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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Cosmic-ray modulation, merged interaction regions, and multifractals

The large-scale heliospheric magnetic field fluctuations and their effects on cosmic rays are discussed. Voyager 2 observations from 1983.0 to 1989.6 are used as input to the model of Perko and Burlaga (1992) to show that the few largest merged interaction regions (MIRs) provide essential details in the cosmic-ray intensity (CRI) profile. It is demonstrated that the largest MIRs determine the basic structure of the radial gradients of the CRI. It is shown that the magnetic field strength during the period 1983.0-1989.6 does have a multifractal structure, but that there are different multifractal structures in the three intervals 1983.0-1985.0, 1985.0-1987.5, and 1987.5-1989.6. The multifractal structure can be described by simple functions, the multifractal spectra. These analytical descriptions can be used to generate realizations of the heliospheric magnetic field with the same statistical properties as those observed by Voyager 2. Thus, the starting point is provided for realistic global statistical models of the 11-yr variation of the Galactic CRI.

Burlaga, L. F.↗
NASA NTRS1996

Accurate Predictions of Mean Geomagnetic Dipole Excursion and Reversal Frequencies, Mean Paleomagnetic Field Intensity, and the Radius of Earth's Core Using McLeod's Rule

The geomagnetic spatial power spectrum R(sub n)(r) is the mean square magnetic induction represented by degree n spherical harmonic coefficients of the internal scalar potential averaged over the geocentric sphere of radius r. McLeod's Rule for the magnetic field generated by Earth's core geodynamo says that the expected core surface power spectrum (R(sub nc)(c)) is inversely proportional to (2n + 1) for 1 less than n less than or equal to N(sub E). McLeod's Rule is verified by locating Earth's core with main field models of Magsat data; the estimated core radius of 3485 kn is close to the seismologic value for c of 3480 km. McLeod's Rule and similar forms are then calibrated with the model values of R(sub n) for 3 less than or = n less than or = 12. Extrapolation to the degree 1 dipole predicts the expectation value of Earth's dipole moment to be about 5.89 x 10(exp 22) Am(exp 2)rms (74.5% of the 1980 value) and the expected geomagnetic intensity to be about 35.6 (mu)T rms at Earth's surface. Archeo- and paleomagnetic field intensity data show these and related predictions to be reasonably accurate. The probability distribution chi(exp 2) with 2n+1 degrees of freedom is assigned to (2n + 1)R(sub nc)/(R(sub nc). Extending this to the dipole implies that an exceptionally weak absolute dipole moment (less than or = 20% of the 1980 value) will exist during 2.5% of geologic time. The mean duration for such major geomagnetic dipole power excursions, one quarter of which feature durable axial dipole reversal, is estimated from the modern dipole power time-scale and the statistical model of excursions. The resulting mean excursion duration of 2767 years forces us to predict an average of 9.04 excursions per million years, 2.26 axial dipole reversals per million years, and a mean reversal duration of 5533 years. Paleomagnetic data show these predictions to be quite accurate. McLeod's Rule led to accurate predictions of Earth's core radius, mean paleomagnetic field intensity, and mean geomagnetic dipole power excursion and axial dipole reversal frequencies. We conclude that McLeod's Rule helps unify geo-paleomagnetism, correctly relates theoretically predictable statistical properties of the core geodynamo to magnetic observation, and provides a priori information required for stochastic inversion of paleo-, archeo-, and/or historical geomagnetic measurements.

