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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Data-driven predictive probability density function control of fiber length stochastic distribution shaping in refining process

Pulp is the most important raw material in paper in paper industries, whose Fiber Length Stochastic Distribution (FLSD) shaping directly determines the energy consumption and paper quality of the subsequent papermaking processes. However, the mean and variance are insufficient to describe the output FLSD shaping, which display non-Gaussian distribution properties. Therefore, the traditional control, method based on the mean and variance of fiber length is difficult to control the FLSD shaping effectively. In this paper, a novel data-driven predictive probability density function (PDF) control method is proposed for output FLSD shaping in refining process. Primarily, in order to improve the approximation accuracy of the PDF employing RBF neural network, the parameters (center value and width) of each RBF basis functions are tuned via utilizing iterative learning control (ILC) law, and the corresponding estimations of weights law can be obtained. Secondly, considering that the conventional linear model of weights vector has drawbacks of low accuracy and weak generalization ability, random vector functional link networks (RVFLNs) based data driven nonlinear modelling method is employed to characterize the prediction model between the input variables and weights vector. Finally, in order to reduce the randomness of the output FLSD, a minimum entropy control method under mean constraint is employed to design the predictive PDF controller for the output FLSD shaping with the help of the established nonlinear stochastic distribution model. Both simulations and experiments are given to illustrate the effectiveness and practicability of the proposed method.

Probability debsity function, Fiber length distrib↗
NASA NTRS1991

Probabilistic structural mechanics research for parallel processing computers

Aerospace structures and spacecraft are a complex assemblage of structural components that are subjected to a variety of complex, cyclic, and transient loading conditions. Significant modeling uncertainties are present in these structures, in addition to the inherent randomness of material properties and loads. To properly account for these uncertainties in evaluating and assessing the reliability of these components and structures, probabilistic structural mechanics (PSM) procedures must be used. Much research has focused on basic theory development and the development of approximate analytic solution methods in random vibrations and structural reliability. Practical application of PSM methods was hampered by their computationally intense nature. Solution of PSM problems requires repeated analyses of structures that are often large, and exhibit nonlinear and/or dynamic response behavior. These methods are all inherently parallel and ideally suited to implementation on parallel processing computers. New hardware architectures and innovative control software and solution methodologies are needed to make solution of large scale PSM problems practical.

Sues, Robert H.↗
DOE OSTI2021

Experimental Realization of the 1D Random Field Ising Model

Here we have measured magnetic-field-induced avalanches in a square artificial spin ice array of interacting nanomagnets. Starting from the ground state ordered configuration, we imaged the individual nanomagnet moments after each successive application of an incrementally increasing field. The statistics of the evolution of the moment configuration show good agreement with the canonical one-dimensional random field Ising model. We extract information about the microscopic structure of the arrays from our macroscopic measurements of their collective behavior, demonstrating a process that could be applied to other systems exhibiting avalanches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Neural chaos: A spectral stochastic neural operator

Building surrogate models for operators with uncertainty quantification capabilities is essential for many engineering applications where randomness–such as variability in material properties, boundary conditions, and initial conditions–is unavoidable. Polynomial Chaos Expansion (PCE) is widely recognized as a go-to method for constructing stochastic surrogates in both intrusive and non-intrusive ways, and it has recently been used in the context of operator learning. However, its application becomes challenging for complex or high-dimensional processes, as achieving accuracy requires higher-order polynomials, which can increase computational demand and/or the risk of overfitting. Furthermore, PCE requires specialized treatments to manage random variables that are not independent, and these treatments may be problem-dependent or may fail with increasing complexity. Here, in this work, we adopt the same formalism as the spectral expansion used in PCE; however, we replace the classical polynomial basis functions with neural network (NN) basis functions to leverage their expressivity. To achieve this, we propose an algorithm that identifies NN-parameterized basis functions in a purely data-driven manner, without any prior assumptions about the joint distribution of the random variables involved, whether independent or dependent, or about their marginal distributions. The proposed algorithm identifies each NN-parameterized basis function sequentially, ensuring they are orthogonal with respect to the data distribution. The basis functions are constructed directly on the joint stochastic variables without requiring a tensor product structure or assuming independence of the random variables. This approach may offer greater flexibility for complex stochastic models, while simplifying implementation compared to the tensor product structures typically used in PCE to handle random vectors. This is particularly advantageous given the current state of open-source packages, where building and training neural networks can be done with just a few lines of code and extensive community support. We demonstrate the effectiveness of the proposed scheme through several numerical examples of varying complexity and provide comparisons with classical PCE.

