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NASA NTRS2020

Nuclear Fusion Reactions in Deuterated Metals

Nuclear fusion reactions of D-D are examined in an environment comprised of high density cold fuel embedded in metal lattices in which a small fuel portion is activated by hot neutrons. Such an environment provides for enhanced screening of the Coulomb barrier due to conduction and shell electrons of the metal lattice, or by plasma induced by ionizing radiation (γ quanta). We show that neutrons are far more efficient than energetic charged particles, such as light particles (e−, e+) or heavy particles (p, d, α) in transferring kinetic energy to fuel nuclei (D) to initiate fusion processes. It is well-known that screening increases the probability of tunneling through the Coulomb barrier. Electron screening also significantly increases the probability of large- versus small-angle Coulomb scattering of the reacting nuclei to enable subsequent nuclear reactions via tunneling. This probability is incorporated into the astrophysical factor S(E). Aspects of screening effects to enable calculation of nuclear reaction rates are also evaluated, including Coulomb scattering and localized heating of the cold fuel, primary D-D reactions, and subsequent reactions with both the fuel and the lattice nuclei. The effect of screening for enhancement of the total nuclear reaction rate is a function of multiple parameters including fuel temperature and the relative scattering probability between the fuel and lattice metal nuclei. Screening also significantly increases the probability of interaction between hot fuel and lattice nuclei increasing the likelilhood of Oppenheimer-Phillips processes opening a potential route to reaction multiplication. We demonstrate that the screened Coulomb potential of the target ion is determined by the nonlinear Vlasov potential and not by the Debye potential. In general, the effect of screening becomes important at low kinetic energy of the projectile. We examine the range of applicability of both the analytical and asymptotic expressions for the well-known electron screening lattice potential energy Ue, which is valid only for E >> Ue (E is the energy in the center of mass reference frame). We demonstrate that for E ≤ Ue, a direct calculation of Gamow factor for screened Coulomb potential is required to avoid unreasonably high values of the enhancement factor f (E) by the analytical—and more so by the asymptotic—formulas.

Vladimir Pines↗

DOE OSTI2021

Machine-Learned Manifold-Based Models for Large Eddy Simulation of Turbulent Combustion

Reduced-order manifold combustion models are commonly used to lower the cost of reacting Large Eddy Simulations (LES) and may be derived either from physical principles as in flamelet models or from data-driven methods like Principal Component Analysis (PCA). In either case, neural networks are increasingly used as part of these models to provide a nonlinear mapping between a small set of pre-defined variables that parameterize the manifold and outputs of interest, such as reaction rates. In this work, we propose a new manifold-based modeling approach that combines the definition of the manifold-parameterizing variables (linear combinations of species), the nonlinear mapping to the outputs, and closure of filtered quantities for LES into the structure of a single neural network. This allows the process used to train the neural network to simultaneously optimize both the functional form of the model and the identities of the inputs to the model. The new approach can flexibly incorporate thermochemical data from any combustion system; if trained on data from 1D flames it can be interpreted as an optimized flamelet model, but it can also be used to learn models from data from more complex configurations. This work presents a priori evaluations of the new approach in both contexts. Evaluation using data from 1D premixed flames demonstrates the physical interpretability of the manifold variables generated by the new approach. Evaluation using data from direct numerical simulations of turbulent flames shows improved predictions relative to either flamelet or PCA-based models in a more complex configuration.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

A DATA EFFICIENT SPARSE MODELING FRAMEWORK FOR POWER ESTIMATION IN WATER TREATMENT SENSING OPERATIONS

With increasing freshwater scarcity, advanced process design mechanisms such as Closed-Circuit Reverse Osmosis (CCRO) and Digital/Physical Twin systems are gaining traction in water treatment and reuse operations. While digital and physical twin models enable improved system insight and control, their development is often expensive and computationally intensive, requiring large volumes of synthetic or experimental data to characterize underlying process dynamics. This work introduces a sparse surrogate modeling framework to estimate power consumption from measured flow and pressure variables, along with their nonlinear polynomial and interaction expansions. To ensure model reliability and reduce overfitting, a two-stage pipeline is proposed. First, a dynamic data filtering algorithm is employed to remove uninformative observations and transient operational states. Second, a sparse penalized regression technique is applied to select a minimal set of parsimonious features. The proposed model achieves high sparsity, retaining only 7 out of 34 candidate features (≈79.41% sparsity) while delivering a root mean square error (RMSE) of 0.072 on the test dataset.

