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NASA NTRS2001

Pick-Up Ion Instabilities at Planetary Magnetospheres

This effort involved the analysis of low frequency waves as observed by the Galileo spacecraft near the Galilean moon, Io. Io is a significant source of material, especially SO2, and various products of dissociation, and further these atoms and molecules are readily ionized. The initial velocity of the ions is essentially that of the neutral species, i.e., the Keplerian velocity. The plasma, on the other hand is co-rotating, and there is a differential flow of the order 57 km/s between the plasma and the neutral particles. Thus pick-up ion instabilities are Rely to occur within the Jovian magnetosphere. Indeed, magnetometer observations from the Galileo spacecraft clearly show ion cyclotron waves that have been identified with a large variety of plasma species, such as O+, S++ (which has the same gyro-frequency as O+), S+, and SO2+. Typically, however, the dominant frequency is near the SO2+ gyro-frequency. The research effort was originally planned to be a team effort between Robert J. Strangeway as the Principal Investigator, and Debbie Huddleston, who was an Assistant Research Geophysicist at UCLA. Unfortunately, Dr. Huddleston took a position within Industry. The effort was therefore descoped, and Dr. Strangeway instead pursued a collaboration with Dr. Xochitl Blanco-Cano, of the Instituto de Geofisica, Universidad Nacional Autonoma de Mexico. This has proved to be a productive collaboration, with several papers and publications arising out of the effort. The magnetic field oscillations near lo generally fall into two types: ion cyclotron waves, with frequencies near an ion gyro-frequency, and lower frequency mirror-mode waves. The ion cyclotron waves are mainly transverse, and frequently propagate along the ambient magnetic field. The mirror-mode waves are compressional waves, and they have essentially zero frequency in the plasma rest frame. One of the purposes of our investigation is to understand what controls the types of wave modes that occur, since both wave modes can be drive unstable by the pressure anisotropy associated with the pick up ions. The pick ion velocity is perpendicular to the ambient magnetic field, and is generally much larger than the thermal velocity, at least initially. At its simplest, we found that the ion cyclotron waves are controlled by the parameters of the species in gyro-resonance with the wave. Thus, while the growth rates for the lower mass (higher gyro-frequency) pick-up ions are generally larger, we found that the heavier SO2+ ion cyclotron waves are generally preferred. This is because one of the effects of the wave instability is to diffuse the ions in pitch angle and energy. The lower mass ions therefore consist of both a ring of recently created pick-up ions, and a thermal background. This thermal background quenches the ion cyclotron instability. SO2+ is different, however. Being a molecule, the species can also dissociate. Our analysis suggests that the dissociation acts on a time scale comparable to or faster than the velocity space diffusion time scale. There are consequently no thermal SO2+ ions to quench the instability. We have also investigated the mirror-mode. This mode can at times grow more rapidly than the individual ion cyclotron waves. This is mainly because the mirror-mode can grow off the pressure anisotropy of the individual species with which the waves are in resonance. Lastly, as part of this effort we have begun to investigate the instability for obliquely propagating modes. Galileo observations show that at times the ion cyclotron waves are significantly elliptically polarized, and further the wave vector is at a large angle to the field, significant growth can occur for oblique propagation. Depending on the group velocity of the waves, it is possible that obliquely propagating modes have higher advective growth rate, but this has yet to be determined.

Strangeway, Robert J.↗

DOE OSTI2022

Site Density Functional Theory and Structural Bioinformatics Analysis of the SARS-CoV Spike Protein and hACE2 Complex

The entry of the SARS-CoV-2, a causative agent of COVID-19, into human host cells is mediated by the SARS-CoV-2 spike (S) glycoprotein, which critically depends on the formation of complexes involving the spike protein receptor-binding domain (RBD) and the human cellular membrane receptor angiotensin-converting enzyme 2 (hACE2). Using classical site density functional theory (SDFT) and structural bioinformatics methods, we investigate binding and conformational properties of these complexes and study the overlooked role of water-mediated interactions. Analysis of the three-dimensional reference interaction site model (3DRISM) of SDFT indicates that water mediated interactions in the form of additional water bridges strongly increases the binding between SARS-CoV-2 spike protein and hACE2 compared to SARS-CoV-1-hACE2 complex. By analyzing structures of SARS-CoV-2 and SARS-CoV-1, we find that the homotrimer SARS-CoV-2 S receptor-binding domain (RBD) has expanded in size, indicating large conformational change relative to SARS-CoV-1 S protein. Protomer with the up-conformational form of RBD, which binds with hACE2, exhibits stronger intermolecular interactions at the RBD-ACE2 interface, with differential distributions and the inclusion of specific H-bonds in the CoV-2 complex. Further interface analysis has shown that interfacial water promotes and stabilizes the formation of CoV-2/hACE2 complex. This interaction causes a significant structural rigidification of the spike protein, favoring proteolytic processing of the S protein for the fusion of the viral and cellular membrane. Moreover, conformational dynamics simulations of RBD motions in SARS-CoV-2 and SARS-CoV-1 point to the role in modification of the RBD dynamics and their impact on infectivity.

