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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Effect of Surface Pressure Integration Methodology on Launch Vehicle Buffet Forcing Functions

The 2014 test of the Space Launch System (SLS) Rigid Buffet Model conducted at the NASA Langley Transonic Dynamics Tunnel employed an extremely high number of unsteady pressure transducers. The high channel count provided an opportunity to examine the effect of transducer placement on the resulting buffet forcing functions (BFFs). Rings of transducers on the forward half of the model were employed to simulate a single-body vehicle. The impact of transducer density, circumferential distribution, and loss of a single transducer on the resulting BFFs were examined. Rings of transducers on the aft half of the SLS model were employed to examine the effect of transducer density and circumferential distribution on BFFs for a multi-body configuration. Transducer placement considerations with respect to model size, facility infrastructure, and data acquisition system capabilities, which affect the integration process, are also discussed.

Sekula, Martin K.↗
NASA NTRS1961

Stability and Control Characteristics of a Small-Scale Model of an Aerial Vehicle Supported by Two Ducted Fans

An investigation has been made to determine the stability and control characteristics in hovering and in forward flight of a free-flight model representing a type of vertical-take-off-and-landing aircraft which utilizes two fixed ducted fans as its sole source of lift and propulsion. The model, having fans 28 inches in diameter, was considered to be approximately one-third the size of a full-scale aircraft. Control moments for most of the hovering tests and all the forward-flight tests were provided by remotely controlled compressed-air jets at the sides and ends of the model. For one brief phase of the hovering investigation a system of vanes in the duct slipstreams was substituted for the jets as a means of roll control. During the forward-flight tests, the model was flown with both the tandem and side-by-side duct arrangements. In hovering the model exhibited strongly divergent oscillations about the pitch and roll axes. The pitching oscillation of the tandem configuration was of a fairly long period and was not particularly difficult to control; the rolling oscillation, however, was of a relatively short period and was extremely difficult to control. Both oscillations could be completely eliminated by the addition of a sufficient amount of artificial damping. The control moments produced by the vane-type roll control system were weak and were accompanied by a side force of appreciable magnitude and undesirable direction. In forward flight the model required an undesirably large nose-down tilt angle for equilibrium at any appreciable speed. A vane was placed transversely in the slipstream of the forward duct of the tandem configuration in an attempt to reduce this tilt angle. The vane was effective in reducing the tilt angle but apparently caused an increase in the power requirements and in the angle-of-attack instability. Without the vane, a forward speed of 30 knots (full scale) required a nose-down tilt angle of about 300. A powerful pitch control moment was required not only to maintain the trim attitude but also to 2 overcome the effects of instability with angle of attack. Less pitch control moment was required for the tandem configuration than for the side-by-side configuration at any given forward speed. The instability in roll increased with forward speed. No forward speeds in excess of about 20 knots (full scale) were achieved until the artificial damping in roll and the yaw control moment were increased appreciably above values which had proved satisfactory for hovering flight.

Parlett, Lysle P.↗
NASA NTRS2014

Tone and Broadband Noise Separation from Acoustic Data of a Scale-Model Contra-Rotating Open Rotor

Renewed interest in contra-rotating open rotor technology for aircraft propulsion application has prompted the development of advanced diagnostic tools for better design and improved acoustical performance. In particular, the determination of tonal and broadband components of open rotor acoustic spectra is essential for properly assessing the noise control parameters and also for validating the open rotor noise simulation codes. The technique of phase averaging has been employed to separate the tone and broadband components from a single rotor, but this method does not work for the two-shaft contra-rotating open rotor. A new signal processing technique was recently developed to process the contra-rotating open rotor acoustic data. The technique was first tested using acoustic data taken of a hobby aircraft open rotor propeller, and reported previously. The intent of the present work is to verify and validate the applicability of the new technique to a realistic one-fifth scale open rotor model which has 12 forward and 10 aft contra-rotating blades operating at realistic forward flight Mach numbers and tip speeds. The results and discussions of that study are presented in this paper.