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

DOE OSTI2026

Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs

Dirichlet-to-Neumann maps enable the coupling of multiphysics simulations across computational subdomains by ensuring continuity of state variables and fluxes at artificial interfaces. We present a novel method for learning Dirichlet-to-Neumann maps on graphs using Gaussian processes, specifically for problems where the data obey a conservation law arising from an underlying partial differential equation. Our approach combines discrete exterior calculus and nonlinear optimal recovery to infer relationships between vertex and edge values. This framework yields data-driven predictions with uncertainty quantification across the entire graph, even when observations are limited to a subset of vertices and edges. By minimizing the reproducing kernel Hilbert space norm while penalizing kernel complexity through maximum likelihood estimation, our method ensures that the resulting surrogate strictly enforces conservation laws without overfitting. We demonstrate our method on two representative applications: subsurface flow in fracture networks and arterial blood flow. Finally, the results demonstrate that the method maintains high accuracy and well-calibrated uncertainty estimates even under severe data scarcity, highlighting its potential for scientific applications where limited data and reliable uncertainty quantification are critical.

Dirichlet-to-Neumann map↗

NASA NTRS2004

An experimental study of the Richtmyer-Meshkov instability in microgravity

Richtmyer-Meshkov (RM) instability occurs when a planar interface separating two fluids of different density is impulsively accelerated in the direction of its normal. It is one of the most fundamental fluid instabilities and is of importance to the fields of astrophysics and inertial confinement fusion. Because RM instability experiments are normally carried out in shock tubes, where the generation of a sharp, well-controlled interface between gases is difficult, there is a scarcity of good experimental results. The experiments presented here use a novel technique that circumvents many of the experimental difficulties that have previously limited the study of RM instability in shock tubes. In these experiments, the instability is generated incompressibly, by bouncing a rectangular tank containing two liquids off of a fixed spring. These experiments, which utilize PLIF flow visualization, yield time-motion image sequences of the nonlinear development and transition to turbulence of the instability that are of a quality unattainable in shock tube experiments. Measurements obtained from these images, therefore, provide benchmark data for the evaluation of nonlinear models for the late-time growth of the instability. Because the run time in these experiments is limited, new experiments in the NASA Glenn 2.2 second drop tower, capable of achieving longer run times, are currently under way.

Rheology/methods↗

DOE OSTI2022

Ion dynamics driven by a strongly nonlinear plasma wake

Abstract In plasma wakefield accelerators, the wave excited in the plasma eventually breaks and leaves behind slowly changing fields and currents that perturb the ion density background. We study this process numerically using the example of a Facility for Advanced aCcelerator Experimental Tests (FACET) experiment where the wave is excited by an electron bunch in the bubble regime in a radially bounded plasma. Four physical effects underlie the dynamics of ions: (1) attraction of ions toward the axis by the fields of the driver and the wave, resulting in formation of a density peak, (2) generation of ion-acoustic solitons following the decay of the density peak, (3) positive plasma charging after wave breaking, leading to acceleration of some ions in the radial direction, and (4) plasma pinching by the current generated during the wave-breaking. The interplay of these effects results in the formation of various radial density profiles, which are difficult to produce in any other way.