Voorhies, Coerte V.↗
DOE OSTI2021

Learning-based approach to plasticity in athermal sheared amorphous packings: Improving softness

The plasticity of amorphous solids undergoing shear is characterized by quasi-localized rearrangements of particles. While many models of plasticity exist, the precise relationship between the plastic dynamics and the structure of a particle’s local environment remains an open question. Previously, machine learning was used to identify a structural predictor of rearrangements called “softness.” Although softness has been shown to predict which particles will rearrange with high accuracy, the method can be difficult to implement in experiments where data are limited and the combinations of descriptors it identifies are often difficult to interpret physically. Here, we address both of these weaknesses, presenting two major improvements to the standard softness method. First, we present a natural representation of each particle’s observed mobility, allowing for the use of statistical models that are both simpler and provide greater accuracy in limited datasets. Second, we employ persistent homology as a systematic means of identifying simple, topologically informed, structural quantities that are easy to interpret and measure experimentally. We test our methods on two-dimensional athermal packings of soft spheres under quasi-static shear. We find that the same structural information that predicts small variations in the response is also predictive of where plastic events will localize. We also find that an excellent accuracy is achieved in athermal sheared packings using simply a particle’s species and the number of nearest neighbor contacts.

36 MATERIALS SCIENCE↗
NASA NTRS1997

On the Use of Statistics in Design and the Implications for Deterministic Computer Experiments

Perhaps the most prevalent use of statistics in engineering design is through Taguchi's parameter and robust design -- using orthogonal arrays to compute signal-to-noise ratios in a process of design improvement. In our view, however, there is an equally exciting use of statistics in design that could become just as prevalent: it is the concept of metamodeling whereby statistical models are built to approximate detailed computer analysis codes. Although computers continue to get faster, analysis codes always seem to keep pace so that their computational time remains non-trivial. Through metamodeling, approximations of these codes are built that are orders of magnitude cheaper to run. These metamodels can then be linked to optimization routines for fast analysis, or they can serve as a bridge for integrating analysis codes across different domains. In this paper we first review metamodeling techniques that encompass design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We discuss their existing applications in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of metamodeling techniques in given situations and how common pitfalls can be avoided.

Simpson, Timothy W.↗
DOE OSTI2019

Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions

Linking in vitro bioactivity and in vivo toxicity on a dose basis enables the use of highthroughput in vitro assays as an alternative to traditional animal studies. In this study, we evaluated assumptions in the use of a high-throughput, physiologically based toxicokinetic (PBTK) model to relate in vitro bioactivity and rat in vivo toxicity data. The fraction unbound in plasma (fup) and intrinsic hepatic clearance (Clint) were measured for rats (for 67 and 77 chemicals, respectively), combined with fup and Clint literature data for 97 chemicals, and incorporated in the PBTK model. Of these chemicals, 84 had corresponding in vitro ToxCast bioactivity data and in vivo toxicity data. For each possible comparison of in vitro and in vivo endpoint, the concordance between the in vivo and in vitro data was evaluated by a regression analysis. For a base set of assumptions, the PBTK results were more frequently better associated than either the results from a “random” model parameterization or direct comparison of the “untransformed” values of AC50 and dose (performed best in 51%, 28%, and 21% of cases, respectively). We also investigated several assumptions in the application of PBTK for IVIVE, including clearance and internal dose selection. One of the better assumptions sets–restrictive clearance and comparing free in vivo venous plasma concentration with free in vitro concentration–outperformed the random and untransformed results in 71% of the in vitro-in vivo endpoint comparisons. These results demonstrate that applying PBTK improves our ability to observe the association between in vitro bioactivity and in vivo toxicity data in general. This suggests that potency values from in vitro screening should be transformed using in vitro-in vivo extrapolation (IVIVE) to build potentially better machine learning and other statistical models for predicting in vivo toxicity in humans

