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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Lifetime Reliability Prediction of Ceramic Structures Under Transient Thermomechanical Loads

An analytical methodology is developed to predict the probability of survival (reliability) of ceramic components subjected to harsh thermomechanical loads that can vary with time (transient reliability analysis). This capability enables more accurate prediction of ceramic component integrity against fracture in situations such as turbine startup and shutdown, operational vibrations, atmospheric reentry, or other rapid heating or cooling situations (thermal shock). The transient reliability analysis methodology developed herein incorporates the following features: fast-fracture transient analysis (reliability analysis without slow crack growth, SCG); transient analysis with SCG (reliability analysis with time-dependent damage due to SCG); a computationally efficient algorithm to compute the reliability for components subjected to repeated transient loading (block loading); cyclic fatigue modeling using a combined SCG and Walker fatigue law; proof testing for transient loads; and Weibull and fatigue parameters that are allowed to vary with temperature or time. Component-to-component variation in strength (stochastic strength response) is accounted for with the Weibull distribution, and either the principle of independent action or the Batdorf theory is used to predict the effect of multiaxial stresses on reliability. The reliability analysis can be performed either as a function of the component surface (for surface-distributed flaws) or component volume (for volume-distributed flaws). The transient reliability analysis capability has been added to the NASA CARES/ Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code. CARES/Life was also updated to interface with commercially available finite element analysis software, such as ANSYS, when used to model the effects of transient load histories. Examples are provided to demonstrate the features of the methodology as implemented in the CARES/Life program.

Nemeth, Noel N.↗
NASA NTRS2024

DNA Break Clustering as a Predictor of Cell Death across Various Radiation Qualities: Influence of Cell Size, Cell Asymmetry, and Beam Orientation

Cosmic radiation, composed of high charge and energy (HZE) particles, causes cellular DNA damage that can result in cell death or mutation that can evolve into cancer. In this work, a cell death model is applied to several cell lines exposed to HZE ions spanning a broad range of linear energy transfer (LET) values. We hypothesize that chromatin movement leads to the clustering of multiple double strand breaks (DSB) within one radiation-induced foci (RIF). The survival probability of a cell population is determined by averaging the survival probabilities of individual cells, which is function of the number of pairwise DSB interactions within RIF. The simulation code RITCARD was used to compute DSB. Two clustering approaches were applied to determine the number of RIF per cell. RITCARD outputs were combined with experimental data from four normal human cell lines to derive the model parameters and expand its predictions in response to ions with LET ranging from ∼0.2keV/μmto∼3000keV/μm. Spherical and ellipsoidal nuclear shapes and two ion beam orientations were modeled to assess the impact of geometrical properties on cell death. The calculated average number of RIF per cell reproduces the saturation trend for high doses and high-LET values that is usually experimentally observed. The cell survival model generates the recognizable bell shape of LET dependence for the relative biological effectiveness (RBE). At low LET, smaller nuclei have lower survival due to increased DNA density and DSB clustering. At high LET, nuclei with a smaller irradiation area either because of a smaller size or a change in beam orientation have a higher survival rate due to a change in the distribution of DSB/RIF per cell. If confirmed experimentally, the geometric characteristics of cells would become a significant factor in predicting radiation-induced biological effects.

cell survival↗
NASA NTRS2010

A Test of Thick-Target Nonuniform Ionization as an Explanation for Breaks in Solar Flare Hard X-Ray Spectra

Solar nonthermal hard X-ray (HXR) flare spectra often cannot be fitted by a single power law, but rather require a downward break in the photon spectrum. A possible explanation for this spectral break is nonuniform ionization in the emission region. We have developed a computer code to calculate the photon spectrum from electrons with a power-law distribution injected into a thick-target in which the ionization decreases linearly from 100% to zero. We use the bremsstrahlung cross-section from Haug (1997), which closely approximates the full relativistic Bethe-Heitler cross-section, and compare photon spectra computed from this model with those obtained by Kontar, Brown and McArthur (2002), who used a step-function ionization model and the Kramers approximation to the cross-section. We find that for HXR spectra from a target with nonuniform ionization, the difference (Delta-gamma) between the power-law indexes above and below the break has an upper limit between approx.0.2 and 0.7 that depends on the power-law index delta of the injected electron distribution. A broken power-law spectrum with a. higher value of Delta-gamma cannot result from nonuniform ionization alone. The model is applied to spectra obtained around the peak times of 20 flares observed by the Ramaty High Energy Solar Spectroscopic Imager (RHESSI from 2002 to 2004 to determine whether thick-target nonuniform ionization can explain the measured spectral breaks. A Monte Carlo method is used to determine the uncertainties of the best-fit parameters, especially on Delta-gamma. We find that 15 of the 20 flare spectra require a downward spectral break and that at least 6 of these could not be explained by nonuniform ionization alone because they had values of Delta-gamma with less than a 2.5% probability of being consistent with the computed upper limits from the model. The remaining 9 flare spectra, based on this criterion, are consistent with the nonuniform ionization model.