3DRISM↗

DOE OSTI2021

Nominal and adversarial synthetic PMU data for standard IEEE test systems

GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2016

High Energy, Narrow Linewidth 1572nm Eryb-Fiber Based MOPA for a Multi-Aperture CO2 Trace-Gas Laser Space Transmitter

Accurate global measurements of tropospheric CO2 mixing ratios are needed to study CO2 emissions and CO2 exchange with the land and oceans. NASA Goddard Space Flight Center (GSFC) is developing a pulsed lidar approach for an integrated path differential absorption (IPDA) lidar to allow global measurements of atmospheric CO2 column densities from space. Our group has developed, and successfully flown, an airborne pulsed lidar instrument that uses two tunable pulsed laser transmitters allowing simultaneous measurement of a single CO2 absorption line in the 1570 nm band, absorption of an O2 line pair in the oxygen A-band (765 nm), range, and atmospheric backscatter profiles in the same path. Both lasers are pulsed at 10 kHz, and the two absorption line regions are sampled at typically a 300 Hz rate. A space-based version of this lidar must have a much larger lidar power-area product due to the x40 longer range and faster along track velocity compared to airborne instrument. Initial link budget analysis indicated that for a 400 km orbit, a 1.5 m diameter telescope and a 10 second integration time, a 2 mJ laser energy is required to attain the precision needed for each measurement. To meet this energy requirement, we have pursued parallel power scaling efforts to enable space-based lidar measurement of CO2 concentrations. These included a multiple aperture approach consists of multi-element large mode area fiber amplifiers and a single-aperture approach consists of a multi-pass Er:Yb:Phosphate glass based planar waveguide amplifier (PWA). In this paper we will present our laser amplifier design approaches and preliminary results.

micropulse lidar↗

NASA NTRS2015

Laser Amplifier Development for the Remote Sensing of CO2 from Space

Accurate global measurements of tropospheric CO2 mixing ratios are needed to study CO2 emissions and CO2 exchange with the land and oceans. NASA Goddard Space Flight Center (GSFC) is developing a pulsed lidar approach for an integrated path differential absorption (IPDA) lidar to allow global measurements of atmospheric CO2 column densities from space. Our group has developed, and successfully flown, an airborne pulsed lidar instrument that uses two tunable pulsed laser transmitters allowing simultaneous measurement of a single CO2 absorption line in the 1570 nm band, absorption of an O2 line pair in the oxygen A-band (765 nm), range, and atmospheric backscatter profiles in the same path. Both lasers are pulsed at 10 kHz, and the two absorption line regions are sampled at typically a 300 Hz rate. A space-based version of this lidar must have a much larger lidar power-area product due to the approximately x40 longer range and faster along track velocity compared to airborne instrument. Initial link budget analysis indicated that for a 400 km orbit, a 1.5 m diameter telescope and a 10 second integration time, a approximately 2 mJ laser energy is required to attain the precision needed for each measurement. To meet this energy requirement, we have pursued parallel power scaling efforts to enable space-based lidar measurement of CO2 concentrations. These included a multiple aperture approach consists of multi-element large mode area fiber amplifiers and a single-aperture approach consists of a multi-pass Er:Yb:Phosphate glass based planar waveguide amplifier (PWA). In this paper we will present our laser amplifier design approaches and preliminary results.

Solid-State Laser Amplifier↗

DOE OSTI2022

Composing preconditioners for multiphysics PDE systems with applications to Generalized MHD