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1993

A petabyte size electronic library using the N-Gram memory engine

A model library containing petabytes of data is proposed by Triada, Ltd., Ann Arbor, Michigan. The library uses the newly patented N-Gram Memory Engine (Neurex), for storage, compression, and retrieval. Neurex splits data into two parts: a hierarchical network of associative memories that store 'information' from data and a permutation operator that preserves sequence. Neurex is expected to offer four advantages in mass storage systems. Neurex representations are dense, fully reversible, hence less expensive to store. Neurex becomes exponentially more stable with increasing data flow; thus its contents and the inverting algorithm may be mass produced for low cost distribution. Only a small permutation operator would be recalled from the library to recover data. Neurex may be enhanced to recall patterns using a partial pattern. Neurex nodes are measures of their pattern. Researchers might use nodes in statistical models to avoid costly sorting and counting procedures. Neurex subsumes a theory of learning and memory that the author believes extends information theory. Its first axiom is a symmetry principle: learning creates memory and memory evidences learning. The theory treats an information store that evolves from a null state to stationarity. A Neurex extracts information data without a priori knowledge; i.e., unlike neural networks, neither feedback nor training is required. The model consists of an energetically conservative field of uniformly distributed events with variable spatial and temporal scale, and an observer walking randomly through this field. A bank of band limited transducers (an 'eye'), each transducer in a bank being tuned to a sub-band, outputs signals upon registering events. Output signals are 'observed' by another transducer bank (a mid-brain), except the band limit of the second bank is narrower than the band limit of the first bank. The banks are arrayed as n 'levels' or 'time domains, td.' The banks are the hierarchical network (a cortex) and transducers are (associative) memories. A model Neurex was built and studied. Data were 50 MB to 10 GB samples of text, data base, and images: black/white, grey scale, and high resolution in several spectral bands. Memories at td, S(m(sub td)), were plotted against outputs of memories at td-1. S(m(sub td)) was Boltzman distributed, and memory frequencies exhibited self-organized criticality (SOC); i.e., 'l/f(sup beta)' after long exposures to data. Whereas output signals from level n may be encoded with B(sub output) = O(-log(2)f(sup beta)) bits, and input data encoded with B(sub input) = O((S(td)/S(td-1))(sup n)), B(sup output)/B(sub input) is much less than 1 always, the Neurex determines a canonical code for data and it is a lossless data compressor. Further tests are underway to confirm these results with more data types and larger samples.

Bugajski, Joseph M.↗
NASA NTRS2018

Bloom Modeling and Prediction of the Harmful Algae Alexandrium in Bellingham Bay, WA

Bellingham, Washington is located near the American-Canadian border in the northwestern part of Washington state, and is bordered on its west side by the Lummi reservation. Between Lummi and Bellingham lies Bellingham bay, which has a history of harmful algae related closures dating back to 1978. The subject of this work is a genus of dinoflagellates: Alexandrium, within which many species have the capacity to produce a suite of toxins known as saxitoxin. These toxins bioaccumulate in bivalves, which in turn cause paralytic shellfish poisoning in marine consumers (mammals, birds, and fish), including humans. Symptoms in humans can range from tingling and numbness to difficulty or inability to breathe, resulting in death. Because of the longstanding history of shellfish gathering among Salish tribes and the fact that Washington is the leading U.S. producer of farmed bivalves, harmful algae blooms impact both native and non-native peoples living in the Salish Sea area negatively. The objective of this work is to identify factors which influence Alexandrium blooms in Bellingham Bay, as well as predict Alexandrium blooms in the future. The method of doing so involves two processes: an initial statistical modeling phase to find in situ and remote sensing observations correlated to bloom density (including, but not limited to: water temperature, chlorophyll-a, salinity, color dissolved organic matter, and discharge rate of local rivers), followed by use of that data as a training set for a recursive neural network. This predictive capacity may inform future closures, help ensure the safety of shellfish consumers, and act as a baseline for future modeling efforts in the region.

