Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “range prediction”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
NASA NTRS1959

Surface Pressure Distribution at Hypersonic Speeds for Blunt Delta Wings at Angle of Attack

Surface pressures were measured over a blunt 60 deg delta wing with extended trailing edge at a Mach number of 5.7, a free-stream Reynolds number of 20,000 per inch, and angles of attack from -10 to +10 deg. Aft of four leading-edge thicknesses the pressure distributions evidenced no appreciable three-dimensional effects and were predicted qualitatively by a method described herein for calculation of pressure distribution in two-dimensional flow. Results of tests performed elsewhere on blunt triangular wings were found to substantiate the near two-dimensionality of the flow and were used to extend the range of applicability of the method of surface pressure predictions to Mach numbers of 11.5 in air and 13.3 in helium.

Creager, Marcus O.↗
DOE OSTI2023

Dark matter from a conformal Dark Sector

We consider theories in which a dark sector is described by a Conformal Field Theory (CFT) over a broad range of energy scales. A coupling of the dark sector to the Standard Model breaks conformal invariance. While weak at high energies, the breaking grows in the infrared, and at a certain energy scale the theory enters a confined (hadronic) phase. One of the hadronic excitations can play the role of dark matter. We study a “Conformal Freeze-In” cosmological scenario, in which the dark sector is populated through its interactions with the SM at temperatures when it is conformal. In this scenario, the dark matter relic density is determined by the CFT data, such as the dimension of the CFT operator coupled to the Standard Model. We show that this simple and highly predictive model of dark matter is phenomenologically viable. The observed relic density is reproduced for a variety of SM operators (“portals”) coupled to the CFT, and the resulting models are consistent with observational constraints. The mass of the COFI dark matter candidate is predicted to be in the keV-MeV range.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1997

Low-Density Aerodynamics of the Stardust Sample Return Capsule

The aerodynamics of the Stardust Sample Return Capsule are analyzed in the low- density, transitional flow regime using free-molecular, Direct Simulation Monte Carlo, Navier-Stokes, and Newtonian methods to provide inputs for constructing a transitional flow bridging relation. The accuracy of this bridging relation in reconstructing the aero- dynamic coefficients given by the more exact methods is presented for a range of flight conditions and vehicle attitudes. There is good agreement between the various prediction methods, and a simple sine-squared bridging relation is shown to provide a reasonably good description of the axial force, normal force, and pitching moment over a range of Knudsen numbers from 0.001 to 10. The predictions show a static instability of the Star- dust capsule in the free-molecular regime that persists well into the transitional flow. The addition of a thin disk to the base of the capsule is shown to remove this static instability. However, the extremely high entry velocity of 12.6 km/s for the proposed trajectory introduces difficult design issues for incorporating this disk caused by the high aerothermal loads that occur even under relatively rarefied conditions.

Wilmoth, Richard G.↗
DOE OSTI2022

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗
DOE OSTI2024

Nonempirical Prediction of the Length-Dependent Ionization Potential in Molecular Chains

The ionization potential of molecular chains is well-known to be a tunable nanoscale property that exhibits clear quantum confinement effects. State-of-the-art methods can accurately predict the ionization potential in the small molecule limit and in the solid-state limit, but for intermediate, nanosized systems prediction of the evolution of the electronic structure between the two limits is more difficult. Recently, optimal tuning of range-separated hybrid functionals has emerged as a highly accurate method for predicting ionization potentials. This was first achieved for molecules using the ionization potential theorem (IPT) and more recently extended to solid-state systems, based on an ansatz that generalizes the IPT to the removal of charge from a localized Wannier function. Here, we study one-dimensional molecular chains of increasing size, from the monomer limit to the infinite polymer limit using this approach. By comparing our results with other localization-based methods and where available with experiment, we demonstrate that Wannier-localization-based optimal tuning is highly accurate in predicting ionization potentials for any chain length, including the nanoscale regime.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

When do short-range atomistic machine-learning models fall short?

In this work, we explore the role of long-range interactions in atomistic machine-learning models by analyzing the effects on fitting accuracy, isolated cluster properties, and bulk thermodynamic properties. Such models have become increasingly popular in molecular simulations given their ability to learn highly complex and multi-dimensional interactions within a local environment; however, many of them fundamentally lack a description of explicit long-range interactions. In order to provide a well-defined benchmark system with precisely known pairwise interactions, we chose as the reference model a flexible version of the Extended Simple Point Charge (SPC/E) water model. Our analysis shows that while local representations are sufficient for predictions of the condensed liquid phase, the short-range nature of machine-learning models falls short in representing cluster and vapor phase properties. These findings provide an improved understanding of the role of long-range interactions in machine learning models and the regimes where they are necessary.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗
DOE OSTI2020