New patch smoothers or relaxation techniques are developed for solving linear matrix equations coming from systems of discretized partial differential equations (PDEs). One key linear solver challenge for many PDE systems arises when the resulting discretization matrix has a near null space that has a large dimension, which can occur in generalized magnetohydrodynamic (GMHD) systems. Patch-based relaxation is highly effective for problems when the null space can be spanned by a basis of locally supported vectors. The patch-based relaxation methods that we develop can be used either within an algebraic multigrid (AMG) hierarchy or as stand-alone preconditioners. These patch-based relaxation techniques are a form of well-known overlapping Schwarz methods where the computational domain is covered with a series of overlapping sub-domains (or patches). Patch relaxation then corresponds to solving a set of independent linear systems associated with each patch. In the context of GMHD, we also reformulate the underlying discrete representation used to generate a suitable set of matrix equations. In general, deriving a discretization that accurately approximates the curl operator and the Hall term while also producing linear systems with physically meaningful near null space properties can be challenging. Unfortunately, many natural discretization choices lead to a near null space that includes non-physical oscillatory modes and where it is not possible to span the near null space with a minimal set of locally supported basis vectors. Further discretization research is needed to understand the resulting trade-offs between accuracy, stability, and ease in solving the associated linear systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Λ Baryon Production in ν¯µ Interactions in the MicroBooNE Detector

The Cabibbo suppressed production of $\Lambda$ baryons in anti-neutrino interactions with nuclei is a rare process that is yet to be measured with a modern neutrino detector with automated reconstruction. The cross section for this process is sensitive to a number of unique nuclear effects, most notably the secondary interactions of the produced hyperon while attempting to escape from the nucleus. Other interactions within the nuclear remnant can impact the estimation of neutrino energy in oscillation measurements, and thus an accurate description of the nuclear environment is required. The strangeness violating hyperon production process is only available to anti-neutrinos. The model of this interaction is implemented into the NuWro neutrino interaction Monte Carlo simulation, and some predictions are presented, focusing on the role of nuclear effects. This model introduces a hyperon-nucleus potential, which calculations from hypernuclear theory permit to be strongly repulsive in th e case of $\Sigma$ baryons. The presence of this potential is found to sculpt the shape of the differential cross section in some variables. The MicroBooNE detector will be described, followed by a description of a measurement of the flux averaged, restricted phase space cross section of Cabibbo suppressed $\Lambda$ baryon production. A sophisticated event selection is employed, as a very large quantity of background neutrino interactions must be removed to perform the measurement with any sensitivity. This selection introduces some novel techniques such as the island finding method, and achieves a background reduction of $\sim 10^6$, with an efficiency of around 7\%. The calculation of the systematic uncertainties will be explained, including two procedures explored to handle sources of background with extremely poor simulation statistics: an in-situ constraint using data from sidebands, and a visual inspection of the data and simulation to remove the troublesome background events. The sensitivity to the $\Lambda$ baryon production cross section is calculated in the form of Bayesian posterior probability distributions, combining the systematic uncertainties with data and simulation statistical uncertainties. As a rare process, the statistical uncertainties are highly non-Gaussian, and the Bayesian approach is applied to include the full shapes of these uncertainties. Data corresponding to $2.2 \times 10^{20}$ protons on target of neutrino mode running and $4.9 \times 10^{20}$ protons on target of anti-neutrino running is analysed. When the data was unblinded, five $\Lambda$ production candidates were selected from the data, consistent with the MC simulation prediction of $5.3 \pm 1.1$ events. The final estimated cross section is $1.8^{+2.0}_{-1.6} \times 10^{-40}$cm$^2/$Ar when employing the sideband constraint procedure. A similar result of $2.0^{+2.2}_{-1.8} \times 10^{-40}$cm$^2/$Ar is obtained when performing the visual scan instead. The methods used in t his analysis are intended to be easily exported to other LArTPC detectors such as the Short Baseline Near Detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

Physics-informed neural networks (PINNs) were recently proposed in [18] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution, while a PDE-induced NN is coupled to the solution NN, and all differential operators are treated using automatic differentiation. Here, we first employ the standard PINN and a stochastic version, sPINN, to solve forward and inverse problems governed by a non-linear advection–diffusion–reaction (ADR) equation, assuming we have some sparse measurements of the concentration field at random or pre-selected locations. Subsequently, we attempt to optimise the hyper-parameters of sPINN by using the Bayesian optimisation method (meta-learning) and compare the results with the empirically selected hyper-parameters of sPINN. In particular, for the first part in solving the inverse deterministic ADR, we assume that we only have a few high-fidelity measurements, whereas the rest of the data is of lower fidelity. Hence, the PINN is trained using a composite multi-fidelity network, first introduced in [12], that learns the correlations between the multi-fidelity data and predicts the unknown values of diffusivity, transport velocity and two reaction constants as well as the concentration field. For the stochastic ADR, we employ a Karhunen–Loève (KL) expansion to represent the stochastic diffusivity, and arbitrary polynomial chaos (aPC) to represent the stochastic solution. Correspondingly, we design multiple NNs to represent the mean of the solution and learn each aPC mode separately, whereas we employ a separate NN to represent the mean of diffusivity and another NN to learn all modes of the KL expansion. For the inverse problem, in addition to stochastic diffusivity and concentration fields, we also aim to obtain the (unknown) deterministic values of transport velocity and reaction constants. The available data correspond to 7spatial points for the diffusivity and 20 space–time points for the solution, both sampled 2000 times. We obtain good accuracy for the deterministic parameters of the order of 1–2% and excellent accuracy for the mean and variance of the stochastic fields, better than three digits of accuracy. In the second part, we consider the previous stochastic inverse problem, and we use Bayesian optimisation to find five hyper-parameters of sPINN, namely the width, depth and learning rate of two NNs for learning the modes. Here, we obtain much deeper and wider optimal NNs compared to the manual tuning, leading to even better accuracy, i.e., errors less than 1% for the deterministic values, and about an order of magnitude less for the stochastic fields.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Regularized inversion of aerosol hygroscopic growth factor probability density function: application to humidity-controlled fast integrated mobility spectrometer measurements