Harmful↗
NASA NTRS2000

Cyclic Oxidation Testing and Modelling: A NASA Lewis Perspective

The Materials Division of the NASA Lewis Research Center has been heavily involved in the cyclic oxidation of high temperature materials for 30 years. Cyclic furnace and burner rig apparati have been developed, refined, and replicated to provide a large scale facility capable of evaluating many materials by a standard technique. Material behavior is characterized by weight change data obtained throughout the test, which has been modelled in a step-wise process of scale growth and spallation. This model and a coupled diffusion model have successfully described cyclic behavior for a number of systems and have provided insights regarding life prediction and variations in the spalling process. Performance ranking and mechanistic studies are discussed primarily for superalloys and coating alloys. Similar cyclic oxidation studies have been performed on steels, intermetallic compounds, thermal barrier coatings, ceramics, and ceramic composites. The most common oxidation test was performed in air at temperatures ranging from 800 deg. to 1600 C, for times up to 10000 h, and for cycle durations of 0.1 to 1000 h. Less controlled, but important, test parameters are the cooling temperature and humidity level. Heating and cooling rates are not likely to affect scale spallation. Broad experience has usually allowed for considerable focus and simplification of these test parameters, while still revealing the principal aspects of material behavior and performance. Extensive testing has been performed to statistically model the compositional effects of experimental alloys and to construct a comprehensive database of complex commercial alloys.

Smialek, J. L.↗
DOE OSTI2026

Mountain Basin Controls on the Snow-to-Streamflow Signal: An AIC-Weighted Multiple Linear Regression Framework

A regression-based analysis quantifies how basin characteristics modulate the snow-to-streamflow signal. First, we use the ERA5-Land reanalysis gridded product (European Centre for Medium Range Weather Forecasts reanalysis 5 -Land component) for 4,655 hydrologic unit code - 10 (HUC10) mountain basins across the western United States (US) for water years 1987–2024. Linear regressions are performed for peak snow water equivalent (SWE) and annual streamflow for each mountain basin. Models use ordinary least squares in Python’s statsmodels package. After which, an Akaike Information Criterion (AIC)–weighted ensemble multiple linear regression (MLR) framework with 47 watershed traits is used to predict the linear regression coefficient of determination (r-squared) defining the ability of peak SWE to predict annual streamflow across all mountain basin. Predictor sets are constrained to avoid multicollinearity by excluding models with variance inflation factors (VIF) greater than 5. Mountain basin traits included in the MLR include seasonal climate, topography, vegetation type and structure, and bedrock geology. Accepted models are considered if their AIC is within 2.0 of the model with the minimum AIC, or best model. To compare predictor influence across acceptable models, we computed standardized regression coefficients. To evaluate structural redundancy among models, we constructed binary inclusion vectors for each acceptable model, denoting whether a predictor was present (1) or absent (0). Core predictor variables are defined as occurring in at least 67% of the acceptable models. For this regional analysis, only one model was found acceptable, with higher snow-to-streamflow translation (higher r-squared) occurring in colder mountain basins with higher relative winter precipitation, more snow accumulation and a lower fraction of annual precipitation that falls in the spring and summer. The second component of the data package uses previously published, high-resolution output from an integrated hydrological model of the East River watershed using the U.S. Geological Survey Groundwater and Surface water Flow model (GSFLOW, doi:10.15485/1998576). East River MLR expands upon the approach described above to explore the response of five streamflow metrics—annual streamflow, runoff efficiency, 7-day minimum flow, low-flow duration, and non-perennial stream fraction to snow system indicators including peak SWE, snow-covered area, snow disappearance date, and the fraction of basin area characterized by low-to-no snow, as well as seasonal precipitation and temperature, and annual hydrologic variables representing soil moisture, evapotranspiration (ET), the partitioning of incoming precipitation to evapotranspiration (ET/P), groundwater storage, and groundwater inflow to streams. MLR was done on all water years (P0: 1987-2024) and for each period as determined in the split analysis using pooled regression techniques (P1: 1987-2011 and P2: 2012-2024) to evaluate shifting predictor variable emphasis on streamflow generation. Results indicate that since 2012, peak SWE has lost statistical strength in its prediction of annual streamflow and runoff efficiency, and the indirect influence of spring temperature has emerged as critically important. Low-flow metrics remain largely influenced by soil moisture, vegetation water use and groundwater inflows with summer precipitation becoming a direct influence on minimum summer flow. Together, these data and Python-based analysis tools provide a framework for identifying the key watershed characteristics that control how streamflow responds to snow from year to year. The package also helps quantify uncertainty in statistical models and assess how snow–streamflow relationships vary across regions and over time. This dataset contains comma-separated values files (.csv), text files (.txt), python code files (.py), figure files (.png), and shapefiles (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .xml). Further details on file contents and MLR execution can be found in the readme file and the FLMD files. Work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