Physics↗

NASA NTRS2006

The Use of RGPS Kinematic Data to Estimate Nonlinear Sea Ice Motion

In current simulations of the interaction between sea ice and its environment, large significance is placed on the deformation of the sea ice. Sea ice deformation is an important process in determining the sea ice thickness distribution across a wide range of space and time scales. Changes in the sea ice thickness distribution affect energy and mass fluxes between the atmosphere and ocean and also the strength of the ice. While most current ice models assume linear variation in the ice motion field to calculate strain, deformation of sea ice occurs through the opening, closing and shearing of ice along discrete linear features. New numerical models are being developed which explicitly account for discontinuities in ice motion, and the need for requisite data sets for model validation has emerged. Multiple buoy data sets, as well as satellite data, have been used to examine the movement and deformation of sea ice. Generally it has been found that the ice motion field has been represented well by buoy data, as well as satellite data over a broad range of scales. However, the underlying deformation (spatial variation in displacement) as represented by different data sets may vary. For the work presented here, sea ice motion In current simulations of the interaction between sea ice and its environment, large significance is placed on the deformation of the sea ice. Sea ice deformation is an important process in determining the sea ice thickness distribution across a wide range of space and time scales. Changes in the sea ice thickness distribution affect energy and mass fluxes between the atmosphere and ocean and also the strength of the ice. While most current ice models assume linear variation in the ice motion field to calculate strain, deformation of sea ice occurs through the opening, closing and shearing of ice along discrete linear features. New numerical models are being developed which explicitly account for discontinuities in ice motion, and the need for requisite data sets for model validation has emerged. Multiple buoy data sets, as well as satellite data, have been used to examine the movement and deformation of sea ice. Generally it has been found that the ice motion field has been represented well by buoy data, as well as satellite data over a broad range of scales. However, the underlying deformation (spatial variation in displacement) as represented by different data sets may vary. For the work presented here, sea ice motio

Pruis, M.↗

DOE OSTI2024

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

Yao, Weigang↗

DOE OSTI2022

Disentangling Memristive and Memcapacitive Effects in Droplet Interface Bilayers Using Dynamic Impedance Spectroscopy

The underlying principles for generating intelligent behavior in living organisms are fundamentally different from those in traditional solid-state circuits. Biomimetic neuromorphic equivalents based on biological membranes offer novel implementation of tunable plasticity and diverse mechanisms to control functionality. Here, dynamic electrochemical impedance spectroscopy (dEIS) to probe diphytanoylphosphatidylcholine (DPhPC) droplet interface bilayers (DIBs) to better understand the differences in molecular level structure/dynamics that give rise to hysteretic loops and neuromorphic, memelement behaviors in lipid bilayers in response to electrical biasing is used. Importantly, this system does not have ion-conducting channels and is, therefore, not expected to show memristive behavior. Surprisingly, both memristive and memcapacitive behaviors by measuring the time-dependent complex impedance of DPhPC DIBs are detected. Furthermore, it is shown that nonlinear memristance can originate from structural changes in the bilayer, affecting its dielectric properties. This novel dEIS application allows for the simultaneous analysis of the system's changing memristive and memcapacitive properties, which originate from different molecular restructuring processes. Moreover, and importantly, access to this type of information increases the number of neuromorphic processes supported simultaneously in a single two-terminal device.

36 MATERIALS SCIENCE↗

NASA NTRS2018

ALMA Observations of Molecular Clouds in Three Group-centered Elliptical Galaxies:NGC 5846, NGC 4636, and NGC 5044

We present new ALMA CO(2–1) observations of two well-studied group-centered elliptical galaxies: NGC 4636 and NGC 5846. In addition, we include a revised analysis of Cycle 0 ALMA observations of the central galaxy in the NGC 5044 group. We find evidence that molecular gas is a common presence in bright group-centered galaxies (BGG). CO line widths are broader than Galactic molecular clouds, and using the reference Milky Way XCO, the total molecular mass ranges from 2.6 x 10(exp 5)M(☉) in NGC 4636 to 6.1 x 10(exp 7) M(☉) in NGC 5044. Complementary observations using the ALMA Compact Array do not exhibit any detection of a CO diffuse component at the sensitivity level achieved by current exposures. The origin of the detected molecular features is still uncertain, but these ALMA observations suggest that they are the end product of the hot gas cooling process and not the result of merger events. Some of the molecular clouds are associated with dust features as revealed by HST dust extinction maps, suggesting that these clouds formed from dust-enhanced cooling. The global nonlinear condensation may be triggered via the chaotic turbulent field or buoyant uplift. The large virial parameter of the molecular structures and correlation with the warm (10(exp 3)–10(exp 5) K)/hot (≥ 10(exp 6)) phase velocity dispersion provide evidence that they are unbound giant molecular associations drifting in the turbulent field, consistent with numerical predictions of the chaotic cold accretion process. Alternatively, the observed large CO line widths may be generated by molecular gas flowing out from cloud surfaces due to heating by the local hot gas atmosphere.