Noise Separation↗
NASA NTRS2013

Coarse-Grain Bandwidth Estimation Scheme for Large-Scale Network

A large-scale network that supports a large number of users can have an aggregate data rate of hundreds of Mbps at any time. High-fidelity simulation of a large-scale network might be too complicated and memory-intensive for typical commercial-off-the-shelf (COTS) tools. Unlike a large commercial wide-area-network (WAN) that shares diverse network resources among diverse users and has a complex topology that requires routing mechanism and flow control, the ground communication links of a space network operate under the assumption of a guaranteed dedicated bandwidth allocation between specific sparse endpoints in a star-like topology. This work solved the network design problem of estimating the bandwidths of a ground network architecture option that offer different service classes to meet the latency requirements of different user data types. In this work, a top-down analysis and simulation approach was created to size the bandwidths of a store-and-forward network for a given network topology, a mission traffic scenario, and a set of data types with different latency requirements. These techniques were used to estimate the WAN bandwidths of the ground links for different architecture options of the proposed Integrated Space Communication and Navigation (SCaN) Network. A new analytical approach, called the "leveling scheme," was developed to model the store-and-forward mechanism of the network data flow. The term "leveling" refers to the spreading of data across a longer time horizon without violating the corresponding latency requirement of the data type. Two versions of the leveling scheme were developed: 1. A straightforward version that simply spreads the data of each data type across the time horizon and doesn't take into account the interactions among data types within a pass, or between data types across overlapping passes at a network node, and is inherently sub-optimal. 2. Two-state Markov leveling scheme that takes into account the second order behavior of the store-and-forward mechanism, and the interactions among data types within a pass. The novelty of this approach lies in the modeling of the store-and-forward mechanism of each network node. The term store-and-forward refers to the data traffic regulation technique in which data is sent to an intermediate network node where they are temporarily stored and sent at a later time to the destination node or to another intermediate node. Store-and-forward can be applied to both space-based networks that have intermittent connectivity, and ground-based networks with deterministic connectivity. For groundbased networks, the store-and-forward mechanism is used to regulate the network data flow and link resource utilization such that the user data types can be delivered to their destination nodes without violating their respective latency requirements.

Cheung, Kar-Ming↗
NASA NTRS1974

The 727 airplane side inlet low-speed performance confirmation model test for refanned JT8D engines

The results of a low-speed wind tunnel test of a 0.3 scale model 727 airplane side inlet for JT8D-100 engines are presented. The objectives of the test were to develop lines for a full-scale flightworthy inlet, to evaluate inlet total pressure recovery and steady-state total pressure distortion, and to obtain model-scale distortion data which can be used in the assessment of the compatibility of the inlet with the JT8D-100 series engines. A secondary objective was to obtain internal/external cowl static pressures for the determination of nacelle loads. Two basic inlet models were tested at static, forward speed, angle-of-attack (inflow angle), and cross-wind conditions. One model was with and one without an acoustic ring. Two modifications to the models were also tested, one with the ring closer to the inlet throat and one with a larger lip. Test measurements consisted of inlet surface static pressure, engine face total pressure, inlet airflow, tunnel total pressure, tunnel total temperature and tunnel velocity. Total pressure traverses were taken directly behind the ring and strut. No dynamic measurements were taken.

Schuehle, A. L.↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square error (RMSE) metric. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent covariates across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is studied for both direct and recursive RF modelling approaches across increasing forecast horizons. Accurate prediction of initial TMP is critical for optimizing RO operations, as it enables the development of robust modelling frameworks by accurately estimating membrane fouling trends, thereby enhancing process efficiency and long-term reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Ultra-filtration(UF) units are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square (RMSE) metric. Accurate prediction of initial TMP is critical for optimizing CCRO operations, as it enables the development of robust modelling frameworks that enhance process efficiency and reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2022

Capturing the interactions between ice sheets, sea level and the solid Earth on a range of timescales: a new “time window” algorithm