Holman, gordon↗
DOE OSTI2024

Constraining Galaxy-Halo connection using machine learning

We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical fits, they often yield likelihood contours that are significantly biased in both mean values and variances relative to the true model parameters. This highlights the importance of careful data processing and algorithm selection in ML applications for galaxy clustering, as even seemingly robust methods can lead to biased results if not applied correctly. ML tools offer a promising approach to exploring the HOD parameter space with significantly reduced computational costs compared to traditional brute-force methods if their robustness is established. Using our ANN-based pipeline, we successfully recreate some standard results from recent literature. Properly restricting the HOD parameter space, transforming the training data, and carefully selecting ML algorithms are essential for achieving unbiased and robust predictions. Among the methods tested, artificial neural networks (ANNs) outperform random forests (RF) and ridge regression in predicting clustering statistics, when the HOD prior space is appropriately restricted. We demonstrate these findings using the projected two-point correlation function (w p (r p )), angular multipoles of the correlation function (ξ ℓ (r)), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining w p (r p ) and VPF improves parameter constraints, adding the multipoles ξ 0 , ξ 2 , and ξ 4 to w p (r p ) does not significantly improve the constraints.

cosmology↗
NASA NTRS1992

Multiple scattering effects on spaceborne lidar

A semianalytic Monte Carlo code originally developed for oceanographic calculations (Poole et al., 1981) has been modified for use in studying multiple scattering of space-based lidar. The approach is very similar to that described by Kunkel and Weinman (1976). The trajectory of each photon is followed from the transmitter through multiple scattering until the photon is either scattered backward out of the atmosphere, scattered forward into the ground and absorbed, or scattered out the sides of the cloud. The probability that the photon will return directly to the detector is computed and summed over all significant scattering events within the field of view of the detector. Multiple scattering of the lidar pulse causes an apparent increase in the transmittance of the medium. Multiple scattering effects for space-based lidar are more significant than for ground-based lidar due to the much larger beam diameter in the atmosphere. These larger diameters are due not only to the greater range between the lidar and the scattering volume, but also the need to maintain relatively large beam divergences to satisfy eye safety restrictions on the laser irradiance at the Earth's surface. The simulations presented here are for a wavelength of 1064 nm and the Deirmendjian C1 phase function, which yields an extinction coefficient of 17.259/km. We have looked at two cases: a space-based lidar at 296 km observing a C1 cloud 293 km from the lidar and, for comparison purposes, a ground-based lidar looking at a C1 cloud with a base height of either 2 km or 5 km. The C1 size distribution roughly approximates that of stratocumulus or altocumulus clouds (aufm Kampe and Weickmann, 1957).

Winker, David M.↗
DOE OSTI2025

Reduced Order Modeling conditioned on monitored features for response and error bounds estimation in engineered systems

Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Although purely data-driven methods are available for ROM construction, schemes that allow to retain a portion of the physics tend to enhance the interpretability and generalization of ROMs. However, physics-based techniques can adversely scale when dealing with nonlinear systems that feature parametric dependencies. This study introduces a generative physics-based ROM that is suited for nonlinear systems with parametric dependencies and is additionally able to provide numerical error bounds associated with the respective estimates. A main contribution of this work is the conditioning of these parametric ROMs to features that can be derived from monitoring measurements, feasibly in an online fashion. This is contrary to most existing ROM schemes, which remain restricted to the prescription of the physics-based, and usually a priori unknown, system parameters. Our work utilizes conditional Variational Autoencoders to continuously map the required reduction bases to a feature vector extracted from limited output measurements, while additionally allowing for a probabilistic assessment of the ROM-estimated Quantities of Interest. An auxiliary task using a neural network-based parametrization of suitable probability distributions is introduced to re-establish the link with physical model parameters. We verify the proposed scheme on a series of simulated case studies incorporating effects of geometric and material nonlinearity under parametric dependencies related to system properties and input load characteristics.

