NACA 64A010 airfoil at 0, 2, and 3.5 deg angle of attack for free-stream conditions of 2,000,000 Reynolds number and 0.8 Mach number. Overall results were in good qualitative agreement with wind tunnel data sets. However, while nondimensional times of six were attained, numerical difficulties prevented any case from reaching a true steady state.

Copper, G. K.↗
NASA NTRS1981

Numerical and classical analysis of V/STOL aircraft using selected propulsion systems

The development needed for the evolution of selected V/STOL research vehicles into optimized antisubmarine warfare (ASW) aircraft configurations, using numerical procedures and traditional analytical methods, has been examined. Three propulsion systems, which represent state-of-the-art development aimed at solving the thrust-vectoring and attitude-control problems of V/STOL aircraft, are analyzed. The use of NASA computer programs for aircraft synthesis (ACSYNT), and for optimizing configurations (COMMIN), coupled with contractor-supplied propulsion system data provides for accurate performance prediction of the selected ASW configurations. Particular emphasis on the transition phase between the research vehicle and the optimized configuration demonstrates the strengths and weaknesses of using generic research aircraft instead of building prototypes to demonstrate new technology

Wilson, S. B., III↗
NASA NTRS1989

Vector quantization

During the past ten years Vector Quantization (VQ) has developed from a theoretical possibility promised by Shannon's source coding theorems into a powerful and competitive technique for speech and image coding and compression at medium to low bit rates. In this survey, the basic ideas behind the design of vector quantizers are sketched and some comments made on the state-of-the-art and current research efforts.

Gray, Robert M.↗
NASA NTRS1992

Adaptive planning for applications with dynamic objectives

We devise a qualitative control layer to be integrated into a real-time multi-agent reactive planner. The reactive planning system consists of distributed planning agents attending to various perspectives of the task environment. Each perspective corresponds to an objective. The set of objectives considered are sometimes in conflict with each other. Each agent receives information about events as they occur, and a set of actions based on heuristics can be taken by the agents. Within the qualitative control scheme, we use a set of qualitative feature vectors to describe the effects of applying actions. A qualitative transition vector is used to denote the qualitative distance between the current state and the target state. We will then apply on-line learning at the qualitative control level to achieve adaptive planning. Our goal is to design a mechanism to refine the heuristics used by the reactive planner every time an action is taken toward achieving the objectives, using feedback from the results of the actions. When the outcome is compared with expectations, our prior objectives may be modified and a new set of objectives (or a new assessment of the relative importance of the different objectives) can be introduced. Because we are able to obtain better estimates of the time-varying objectives, the reactive strategies can be improved and better prediction can be achieved.

Hadavi, Khosrow↗
NASA NTRS1993

Hidden Markov models for fault detection in dynamic systems

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) perpendicular to x), 1 less than or equal to i is less than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

Smyth, Padhraic J.↗
NASA NTRS1995

Hidden Markov models for fault detection in dynamic systems

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) (vertical bar)/x), 1 less than or equal to i isless than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

Smyth, Padhraic J.↗
NASA NTRS1994

Measurement Capabilities of Single-Pulse Planar Doppler Velocimetry

Preliminary investigations are described of a method that is capable of measuring instantaneous, 3-D, velocity vectors everywhere in a light sheet generated by a pulsed laser. The technique, here called Planar Doppler Velocimetry (PDV), is a variation of a new concept for velocity measurements that was called Doppler Global Velocimetry (DGV) in its original disclosure. The concept relies on the use of a narrowband laser and measurements of the Doppler shift of scattered light from particles moving with a flow. The Doppler shift is recorded as a variation in transmission through a sharp-edged spectral filter provided by iodine vapor in a cell. Entire fields of velocity can be determined by using a solid-state camera to record the intensity variations throughout the field of view. However, the implementation of DGV has been centered principally on the use of high power, continuous-wave, ion lasers and measurement times that are determined by the 30-ms framing times of standard video cameras. Hence, they provide velocity fields that are averaged in time at least over that period. On the other hand, the PDV concept described in this presentation incorporates a high energy, repetitively pulsed, Nd-YAG laser that is injection-seeded to make it narrowband and then frequency-doubled to provide light at frequencies absorbed by the iodine vapor. The duration of each pulse is less than 10 nanoseconds. When used in combination with nonstandard, scientific quality, solid state cameras, a sequence of images can be obtained that provides instantaneous velocity vectors everywhere in the field of view. The investigations described in this paper include an accurate characterization of the iodine cell spectral behavior and its influence on the PDV measurements, a derivation of the PDV signal analysis requirements, and the unique aspects of the pulsed laser behavior related to this application. In addition, PDV measurements are to be demonstrated using data from a rotating wheel target and from the flow of a subsonic jet. Initially, single optical fiber light collection and photomultiplier detectors will be substituted for solid state cameras. Those results will allow the determination of the fundamental limitations of the PDV technique without the complications of image acquisition and processing. They will then be used to provide an analysis of the measurement capabilities of PDV both in small aerodynamic research wind tunnels and in large wind tunnels designed for production airframe and propulsion testing. Future plans include the implementation of solid state cameras and the development of the required image acquisition and processing software. Eventually, the PDV technique will be applied to an aerodynamic research program related to transonic wing flutter.