Retreat and advance of ice sheets perturb the gravitational field, solid surface and rotation of the Earth, leading to spatially variable sea-level changes over a range of timescales $\textit{O}$ (10 0–6 years), which in turn feed back onto ice-sheet dynamics. Coupled ice-sheet–sea-level models have been developed to capture the interactive processes between ice sheets, sea level and the solid Earth, but it is computationally challenging to capture short-term interactions $\textit{O}$ (10 0–2 years) precisely within longer $\textit{O}$ (10 3–6 years) simulations. The standard forward sea-level modelling algorithm assigns a uniform temporal resolution in the sea-level model, causing a quadratic increase in total CPU time with the total number of input ice history steps, which increases with either the length or temporal resolution of the simulation. In this study, we introduce a new “time window” algorithm for 1D pseudo-spectral sea-level models based on the normal mode method that enables users to define the temporal resolution at which the ice loading history is captured during different time intervals before the current simulation time. Utilizing the time window, we assign a fine temporal resolution $\textit{O}$ (10 0–2 years) for the period of ongoing and recent history of surface ice and ocean loading changes and a coarser temporal resolution $\textit{O}$ (10 3–6 years) for earlier periods in the simulation. This reduces the total CPU time and memory required per model time step while maintaining the precision of the model results. We explore the sensitivity of sea-level model results to the model temporal resolution and show how this sensitivity feeds back onto ice-sheet dynamics in coupled modelling. We apply the new algorithm to simulate sea-level changes in response to global ice-sheet evolution over two glacial cycles and the rapid collapse of marine sectors of the West Antarctic Ice Sheet in the coming centuries and provide appropriate time window profiles for each application. The time window algorithm reduces the total CPU time by ~ 50 % in each of these examples and changes the trend of the total CPU time increase from quadratic to linear. This improvement would increase with longer simulations than those considered here. Our algorithm also allows for coupling time intervals of annual temporal scale for coupled ice-sheet–sea-level modelling of regions such as West Antarctica that are characterized by rapid solid Earth response to ice changes due to the thin lithosphere and low mantle viscosities.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1997

National Transonic Facility Model and Tunnel Vibrations

Since coming online in 1984, the National Transonic Facility (NTF) cryogenic wind tunnel at the NASA Langley Research Center has provided unique high Reynolds number testing capability. While turbulence levels in the tunnel, expressed in terms of percent dynamic pressure, are typical of other transonic wind tunnels, the significantly increased load levels utilized to achieve flight Reynolds numbers, in conjunction with the unique structural design requirements for cryogenic operation, have brought forward the issue of model and model support structure vibrations. This paper reports new experimental measurements documenting aerodynamic and structural dynamics processes involved in such vibrations experienced in the NTF. In particular, evidence of local un-steady airloads developed about the model support strut is shown and related to well documented acoustic features known as "Parker" modes. Two-dimensional unsteady viscous computations illustrate this model support structure loading mechanism.

Edwards, John W.↗
DOE OSTI2024

Asymptotic-state prediction for fast flavor transformation in neutron star mergers

Neutrino flavor instabilities appear to be omnipresent in dense astrophysical environments, thus presenting a challenge to large-scale simulations of core-collapse supernovae and neutron star mergers (NSMs). Subgrid models offer a path forward, but require an accurate determination of the local outcome of such conversion phenomena. Focusing on “fast” instabilities, related to the existence of a crossing between neutrino and antineutrino angular distributions, we consider a range of analytical mixing schemes, including a new, fully three-dimensional one, and also introduce a new machine learning (ML) model. We compare the accuracy of these models with the results of several thousands of local dynamical calculations of neutrino evolution from the conditions extracted from classical NSM simulations. Our ML model shows good overall performance, but struggles to generalize to conditions from a NSM simulation not used for training. The multidimensional analytic model performs and generalizes even better, while other analytic models (which assume axisymmetric neutrino distributions) do not have reliably high performances, as they notably fail as expected to account for effects resulting from strong anisotropies. As a result, the ML and analytic subgrid models extensively tested here are both promising, with different computational requirements and sources of systematic errors.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1986

Development of perturbations in the boundary layer

The transition of laminar flows into turbulent flows in a boundary layer is discussed. The individual aspects of the transition process, observed under controllable model conditions are examined. The aspect of this problem, namely the development or excitation of the natural oscillations in the boundary layer, the so-called Tollmin-Schlichting waves is covered. Three types of excitation of these waves are considered: (1) distributed generation throughout the boundary layer; (2) generation in the vicinity of the forward edge of a model, having either a sharp edge or an edge with a large radius or curvature, and (3) generation in a developed boundary layer by means of a focused effect.