Abstract. Aerosol hygroscopic growth plays an important role in atmospheric particle chemistry and the effects of aerosol on radiation and hence climate. The hygroscopic growth is often characterized by a growth factor probability density function (GF-PDF), where the growth factor is defined as the ratio of the particle size at a specified relative humidity to its dry size. Parametric, least-squares methods are the most widely used algorithms for inverting the GF-PDF from measurements of the humidified tandem differential mobility analyzer (HTDMA) and have been recently applied to the GF-PDF inversion from measurements of the humidity-controlled fast integrated mobility spectrometer (HFIMS). However, these least-squares methods suffer from noise amplification due to the lack of regularization in solving the ill-posed problem, resulting in significant fluctuations in the retrieved GF-PDF and even occasional failures of convergence. In this study, we introduce nonparametric, regularized methods to invert the aerosol GF-PDF and apply them to HFIMS measurements. Based on the HFIMS kernel function, the forward convolution is transformed into a matrix-based form, which facilitates the application of the nonparametric inversion methods with regularizations, including Tikhonov regularization and Twomey's iterative regularization. Inversions of the GF-PDF using the nonparameteric methods with regularization are demonstrated using HFIMS measurements simulated from representative GF-PDFs of ambient aerosols. The characteristics of reconstructed GF-PDFs resulting from different inversion methods, including previously developed least-squares methods, are quantitatively compared. The result shows that Twomey's method generally outperforms other inversion methods. The capabilities of Twomey's method in reconstructing the pre-defined GF-PDFs and recovering the mode parameters are validated.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2003

Petrology and Physics of Magma Ocean Crystallization

Early Mars is thought to have been melted significantly by the conversion of kinetic energy to heat during accretion of planetesimals. The processes of solidification of a magma ocean determine initial planetary compositional differentiation and the stability of the resulting mantle density profile. The stability and compositional heterogeneity of the mantle have significance for magmatic source regions, convective instability, and magnetic field generation. Significant progress on the dynamical problem of magma ocean crystallization has been made by a number of workers. The work done under the 2003 MFRP grant further explored the implications of early physical processes on compositional heterogeneity in Mars. Our goals were to connect early physical processes in Mars evolution with the present planet's most ancient observable characteristics, including the early, strong magnetic field, the crustal dichotomy, and the compositional characteristics of the SNC meteorite's source regions as well as their formation as isotopically distinct compositions early in Mars's evolution. We had already established a possible relationship between the major element compositions of SNC meteorite sources and processes of Martian magma ocean crystallization and overturn, and under this grant extended the analysis to the crucial trace element and isotopic SNC signatures. This study then demonstrated the ability to create and end the magnetic field through magma ocean cumulate overturn and subsequent cooling, as well as the feasibility of creating a compositionally- and volumetrically-consistent crustal dichotomy through mode-1 overturn and simultaneous adiabatic melting.