Polynomial chaos expansion↗
NASA NTRS1994

Acquisition and processing of data for isotope-ratio-monitoring mass spectrometry

Methods are described for continuous monitoring of signals required for precise analyses of 13C, 18O, and 15N in gas streams containing varying quantities of CO2 and N2. The quantitative resolution (i.e. maximum performance in the absence of random errors) of these methods is adequate for determination of isotope ratios with an uncertainty of one part in 10(5); the precision actually obtained is often better than one part in 10(4). This report describes data-processing operations including definition of beginning and ending points of chromatographic peaks and quantitation of background levels, allowance for effects of chromatographic separation of isotopically substituted species, integration of signals related to specific masses, correction for effects of mass discrimination, recognition of drifts in mass spectrometer performance, and calculation of isotopic delta values. Characteristics of a system allowing off-line revision of parameters used in data reduction are described and an algorithm for identification of background levels in complex chromatograms is outlined. Effects of imperfect chromatographic resolution are demonstrated and discussed and an approach to deconvolution of signals from coeluting substances described.

NASA Discipline Exobiology↗
DOE OSTI2022

Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps

Abstract An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R 2 using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R 2 = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points. Significance Statement Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Shallow Marine High-Resolution 3D Seismics for Above-Zone CO2 Monitoring: A Case Study in Offshore Japan

Shallow marine high-resolution 3D (HR3D) seismic utilizes a novel seismic acquisition technology for imaging fine geological features not revealed by conventional 3D surveys. HR3D technology has been previously used for shallow subsurface studies, however is rarely implemented for monitoring injected fluids, and has not previously been used to monitor an active CO2 storage project. In this study, we for the first time demonstrate the acquisition, processing and initial interpretation of a HR3D seismic survey in Tomakomai, offshore Japan, where a large-scale CO2 storage project is being undertaken. As a seismic monitoring tool, the HR3D survey images the geologic overburden (< 1000 ms), aiming to detect potential CO2 leakage pathways above the injection reservoir. The HR3D acquisition system comprises a 210 in3 GI air gun and 4 streamers that are 25 m in length with 10 m inline separation. Each streamer has 8 channels with a 3.125 m group interval, yielding a very small final bin size of 3 x 3 m. The short offset and low-fold coverage of the HR3D data creates new challenges in seismic data processing, which demands a different data processing workflow tailored to the HR3D acquisition. We present a data processing workflow involving multiple software processing packages, which tackled high-resolution related static issues and significantly reduced various noise elements. Dip-steering and structural filtering were employed on the stacked volume to further enhance the lateral coherence and to remove random noise and suppress acquisition footprints. The final volume shows flat and coherent above-zone stratigraphy, suggesting a good seal quality above the CO2 reservoir. The successful implementation of HR3D acquisition and the customized processing workflow verify the feasibility of HR3D seismics for the above- zone monitoring of offshore CO2 injection and storage projects.

Feng, Ye Erickson↗
DOE OSTI2018

SHALLOW MARINE HIGH-RESOLUTION 3D SEISMICS FOR ABOVE- ZONE CO2MONITORING: A CASE STUDY IN OFFSHORE JAPAN

Shallow marine high-resolution 3D (HR3D) seismic utilizes a novel seismic acquisition technology for imaging fine geological features not revealed by conventional 3D surveys. HR3D technology has been previously used for shallow subsurface studies, however is rarely implemented for monitoring injected fluids, and has not previously been used to monitor an active CO2 storage project. In this study, we for the first time demonstrate the acquisition, processing and initial interpretation of a HR3D seismic survey in Tomakomai, offshore Japan, where a large-scale CO2 storage project is being undertaken. As a seismic monitoring tool, the HR3D survey images the geologic overburden (< 1000 ms), aiming to detect potential CO2 leakage pathways above the injection reservoir. The HR3D acquisition system comprises a 210 in3 GI air gun and 4 streamers that are 25 m in length with 10 m inline separation. Each streamer has 8 channels with a 3.125 m group interval, yielding a very small final bin size of 3 x 3 m. The short offset and low-fold coverage of the HR3D data creates new challenges in seismic data processing, which demands a different data processing workflow tailored to the HR3D acquisition. We present a data processing workflow involving multiple software processing packages, which tackled high-resolution related static issues and significantly reduced various noise elements. Dip-steering and structural filtering were employed on the stacked volume to further enhance the lateral coherence and to remove random noise and suppress acquisition footprints. The final volume shows flat and coherent above-zone stratigraphy, suggesting a good seal quality above the CO2 reservoir. The successful implementation of HR3D acquisition and the customized processing workflow verify the feasibility of HR3D seismics for the above-zone monitoring of offshore CO2 injection and storage projects.