McKenzie, Robert L.↗
NASA NTRS2022

Deep Interacting Multiple Model Filtering

In this paper, a deep learning-based multiple model estimation framework is presented for the state estimation of hybrid dynamical systems from high dimensional observations such as camera images. A low dimensional vector which represents the measurement of the latent dynamical system and its corresponding variance are learned using a deep encoder neural network. An Interacting Multiple Model (IMM) filter is used to generate the latent state estimates and covariances using multiple dynamical models, which can be learned using backpropagation through time. The state estimates of the dynamical system and the corresponding covariance matrix are generated from the latent state estimates and covariance using a deep decoder neural network. The whole network is trained in an end-to-end manner using a loss function which minimizes the negative log-likelihood of the neural network parameters. Simulation results are presented using a 2D bouncing ball example and estimation error statistics are computed which demonstrates the accuracy and consistency of the estimation.

Ghananeel Rotithor↗
NASA NTRS2011

State-Based Implicit Coordination and Applications

In air traffic management, pairwise coordination is the ability to achieve separation requirements when conflicting aircraft simultaneously maneuver to solve a conflict. Resolution algorithms are implicitly coordinated if they provide coordinated resolution maneuvers to conflicting aircraft when only surveillance data, e.g., position and velocity vectors, is periodically broadcast by the aircraft. This paper proposes an abstract framework for reasoning about state-based implicit coordination. The framework consists of a formalized mathematical development that enables and simplifies the design and verification of implicitly coordinated state-based resolution algorithms. The use of the framework is illustrated with several examples of algorithms and formal proofs of their coordination properties. The work presented here supports the safety case for a distributed self-separation air traffic management concept where different aircraft may use different conflict resolution algorithms and be assured that separation will be maintained.

Narkawicz, Anthony J.↗
DOE OSTI2024

Structural mode coupling in perovskite oxides using hypothesis-driven active learning

Abstract Finding the ground-state structure with minimum energy is paramount to designing any material. In ABO 3 -type perovskite oxides with Pnma symmetry, the lowest energy phase is driven by an inherent trilinear coupling between the two primary order parameters such as rotation and tilt with antiferroelectric displacement of the A-site cations as established via hybrid improper ferroelectric mechanism. Conventionally, finding the relevant mode coupling driving phase transition requires performing first-principles calculations which is computationally time-consuming as well as expensive. It involves following an intuitive iterative hit and trial method of (a) adding two or multiple mode vectors, followed by (b) evaluating which combination would lead to the ground-state energy. In this study, we show how a hypothesis-driven active learning framework can identify suitable mode couplings within the Landau free energy expansion with minimal information on amplitudes of modes for a series of double perovskite oxides with A-site layered, columnar and rocksalt ordering. This scheme is expected to be applicable universally for understanding atomistic mechanisms derived from various structural mode couplings behind functionalities, for e.g. polarization, magnetization and metal–insulator transitions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Hall effect in LaCrGe 3 at pressures up to 24 kbar and a 𝐻−𝑇 phase diagram of the pressure-induced magnetic phase

LaCrGe 3 is a metallic, itinerant ferromagnet with 𝑇 𝐶 ≈ 86K and an easy magnetic direction along its hexagonal 𝑐 axis. Here we present studies of the 𝐻 ∥ 𝑐 Hall effect in LaCrGe 3 at ambient pressure and under pressures up to 24 kbar. At ambient pressure the anomalous Hall conductivity in the ferromagnetic state reaches a large value of ∼520 Ω − 1 cm −1 . The overall data are consistent with the behavior expected for a ferromagnet with magnetic field applied along its easy axis. Measurements under pressure yield an 𝐻 = 0, 𝑃−𝑇 phase diagram consistent with earlier publications with 𝑇 𝐶 being suppressed with pressure and nonferromagnetic phases appearing for 𝑃 >15 kbar. The data suggest that the observed 𝑇 𝐶 suppression is due to the pressure-dependent exchange interactions rather than suppression of the size of the magnetic moment. Field-dependent Hall resistivity in the higher-pressure, nonferromagnetic state is consistent with the net moment being zero for 𝐻=0. In a finite field there is a sharp anomaly comparable to the one associated with a spin-flip metamagnetic transition. The 𝐻−𝑇 phase diagrams corresponding to a pressure-induced magnetic phase are constructed for several pressures. In conclusion, the Hall data suggest the possibility of the pressure-induced nonferromagnetic phase being a combination of multiple small regions of antiferromagnetic phases with different wave vectors.

Bud'ko, Sergey L. [Ames Laboratory, and Iowa State↗