Elkins-Tanton, Linda T.↗

NASA NTRS2009

Complementary Paired G4FETs as Voltage-Controlled NDR Device

It is possible to synthesize a voltage-controlled negative-differential-resistance (NDR) device or circuit by use of a pair of complementary G4FETs (four-gate field-effect transistors). [For more information about G4FETs, please see the immediately preceding article]. As shown in Figure 1, the present voltage-controlled NDR device or circuit is an updated version of a prior NDR device or circuit, known as a lambda diode, that contains a pair of complementary junction field-effect transistors (JFETs). (The lambda diode is so named because its current-versus- voltage plot bears some resemblance to an upper-case lambda.) The present version can be derived from the prior version by substituting G4FETs for the JFETs and connecting both JFET gates of each G4FET together. The front gate terminals of the G4FETs constitute additional terminals (that is, terminals not available in the older JFET version) to which one can apply control voltages VN and VP. Circuits in which NDR devices have been used include (1) Schmitt triggers and (2) oscillators containing inductance/ capacitance (LC) resonant circuits. Figure 2 depicts such circuits containing G4FET NDR devices like that of Figure 1. In the Schmitt trigger shown here, the G4FET NDR is loaded with an ordinary inversion-mode, p-channel, metal oxide/semiconductor field-effect transistor (inversion-mode PMOSFET), the VN terminal of the G4FET NDR device is used as an input terminal, and the input terminals of the PMOSFET and the G4FET NDR device are connected. VP can be used as an extra control voltage (that is, a control voltage not available in a typical prior Schmitt trigger) for adjusting the pinch-off voltage of the p-channel G4FET and thereby adjusting the trigger-voltage window. In the oscillator, a G4FET NDR device is loaded with a conventional LC tank circuit. As in other LC NDR oscillators, oscillation occurs because the NDR counteracts the resistance in the tank circuit. The advantage of this G4FET-NDR LC oscillator over a conventional LC NDR oscillator is that one can apply a time-varying signal to one of the extra control input terminals (VN or VP) to modulate the conductance of the NDR device and thereby amplitude-modulate the output signal.

Mojarradi, Mohammad↗

NASA NTRS2015

In-Flight Aeroelastic Stability of the Thermal Protection System on the NASA HIAD, Part II: Nonlinear Theory and Extended Aerodynamics

Conical shell theory and a supersonic potential flow aerodynamic theory are used to study the nonlinear pressure buckling and aeroelastic limit cycle behavior of the thermal protection system for NASA's Hypersonic Inflatable Aerodynamic Decelerator. The structural model of the thermal protection system consists of an orthotropic conical shell of the Donnell type, resting on several circumferential elastic supports. Classical Piston Theory is used initially for the aerodynamic pressure, but was found to be insufficient at low supersonic Mach numbers. Transform methods are applied to the convected wave equation for potential flow, and a time-dependent aerodynamic pressure correction factor is obtained. The Lagrangian of the shell system is formulated in terms of the generalized coordinates for all displacements and the Rayleigh-Ritz method is used to derive the governing differential-algebraic equations of motion. Aeroelastic limit cycle oscillations and buckling deformations are calculated in the time domain using a Runge-Kutta method in MATLAB. Three conical shell geometries were considered in the present analysis: a 3-meter diameter 70 deg. cone, a 3.7-meter 70 deg. cone, and a 6-meter diameter 70 deg. cone. The 6-meter configuration was loaded statically and the results were compared with an experimental load test of a 6-meter HIAD. Though agreement between theoretical and experimental strains was poor, the circumferential wrinkling phenomena observed during the experiments was captured by the theory and axial deformations were qualitatively similar in shape. With Piston Theory aerodynamics, the nonlinear flutter dynamic pressures of the 3-meter configuration were in agreement with the values calculated using linear theory, and the limit cycle amplitudes were generally on the order of the shell thickness. The effect of axial tension was studied for this configuration, and increasing tension was found to decrease the limit cycle amplitudes when the circumferential elastic supports were neglected, but resulted in more complex behavior when the supports were included. The nominal flutter dynamic pressure of the 3.7-meter configuration was significantly lower than that of the 3-meter, and it was found that two sets of natural modes coalesce to flutter modes near the same dynamic pressure. This resulted in a significant drop in the limit cycle frequencies at higher dynamic pressures, where the flutter mode with the lower frequency becomes more critical. Pre-buckling pressure loads and the aerodynamic pressure correction factor were studied for all geometries, and these effects resulted in significantly lower flutter boundaries compared with Piston Theory alone. The maximum dynamic pressure predicted by aerodynamic simulations of a proposed 3.7-meter HIAD vehicle was still lower than any of the calculated flutter dynamic pressures, suggesting that aeroelastic effects for this vehicle are of little concern.