Dovgal, A. V.↗
DOE OSTI2023

Encoding nonlinear and unsteady aerodynamics of limit cycle oscillations using nonlinear sparse Bayesian learning

This article investigates the applicability of a recently proposed, nonlinear sparse Bayesian learning (NSBL) algorithm to identify and estimate the complex aerodynamics of limit cycle oscillations. NSBL provides a semi-analytical framework for determining the data-optimal sparse model nested within a (potentially) over-parameterized model. This is particularly relevant to nonlinear dynamical systems where modelling approaches involve the use of physics-based and data-driven components. In such cases, the data-driven components, where analytical descriptions of the physical processes are not readily available, are often prone to overfitting, meaning that the empirical aspects of these models will often involve the calibration of an unnecessarily large number of parameters. While an overparameterized model may fit the observed data well, such models may be inadequate for making predictions in regimes that are different from those wherein the data were recorded. In view of this, it is desirable to not only calibrate the model parameters, but also identify the optimal compromise between data fit and model complexity. In this article, we exhibit the optimal model discovery for an aeroelastic system wherein the structural dynamics are well-known and described by a differential equation model, coupled with a semi-empirical aerodynamic model for laminar separation flutter, resulting in low-amplitude limit cycle oscillations (LCO). To illustrate the performance of the algorithm, in this article, we use synthetic data and demonstrate the ability of the algorithm to correctly rediscover the optimal model and model parameters, given a known data-generating model. The synthetic data are generated from a forward simulation of a known differential equation model with parameters selected so as to mimic the dynamics observed in wind-tunnel experiments. Subsequently, we demonstrate the performance of the algorithm for model selection using noisy LCO data from wind tunnel experiments. As there is no ground truth available for the experimental data case, we provide a comparison between NSBL and Bayesian model selection to validate the results, and demonstrate the use of NSBL as an efficient alternative to traditional methods.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Horizontal energy propagation in a baroclinic atmosphere with meridional and zonal structure

A baroclinic model was developed. The model is linear, steady state, baroclinic (2 level) and based on the primitive equations. The unique features of the model are: (1) The basic state u and T fields are functions of wavelength, theta, and rho, (2) A mean meridional circulation v, w, which may be a function of wavelength, theta, and rho, and p may be included, (3) Dissipation may be a function of theta, wavelength, rho, p (e.g., differ over land and sea). Because the basic state is a function of longitude, the equations are not separable in wavelength and the usual semi-spectral solution procedure is not applicable. The model solutions can be found in a straight forward (brute force) way by discretizing the model equations directly on a wavelength, theta, rho, p grid. There are six dependent (perturbation) variables: u, v, and geopotential at levels 1 and 2. The thermal forcing is prescribed at the intermediate vertical level. The linearized equations may be written.

Youngblut, C.↗
NASA NTRS2010

The Late Peaking Afterglow of GR8 100418A

GRB 100418A is a long Gamma-Ray Burst at redshift z=0.6235 discovered with the Swift Gamma-Ray Burst Explorer with unusual optical and X-ray light curves ' After an initial short-lived, rapid decline in X-rays, the optical and X-ray light curves observed with Swift are approximately flat or rising slightly out to at least approx.7 ks after the trigger, peak at approx.50 ks, and then follow an approximately power-law decay. Such a long optical plateau and late peaking is rarely seen in 6R8 afterglows. Observations with REM during a gap in the Swift coverage indicate a bright optical flare at approx.25 ks, The long plateau phase of the afterglow is interpreted using either a model with continuous injection of energy into the forward shock of the burst or a model in which the 'et of the burst is viewed off-axis. In both models the isotropic kinetic energy in the late afterglow after the plateau phase is >100 times the 10(exp 51) erg of the prompt isotropic gamma-ray energy release. The energy injection model is favored because the off-axis 'et model would require the intrinsic $T f801$ for the GR8 'et viewed on-axis to be very short, approx.10 ms, and the intrinsic isotropic gamma-ray energy release and the true jet energy to be much higher than the typical values of known short GRBs^ The non-detection of a 'et break up to approx.2 Ms indicates a jet half-opening angle of at least 14 degrees, and a relatively high collimation-corrected 'et energy of at least 10(exp 52) erg.