On decoupling the integrals of cosmological perturbation theory

ABSTRACT Perturbation theory (PT) is often used to model statistical observables capturing the translation and rotation-invariant information in cosmological density fields. PT produces higher order corrections by integration over linear statistics of the density fields weighted by kernels resulting from recursive solution of the fluid equations. These integrals quickly become high dimensional and naively require increasing computational resources the higher the order of the corrections. Here, we show how to decouple the integrands that often produce this issue, enabling PT corrections to be computed as a sum of products of independent 1D integrals. Our approach is related to a commonly used method for calculating multiloop Feynman integrals in quantum field theory, the Gegenbauer Polynomial x-Space Technique. We explicitly reduce the three terms entering the 2-loop power spectrum, formally requiring 9D integrations, to sums over successive 1D radial integrals. These 1D integrals can further be performed as convolutions, rendering the scaling of this method Nglog Ng with Ng the number of grid points used for each fast Fourier transform. This method should be highly enabling for upcoming large-scale structure redshift surveys where model predictions at an enormous number of cosmological parameter combinations will be required by Monte Carlo Markov Chain searches for the best-fitting values.

Slepian, Zachary↗
NASA NTRS1994

Assessment of the SFC database for analysis and modeling

SFC is one of the four clusters that make up the Integrated Work Control System (IWCS), which will integrate the shuttle processing databases at Kennedy Space Center (KSC). The IWCS framework will enable communication among the four clusters and add new data collection protocols. The Shop Floor Control (SFC) module has been operational for two and a half years; however, at this stage, automatic links to the other 3 modules have not been implemented yet, except for a partial link to IOS (CASPR). SFC revolves around a DB/2 database with PFORMS acting as the database management system (DBMS). PFORMS is an off-the-shelf DB/2 application that provides a set of data entry screens and query forms. The main dynamic entity in the SFC and IOS database is a task; thus, the physical storage location and update privileges are driven by the status of the WAD. As we explored the SFC values, we realized that there was much to do before actually engaging in continuous analysis of the SFC data. Half way into this effort, it was realized that full scale analysis would have to be a future third phase of this effort. So, we concentrated on getting to know the contents of the database, and in establishing an initial set of tools to start the continuous analysis process. Specifically, we set out to: (1) provide specific procedures for statistical models, so as to enhance the TP-OAO office analysis and modeling capabilities; (2) design a data exchange interface; (3) prototype the interface to provide inputs to SCRAM; and (4) design a modeling database. These objectives were set with the expectation that, if met, they would provide former TP-OAO engineers with tools that would help them demonstrate the importance of process-based analyses. The latter, in return, will help them obtain the cooperation of various organizations in charting out their individual processes.

Centeno, Martha A.↗
DOE OSTI2025

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials

Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti 3 C 2 T 2–x as a representative system, we find that electrical conductivity exhibits an emergent peak near the order–disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids.

MXene↗
NASA NTRS2014

Turbulent Concentration of MM-Size Particles in the Protoplanetary Nebula: Scaled-Dependent Multiplier Functions