Goldman, Benjamin D.↗

NASA NTRS2021

Synthesis and Characterization of Fe(III)-Fe(II)- Mg- Al Smectite Solid Solutions and Implications for Planetary Science

This study demonstrates the synergies and limits of multiple measurement types for the detection of smectite chemistry and oxidation state on planetary surfaces to infer past geochemical conditions. Smectite clay minerals are common products of water-rock interactions throughout the solar system, and their detection and characterization provides important clues about geochemical conditions and past environments if sufficient information about their composition can be discerned. Here, we synthesize and report on the spectroscopic properties of a suite of smectite samples that span the intermediate compositional range between Fe(II), Fe(III), Mg, and Al end-member species using bulk chemical analyses, X-ray diffraction, Vis/IR reflectance spectroscopy, UV and green-laser Raman spectroscopy, and Mössbauer spectroscopy. Our data show that smectite composition and the oxidation state of octahedral Fe can be reliably identified in the near infrared on the basis of combination and fundamental metal-OH stretching modes between 2.1–2.9 μm, which vary systematically with chemistry. Smectites dominated by Mg or Fe(III) have spectrally distinct fundamental and combination stretches, whereas Al-rich and Fe(II)-rich smectites have similar fundamental minima near 2.76 μm, but have distinct combination M-OH features between 2.24 and 2.36 μm. We show that with expanded spectral libraries that include intermediate composition smectites and both Fe(III) and Fe(II) oxidation states, more refined characterization of smectites from MIR data is now possible, as the position of the 450 cm–1 absorption shifts systematically with octahedral Fe content, although detailed analysis is best accomplished in concert with other characterization methods. Our data also provide the first Raman spectral libraries of smectite clays as a function of chemistry, and we demonstrate that Raman spectroscopy at multiple excitation wavelengths can qualitatively distinguish smectite clays of different structures and can enhance interpretation by other types of analyses. Our sample set demonstrates how X-ray diffraction can distinguish between dioctahedral and trioctahedral smectites using either the (02,11) or (06,33) peaks, but auxiliary information about chemistry and oxidation state aids in specific identifications. Finally, the temperature-dependent isomer shift and quadrupole splitting in Mössbauer data are insensitive to changes in Fe content but reliability differentiates Fe within the smectite mineral structure.

Valerie K Fox↗

NASA NTRS2002

Optimal Design of Functionally Graded Metallic Foam Insulations

The focus of our work has been on developing an insight into the physics that govern the optimum design of thermal insulation for use in thermal protection systems of launch vehicle. Of particular interest was to obtain optimality criteria for designing foam insulations that have density (or porosity) distributions through the thickness for optimum thermal performance. We investigate the optimum design of functionally graded thermal insulation for steady state heat transfer through the foam. We showed that the heat transfer in the foam has competing modes, of radiation and conduction. The problem assumed a fixed inside temperature of 400 K and varied the aerodynamic surface heating on the outside surface from 0.2 to 1.0 MW/sq m. The thermal insulation develops a high temperature gradient through the thickness. Investigation of the model developed for heat conduction in foams showed that at high temperatures (as on outside wall) intracellular radiation dominates the heat transfer in the foam. Minimizing radiation requires reducing the pore size, which increases the density of the foam. At low temperatures (as on the inside wall), intracellular conduction (of the metal and air) dominates the heat transfer. Minimizing conduction requires increasing the pore size. This indicated that for every temperature there was an optimum value of density that minimized the heat transfer coefficient. Two optimization studies were performed. One was to minimize the heat transmitted though a fixed thickness insulation by varying density profiles. The second was to obtain the minimum mass insulation for specified thickness. Analytical optimality criteria were derived for the cases considered. The optimality condition for minimum heat transfer required that at each temperature we find the density that minimizes the heat transfer coefficient. Once a relationship between the optimum heat transfer coefficient and the temperature was found, the design problem reduced to the solution of a simple nonlinear differential equation. Preliminary results of this work were presented at the American Society of Composites meeting, and the final version was submitted for publication in the AIAA Journal. In addition to minimizing the transmitted heat, we investigated the optimum design for minimum weight given an acceptable level of heat transmission through the insulation. The optimality criterion developed was different from that obtained for minimizing beat transfer coefficient. For minimum mass design, we had to find for a given temperature the optimum density, which minimized the logarithmic derivative of the insulation thermal conductivity with respect to its density. The logarithmic derivative is defined as the ratio of relative change in the dependent response (thermal conductivity) to the relative change in the independent variable (density). The results have been documented as a conference paper that will be presented at the upcoming AIAA.