Conditional VAEs↗
NASA NTRS2007

Assessment of Immune Status, Latent Viral Reactivation and Stress during Long Duration Bed Rest as an Analog for Spaceflight

As logistical access for in-flight space research becomes more limited, the use of ground based spaceflight analogs for life science studies will increase. These studies are particularly important as NASA progresses towards the Lunar and eventually Mars missions outlined in the 2005 Vision for Space Exploration. Countermeasures must be developed to mitigate the clinical risks associated with exploration class space missions. In an effort to coordinate studies across multiple disciplines, NASA has selected 90-day bed rest as the analog of choice, and initiated the Flight Analogs Project to implement research studies with or without the evaluation of countermeasures. Although bed rest is not the analog of choice to evaluate spaceflight-associated immune dysfunction, a standard Immune Assessment was developed for subjects participating in the 90-day bed best studies. The Immune Assessment consists of: leukocyte subset distribution, T cell functional responses, intracellular cytokine production profiles, latent viral reactivation, virus specific T cell levels, virus specific T cell function, stress hormone levels and a behavioral assessment using stress questionnaires. The purpose of the assessment during the initial studies (without countermeasure) is to establish control data against which future studies (with countermeasure) will be evaluated. It is believed that some of the countermeasures planned to be evaluated in future studies, such as exercise, pharmacologic intervention or nutritional supplementation, have the ability to impact immune function. Therefore immunity will likely be monitored during those studies. The data generated during the first three control studies showed that the subjects in general did not display altered peripheral leukocyte subsets, constitutive immune activation, significant latent viral reactivation (EBV, VZV) or altered T cell function. Interestingly, for some subjects the level of constitutively activated T cells (CD8+/CD69+) and virus-specific T cells (CMV and EBV) both decreased during the studies. This likely reflects the isolation of the subjects (from an immunological perspective) and absence of everyday subclinical challenges to the immune system. Cortisol levels (plasma and saliva) did not vary significantly during the studies. This probably reflects a lack of physiological stress during the study and the stress of readaptation to the 1xG environment at R+1. These data demonstrate the absence of significant immune alteration during 90-day bed rest, and establish control data against which future studies (including countermeasures) may be compared.

Crucian, Brian E.↗
NASA NTRS2004

The Investigation Of Carbon Contamination And Sputtering Effects Of Xenon Ion Thrusters

The Electro-Physics Branch of the NASA Glenn Research Center investigates the effect of atomic oxygen, environmental durability of high performance power materials and surfaces, and low earth orbit. One of its current projects involves the analysis of ion thrusters. Ion thrusters are devices that initiate a beam of ions to a target area. The type of ion thruster that I have been working with this Summer of 2004 emits positively charged Xenon (Xe(+)) atoms through two grids, the screen grid and the accelerator grid, after it enters an ionization chamber. Insulators are used to mechanically hold and separate these two grids. A propellant isolator, an instrument that closely resembles insulators, is placed in front of the ionization chamber. Both the insulator and isolator are made with a ceramic compound and filled with insulating beads. The main difference between the two devices is that the propellant isolator allows gas to flow through, in this case, the gas is Xe(+) and the insulators do not. In order to avoid carbon deposits and other contaminating chemicals to settle on the insulators and propellant isolator, a metal shadow shield is placed around them. These shadow shields function as a protectant and can be shaped in numerous configurations. Part of my job responsibility this summer is to investigate the effectiveness of different shadow shields that are utilized on three different ion engines: the NSTAR (NASA Solar Electric Propulsion Technology Application Readiness), JIMO (Jupiter Icy Moons Orbiter), and NEXIS (Nuclear Electric Xenon Ion System). Using calculus and other mathematical tactics, I was asked to find the total flux of carbon contamination that was able to pass the protectant shadow shield. I familiarized myself with the software program, MathCad2004, to help perform some mathematical computations such as complex integration. Another method of studying the probability of contamination is by experimental simulation. After attaining the precise parameters of the actual shadow shields, I created replicas of three types of shadow shielding to be used to undergo testing. It will be placed in a machine that produces carbon atoms at a high temperature of 200 C. or beam is aimed at a targeted material. As a result of this collision, atoms and other particles are ejected out of the target surface. Another part of my internship consisted of research on sputter ejection, or the angle distribution of sputtered material. This research entailed finding the past results of sputter ejection investigation as well as creating another type of mock simulation. Other minor projects include calculating the path of Xe(+) gas through the insulating beads of the isolators and assisting my mentor in collecting data for his paper for the Joint Propulsion Conference & Exhibit to be held July 11-14,2004 in Fort Lauderdale, Florida.

