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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Quantitative Global Heat Transfer in a Mach-6 Quiet Tunnel

This project developed quantitative methods for obtaining heat transfer from temperature sensitive paint (TSP) measurements in the Mach-6 quiet tunnel at Purdue, which is a Ludwieg tube with a downstream valve, moderately-short flow duration and low levels of heat transfer. Previous difficulties with inferring heat transfer from TSP in the Mach-6 quiet tunnel were traced to (1) the large transient heat transfer that occurs during the unusually long tunnel startup and shutdown, (2) the non-uniform thickness of the insulating coating, (3) inconsistencies and imperfections in the painting process and (4) the low levels of heat transfer observed on slender models at typical stagnation temperatures near 430K. Repeated measurements were conducted on 7 degree-half-angle sharp circular cones at zero angle of attack in order to evaluate the techniques, isolate the problems and identify solutions. An attempt at developing a two-color TSP method is also summarized.

Sullivan, John P.↗
NASA NTRS2018

Resolving Faint Structures in the Debris Disk Around TWA 7* Tentative Detections of an Outer Belt, a Spiral Arm, and a Dusty Cloud

Context. Debris disks are the intrinsic by-products of the star and planet formation processes. Most likely due to instrumental limitations and their natural faintness, little is known about debris disks around low mass stars, especially when it comes to spatially resolved observations. Aims. We present new VLT/SPHERE IRDIS dual-polarization imaging (DPI) observations in which we detect the dust ring around the M2 spectral type star TWA 7. Combined with additional angular differential imaging observations we aim at a fine characterization of the debris disk and setting constraints on the presence of low-mass planets. Methods. We modeled the SPHERE DPI observations and constrain the location of the small dust grains, as well as the spectral energy distribution of the debris disk, using the results inferred from the observations, and performed simple N-body simulations. Results. We find that the dust density distribution peaks at ~0.72′′ (25 au), with a very shallow outer power-law slope, and that the disk has an inclination of ~13° with a position angle of ~91° east of north. We also report low signal-to-noise ratio detections of an outer belt at a distance of ~1.5′′ (~52 au) from the star, of a spiral arm in the southern side of the star, and of a possible dusty clump at 0.11′′. These findings seem to persist over timescales of at least a year. Using the intensity images, we do not detect any planets in the close vicinity of the star, but the sensitivity reaches Jovian planet mass upper limits. We find that the SED is best reproduced with an inner disk at ~0.2′′ (~7 au) and another belt at 0.72′′ (25 au). Conclusions. We report the detections of several unexpected features in the disk around TWA 7. A yet undetected 100 Solar Mass planet with a semi-major axis at 20−30 au could possibly explain the outer belt as well as the spiral arm. We conclude that stellar winds are unlikely to be responsible for the spiral arm.

VLT/SPHERE IRDIS↗
NASA NTRS2019

AladynPi – Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application

This report provides an overview and description of commands used in the Computational Materials mini-application, AladynPi. AladynPi is an extension of a previously released mini-application, Aladyn (https://github.com/nasa/aladyn; Yamakov, V.I., and Glaessgen, E.H., NASA/TM-2018-220104). Aladyn and AladynPi are basic molecular dynamics codes written in FORTRAN 2003, which are designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory method. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. In Aladyn, the ANN gives directly the energy of interatomic interactions. In AladynPi, the ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). The parameterized BOP function is then used to calculate the energy. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing manycore architectures based on graphic processing units (GPUs). The effort is supported by the High Performance Computing incubator (HPCi) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗
NASA NTRS2021

Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model

The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. The combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

Robert S. Schrom↗
NASA NTRS2021

Trend Detection of Atmospheric Time Series: Incorporating Appropriate Uncertainty Estimates and Handling Extreme Events

This paper is aimed at atmospheric scientists without formal training in statistical theory. Its goal is to, 1) provide a critical review of the rationale for trend analysis of the time series typically encountered in the field of atmospheric chemistry; 2) describe a range of trend-detection methods; and 3) demonstrate effective means of conveying the results to a general audience. Trend detections in atmospheric chemical composition data are often challenged by a variety of sources of uncertainty, which often behave differently to other environmental phenomena such as temperature, precipitation rate, or stream flow, and may require specific methods depending on the science questions to be addressed. Some sources of uncertainty can be explicitly included in the model specification, such as autocorrelation and seasonality, but some inherent uncertainties are difficult to quantify, such as data heterogeneity and measurement uncertainty due to the combined effect of short- and long-term natural variability, instrumental stability, and aggregation of data from sparse sampling frequency. Failure to account for these uncertainties might result in an inappropriate inference of the trends and their estimation errors. On the other hand, the variation in extreme events might be interesting for different scientific questions, for example, the frequency of extremely high surface ozone events and their relevance to human health. In this study we aim to, 1) review trend detection methods for addressing different levels of data complexity in different chemical species; 2) demonstrate that the incorporation of scientifically interpretable covariates can outperform pure numerical curve fitting techniques in terms of uncertainty reduction and improved predictability; 3) illustrate the study of trends based on extreme quantiles that can provide insight beyond standard mean or median based trend estimates; and 4) present an advanced method of quantifying regional trends based on the inter-site correlations of multi-site data. All demonstrations are based on time series of observed trace gases relevant to atmospheric chemistry, but the methods can be applied to other environmental data sets.