Haftka, Raphael T.↗

DOE OSTI2025

STILGAR End-of-Project Report

The Subsurface Tunnel Imaging LeveraGed by Analysis of Rayleigh wave ellipticity (STILGAR) project demonstrated an integrated geophysical approach for detecting, locating, and characterizing underground structural changes using dense seismic arrays and advanced inversion techniques. Field campaigns were conducted at two operational mines—the Redmond salt mine (Utah) and Graymont Pleasant Gap limestone mine (Pennsylvania)—providing real-world testbeds for monitoring anthropogenic subsurface activity. At the Redmond salt mine, seismic interferometry combined with back-projection inversion successfully identified continuous, low-amplitude signals from mining operations. The approach differentiated stationary from migrating anthropogenic sources, captured daily operational cycles, and validated the potential of passive seismic monitoring for remote detection of underground activity. At the Graymont Pleasant Gap mine, two dense seismic deployments in the spring and fall of 2023 generated over 4 TB of high-resolution data. Key outcomes included the relocation of 199 underground and 8 surface explosions with accuracies within tens of meters and the development of a 3D P-wave velocity model using the triple-difference tomography algorithm (tomoTD) that resolved major structural features such as the mine entrance, low-velocity tunnels, and roof-collapse areas. Ambient noise cross-correlation and back-projection analyses revealed persistent sources linked to ongoing mining activity, whereas horizontal-to-vertical spectral ratio (HVSR) and ellipticity studies confirmed stable site responses across seasons and identified soil thickness trends consistent with regional erosional and depositional processes. Checkerboard and sensitivity tests further validated the robustness of the tomographic results. Overall, the findings emphasize that although significant progress has been made in subsurface imaging, further work is needed to enhance the detection and localization of underground structures. Accurate imaging requires higher frequencies, yet anthropogenic sources tend to dominate the seismic record at those frequencies, and high-frequency surface waves are affected by higher modes that complicate interpretation. The improved detection and localization of human-induced signals enabled detailed temporal and spatial mapping of daily mine operations, demonstrating the feasibility of continuous anthropogenic source monitoring. Sensitivity to signals from nontraditional sources, such as fan operations, highlights the broader applicability of this approach to other industrial environments in which continuous and impulsive signals are present. The field campaigns produced a substantial volume of high-quality seismic data, supporting the development and testing of new methods for seismic source characterization and subsurface imaging. Future deployments should include sensors capable of recording lower frequencies to probe deeper structures, increase bandwidth to enhance resolution and sensitivity to both shallow and deep targets, and collect additional large-scale datasets to refine imaging and source characterization techniques. Moreover, conducting 3D modeling studies of seismic wavefields at higher frequencies will provide a better understanding of wave scattering and cavity–wavefield interactions in complex underground environments. In conclusion, the STILGAR project demonstrated that integrated seismic monitoring can effectively characterize underground operations, capturing both natural and anthropogenic signals. The approaches developed provide a foundation for improved detection, localization, and imaging of subsurface structures and are directly transferable to broader industrial monitoring applications.

58 GEOSCIENCES↗

NASA NTRS1995

A new criterion for bar-forming instability in rapidly rotating gaseous and stellar systems. 1: Axisymmetric form

We analyze previous results on the stability of uniformly and differentialy rotating, self-gravitating, gaseous and stellar, axisymmetric systems to derive a new stability criterion for the appearance of torodial, m = 2 intermediate or I-modes and bar modes. In the process, we demonstrate that the bar modes in stellar systems and the m = 2 I-modes in gaseous systems have many common physical characteristics and only one substantial difference: because of the anisotropy of the stress tensor, dynamical instability sets in at lower rotation in stellar systems. This difference is reflected also in the new stability criterion. The new stability parameter alpha equals (T(sub J))/(absolute value of W) is formulated first for uniformly rotating systems and is based on the angular momentum content rather than on the energy content of a system. (T(sub J) is defined as ((L)(Omega(sub J)))/2; L is the total angular momentum; Omega(sub J) is the Jeans frequency introduced by self-gravity; and W is the total gravitational potential energy.) For stability of stellar systems alpha less than or equal to 0.254-0.258 while alpha less than or equal to 0.341-0.354 for stability of gaseous systems. For uniform rotation, one can write alpha = ((ft)/2)(exp 1/2), where t is defined as T/(absolute value of W), T is the total kinetic energy due to rotation, and f is a function characteristic of the topology/connectedness and the geometric shape of a system. Equivalently, alpha equals t/(chi), where chi is defined as Omega/Omega(sub J) and Omega is the rotation frequency. Using these forms, alpha can be extended to and calculated for a variety of differentially rotating, gaseous and stellar, axisymmetric disk and spheroidal models whose equilibrium structures and stability characteristics are known. In this paper, we also estimate alpha for gaseous torodial models and for stellar disk systems embedded in an inert or responsive 'halo.' We find that the new stability criterion holds equally well for all these previously published axisymmetric models.