Feng, Ye E.↗
DOE OSTI2018

Improved high temperature radiation damage tolerance in a three-phase ceramic with heterointerfaces

Abstract Radiation damage tolerance for a variety of ceramics at high temperatures depends on the material’s resistance to nucleation and growth of extended defects. Such processes are prevalent in ceramics employed for space, nuclear fission/fusion and nuclear waste environments. This report shows that random heterointerfaces in materials with sub-micron grains can act as highly efficient sinks for point defects compared to grain boundaries in single-phase materials. The concentration of dislocation loops in a radiation damage-prone phase (Al 2 O 3 ) is significantly reduced when Al 2 O 3 is a component of a composite system as opposed to a single-phase system. These results present a novel method for designing exceptionally radiation damage tolerant ceramics at high temperatures with a stable grain size, without requiring extensive interfacial engineering or production of nanocrystalline materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Energy transfer in random-matrix ensembles of Floquet Hamiltonians

Here, we explore the statistical properties of energy transfer in ensembles of doubly driven random-matrix Floquet Hamiltonians based on universal symmetry arguments. The energy-pumping efficiency distribution P⁡($\overline{E}$) is associated with the Hamiltonian parameter ensemble and the eigenvalue statistics of the Floquet operator. For specific Hamiltonian ensembles, P⁡($\overline{E}$) undergoes a transition which cannot be associated with a symmetry breaking of the instantaneous Hamiltonian. The Floquet eigenvalue spacing distribution indicates the considered ensembles constitute generic nonintegrable Hamiltonian families. As a step towards Hamiltonian engineering, we develop a machine-learning classifier to understand the relative parameter importance in resulting high-conversion efficiency. We propose random Floquet Hamiltonians as a general framework to investigate frequency conversion effects in a class of generic dynamical processes beyond adiabatic pumps.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Quantum algorithm to simulate Lindblad master equations

We present a quantum algorithm for simulating a family of Markovian master equations that can be realized through a probabilistic application of unitary channels and state preparation. Our approach employs a second-order product formula for the Lindblad master equation, achieved by decomposing the dynamics into dissipative and Hamiltonian components and replacing the dissipative segments with randomly compiled, easily implementable elements. The sampling approach eliminates the need for ancillary qubits to simulate the dissipation process and reduces the gate complexity in terms of the number of jump operators. We provide a rigorous performance analysis of the algorithm. We also extend the algorithm to time-dependent Lindblad equations, generalize the family of Markovian master equations it can be applied to, and explore applications beyond the Markovian noise model. A new error bound, in terms of the diamond norm, for second-order product formulas for time-dependent Liouvillians is provided that might be of independent interest. Published by the American Physical Society 2025

Borras, Evan (ORCID:000900017709037X)↗
DOE OSTI2021

Application of Markov Chain Monte Carlo Methods for Uncertainty Quantification in Inverse Transport Problems

Determination of the components of a radioactive source/shield system using the system’s radiation signature is of great importance in homeland security, material safeguards, and waste management. Although significant progress has been made toward solving this inverse transport problem in recent years, work remains to be done to quantify the uncertainty in reconstructed results. In this article we apply two Markov chain Monte Carlo (MCMC) approaches, the delayed rejection adaptive metropolis (DRAM) and differential evolution adaptive metropolis (DREAM) methods, to solve inverse problems and quantify uncertainty. The DRAM method uses delayed rejection combined with global adaptation of the proposal covariance matrix. Furthermore, the DREAM method hybridizes MCMC sampling with the differential evolution (DE) algorithm. In numerical test cases, the DRAM and DREAM methods are shown to be superior to a first-order inverse Hessian approach for problems with noisy data and multiple unknown quantities, with DREAM converging to the posterior distribution more quickly than DRAM. The DREAM and DRAM results indicate that a full posterior distribution is required to quantify uncertainty in many inverse transport problems.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