Evaluation of a New Chemical Mechanism for 2-Amino-2-methyl-1-propanol in a Reactive Environment from CSIRO Smog Chamber Experiments

Amines are considered as an emerging class of atmospheric pollutants that are of great importance to atmospheric chemistry and new particle formation. As a typical amine, 2-amino-2-methyl-1-propanol (AMP) is one of the proposed solvents for capturing CO 2 from flue gas streams in amine-based post-combustion CO 2 capture plants, and it is expected to result in AMP emission and secondary products formation in the atmosphere. However, the current knowledge of its atmospheric chemistry and kinetics is poorly understood, particularly in a reactive environment. As such, in this work we used the CSIRO smog chamber to study the photo-oxidation of AMP in the presence of VOCs–NOx surrogate mixtures over a range of initial amine concentrations. O 3 formation was significantly inhibited when AMP was added to the surrogate VOCs–NOx mixtures, implying that AMP could alter known atmospheric chemical reaction pathways and the prevailing reactivity. Simultaneously, a large amount of AMP-derived secondary aerosol was formed, with a considerably high aerosol mass yield (i.e. ratio of aerosol formed to reacted AMP) of 1.06±0.20. Based on updated knowledge of its kinetics, oxidation pathways and product yields, we have developed a new mechanism (designated as CSIAMP-19) and integrated into the Carbon Bond 6 (CB6) chemical mechanism, and evaluated it against available smog chamber data. Compared with the existing AMP mechanism (designated as CarterAMP-08), the modified CB6 with CSIAMP-19 mechanism improves prediction against AMP–VOCs–NOx experiments across a range of initial AMP concentrations, within ±10% model error for gross ozone production. Our results contribute to scientific understanding of AMP photochemistry and to the development of the chemical mechanism of other amines. The updated AMP chemical reactions scheme can be further embedded into the chemical transport model for regional modelling scenarios where AMP-related emissions are of concern.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2011

TPSAS-NF1676L-12387-DND

Fast-time wake prediction models are semi-empirical, and are designed to predict aircraft wake vortex information behind a generating aircraft. They are developed using theoretical concepts and often calibrated with data obtained from field measurements, laboratory studies, or numerical simulations. They rely upon inputs representing the generating aircraft (such as weight, airspeed, wingspan, lateral position, and altitude) and atmospheric environment (such as ambient winds, turbulence, and temperature lapse rate). Other input may include the type of aircraft (e.g., B-747- 400). Output from these models may include time histories of the wake vortex strength, and position, as well as a prediction of the uncertainties in strength and position. Fast-time models should provide solutions in a fraction of a second, and hence is an enabling technology able to perform in a real-time operational system, such as dynamic spacing systems envisioned by the Aircraft Vortex Spacing System or other Super Density Operations concepts. Other applications of fast-time models include 1) guidance for wake separation standards for new aircraft, 2) optimization of separation standards for existing aircraft, 3) guidance for setting vertical separation standards during cruise, 4) accident reconstructions, and 5) testing of new concepts for safe increases in airport capacity for both single and multiple runway configurations. Fast-time models are based on a number of assumptions which limit their accuracy and range of application. For example, they do not predict the meandering vortex paths that can result from the wake interaction with atmospheric turbulence. However, these models should account for the mean vortex position and may predict the level of uncertainty. This presentation describes a study in which comparative evaluations of fast-time wake transport and decay models were conducted using data sets from previous field programs taken at MEM (1995), DFW (1997, 1999, 2000), and Denver (2005, 2006) airports. These data sets contain, meteorological data, lidar measurements of wake vortices, and the aircraft parameters associated with these vortices.

Fred H Proctor↗
NASA NTRS2001

A Diagnostic Analysis of the Kennedy Space Center LDAR Network: Data Characteristics - 1

An analytic framework is developed in which to analyze climatological VHF (66 MHz) radiation measurements taken by the Kennedy Space Center Lightning Detection and Ranging (LDAR) network. A 19 month noise-filtered sample of LDAR observations is examined using this framework. It is found that the climatological impulsive VHF source density as observed by LDAR falls off approximately 10 dB every 71 km of ground range away from the network centroid (a 31 km e-folding scale). The underlying vertical distribution of impulsive VHF sources is approximately normally distributed with a mean altitude of 9 km and a standard deviation of 2.7 km; this implies that the loss of below-horizon sources has a negligible effect on column-integrated source densities within a 200 km ground range. At medium to far ranges, location errors are primarily radial and have a slightly asymmetric distribution whose standard deviation increases as r(exp 2). Error moments estimated from observed lightning are significantly higher than those from aircraft-based signal generator or analytic estimates. LDAR bulk flash detection efficiency is predicted to be above 90% to 94-113 km range from the network centroid and to fall below 10% at ranges greater than 200-240 km.