Prak, Moline K.↗
NASA NTRS2003

Self-Supervised Dynamical Systems

Some progress has been made in a continuing effort to develop mathematical models of the behaviors of multi-agent systems known in biology, economics, and sociology (e.g., systems ranging from single or a few biomolecules to many interacting higher organisms). Living systems can be characterized by nonlinear evolution of probability distributions over different possible choices of the next steps in their motions. One of the main challenges in mathematical modeling of living systems is to distinguish between random walks of purely physical origin (for instance, Brownian motions) and those of biological origin. Following a line of reasoning from prior research, it has been assumed, in the present development, that a biological random walk can be represented by a nonlinear mathematical model that represents coupled mental and motor dynamics incorporating the psychological concept of reflection or self-image. The nonlinear dynamics impart the lifelike ability to behave in ways and to exhibit patterns that depart from thermodynamic equilibrium. Reflection or self-image has traditionally been recognized as a basic element of intelligence. The nonlinear mathematical models of the present development are denoted self-supervised dynamical systems. They include (1) equations of classical dynamics, including random components caused by uncertainties in initial conditions and by Langevin forces, coupled with (2) the corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces, denoted supervising forces, composed of probability densities and functionals thereof. The equations of classical mechanics represent motor dynamics that is, dynamics in the traditional sense, signifying Newton s equations of motion. The evolution of the probability densities represents mental dynamics or self-image. Then the interaction between the physical and metal aspects of a monad is implemented by feedback from mental to motor dynamics, as represented by the aforementioned fictitious forces. This feedback is what makes the evolution of probability densities nonlinear. The deviation from linear evolution can be characterized, in a sense, as an expression of free will. It has been demonstrated that probability densities can approach prescribed attractors while exhibiting such patterns as shock waves, solitons, and chaos in probability space. The concept of self-supervised dynamical systems has been considered for application to diverse phenomena, including information-based neural networks, cooperation, competition, deception, games, and control of chaos. In addition, a formal similarity between the mathematical structures of self-supervised dynamical systems and of quantum-mechanical systems has been investigated.

Zak, Michail↗
NASA NTRS1976

The stellar content of the nuclear regions of Sc galaxies

Stellar-population syntheses based on absolute spectral energy distributions over the wavelength range from 3300 to 10,400 A are used to determine the stellar content of the nuclear regions of seven nearby Sc galaxies (NGC 628, 1073, 1084, 1637, 2903, 4321, and 5194). A linear-programming procedure is employed to construct models of the overall stellar populations whose spectra closely match those of the seven galaxies. Absolute measurements of the emission-line spectra of the nuclear regions are also provided. It is found that: (1) intrinsic reddening is probably present in each nuclear region; (2) the upper main sequence is substantially populated in most of the models; (3) the lower main sequence contributes insignificantly to the luminosity in all optimal solutions; (4) substantial contributions are made by evolved M stars at long wavelengths in all the models; (5) the model photometric M/L ratios are low, of the order of unity; and (6) the O-B stars arising naturally in the population models are just sufficient to provide the observed nuclear ionization in all the galaxies except NGC 5194, which may be collisionally ionized. The properties of the nuclear regions are shown to be consistent with the existence of a common initial mass function for star formation and a variety of time dependences for the star-formation process. A possibly significant correlation is noted between nuclear stellar content and overall dynamical properties in four of the galaxies.