Pasquale Temi↗

NASA NTRS1995

On the Feasibility of Satellite Altimeter Tomography

The time-varying field of sea surface height (SSH) reflects a large number of dynamical processes including a broad range of baroclinic waves. Inertia-gravity (IG) and Rossby waves (RW) are readily detected in power spectra F(k) of SSH spatial variations, as well as in wavenumber-frequency spectral, ,k), and autocorrelation functions W(). these statistical characteristics contain information on dynamical processes in the upper thermocline. We show that the well-known 3-segment shape of 1-d spectra (based on SSH variations along satellite tracks) is determined by the Internal Rossby radius of deformation and by the degree of nonlinearity of baroclinic IG waves. Analyzing altimeter-based spectra of SSH variations in the light of a recently proposed theory for IG wave turbulence, we demonstrate that baroclinic IG waves are typically highly nonlinear. For a number of mid- and high-latitude regions, the internal Rossby radius and the wave nonlinearity are estimated from T/P data and shown to agree with expected values. The degree of the wave nonlinearity is of great practical interest. In particular, it tells us about the intensity of internal wave breaking. Furthermore, estimating the velocity of baroclinic RW we derive additional information on the upper layer stratification. These experiments point to the feasibility of using observed SSH field statistics (such F(k) and W()) to monitor properties of and dynamical processes in the upper mixed layer of the ocean.

Satellite Altimeter↗

DOE OSTI2022

Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure

Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a natural route to optimise these processes and bring theory and experiments together. Here, we propose a scheme that integrates machine learning with high-performance simulations and scattering measurements, covering the pipeline of typical neutron experiments. Our approach uses nonlinear autoencoders trained on realistic simulations along with a fast surrogate for the calculation of scattering in the form of a generative model. We demonstrate this approach in a highly frustrated magnet, Dy 2 Ti 2 O 7 , using machine learning predictions to guide the neutron scattering experiment under hydrostatic pressure, extract material parameters and construct a phase diagram. Our scheme provides a comprehensive set of capabilities that allows direct integration of theory along with automated data processing and provides on a rapid timescale direct insight into a challenging condensed matter system.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Design and Fabrication of Quantum Information Processing Devices (Final Report)

The goal of this work is to develop a wideband Josephson Parametric Amplifier (JPA) by the combination of a coplanar impedance transformer and a lumped Josephson Parametric Amplifier. Josephson Parametric Amplifiers take advantage of the nonlinear inductance of Josephson junctions and the nearly lossless superconductor circuits to achieve wave mixing and amplification with only quantum-limited added noise, which makes it a key component in quantum measurements, such as single-shot qubit measurement. However, the current JPAs generally have a relatively high quality factor and small bandwidth (around 50MHz to 100MHz), which limits their ability in measuring signals from a broad spectrum. Therefore, the work planned to use a coplanar impedance transformer to enhance the coupling between JPA and the signals, resulting in a small quality factor and large bandwidth. By decreasing the quality factor of JPA to around 3, one can achieve around 20dB gain within a bandwidth of several hundred MHz. Moreover, the wideband feature allows this device to explore the frequency dependence of the coupling between the JPA and the signals. Because the impedance transformer is based on a coplanar transmission line and requires no modifications of the lumped JPA, it is relatively straightforward to embed it in the current fabrication, package, and testing process for lumped JPA. The impedance transformer can be fabricated by liftoff or etching process with the lumped JPA together, which adds no fabrication complexity. The only difference is the size, which is around 3~5 times larger than lumped JPA. This may make the number of available chips on a single wafer lower. As for the public benefits, the development of this wideband JPA will make it possible to use just a single JPA device for multi-frequency signals, for example, the readout signals of a superconducting qubit chip with multiplexed readout bus. This would greatly decrease the demand for JPAs in quantum measurements.

42 ENGINEERING↗

NASA NTRS1983

A theory of rotating stall of multistage axial compressors

A theoretical analysis was made of rotating stall in axial compressors of many stages, finding conditions for a permanent, straight-through traveling disturbance, with the steady compressor characteristic assumed known, and with simple lag processes ascribed to the flows in the inlet, blade passages, and exit regions. For weak disturbances, predicted stall propagation speeds agree well with experimental results. For a locally-parabolic compressor characteristic, an exact nonlinear solution is found and discussed. For deep stall, the stall-zone boundary is most abrupt at the trailing edge, as expected. When a complete characteristic having unstalling and reverse-flow features is adopted, limit cycles governed by a Lienard's equation are found. Analysis of these cycles yields predictions of recovery from rotating stall; a relaxation oscillation is found at some limiting flow coefficient, above which no solution exists. Recovery is apparently independent of lag processes in the blade passages, but instead depends on the lags originating in the inlet and exit flows, and also on the shape of the given characteristic diagram. Small external lags and tall diagrams favor early recovery. Implications for future research are discussed.