Trace gas↗
DOE OSTI2014

Phytozome Comparative Plant Genomics Portal

The Dept. of Energy Joint Genome Institute is a genomics user facility supporting DOE mission science in the areas of Bioenergy, Carbon Cycling, and Biogeochemistry. The Plant Program at the JGI applies genomic, analytical, computational and informatics platforms and methods to: 1. Understand and accelerate the improvement (domestication) of bioenergy crops 2. Characterize and moderate plant response to climate change 3. Use comparative genomics to identify constrained elements and infer gene function 4. Build high quality genomic resource platforms of JGI Plant Flagship genomes for functional and experimental work 5. Expand functional genomic resources for Plant Flagship genomes

09 BIOMASS FUELS↗
DOE OSTI2025

SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression

Abstract Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining comparable accuracy (Tsoi et al 2024 EPJ Web Conf. 295 09036). These capabilities are highly valuable in environments with stringent computational resource constraints, such as high-energy physics experiments at the CERN Large Hadron Collider. However, finding compact expressions for high-dimensional datasets remains challenging due to the inherent limitations of genetic programming (GP), the search algorithm of most symbolic regression (SR) methods. Contrary to GP, the NN approach to SR offers scalability to high-dimensional inputs and leverages gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which can result in significant performance loss. In this work, we propose S y m b o l N e t , a NN approach to SR specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs. This framework allows dynamic pruning of model weights, input features, and mathematical operators in a single training process, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term for each pruning type, which can adaptively adjust its strength, leading to convergence at a target sparsity ratio. Unlike most existing SR methods that struggle with datasets containing more than O ( 10 ) inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).

Tsoi, Ho Fung (ORCID:0000000225502184)↗
DOE OSTI2022

Simultaneous inference of plate boundary stresses and mantle rheology using adjoints: large-scale 2-D models

SUMMARY Plate motions are a primary surface constraint on plate and mantle dynamics and rheology, plate boundary stresses and the occurrence of great earthquakes. Within an optimization method, we use plate motion data to better constrain uncertain mantle parameters. For the optimization problem characterizing the maximum a posteriori rheological parameters we derive gradients using adjoints and expressions to approximate the posterior distributions for stresses within plate boundaries. We apply these methods to a 2-D cross section from the western to eastern Pacific, with temperature distributions and fault zone geometries developed primarily from seismic and plate motion data. We find that the best-fitting stress exponent, n, is about 2.8 and the yield stress about 100 MPa or less. The normal stress on the interplate fault zones is about 100 MPa and the shear stresses about 10 MPa or less.

Rudi, Johann (ORCID:0000000265639265)↗
DOE OSTI2021

Cosmic Inference: Constraining Parameters with Observations and a Highly Limited Number of Simulations

We look at cosmological probes that pose an inverse problem where the measurement result is obtained through observations, and the objective is to infer values of model parameters that characterize the underlying physical system-our universe, from these observations and theoretical forward-modeling. The only way to accurately forward-model physical behavior on small scales is via expensive numerical simulations, which are further “emulated” due to their high cost. Emulators are commonly built with a set of simulations covering the parameter space with Latin hypercube sampling and an interpolation procedure; the aim is to establish an approximately constant prediction error across the hypercube. In this paper, we provide a description of a novel statistical framework for obtaining accurate parameter constraints. The proposed framework uses multi-output Gaussian process emulators that are adaptively constructed using Bayesian optimization methods with the goal of maintaining a low emulation error in the region of the hypercube preferred by the observational data. In this paper, we compare several approaches for constructing multi-output emulators that enable us to take possible inter-output correlations into account while maintaining the efficiency needed for inference. Using a Lyα forest flux power spectrum, we demonstrate that our adaptive approach requires considerably fewer-by a factor of a few in the Lyα P(k) case considered here-simulations compared to the emulation based on Latin hypercube sampling, and that the method is more robust in reconstructing parameters and their Bayesian credible intervals.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Inferring the Energetics of CO 2 –Aniline Adduct Formation from Vibrational Spectroscopy

