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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Evaluating a Semi-Quantitative, Spectrophotometric Method for the Rapid Determination of Total Rare Earth Element Concentrations in Citrate Leaching Solutions

Quick, reliable methods to measure the rare earth elements (REEs) in process streams are needed to support real-time parameterization and monitoring for a burgeoning number of REE extraction schemes. Such methods would ideally be fast, reproducible, and field-deployable. This study evaluates the feasibility of using a chromogenic indicator, arsenazo(III) (hereafter abbreviated as Arsz(III)) to detect the concentration of REEs in complex citrate leaching solutions using a spectrophotometer. Arsz(III) forms a chromogenic complex with the lanthanide elements that produces a blue/purple color detectable in the visible light range. Matrices of solutions containing varied concentrations of calcium, iron, and lanthanum were scanned in the presence of an Arsz(III) indicator to generate a modeled relationship that could be used to back calculate REE concentrations. While relatively quick (scan times on the order of seconds), the results indicated that the calcium and iron concentrations of typical mining solutions overwhelm the signal of the REEs, to the extent that sensitivity of the method is no less than 50 µM REEs. These results demonstrate the difficulties of accurately measuring trace metals in complex solutions, although the method may still hold utility for processes with significantly lower calcium and iron concentrations or REE concentrations much greater than 50 µM.

36 MATERIALS SCIENCE↗
NASA NTRS2007

Topology Synthesis of Structures Using Parameter Relaxation and Geometric Refinement

Typically, structural topology optimization problems undergo relaxation of certain design parameters to allow the existence of intermediate variable optimum topologies. Relaxation permits the use of a variety of gradient-based search techniques and has been shown to guarantee the existence of optimal solutions and eliminate mesh dependencies. This Technical Publication (TP) will demonstrate the application of relaxation to a control point discretization of the design workspace for the structural topology optimization process. The control point parameterization with subdivision has been offered as an alternative to the traditional method of discretized finite element design domain. The principle of relaxation demonstrates the increased utility of the control point parameterization. One of the significant results of the relaxation process offered in this TP is that direct manufacturability of the optimized design will be maintained without the need for designer intervention or translation. In addition, it will be shown that relaxation of certain parameters may extend the range of problems that can be addressed; e.g., in permitting limited out-of-plane motion to be included in a path generation problem.

Hull, P. V.↗
DOE OSTI2021

Uncertainty of SW Cloud Radiative Effect in Atmospheric Models Due to the Parameterization of Liquid Cloud Optical Properties

Clouds are largely responsible for the spread of climate models predictions. Here we focus on the uncertainties in cloud shortwave radiative effect due to the parameterization of liquid cloud single scattering properties (SSPs) from liquid water content (LWC) and droplet number concentration (N), named parameterization of cloud optical properties. Uncertainties arise from not accounting for the droplet size distribution (DSD)—which affects the estimation of the effective radius (r eff ) and modulates the r eff -dependency of the SSPs—and from averaging SSPs over wide spectral bands. To assess these uncertainties a series of r eff -dependent SSPs parameterizations corresponding to various DSDs and spectral averaging methods are derived and implemented in a radiative code. Combined with the DSD-dependent estimation of r eff they are used to compute the bulk radiative properties (reflectance, transmittance, absorptance) of various clouds (defined in terms of LWC and N), including a homogeneous cloud, more realistic case studies, and outputs of a climate model. The results show that the cloud radiative forcing can vary up to 20% depending on the assumed DSD. Likewise, differences up to 20% are obtained for heating rates. The estimation of r eff is the main source of uncertainty, while the SSPs parameterization contributes to around 20% of the total uncertainty. Spectral averaging is less an issue, except for atmospheric absorption. Overall, global shortwave cloud radiative effect can vary by 6 W m –2 depending on the assumed DSD shape, which is about 13% of the best observational estimate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Non-Blind Deblurring for Fluorescence: A Deformable Latent Space Approach with Kernel Parameterization