Christodoulou, Dimitris M.↗

NASA NTRS2011

Receiver Signal to Noise Ratios for IPDA Lidars Using Sine-wave and Pulsed Laser Modulation and Direct Detections

Integrated path differential absorption (IPDA) lidar can be used to remotely measure the column density of gases in the path to a scattering target [1]. The total column gas molecular density can be derived from the ratio of the laser echo signal power with the laser wavelength on the gas absorption line (on-line) to that off the line (off-line). 80th coherent detection and direct detection IPDA lidar have been used successfully in the past in horizontal path and airborne remote sensing measurements. However, for space based measurements, the signal propagation losses are often orders of magnitude higher and it is important to use the most efficient laser modulation and detection technique to minimize the average laser power and the electrical power from the spacecraft. This paper gives an analysis the receiver signal to noise ratio (SNR) of several laser modulation and detection techniques versus the average received laser power under similar operation environments. Coherent detection [2] can give the best receiver performance when the local oscillator laser is relatively strong and the heterodyne mixing losses are negligible. Coherent detection has a high signal gain and a very narrow bandwidth for the background light and detector dark noise. However, coherent detection must maintain a high degree of coherence between the local oscillator laser and the received signal in both temporal and spatial modes. This often results in a high system complexity and low overall measurement efficiency. For measurements through atmosphere the coherence diameter of the received signal also limits the useful size of the receiver telescope. Direct detection IPDA lidars are simpler to build and have fewer constraints on the transmitter and receiver components. They can use much larger size 'photon-bucket' type telescopes to reduce the demands on the laser transmitter. Here we consider the two most widely used direct detection IPDA lidar techniques. The first technique uses two CW seeder lasers, one on-line and one offline that are intensity modulated by two different frequency sine-waves signals before being amplified by a common laser amplifier. The receiver uses narrowband amplitude demodulation, or lock-in, Signal processing at the given laser modulation frequencies [3,4]. The laser transmitter operates in a quasi CW mode with the peak power equal to twice the average power. The on-line and off-line lasers can be transmitted at the same time without interference. Another direct detection technique uses a low duty cycle pulsed laser modulation [5,6] with the laser wavelengths alternating between on-line and off-line on successive pulses. The receiver uses time resolved detection and can also provide simultaneous target range measurement. With a lower laser duty cycle it requires a much higher peak laser power for the same average power.

Sun, Xiaoli↗

DOE OSTI2026

Gearbox bearing crack growth prognostics and uncertainty quantification with physics-informed machine learning

This paper introduces the extreme theory of functional connections (X-TFC), a physics-informed machine learning algorithm, and tailors it to estimate the remaining useful life (RUL) of wind turbine gearbox bearings experiencing fatigue crack growth. Unlike purely data-driven methods, X-TFC embeds a physics model, based on Head's theory in this work, into its training objective. The core of X-TFC is a random-projection single-layer neural network trained via an extreme learning machine, which requires only limited damage progression data and solves for output weights with a least-squares optimization algorithm. A composite loss function balances the network's fit to observed degradation data against the residuals of the governing crack growth differential equation, ensuring the learned damage trajectory remains physically plausible. When applied to a vibration-based health-index (HI) dataset measured during the growth of a crack on the inner ring of a high-speed bearing in a wind turbine gearbox (Bechhoefer and Dubé, 2020), X-TFC achieves near-zero prediction bias. Even when trained on only the first 10 %–20 % of the damage progression data, with sufficient physics weighting its predictions remain monotonic and smooth, delivering high prognosability and trendability. To quantify the epistemic uncertainty, we employ a Monte Carlo ensemble of independently initialized X-TFC models trained on noise-perturbed data, which yields confidence intervals around each RUL estimate and captures both model-parameter and epistemic uncertainty. In addition to a vibration-based HI, we demonstrate that the proposed framework can be directly applied to a supervisory control and data acquisition (SCADA) data-based HI (Eftekhari Milani et al., 2026) measured during similar wind turbine gearbox bearing crack faults, preserving its accuracy and interpretability. This extension shows the versatility of our approach, which is applicable to bearings of multiple gearbox manufacturers, models, and ratings using only SCADA data. By integrating domain knowledge with machine learning, X-TFC offers a rapid, reliable tool for crack prognostics. Its adaptability to other bearing failure modes, such as pitch bearing ring cracks, positions X-TFC as a powerful enabler of data-driven, physics-informed asset management in the wind energy sector and beyond.

17 WIND ENERGY↗