Boccippio, D. J.↗
DOE OSTI2021

Natural soil microbiome variation affects spring foliar phenology with consequences for plant productivity and climate-driven range shifts

Identifying the potential for natural soil microbial communities to predictably affect complex plant traits is an important frontier in climate change research. Plant phenology varies with environmental and genetic factors, but few studies have examined if the soil microbiome interacts with plant population differentiation to affect phenology and ecosystem function. We compared soil microbial variation in a widespread tree species (Populus angustifolia) with different soil inoculum treatments in a common garden environment to test how the soil microbiome affects spring foliar phenology and subsequent biomass growth. We hypothesized and show that: 1) soil bacterial and fungal communities vary with tree conditioning from different populations and elevations, 2) this soil community variation influences patterns of foliar phenology and plant growth across populations and elevation gradients, and 3) transferring lower elevation plant genotypes to higher elevation soil communities delayed foliar phenology, thereby shortening the growing season and reducing annual biomass production. Our findings show the importance of plant-soil interactions that help shape the timing of tree foliar phenology and productivity. Here, these geographic patterns in plant population x microbiome interactions also broaden our understanding of how soil communities impact plant phenotypic variation across key climate change gradients, with consequences for ecosystem functioning.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1976

Time-temperature-stress dependence of boron fiber deformation

The time-dependent deformation of boron fibers over the temperature range from -190 to 800 C is studied by flexural stress relaxation and flexural internal friction techniques on 203-micron diam specimen fibers commercially produced by chemical vapor deposition (CVD) on a 13-micron tungsten substrate. It is shown that up to at least 800 C all nonelastic behavior observed during axial deformation of CVD boron fibers can be explained solely by anelastic mechanisms and that although creep strains are small, boron fiber anelasticity can produce significant mechanical effects which would otherwise be neglected under the elastic approximation. Relations are obtained to demonstrate the considerable effects of anelasticity on such fiber/composite properties as modulus, creep, creep recovery, stress relaxation, and damping capacity. For an elastic-core/anelastic-sheath model, boron fibers on tungsten substrates are shown to have predictable fracture stresses for time-temperature conditions ranging from impact to long-time stress rupture. Possible techniques for altering these stresses are discussed.

Dicarlo, J. A.↗
NASA NTRS2015

DDA Computations of Porous Aggregates with Forsterite Crystals: Effects of Crystal Shape and Crystal Mass Fraction

Porous aggregate grains are commonly found in cometary dust samples and are needed to model cometary IR spectral energy distributions (SEDs). Models for thermal emissions from comets require two forms of silicates: amorphous and crystalline. The dominant crystal resonances observed in comet SEDs are from Forsterite (Mg2SiO4). The mass fractions that are crystalline span a large range from 0.0 < or = fcrystal < or = 0.74. Radial transport models that predict the enrichment of the outer disk (>25 AU at 1E6 yr) by inner disk materials (crystals) are challenged to yield the highend-range of cometary crystal mass fractions. However, in current thermal models, Forsterite crystals are not incorporated into larger aggregate grains but instead only are considered as discrete crystals. A complicating factor is that Forsterite crystals with rectangular shapes better fit the observed spectral resonances in wavelength (11.0-11.15 microns, 16, 19, 23.5, 27, and 33 microns), feature asymmetry and relative height (Lindley et al. 2013) than spherically or elliptically shaped crystals. We present DDA-DDSCAT computations of IR absorptivities (Qabs) of 3 micron-radii porous aggregates with 0.13 < or = fcrystal < or = 0.35 and with polyhedral-shaped Forsterite crystals. We can produce crystal resonances with similar appearance to the observed resonances of comet Hale- Bopp. Also, a lower mass fraction of crystals in aggregates can produce the same spectral contrast as a higher mass fraction of discrete crystals; the 11micron and 23 micron crystalline resonances appear amplified when crystals are incorporated into aggregates composed otherwise of spherically shaped amorphous Fe-Mg olivines and pyroxenes. We show that the optical properties of a porous aggregate is not linear combination of its monomers, so aggregates need to be computed. We discuss the consequence of lowering comet crystal mass fractions by modeling IR SEDs with aggregates with crystals, and the implications for radial transport models of our protoplanetary disk.