Control of atmospheric CO 2 is an important contemporary scientific and engineering challenge. Towards this goal, the reaction of CO 2 with amines to form carbamate bonds is an established method for CO 2 capture. However, controllable reversal of this reaction remains difficult and requires tuning the energetics of the carbamate bond. Through IR spectroscopy, we show that a characteristic frequency observed upon carbamate formation varies as a function of the substituent’s Hammett parameter for a family of para- substituted anilines. We present computational evidence that the vibrational frequency of the adducted CO 2 serves as a predictor of the energy of formation of the carbamate. Electron donating groups typically enhance the driving force of carbamate formation by transferring more charge to the adducted CO 2 and thus increasing the occupancy of the anti-bonding orbital in the carbon-oxygen bonds. Increased occupancy of the anti-bonding orbital within adducted CO 2 indicates a weaker bond, leading to a red shift in the characteristic carbamate frequency. As a result, our work serves the large field of CO 2 capture research where spectroscopic observables, such as IR frequencies, are more easily obtainable and can stand in as a descriptor of driving forces.

Amines↗
NASA NTRS1993

Inferring global characteristics of current sheet from local measurements

A new procedure to extract global characteristics of a current sheet based on local measurements of magnetic field, plasma, and current density from a single spacecraft is described. By adopting suitable current density profiles, this method permits an estimate of the following parameters: (1) the asymptotic magnetic field strength outside the current sheet, (2) the north-south thickness of the current sheet, (3) the spacecraft distance from the center of the current sheet, (4) the magnetic field line curvature at the center of the current sheet, (5) the volume current density at the center of the current sheet, (6) the integrated current density across the thickness of the current sheet, and (7) the kappa parameter used by many researchers in the study of particle orbits in a current sheet geometry.

Lui, A. T. Y.↗
DOE OSTI2022

Code Verification and Solution Verification framework in pin-resolved neutron transport code MPACT

Program verification in scientific computing encompasses the application of formal and mathematical techniques to a scientific computing code for its credibility, accuracy, and validity. Code Verification identifies bugs and performance issues in the software development stage. Solution Verification assesses the applicability of the code and the accuracy of the solution to problems of interest. Both activities utilize application cases and quantify the error against prescribed acceptance criteria. However, simply executing more application cases does not guarantee stronger or more comprehensive credibility. Here, we establish a verification framework that involves Code Verification and Solution Verification, both of which work together such that the overarching goal of “converge to the correct answer for the intended application” can be reasonably inferred. The application of such a verification framework is demonstrated using the pin-resolved neutron transport code MPACT, where standard unit tests and regression tests are covered, and where the Method of Exact Solutions and the Method of Manufactured Solutions are successfully used. Additionally, the applicability of Method of Manufactured Solutions is extended to the OECD/NEA C5G7 benchmark problems of practical material and geometric configurations. Solution Verification activities are demonstrated on a practical hierarchy of application models of increasing complexity ranging from 2D pin cell problems to 3D assembly problems. The convergence behavior and rate of convergence with respect to each individual variable are studied and provided. This framework can be adapted broadly to other fields involving scientific computing codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Parametrizations of the spin density matrix

Highlights: • Proposed two methods for parametrizing the SDM of spinning, unstable particles. • All non-negativity conditions fulfilled; adaptability to various constraints. • First method: based on a theorem; applicable in a simple and flexible way. • Second method: particularly suitable for especial conditions. • Applications: two of them interesting in the search for new physics. We propose for the spin density matrix two parametrizations which automatically fulfill the non-negativity conditions, without setting any bound on the parameters. The first one relies on a theorem, that we prove, and it is rather simple and easily adaptable to some specific reactions, where, for example, parity is conserved or angular momentum conservation entails selection rules. Moreover, in the case when the rank is less than the order of the density matrix, we show how to improve the fits to the data, either by implementing previous suggestions, or by elaborating an alternative method, for which we prove a second theorem. Our second parametrization is a variant of previous treatments, it appears suitable for some particular processes. Moreover, we discuss about the possibility of inferring the elements of the density matrix from the differential decay width. Last, we illustrate various examples of current interest, both in strong and weak decays; some of them may be helpful in the investigation of physics beyond the standard model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Structure of Gas Phase Monohydrated Nicotine: Implications for Nicotine’s Native Structure in the Acetylcholine Binding Protein