Marshall, Frank↗
NASA NTRS2022

Revisiting the Ground Magnetic Field Perturbations Challenge: A Machine Learning Perspective

Forecasting ground magnetic field perturbations has been a long-standing goal of the space weather community. The availability of ground magnetic field data and its potential to be used in geomagnetically induced current studies, such as risk assessment, have resulted in several forecasting efforts over the past few decades. One particular community effort was the Geospace Environment Modeling (GEM) challenge of ground magnetic field perturbations that evaluated the predictive capacity of several empirical and first principles models at both mid- and high-latitudes in order to choose an operative model. In this work, we use three different deep learning models-a feed-forward neural network, a long short-term memory recurrent network and a convolutional neural network-to forecast the horizontal component of the ground magnetic field rate of change (dB H /dt) over 6 different ground magnetometer stations and to compare as directly as possible with the original GEM challenge. We find that, in general, the models are able to perform at similar levels to those obtained in the original challenge, although the performance depends heavily on the particular storm being evaluated. We then discuss the limitations of such a comparison on the basis that the original challenge was not designed with machine learning algorithms in mind.

Victor A. Pinto↗
DOE OSTI2026

Bayesian chain graph models to characterize microbe-environment dynamics

Microbiome data require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes. While a multiresponse linear regression model seems like a straight-forward solution, we argue that treating it as a graphical model is problematic given that the regression coefficient matrix does not encode the conditional dependence structure between response and predictor nodes. This observation is especially important in biological settings when we have prior knowledge on the edges from specific experimental interventions that can only be properly encoded under a conditional dependence model. Here, we propose a chain graph model with two sets of nodes (predictors and responses) whose solution yields a graph with edges that indeed represent conditional dependence, thus agreeing with the experimenter's intuition on the average behavior of nodes under treatment. The solution to our model is sparse via the Bayesian linear regression (LASSO). In addition, we propose an adaptive extension so that different shrinkages can be applied to different edges to incorporate edge-specific prior knowledge. Our model is computationally inexpensive through an efficient Gibbs sampling algorithm and can account for binary, counting, and compositional responses via an appropriate hierarchical structure. We test the performance of our model in a variety of simulated datasets, thereby showing superior performance to state-of-the-art approaches. We further apply our model to human gut and soil microbial compositional datasets, and we highlight that CG-LASSO can estimate biologically meaningful network structures in the data.

compositional data↗
NASA NTRS1978

Aerodynamic characteristics of a hypersonic research airplane concept having a 70 deg swept double-delta wing at Mach number 0.2

A wind-tunnel of the static longitudinal, lateral and directional stability characteristics of a hypersonic research airplane concept having a 70 deg swept double-delta wing was conducted in the Langley low-turbulence pressure tunnel. The configuration variables included wing planform, tip fins, center fin, and scramjet engine modules. A mach number of 0.2 was investigated over a Reynolds number (based on fuselage length) range of 2,200,000 to 19.75 x 1,000,000 (with a majority of tests at 10.0 x 1,000,000. Tests were conducted through an angle-of-attack range from about -2 deg to 34 deg at angles of sideslip of 0 deg to 5 deg, and at elevon deflection of 0 deg, -5 deg, -10 deg, -15 deg, and -20 deg. The drag coefficient of the integrated scramjet engine appears relatively constant with Reynolds number at the test Mach number of 0.2. Mild pitch-up was exhibited by the models equipped with tip fins. The forward delta, a highly swept forward portion of the wing, was destabilizing. The center fin model has a higher trimmed maximum lift-drag ratio and a wider trim lift and angle-of-attack range than the tip fin model. Both the tip fin models and center fin models exhibited positive dihedral effect and positive directional stability. Roll control was positive for the tip fin model, but yaw due to roll control was unfavorable.

Penland, J. A.↗
DOE OSTI2023

Lessons Learned from RELAP5-3D Modeling of HTTF

Modeling of HTTF using the RELAP5-3D ring model has shown an inability to predict transient temperature rise during HTTF experiments. This presentation provides an overview of the RELAP5-3D ring model, the HTTF modeling activities using the ring model, and reasons why the models failed to reproduce measured temperature rise. It also presents an overview of the path forward for RELAP5-3D modeling of HTTF to better predict the behavior of HTTF experiments

22 GENERAL STUDIES OF NUCLEAR REACTORS↗