New Capability at ORNL: High Precision Uranium Titration

Destructive analytical measurements establish a nuclear facility’s nuclear material inventory and inventory differences for Nuclear Material Accountancy and Control. A nuclear laboratory’s ability to perform accurate high-precision analytical measurements is key for tracking large inventories within a facility’s material balance areas and during production to track material movement through dynamic processes. For uranium, these measurements are made by using several established high-precision measurement protocols. These include isotope dilution mass spectrometry, gravimetry, and potentiometric titrations. Whatever measurement technique a nuclear lab chooses to use, reference materials (RMs) with certified attributes and accompanying uncertainties are used to calibrate measurement systems, and are the cornerstone for accurate results. In addition to calibration, RMs provide for metrological traceability, are used for method development and validation, and thus provide critical evaluations of the appropriateness and performance of analytical processes used. Evaluations may include validation that a method is fit-for-purpose, quantification of systematic and random biases, and the evaluation of long-term and short-term performance metrics. High-precision measurement techniques require that measured attributes be certified to a high degree of precision in the RMs used—ultimately, to a higher degree than that of the measurement technique itself. The US authority on the production of special nuclear material Certified Reference Materials is the NBL Program Office (NBLPO), formally known as New Brunswick Laboratory (NBL). The NBLPO is responsible for the sales and distribution of existing NBL certified reference materials (CRMs) and for the production of the next generation of nuclear RMs. To accomplish its mission, NBLPO is establishing key base capabilities within the DOE laboratory complex that formerly existed at the NBL laboratory. The Nuclear Analytical Chemistry (NAC) section within the Chemical Sciences Division (CSD) at Oak Ridge National Laboratory (ORNL) is currently working with NBLPO to set up laboratory and measurement capabilities to provide measurements and capabilities for production and/or recertification of existing and future CRMs for uranium assay. The NBL-developed high-precision titration (HPT) method is a critically-evaluated, extremely precise and accurate primary method utilized for the determination of uranium content in a variety of uranium materials. The HPT method, combined with detailed balance weighing protocols, provides for an analytical methodology that is unsurpassed in precision and one in which all sources of error have been evaluated, a requirement of CRM certification. HPT capability within the United States was lost with the closure of the labs at NBL. The NAC has been collaborating with NBLPO to stand up and demonstrate the capability to perform uranium assay via HPT at ORNL. HPT can produce results with an expanded uncertainty of approximately 0.01% for pure uranium compounds, with typical precisions of <0.006%. The major tasks required to stand up the method at ORNL were the refurbishment of a dedicated lab and equipment setup, procedure development, analyst training, establishing method-specific quality assurance, and qualification of an analyst to perform the method. This report summarizes these tasks, outlines the documents drafted, and gives the outcome of the qualification titrations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2019

Co-Continuous Phase Nanofiber Composites

We report on the development of a co-continuous phase composite with a polyacrylonitrile (PAN nanofiber network integrated into an acrylonitrile-butadiene-styrene (ABS) matrix that offers improved mechanical strength. Homogeneous dispersion of nanomaterials in a matrix can be challenging and typically involves mechanical mixing or chemical modification. This work overcomes this obstacle by taking an inverse approach in that a nanoscale network is synthesized then infiltrated with polymer instead of attempting to disperse the nanomaterial in the polymer. This not only enables excellent homogeneity but also allows the nanoscale phase to be continuous throughout the composite. Here, PAN is electrospun to form a random network of nanofibers having diameters of roughly 200 nm, which is then infiltrated with ABS using solution processing. By optimizing the heat treatments and ABS concentrations, the tensile strength and elastic modulus were improved by 208% and 313%, respectively, as compared to the bare PAN nanofibers. Since there has been significant previous research efforts on electrospinning polymers, many different nanofiber and polymer systems can be made into composites using the methodology established in this work. Therefore, this research offers a new route to create a co-continuous composite with the aim to replace some existing fiber reinforced composites.

Bowland, Christopher↗
NASA NTRS1974

A method for determining the preferred orientation of crystallites normal to a surface

It is pointed out that in principle all of the preferred-orientation information for the direction normal to a surface will be contained in the intensity differences between X-ray diffraction peaks for a sample with a preferred orientation and one with a fully random crystallite orientation. An experimental investigation is discussed along with questions regarding the procedure used in the evaluation process.

Snyder, R. L.↗
NASA NTRS1985

Investigation of dynamic noise affecting geodynamics information in a tethered subsatellite

Work performed as part of an investigation of noise affecting instrumentation in a tethered subsatellite, was studied. The following specific topics were addressed during the reporting period: a method for stabilizing the subsatellite against the rotational effects of atmospheric perturbation was developed; a variety of analytic studies of tether dynamics aimed at elucidating dynamic noise processes were performed; a novel mechanism for coupling longitudinal and latitudinal oscillations of the tether was discovered, and random vibration analysis for modeling the tethered subsatellite under atmospheric perturbation were studied.

Gullahorn, G. E.↗