The initial accretion of primitive bodies (asteroids and TNOs) from freely-floating nebula particles remains problematic. Here we focus on the asteroids where constituent particle (read "chondrule") sizes are observationally known; similar arguments will hold for TNOs, but the constituent particles in those regions will be smaller, or will be fluffy aggregates, and are unobserved. Traditional growth-bysticking models encounter a formidable "meter-size barrier" [1] (or even a mm-cm-size barrier [2]) in turbulent nebulae, while nonturbulent nebulae form large asteroids too quickly to explain long spreads in formation times, or the dearth of melted asteroids [3]. Even if growth by sticking could somehow breach the meter size barrier, other obstacles are encountered through the 1-10km size range [4]. Another clue regarding planetesimal formation is an apparent 100km diameter peak in the pre-depletion, pre-erosion mass distribution of asteroids [5]; scenarios leading directly from independent nebula particulates to this size, which avoid the problematic m-km size range, could be called "leapfrog" scenarios [6-8]. The leapfrog scenario we have studied in detail involves formation of dense clumps of aerodynamically selected, typically mm-size particles in turbulence, which can under certain conditions shrink inexorably on 100-1000 orbit timescales and form 10-100km diameter sandpile planetesimals. The typical sizes of planetesimals and the rate of their formation [7,8] are determined by a statistical model with properties inferred from large numerical simulations of turbulence [9]. Nebula turbulence can be described by its Reynolds number Re = L/eta sup(4/3), where L = ETA alpha sup (1/2) the largest eddy scale, H is the nebula gas vertical scale height, and α the nebula turbulent viscosity parameter, and η is the Kolmogorov or smallest scale in turbulence (typically about 1km), with eddy turnover time tη. In the nebula, Re is far larger than any numerical simulation can handle, so some physical model is needed to extend the results of numerical simulations to nebula conditions.

protoplanetary nebula↗
NASA NTRS2011

Space-Time Data Fusion

Space-time Data Fusion (STDF) is a methodology for combing heterogeneous remote sensing data to optimally estimate the true values of a geophysical field of interest, and obtain uncertainties for those estimates. The input data sets may have different observing characteristics including different footprints, spatial resolutions and fields of view, orbit cycles, biases, and noise characteristics. Despite these differences all observed data can be linked to the underlying field, and therefore the each other, by a statistical model. Differences in footprints and other geometric characteristics are accounted for by parameterizing pixel-level remote sensing observations as spatial integrals of true field values lying within pixel boundaries, plus measurement error. Both spatial and temporal correlations in the true field and in the observations are estimated and incorporated through the use of a space-time random effects (STRE) model. Once the models parameters are estimated, we use it to derive expressions for optimal (minimum mean squared error and unbiased) estimates of the true field at any arbitrary location of interest, computed from the observations. Standard errors of these estimates are also produced, allowing confidence intervals to be constructed. The procedure is carried out on a fine spatial grid to approximate a continuous field. We demonstrate STDF by applying it to the problem of estimating CO2 concentration in the lower-atmosphere using data from the Atmospheric Infrared Sounder (AIRS) and the Japanese Greenhouse Gasses Observing Satellite (GOSAT) over one year for the continental US.

Greenhouse Gases Observing Satellite (GOSAT)↗
DOE OSTI2023

An asymptotic approach for the statistical thermodynamics of certain model systems

In classical statistical thermodynamics, calculating the configuration integral is both vital and elusive. Analytic relations for configuration integrals are desirable for modeling purposes, but it is typically impossible to obtain them. Certain systems become analytically tractable after replacing steep potential energies with harmonic potentials or athermal rigid constraints, but these approximations are often inadequate, especially when modeling the stretching of molecules. It is therefore necessary to develop a systematic approach to improve upon the approximations provided by these reference systems. Here, a general asymptotic approach is introduced, where the configuration integral for the full system is obtained in terms of that of the reference system and several corrections. This asymptotic approach is first demonstrated using the simple example of a classical three-dimensional oscillator. Next, the approach is applied to modeling the stretching of single polymer chains and to modeling thermally assisted crack growth, where results are verified with respect to numerical calculations. Overall, this asymptotic approach is a valid and effective tool for statistical thermodynamics in general.