Turnrose, B. E.↗
NASA NTRS1994

Kinetic and thermodynamic properties of a convecting plasma in a two-dimensional dipole field

Charged particle guiding center motion is considered in the magnetic field of a two-dimensional ('line') dipole on which is superimposed a small, static, perpendicular electric field. The parallel equation of motion is that of a simple harmonic oscillator for cos theta, the cosine of magnetic colatitude theta. Equations for the perpendicular electric and magnetic drifts are derived as well as their bounce-averaged forms. The latter are solved to yield a bounce-averaged guiding center trajectory, which is the same as that obtained from conversation of magnetic moment mu, longitudinal invariant J, and total (kinetic plus electrostatic) energy K. The algebraic simplicity of the trajectory equations is also manifest in the forms of the invariants. An interesting result is that guiding centers drift in such a way that they preserve the values of their equatorial pitch angles and (equivalently) mirror latitudes. The most general Maxwellian form of the equilibrium one-particle distribution function f is constructed from the invariants, and spatially varying density and pressure moments, parallel and perpendicular to the magnetic field, are identified. Much of the paper deals with the more restricted problem in which f is specified as a bi-Maxwellian over a straight line of finite length in the equatorial plane of the dipole and perpendicular to field lines. This might be thought of as specifying a cross-tail ion injection source; our formalism then describes the subsequent spatial development. The distribution away from the source is a scaled bi-Maxwellian but one that is cut off at large and small kinetic energies, which depend on position. Density and pressure components are reduced from the values they would have if the total content of individual flux tubes convected intact. The equatorial and meridional variations of density and pressure components are examined and compared systematically for the isotropic and highly anisotropic situations. There appears to be little qualitative difference due to anisotropy. An anisotropy measure is defined, and its spatial variation determined as a signature of possible MHD instability. Extreme values are found, larger than at the source, but the plasma beta in such regions is probably so low as to render the effect inconsequential energetically. Finally, the possible consequence of 'nonadia- batic' pressure profiles on electrostatic interchanges is considered, and a boundary delineating stabilizing and destabilizing regions determined.

Huang, T. S.↗
NASA NTRS2018

Representativeness of CO and O3 Along ATom Transects Derived from GEOS-5 and GMI-CTM Simulations

One major goal for the NASA Atmospheric Tomography Mission (ATom) is producing an observation-based chemical climatology to represent the atmospheric heterogeneity. In this study, we use CO and O3 observations and global atmospheric model simulations to examine the spatial representativeness of the ATom-1 and -2 transects within a 4D framework provided by the NASA GEOS-5 and GMI-CTM models. Based on the probability density functions, we find that the variability of CO and O3 along the flight tracks is well hindcast by the model when sampled per ATom flights. The CO variations along the ATom-transect are likely representative of the typical CO variations over the whole Pacific basin during both the ATom-1 and -2 periods, the northern Atlantic during the ATom-1 period, and the tropical Atlantic in the ATom-2 period. Over southern Atlantic, CO along the ATom-1 transects is likely less well mixed than that of the broader region, but is still representative of the median CO concentration. CO along the ATom-2 transect is likely higher than the median CO concentration over this region. For O3, the agreements between PDFs of O3 sampled along the ATom transects and over the broader regions are fair to good over all six regions (Scores > 0.65) with notable discrepancies over some regions. For example, in ATom-1 over the northern Pacific and Atlantic, the transect samples air masses with higher O3 levels. During ATom-2, the transect over-represents the occurrence of O3 plumes over tropical Pacific. Over the southern Pacific and Atlantic for both ATom-1 and -2, the transects have a less uniform distribution compared to the surrounding basins, but still represent the median O3 abundance. Overall, we conclude in most cases that ATom measurements represent the statistical variations of these two species over the ocean basins at the time of measurement. Higher-order statistics, including covariance of species, has not been tested in this study.