We report N\non-blind deblurring (NBD) is a modeling method of the image deblurring problem in computer vision, where the blurring kernel is known or can be externally estimated. In this paper, we attempt to solve a parametric NBD problem, inspired by the simultaneous acquisition of ptychography and fluorescent imaging (FI). Ptychography is an imaging method that favors larger probes, i.e. convolutional kernels, while FI relies on a small probe for high resolution. Also, the kernel can be solved during ptychographic reconstruction. With Ptycho-FI using the same larger kernel, we can perform NBD on the blurred fluorescent images to achieve high-resolution FI, and thus speed up the experiments. To this end, we design a deep latent space deformation network that is directly parameterized by the kernel. The network consists of three components: encoder, deformer, and decoder, where the deformer is specifically meant to rectify the latent space representations of blurred images to a standard latent space, regardless of the kernel. The deformation network is trained with a two-stage training scheme. We conduct extensive experiments to confirm that our parametric model can adapt to drastically different blurring kernels and perform robust deblurring.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

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

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

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experiments

Observationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme’s development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is introduced using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS—flexibility being a key feature of this approach—are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit—a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the further benefit of rigorously quantified uncertainty of these estimates.

58 GEOSCIENCES↗
NASA NTRS1974

A parameterization for the absorption of solar radiation in the earth's atmosphere

A method is described for rapidly computing the amount of solar energy absorbed at the earth's surface and in the atmosphere as a function of altitude. The method is a parametric treatment, but the form of the solution and the coefficients involved are based on accurate multiple-scattering computations. In this treatment the absorption varies with the amount and type of clouds, the humidity, the zenith angle of the sun, and the albedo of the earth's surface. Within the stratosphere the absorption also depends on the vertical distribution of ozone. This parameterization for solar radiation is being used in current versions of the global atmospheric circulation model developed at the Goddard Institute for Space Studies.

Lacis, A. A.↗
DOE OSTI2021

Bond order predictions using deep neural networks

Machine learning is an extremely powerful tool for the modern theoretical chemist since it provides a method for bypassing costly algorithms for solving the Schrödinger equation. Already, it has proven able to infer molecular and atomic properties such as charges, enthalpies, dipoles, excited state energies, and others. Most of these machine learning algorithms proceed by inferring properties of individual atoms, even breaking down total molecular energy into individual atomic contributions. In this paper, we introduce a modified version of the Hierarchically Interacting Particle Neural Network (HIP-NN) capable of making predictions on the bonds between atoms rather than on the atoms themselves. We train the modified HIP-NN to infer bond orders for a large number of small organic molecules as computed via the Natural Bond Orbital package. We demonstrate that the trained model is extensible to molecules much larger than those in the training set by studying its performance on the COMP6 dataset. This method has applications in cheminformatics and force field parameterization and opens a promising future for machine learning models to predict other quantities that are defined between atoms such as density matrix elements, Hamiltonian parameters, and molecular reactivities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Computationally efficient Bayesian quantum state tomography

We describe a method for Bayesian quantum state estimation combining efficient parameterization, a pseudo-likelihood, and advanced numerical sampling techniques. Examples reveal significant computational speedup, indicating the approach’s promise in practical quantum state tomography.

Lukens, Joseph↗
DOE OSTI2021

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. Furthermore, 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.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Active vibration mitigation of distributed parameter, smart-type structures using Pseudo-Feedback Optimal Control (PFOC)

A new, near-optimal feedback control technique is introduced that is shown to provide excellent vibration attenuation for those distributed parameter systems that are often encountered in the areas of aeroservoelasticity and large space systems. The technique relies on a novel solution methodology for the classical optimal control problem. Specifically, the quadratic regulator control problem for a flexible vibrating structure is first cast in a weak functional form that admits an approximate solution. The necessary conditions (first-order) are then solved via a time finite-element method. The procedure produces a low dimensional, algebraic parameterization of the optimal control problem that provides a rigorous basis for a discrete controller with a first-order like hold output. Simulation has shown that the algorithm can successfully control a wide variety of plant forms including multi-input/multi-output systems and systems exhibiting significant nonlinearities. In order to firmly establish the efficacy of the algorithm, a laboratory control experiment was implemented to provide planar (bending) vibration attenuation of a highly flexible beam (with a first clamped-free mode of approximately 0.5 Hz).