Crystal Shape and Crystal Mass Fraction↗
DOE OSTI2024

Upsampling Monte Carlo reactor simulation tallies in depleted LWR assemblies fueled with LEU and HALEU using a convolutional neural network

Simulating nuclear reactor cores at the highest achievable spatial and energy resolution is critical in modeling these systems accurately. Increasing the resolution, however, can dramatically increase the memory and central processing unit time required to run simulations. A convolutional neural network was shown previously to accurately upsample tally results of simulated light water reactor assemblies fueled with fresh, low enriched uranium. Here, we show that a convolutional neural network can be used to upsample tally results in assemblies containing fresh and depleted fuel enriched from 1.6 to 19.9 atom percent. The network was trained using neutron flux tallies from simulations of light water reactor assemblies with a range of fuel and coolant temperatures and a diverse selection of geometries. Accurate predictions of flux tallies are possible even on test assemblies with geometries and burnup levels well outside the range of those present in the training and validation data. The network improves the data density by a factor of 8 over a broad range of light water reactor assemblies while incurring insignificant additional computational cost to a Monte Carlo simulation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1987

Theoretical analysis of HVAC duct hanger systems

Several methods are presented which, together, may be used in the analysis of duct hanger systems over a wide range of frequencies. The finite element method (FEM) and component mode synthesis (CMS) method are used for low- to mid-frequency range computations and have been shown to yield reasonably close results. The statistical energy analysis (SEA) method yields predictions which agree with the CMS results for the 800 to 1000 Hz range provided that a sufficient number of modes participate. The CMS approach has been shown to yield valuable insight into the mid-frequency range of the analysis. It has been demonstrated that it is possible to conduct an analysis of a duct/hanger system in a cost-effective way for a wide frequency range, using several methods which overlap for several frequency bands.

Miller, R. D.↗
NASA NTRSDate not supplied

Evaluation of the Accuracy of the Load Prediction Equations of Low-Load Balance Calibration Data

Accuracy and reliability of the load prediction equations of a low-load calibration data set of a force balance were investigated. First, independent load prediction equations were generated from the data of a full-load and low-load machine calibration. Then, the low-load data set was processed as a set of precision check loads for the load prediction equations that were obtained from the full-load and low-load calibrations. Finally, the load prediction equations of the two calibrations were applied to four manual check load data sets that were recorded between 2009 and 2022. The residuals of the predicted calibration and check loads were compared in all cases. No systematic improvement of the load prediction accuracy was observed when the load prediction equations of the low-load calibration were applied to the check load data sets. Therefore, it is recommended to apply a full-load calibration to a six-component balance even if the balance is not used across its entire design envelope. This approach has the advantage that the resulting load prediction equations are less likely to be applied outside of the calibration load ranges. In addition, it is expected that the numerical estimates of the bridge sensitivities are more reliable assuming that the uncertainties of the applied loads and measured outputs of the calibration data are more or less constant across the load range of the balance.

strain-gage balance↗
DOE OSTI2026

Dwarf Galaxies at Cosmic Noon: New JWST Constraints on Satellite Models and Subhalo Tidal Evolution

The advent of JWST has revolutionized the study of faint satellite galaxies at z ≳ 1, enabling statistical constraints on galaxy evolution and the galaxy–halo connection in a previously unexplored mass and redshift regime. We compare satellite abundances at 1 < z < 3.5 from recent JWST observations with predictions from cosmological dark-matter-only zoom-in simulations. We identify and quantify several sources of biases that can impact theoretical satellite counts, finding that assumptions about subhalo tidal evolution introduce the largest uncertainty in predictions for the satellite mass function. Using a flexible galaxy disruption model, we explore a range of disruption scenarios, spanning hydrodynamically motivated and idealized prescriptions, to bracket plausible physical outcomes. We show that varying galaxy durability can change the predicted satellite mass functions by a factor of ∼3.5. The JWST data and our fiducial model are consistent within 1σ–2σ across the full redshift (1 < z < 3.5) and stellar mass (M⋆ > 10 7 M ⊙ ) range probed. We find evidence that subhalos are at least as long-lived as predicted by hydrodynamic simulations. Our framework will enable robust constraints on the tidal evolution of subhalos with future observations. This work presents the first direct comparison between cosmological models and observations of the high-redshift satellite population in this low-mass regime. These results showcase JWST’s emerging power to test structure formation in the first half of the Universe in a new domain and to constrain the physical processes driving the evolution of low-mass galaxies across cosmic time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Transport model predictions of 225 Ac production cross sections via energetic p, d and α irradiation of 232 Th targets

Monte Carlo transport codes PHITS and MCNP6 were used to calculate the production cross sections of 225,227 Ac, 227,229 Th, 223,225 Ra, and 229,230,231 Pa via the bombardment of a 232 Th target with energetic protons, deuterons, and α-particles. The incident projectile energies ranged between 10 and 800 MeV/nucleon. When possible, the predicted production cross sections were compared with the available experimental data and other predictions. Here, the degree of the codes’ abilities to match the measured data provides a qualitative assessment of the codes’ abilities to predict data from similar, but unmeasured, projectile/target systems. In addition, a comparison between calculated cross sections and data may provide insight into possible improvements in the physics models employed by those transport codes.

223Ra↗