ozone↗
DOE OSTI2026

Enabling probabilistic learning on manifolds through double diffusion maps

Here, we present a generative learning framework for probabilistic sampling that extends Probabilistic Learning on Manifolds (PLoM), which is designed to generate statistically consistent realizations of a random vector in a finite-dimensional Euclidean space, informed by a (representative) set of observations. In its original form, PLoM constructs a reduced-order probabilistic model by combining three main components: (a) kernel density estimation to approximate the underlying probability measure, (b) Diffusion Maps to characterize the manifold of the data, and (c) a reduced-order Itô Stochastic Differential Equation (ISDE) to sample from the learned distribution. However, its sampling dynamics are posed in the ambient space and the retained number of reduced coordinates is chosen by projection-reconstruction error. In practice, this often (i) requires more coordinates than the data’s intrinsic dimension to achieve stable sampling and (ii) lacks a smooth, basis-independent lifting back to the data domain; moreover, standard Diffusion Maps emphasize harmonic eigenfunctions and can miss non-harmonic latent structure. We address these limitations by decoupling geometry learning from sampling: a first Diffusion Maps pass identifies non-harmonic coordinates on which we formulate a full-order ISDE directly in the latent space, while Double Diffusion Maps captures multiscale geometric features and Geometric Harmonics (GH) learns a smooth lifting map to the ambient variables that is independent of the particular diffusion basis. This hybrid design preserves the system’s dynamical richness with a compact geometric representation and enables principled out-of-sample inference. The effectiveness and robustness of the proposed method are illustrated through two numerical studies: one based on data generated from two-dimensional Hermite polynomial functions and another based on high-fidelity simulations of a detonation wave in a reactive flow.

Double diffusion maps↗
NASA NTRS2001

Validation of TRMM Satellite Rainfall Products Over Oklahoma for a Three Year Period (1998-2000)

This study evaluates the level 2 and level 3 rainfall products from the Tropical Rainfall Measuring Mission gridded distribution of 118 high resolution tipping buckets from the Oklahoma Mesonet. The Tropical Rainfall Measuring Mission, a joint satellite mission between NASA and the National Space Development Agency (NASDA) of Japan, was designed to estimate global precipitation between 40 S and 40 N latitude. The TRMM satellite, consisting of two main precipitation sensors, a passive microwave (TMI) and precipitation radar (PR) sensors, was launched in 1997. Although the great advantage of TRMM has been its ability to sample precipitation globally over the tropical oceans in places where ground sensors do not exist, regional points over land still offer the best opportunity for validating these rain estimates. For this study, the gauge data was gridded at I x I degree resolution between 34 and 36 N and -95 and -100 W. The location of Oklahoma is somewhat unique, in that, it is located near the turning point for the satellite. This study investigates effects of temporal sampling on the satellite measurements and the resulting rainfall bias observed between space and ground based sensors. The first part of the study analyzes the monthly rainfall statistics of the 3AI2 (TMI), the 3A25 (PR) and the 31142 (combined sensors) with the rain gauges. It uses regression techniques and a computation of the probability density function (PDF) and cumulative probability density function (CDF) for each sensor to evaluate the effects of temporal sampling on the satellite products and the sufficiency of using two overpasses a day to estimate rainfall on a monthly scale. In further probing this issue, the study also looked at the direct coincidence of ground and satellite measurements by comparing instantaneous comparisons obtained from the level 2 orbital track data, e.g. 2A12, 2A25. Lastly, the effects of temporal sampling were further studied by sub-sampling the gauge data only at overpass times. This methodology provides a quantitative way of separately inferring the fraction of bias due to temporal sampling and the rainfall algorithm. In addition to showing the salient effects of temporal sampling, the study also revisits the historical problem of correlating point gauge estimates with area estimates, though, the problem is ameliorated by averaging and gridding the data to the area resolution scale of the satellite.

Fisher, Brad↗
DOE OSTI2024

Simulation-based inference for parameter estimation of complex watershed simulators

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Medium-range order and compositional correlation in metallic glasses

The compositional atomic ordering in metallic glasses was studied by simulation focusing on the medium-range order (MRO). Many metallic alloy liquids and glasses show MRO characterized by the oscillations in the atomic pair-distribution function (PDF) beyond the first peak, which decay exponentially with distance. To study the effects of the local chemical order on MRO, we examine the compositionally resolved PDF and its MRO for models of various binary metallic alloy glasses. We show that compositional ordering is limited mostly to the nearest-neighbor atoms and the MRO is largely independent of the compositional order. For some elements that strongly repel each other in the alloy, a second MRO periodicity is observed owing to the distinct correlations among them. These results are discussed in light of the idea that the MRO oscillations in the PDF describe the correlations in the atomic density fluctuations, rather than the detailed local atomic structure.