Moore, F. K.↗

NASA NTRS2000

Viscoelastic flow modeling in the extrusion of a dough-like fluid

This work attempts to investigate the effect of viscoelasticity and three-dimensional geometry in screw channels. The Phan-Thien Tanner (PTT) constitutive equation with simplified model parameters was solved in conjunction with the flow equations. Polyflow, a commercially available finite element code was used to solve the resulting nonlinear partial differential equations. The PTT model predicted one log scale lower pressure buildup compared to the equivalent Newtonian results. However, the velocity profile did not show significant changes for the chosen PTT model parameters. Past Researchers neglected viscoelastic effects and also the three dimensional nature of the flow in extruder channels. The results of this paper provide a starting point for further simulations using more realistic model parameters, which may enable the food engineer to more accurately scale-up and design extrusion processes.

NASA Discipline Life Support Systems↗

DOE OSTI2020

A multirate mass transfer model to represent the interaction of multicomponent biogeochemical processes between surface water and hyporheic zones (SWAT-MRMT-R 1.0)

Abstract. Surface water quality along river corridors can be modulated by hyporheic zones (HZs) that are ubiquitous and biogeochemically active. Watershed management practices often ignore the potentially important role of HZs as a natural reactor. To investigate the effect of hydrological exchange and biogeochemical processes on the fate of nutrients in surface water and HZs, a novel model, SWAT-MRMT-R, was developed coupling the Soil and Water Assessment Tool (SWAT) watershed model and the reaction module from a flow and reactive transport code (PFLOTRAN). SWAT-MRMT-R simulates concurrent nonlinear multicomponent biogeochemical reactions in both the channel water and its surrounding HZs, connecting the channel water and HZs through hyporheic exchanges using multirate mass transfer (MRMT) representation. Within the model, HZs are conceptualized as transient storage zones with distinguished exchange rates and residence times. The biogeochemical processes within HZs are different from those in the channel water. Hyporheic exchanges are modeled as multiple first-order mass transfers between the channel water and HZs. As a numerical example, SWAT-MRMT-R is applied to the Hanford Reach of the Columbia River, a large river in the United States, focusing on nitrate dynamics in the channel water. Major nitrate contaminants entering the Hanford Reach include those from the legacy waste, irrigation return flows (irrigation water that is not consumed by crops and runs off as point sources to the stream), and groundwater seepage resulting from irrigated agriculture. A two-step reaction sequence for denitrification and an aerobic respiration reaction is assumed to represent the biogeochemical transformations taking place within the HZs. The spatially variable hyporheic exchange rates and residence times in this example are estimated with the basin-scale Networks with EXchange and Subsurface Storage (NEXSS) model. Our simulation results show that (1), given a residence time distribution, how the exchange fluxes to HZs are approximated when using MRMT can significantly change the amount of nitrate consumption in HZs through denitrification and (2) source locations of nitrate have a different impact on surface water quality due to the spatially variable hyporheic exchanges.

58 GEOSCIENCES↗

NASA NTRS1971

Nonlinear, nonlaminar-3D computation of electron motion through the output cavity of a klystron

The equations of motion used in the computation are discussed along with the space charge fields and the integration process. The following assumptions were used as a basis for the computation: (1) The beam is divided into N axisymmetric discs of equal charge and each disc into R rings of equal charge. (2) The velocity of each disc, its phase with respect to the gap voltage, and its radius at a specified position in the drift tunnel prior to the interaction gap is known from available large signal one dimensional programs. (3) The fringing rf fields are computed from exact analytical expressions derived from the wave equation assuming a known field shape between the tunnel tips at a radius a. (4) The beam is focused by an axisymmetric magnetic field. Both components of B, that is B sub z and B sub r, are taken into account. (5) Since this integration does not start at the cathode but rather further down the stream prior to entering the output cavity it is assumed that each electron moved along a laminar path from the cathode to the start of integration.

Albers, L. U.↗