In this work, we report a joint experimental–theoretical study of the never reported before structure and infrared spectra of gas phase monohydrated nicotine (NIC) and nornicotine (NOR) and use them to assign their protonation sites. NIC’s biological activity is strongly affected by its protonation site, namely, the pyrrolidine (Pyrro-NICH + , anticipated active form) and pyridine (Pyri-NICH + ) forms; however, these have yet to be directly experimentally determined in either the nicotinic acetylcholine receptor (nAChR, no water present) or the acetylcholine-binding protein (AChBP, a single water molecule is present) but can only be inferred to be Pyrro-NICH + from the intermolecular distance to the neighboring residues (i.e., tryptophan). Our temperature-controlled double ion trap infrared spectroscopic experiments assisted by the collisional stripping method and high-level theoretical calculations yield the protonation ratio of Pyri:Pyrro = 8:2 at 240 K for the gas phase NICH + ···(H 2 O) complex, which resembles the molecular cluster present in the AChBP. Therefore, a single water molecule in the gas phase enhances this ratio in NICH + relative to the 3:2 for the nonhydrated gas phase NICH + in a trend that contrasts with the almost exclusive presence of Pyrro-NICH + in aqueous solution. In contrast, the Pyri-NORH + protomer is exclusively observed, a fact that may correlate with its weaker biological activity.

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

Distributed Acoustic Sensing to Estimate the Permeability

Optical fiber in a borehole can be interrogated with distributed acoustic sensors (DAS) to capture fracture displacements with the potential to map surrounding fracture networks. We designed a laboratory experiment to test the capability of DAS to determine borehole flow characteristics, and we show that for the first time DAS can be used to remotely estimate permeability. Optical fiber was wrapped around a bead filled pipe and the pressure drop and flow velocity were measured to directly calculate permeability. A machine learning model using statistical features from continuous DAS estimated the bulk permeability. Fluid interactions with the permeable material demonstrate insufficient resolution using DAS amplitude-based measurements for estimating pressure drop to infer permeability. Variations in the spectral domain relate DAS measurements to the pressure drop and provide consistent permeability estimates. Resolution with DAS is sufficient to estimate permeability and provides a reliable method to monitor at depth in borehole conditions.

58 GEOSCIENCES↗
DOE OSTI2014

Study of heat sources interacting in integrated circuits by laser mirage effect

This work exploits the mirage effect to analyze multiple heat sources thermally interacting in an integrated circuit (IC) by means of a probe IR laser beam, which strikes on the die lateral walls and passes through the die substrate. Under such conditions, the criteria for locating such hot spots, as well as their relative power dissipation, are discussed on the basis of a theoretical model inferred in this work. Finally, the technique feasibility is shown in a real application scenario, obtaining 5-μm spatial lateral resolution and an error in power dissipation measurements below 5%. This method may become a practical alternative to usual off-chip techniques for inspecting hot spots in ICs and to experimentally characterize heat flow in the semiconductor substrate.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Using multiple neutron time of flight detectors to determine the hot spot velocity

An important diagnostic value of a shot at the National Ignition Facility is the resultant center-of-mass motion of the imploding capsule. This residual velocity reduces the efficiency of converting laser energy into plasma temperature. A new analysis method extracts the effective hot spot motion by using information from multiple neutron time-of-flight (nToF) lines-of-sight (LoSs). This technique fits a near Gaussian spectrum to the nToF scope traces and overcomes reliance on models to relate the plasma temperature to the mean energy of the emitted neutrons. This method requires having at least four nToF LoSs. The results of this analysis will be compared to an approach where each LoS is analyzed separately and a model is employed to infer the mean energy of the emitted neutrons

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part I: Scheme Description

A novel framework is proposed for the bulk parameterization of rain microphysics: the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS). It is designed to facilitate direct constraint by observations using Bayesian inference. BOSS combines existing process-level microphysical knowledge with flexible process rate formulations and parameters constrained by observations within a Bayesian framework. Furthermore, using a raindrop size distribution (DSD) normalization method that relates DSD moments to one another via generalized power series, generalized multivariate power expressions are derived for the microphysical process rates as functions of a set of prognostic DSD moments. The scheme is flexible and can utilize any number and combination of prognostic moments and any number of terms in the process rate formulations. This means that both uncertainty in parameter values and structural uncertainty associated with the process rate formulations can be investigated systematically, which is not possible using traditional schemes. In this paper, BOSS is compared to two- and three-moment versions of a traditional bulk rain microphysics scheme (denoted as MORR). It is demonstrated that some process formulations in MORR are analytically equivalent to the generalized power expressions in BOSS using one or two terms, while others are not. BOSS is able to replicate the behavior of MORR in idealized one-dimensional rainshaft tests, but with a much more flexible and systematic design. Part II of this study describes the application of BOSS to derive rain microphysical process rates and posterior parameter distributions in Bayesian experiments using Markov chain Monte Carlo sampling constrained by synthetic observations.

58 GEOSCIENCES↗