Atomic structure↗
NASA NTRS2001

Space flight affects magnocellular supraoptic neurons of young prepuberal rats: transient and permanent effects

Effects of microgravity on postural control and volume of extracellular fluids as well as stress associated with space flight may affect the function of hypothalamic neurosecretory neurons. Since environmental modifications in young animals may result in permanent alterations in neuroendocrine function, the present study was designed to determine the effect of a space flight on oxytocinergic and vasopressinergic magnocellular hypothalamic neurons of prepuberal rats. Fifteen-day-old Sprague-Dawley female rats were flown aboard the Space Shuttle Columbia (STS-90, Neurolab mission, experiment 150) for 16 days. Age-matched litters remained on the ground in cages similar to those of the flight animals. Six animals from each group were killed on the day of landing and eight animals from each group were maintained under standard vivarium conditions and killed 18 weeks after landing. Several signs of enhanced transcriptional and biosynthetic activity were observed in magnocellular supraoptic neurons of flight animals on the day of landing compared to control animals. These include increased c-Fos expression, larger nucleoli and cytoplasm, and higher volume occupied in the neuronal perikaryon by mitochondriae, endoplasmic reticulum, Golgi apparatus, lysosomes and cytoplasmic inclusions known as nematosomes. In contrast, the volume occupied by neurosecretory vesicles in the supraoptic neuronal perikarya was significantly decreased in flight rats. This decrease was associated with a significant decrease in oxytocin and vasopressin immunoreactive levels, suggestive of an increased hormonal release. Vasopressin levels, cytoplasmic volume and c-Fos expression returned to control levels by 18 weeks after landing. These reversible effects were probably associated to osmotic stimuli resulting from modifications in the volume and distribution of extracellular fluids and plasma during flight and landing. However, oxytocin levels were still reduced at 18 weeks after landing in flight animals compared to controls. This indicates that space flight during prepuberal age may induce irreversible modifications in the regulation of oxytocinergic neurons, which in turn may result in permanent endocrine and behavioral impairments.

NASA Experiment Number 9301150↗
DOE OSTI2025

Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker–Planck Equations

The Fokker-Planck (FP) equation is a foundational partial differential equation (PDE) in stochastic processes involving Brownian motions. However, the curse of dimensionality (CoD) poses a formidable challenge when dealing with high-dimensional FP equations. Although Monte Carlo simulation and (vanilla) Physics-Informed Neural Networks (PINNs) have shown the potential to tackle CoD, both methods exhibit significant numerical errors in high dimensions when dealing with the probability density function (PDF) associated with Brownian motion. The point-wise PDF values tend to decrease exponentially as dimensionality increases, surpassing the precision of numerical simulations and resulting in substantial errors. In addition, due to its massive sampling, Monte Carlo fails to offer fast sampling. Modeling the logarithm likelihood (LL) via vanilla PINNs transforms the FP equation into a notoriously difficult Hamilton-Jacobi-Bellman (HJB) equation, which is impractical for PINN learning, whose error grows rapidly with dimension. To this end, we propose a novel approach utilizing a score-based solver to fit the score function in stochastic differential equations (SDEs). The score function, defined as the gradient of the LL, plays a fundamental role in inferring LL and PDF and enables fast SDE sampling, offering an effective means to overcome the CoD. Three fitting methods, Score Matching (SM), Sliced Score Matching (SSM), and Score-PINN, are introduced, each contributing unique advantages in computational complexity, accuracy, and generality. The proposed score-based SDE solver operates in two stages: first, employing score matching or Score-PINN to acquire the score function; and second, solving the LL via an ordinary differential equation (ODE) using the obtained score function. Comparative evaluations across these methods showcase varying trade-offs. The proposed methodology is evaluated across diverse SDEs, including anisotropic Ornstein-Uhlenbeck processes, geometric Brownian motion, and Brownian motion with varying eigenspace. We also test various distributions, including Gaussian, Log-normal, Laplace, and Cauchy distributions. The numerical results demonstrate the score-based SDE solver’s stability, speed, and performance across different experimental settings, solidifying its potential as a solution to CoD for high-dimensional FP equations.

97 MATHEMATICS AND COMPUTING↗