Patten, W. N.↗
NASA NTRS1992

Satellite-derived surface latent heat fluxes in a rapidly intensifying marine cyclone

Surface latent heat fluxes are estimated in the vicinity of a rapidly deepening cyclone before and during its period of most rapid intensification. This was done with a bulk parameterization scheme and remotely sensed input data. A method is studied for estimating the difference in specific humidity between the surface and a ten meter height using the Special Sensor Microwave/Imager (SSM/I) observed integrated water vapor field and a sea surface temperature analysis. The surface latent heat flux fields generally have estimated errors below 40 pct. south of 40 deg. N and outside the region of high integrated water vapor values associated with frontal bands. The method of estimating surface latent heat fluxes for the case study was found to be usable in most regions of the Northwest Atlantic Ocean except for those locations directly adjacent to coastlines in instances of offshore flow and in the vicinity of surface fronts.

Miller, Douglas K.↗
NASA NTRS1992

Satellite-derived surface latent heat fluxes in a rapidly intensifying marine cyclone

The aim of this article is to estimate surface latent heat fluxes in the vicinity of a rapidly deepening cyclone before and during its period of most rapid intensification. This is done with a bulk parameterization scheme and remotely sensed input data. A method for estimating the difference in specific humidity between the surface and a 10-m height is investigated using the Special Sensor Microwave/Imager-observed integrated water vapor field and a sea surface temperature analysis. The surface latent heat flux fields generally have estimated errors below 40 percent south of 40 deg N and outside the region of high integrated water vapor values associated with frontal bands. The method of estimating surface latent heat fluxes for the case study was found to be usable in most regions of the northwest Atlantic Ocean except for those locations directly adjacent to coastlines in instances of offshore flow and in the vicinity of surface fronts.

Miller, Douglas K.↗
DOE OSTI2022

Bayesian Monte Carlo Evaluation Framework for Imperfect Data and Models [Abstract]

Nuclear data evaluation methods conventionally make the following assumptions: prior and posterior probability distribution functions (PDFs) of all model parameters and data are normal (Gaussian); the linear approximation is sufficiently accurate for minimization of a cost function (even for non-linear models); and that both the model (of, e.g., neutron cross section) and experimental data (including their covariance data) are perfect. These assumptions are inherent to the well-known generalized linear least squares (GLLS) minimization method commonly used for evaluations of resolved resonance region (RRR) neutron cross sections. However, these assumptions are often not justified due to the presence of non-normal PDFs, non-linear models (e.g. R -matrix formalism), and inherent imperfections in data and models (e.g. discrepant data sets, discrepancies between the previous evaluation and newly measured data, or imperfect covariance data). We remove the said assumptions in a mathematical framework of Bayes’ theorem, and implement it using the Metropolis-Hastings Monte Carlo method. Parameters of a new kind are introduced to parameterize inherent imperfections, e.g. , any discrepancies between the theoretical model and measured data. These new parameters enable evaluators to quantify their expert judgement about any discrepancies or imperfections in a reproducible manner. We demonstrate the framework with an ongoing evaluation of 233 U in the eV region using the ENDF-B/VIII library and transmission data measured by Guber, et al. , and compare the posterior parameters to those obtained by conventional evaluation methods.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Rapidly-convergent flux-surface shape parameterization

We propose a novel flux-surface parameterization suitable for local MHD equilibrium calculations with strongly-shaped flux surfaces. The method is based on a systematic expansion in a small number of intuitive shape parameters, and reduces to the well-known Miller D-shaped parameterization in the limit where some of the coefficients are set to zero. Here, the new parameterization is valid for up-down asymmetric plasmas and provides an improvement to the Miller form. Simultaneously, the method is rapidly convergent and requires only about half the number of shape parameters as a general Fourier representation in the pedestal.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Calculation of Nuclear Reactor Cooling Tower Performance With Limited Data Streams