Buche, Michael Robert↗
NASA NTRS2019

Large Contribution of Meteorological Factors to Inter-Decadal Changes in Regional Aerosol Optical Depth

Aerosol optical depth (AOD) has become a crucial metric for assessing global climate change. Although global and regional AOD trends have been studied extensively, it remains unclear what factors are driving the inter-decadal variations in regional AOD and how to quantify the relative contribution of each dominant factor. This study used a long-term (1980–2016) aerosol dataset from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis, along with two satellite-based AOD datasets (MODIS/Terra and MISR) from 2001 to 2016, to investigate the long-term trends in global and regional aerosol loading. Statistical models based on emission factors and meteorological parameters were developed to identify the main factors driving the inter-decadal changes of regional AOD and to quantify their contribution. Evaluation of the MERRA-2 AOD with the ground-based measurements of AERONET indicated significant spatial agreement on the global scale (r= 0.85, root-mean-square error = 0.12, mean fractional error = 38.7 %, fractional gross error = 9.86 % and index of agreement = 0.94). However, when AOD observations from the China Aerosol Remote Sensing Network (CARSNET) were employed for independent verification, the results showed that MERRA-2 AODs generally underestimated CARSNET AODs in China (relative mean bias = 0.72 and fractional gross error =−34.3 %). In general, MERRA-2 was able to quantitatively reproduce the annual and seasonal AOD trends on both regional and global scales, as observed by MODIS/Terra, although some differences were found when compared to MISR. Over the 37-year period in this study, significant decreasing trends were observed over Europe and the eastern United States. In contrast, eastern China and southern Asia showed AOD increases, but the increasing trend of the former reversed sharply in the most recent decade. The statistical analyses suggested that the meteorological parameters explained a larger proportion of the AOD variability (20.4 %–72.8 %) over almost all regions of interest (ROIs) during 1980–2014 when compared with emission factors (0 %–56 %). Further analysis also showed that SO2 was the dominant emission factor, explaining 12.7 %–32.6 % of the variation in AOD over anthropogenic-aerosol-dominant regions, while black carbon or organic carbon was the leading factor over the biomass-burning-dominant (BBD) regions, contributing 24.0 %–27.7 % of the variation. Additionally, wind speed was found to be the leading meteorological parameter, explaining 11.8 %–30.3 % of the variance over the mineral-dust-dominant regions, while ambient humidity (including soil moisture and relative humidity) was the top meteorological parameter over the BBD regions, accounting for 11.7 %–35.5 % of the variation. The results of this study indicate that the variation in meteorological parameters is a key factor in determining the inter-decadal change in regional AOD.