Monitoring of cooling tower performance in a nuclear reactor facility is necessary to ensure safe operation; however, instrumentation for measuring performance characteristics can be difficult to install and may malfunction or break down over long duration experiments. This paper describes employing a thermodynamic approach to quantify cooling tower performance, the Merkel model, which requires only five parameters, namely, inlet water temperature, outlet water temperature, liquid mass flowrate, gas mass flowrate, and wet bulb temperature. Using this model, a general method to determine cooling tower operation for a nuclear reactor was developed in situations when neither the outlet water temperature nor gas mass flowrate are available, the former being a critical piece of information to bound the Merkel integral. Furthermore, when multiple cooling tower cells are used in parallel (as would be in the case of large-scale cooling operations), only the average outlet temperature of the cooling system is used as feedback for fan speed control, increasing the difficulty of obtaining the outlet water temperature for each cell. To address these shortcomings, this paper describes a method to obtain individual cell outlet water temperatures for mechanical forced-air cooling towers via parametric analysis and optimization. In this method, the outlet water temperature for an individual cooling tower cell is acquired as a function of the liquid-to-gas ratio (L/G). Leveraging the tight tolerance on the average outlet water temperature, an error function is generated to describe the deviation of the parameterized L/G to the highly controlled average outlet temperature. The method was able to determine the gas flowrate at rated conditions to be within 3.9% from that obtained from the manufacturer’s specification, while the average error for the four individual cooling cell outlet water temperatures were 1.6 °C, -0.5 °C, -1.0 °C, and 0.3 °C.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Coordination and control – limits in standard representations of multi-reservoir operations in hydrological modeling

Abstract. Major multi-reservoir cascades represent a primary mechanism for dealing with hydrologic variability and extremes within institutionally complex river basins worldwide. These coordinated management processes fundamentally reshape water balance dynamics. Yet, multi-reservoir coordination processes have been largely ignored in the increasingly sophisticated representations of reservoir operations within large-scale hydrological models. The aim of this paper is twofold, namely (i) to provide evidence that the common modeling practice of parameterizing each reservoir in a cascade independently from the others is a significant approximation and (ii) to demonstrate potential unintended consequences of this independence approximation when simulating the dynamics of hydrological extremes in complex reservoir cascades. We explore these questions using the Water Balance Model, which features detailed representations of the human infrastructure coupled to the natural processes that shape water balance dynamics. It is applied to the Upper Snake River basin in the western US and its heavily regulated multi-reservoir cascade. We employ a time-varying sensitivity analysis that utilizes the method of Morris factor screening to explicitly track how the dominant release rule parameters evolve both along the cascade and in time according to seasonal high- and low-flow events. This enables us to address aim (i) by demonstrating how the progressive and cumulative dominance of upstream releases significantly dampens the ability of downstream reservoir rules' parameters to influence flow conditions. We address aim (ii) by comparing simulation results with observed reservoir operations during critical low-flow and high-flow events in the basin. Our time-varying parameter sensitivity analysis with the method of Morris clarifies how independent single-reservoir parameterizations and their tacit assumption of independence leads to reservoir release behaviors that generate artificial water shortages and flooding, whereas the observed coordinated cascade operations avoided these outcomes for the same events. To further explore the role of (non-)coordination in the large deviations from the observed operations, we use an offline multi-reservoir water balance model in which adding basic coordination mechanisms drawn from the observed emergency operations is sufficient to correct the deficiencies of the independently parameterized reservoir rules from the hydrological model. These results demonstrate the importance of understanding the state–space context in which reservoir releases occur and where operational coordination plays a crucial role in avoiding or mitigating water-related extremes. Understanding how major infrastructure is coordinated and controlled in major river basins is essential for properly assessing future flood and drought hazards in a changing world.

54 ENVIRONMENTAL SCIENCES↗