Huizheng Che↗
DOE OSTI2019

Probability of Pinching Failure in PWR SNF after Long-Term Storage

A probabilistic assessment was performed on pinching failure of PWR SNF rod cladding under accidental SNF cask transport conditions. a set of 3,000 rod pinching scenarios was analyzed, with different geometrical, material, and loading parameters, using a Latin Hypercube Sampling (LHS). The investigated accidents include train derailments leading to cask collisions or cask drops. Pinching failure was related to cladding offset strain, defined as the plastic deformation interval in radial cladding direction normalized with respect to the outer cladding diameter. Finite element (FE) models were used to compute SNF rod deformations and the offset strain demand OSD in each of the assessed pinching scenarios. The numerical models account for the contribution of the degraded fuel pellets to rod pinching stiffness. The offset strain capacity OSC was computed using regression and interpolation models based on ring compression (RCT) test results, and account for hydride-related effects on cladding ductility. The results show a low probability of pinching failure. Hydride-related cladding embrittlement affects the structural integrity of PWR SNF rods after long-term dry storage of up to 300 years. Radial hydride patterns or large amounts of circumferentially oriented hydrides reduce the cladding ductile deformation capability when pinched at low material temperatures. The probability of hydride-related pinching failure of PWR cladding is estimated after long-term dry storage to address: How do PWR SNF rod conditions like hydrogen content, vacuum drying peak cladding hoop stress σ{sub θ}, and cladding material temperature affect cladding ductility? Is hydride-related cladding embrittlement a material degradation mechanism that affects the structural integrity of PWR SNF rods under pinching loading? What are the controlling parameters for deformation demands in PWR SNF rods under pinching loading? Is the pinching load amplitude an important parameter that controls the probability of pinching failure? How do parameters like pellet-cladding gap or fuel pellet degradation affect the pinching loading capacity of PWR SNF rods? Statistical modelling of PWR SNF rod conditions in U.S. inventory: A pseudo rod database was generated based on EIA database. Analysis of train accident scenarios: a probability density function (pdf) of a pinching load distribution P{sub 9m} of a 9-m cask drop is linearly scaled to generate a more representative: distribution P{sub AS}. The new distribution considers train collision velocities, cask drops for on- and off-bridge accidents, and cask impact orientation. LHS generates 3,000 rod pinching scenarios based on nine independent geometrical, material, and loading parameters. Probabilistic FE Analysis: The LHS realizations are used to generate 3,000 FE models. The FE analysis is conducted for P{sub 9m}, and P{sub AS}. The workflow is automatized. The OSC is computed with regression and interpolation models, as a function of the hydrogen content C{sub H}, vacuum drying peak σ{sub θ}, and SNF temperature T{sub FR}: The regression data was obtained in RCTs on PWR cladding. The OSD is derived from the cladding deformations in the FE models. Cladding failure was identified via a comparison of OSC and OSD. A 300 and a 30 years-of-dry-storage duration is investigated: The dry-storage period affects the expected SNF temperature and consequently, the OSC and the OSD. The material temperature LHS population is adapted to account for a SNF temperature variation. The probability of hydride-related, ductile cladding failure during a severe transportation accident is not significantly affected by the pinching load amplitude: The probability is slightly reduced from a range of less than 0.0333% to 0.1667% for P{sub 9m}, to a range of less than 0.0333% to 0.1333% for P{sub AS}, depending on the vacuum drying peak cladding hoop stress modelling assumptions. Further, the SNF dry-storage duration does not significantly affect the probability of ductile pinching failure. Based on available train accidents data, the pinching failure probability for a 5,000 km long transportation route ranges from less than 1.62 E-7 to a value of 6.47 E-7, depending on the vacuum drying peak cladding hoop stress modelling assumptions. The pinching load amplitude is not a controlling parameter for the probability of ductile cladding pinching failure, and does not control the cladding deformations once pellet-cladding contact is established, The pellet-cladding gap width, however, plays a key role. Fuel pellet degradation only affects the deformation capacity, if the pellet stiffness is significantly reduced (more than 90%). Only highly embrittled cladding is likely to fail. The deformation demands on the cladding under pinching loading are small.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

Neuro-behavioral Consequences of Low Dose Radiation Social Isolation and Sex Differences in the Longevity MCAT Mouse Model

The physiological responses to spaceflight elicit wide-ranging consequences and resemble aspects of aging on Earth. Previous studies have shown that oxidative damage via reactive oxygen species (ROS), contributes to aging-related pathologies. Our study uses 1-year old C57BL/6NJ male and female mice (astronaut-relevant age) that underwent exposure to 0.5 gray of gamma radiation together with social isolation and were euthanized 12 weeks after. We used the longevity MCAT mouse model in which human catalase is overexpressed in the mitochondria, for ROS quenching. We aimed to determine whether in older mice quenching ROS, will mitigate the neuro-behavioral consequences of low dose ionizing radiation and/or social isolation and whether the outcomes will differ in males and females. We have performed five mission relevant behavioral tests which focused on performance, memory, physical stance, and stress. We have detected both sex and radiation effects; the older females look physically better are faster and perform better almost in all behavioral tests compared to their male counterparts. On the other hand, they are more sensitive to low dose radiation in many cases, in some cases this effect was indeed mitigated in the MCAT mice, pointing out to the importance of ROS in response to radiation stress and social isolation. We have measured plasma (7- and 90-days post radiation), cytokines, corticosterone and hippocampal cytokine and microglial activation at the end of the experiment. We saw significant changes in the plasma markers due to radiation, sex, and genotype in both short and long post radiation period and detected long term sex and radiation effects in the brain. Our focus is now on applying advanced statistical modeling to corelate the behavioral tests with our recent molecular findings to look for specific biomarkers that could predict behavioral deficits.

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