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DOE OSTI2024

Exchange–correlation entropy from the generalized thermal adiabatic connection

Warm dense matter is a highly energetic phase characterized by strong correlations, thermal effects, and quantum mechanical electrons. Thermal density functional theory is commonly used in simulations of this challenging phase, driving the development of temperature-dependent approximations to the exchange–correlation free energy. Approaches using the adiabatic connection formula are well known at zero temperature and have been recently leveraged at non-zero temperatures as well. In this work, a generalized thermal adiabatic connection (GTAC) formula is proposed, introducing a fictitious temperature parameter. This allows extraction of the exchange–correlation entropy S XC using simulated interaction strength scaling. Here, this procedure uses a Hellmann–Feynman approach to express the exchange–correlation entropy in terms of a temperature- and interaction strength-dependent exchange–correlation potential energy. In addition, analysis of S XC as a function of interaction strength suggests new forms for approximations, and GTAC itself offers a new framework for exploring both the exact and approximate interplay of temperature, density, and interaction strength across a wide range of conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Twenty-first century hydroclimate: A continually changing baseline, with more frequent extremes

Variability in hydroclimate impacts natural and human systems worldwide. In particular, both decadal variability and extreme precipitation events have substantial effects and are anticipated to be strongly influenced by climate change. From a practical perspective, these impacts will be felt relative to the continuously evolving background climate. Removing the underlying forced trend is therefore necessary to assess the relative impacts, but to date, the small size of most climate model ensembles has made it difficult to do this. Here we use an archive of large ensembles run under a high-emissions scenario to determine how decadal “megadrought” and “megapluvial” events—and shorter-term precipitation extremes—will vary relative to that changing baseline. When the trend is retained, mean state changes dominate: In fact, soil moisture changes are so large in some regions that conditions that would be considered a megadrought or pluvial event today are projected to become average. Time-of-emergence calculations suggest that in some regions including Europe and western North America, this shift may have already taken place and could be imminent elsewhere: Emergence of drought/pluvial conditions occurs over 61% of the global land surface (excluding Antarctica) by 2080. Relative to the changing baseline, megadrought/megapluvial risk either will not change or is slightly reduced. However, the increased frequency and intensity of both extreme wet and dry precipitation events will likely present adaptation challenges beyond anything currently experienced. In many regions, resilience against future hazards will require adapting to an ever-changing “normal,” characterized by unprecedented aridification/wetting punctuated by more severe extremes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Digitally manufactured air plasma-on-water reactor for nitrate production

The sustainable production of food to support the increasing world population is one of humanity’s most pressing challenges. Plasma activated water, produced using renewable energy, can help fulfill plants’ needs in sustainable agriculture approaches. The design, implementation, and characterization of a digitally manufactured air plasma-on-water reactor (POWR) for the synthesis of nitrate as green nitrogen fertilizer is presented. The interaction of air plasma-generated reactive oxygen and nitrogen species with water produces nitrate (NO 3 - ) and related species, which are the main nitrogen-containing nutrients for plants. The mild conditions of the operation of the POWR opens the possibility to use plastics, particularly through digital manufacturing strategies such as 3D-printing, for its fabrication. A pin-to-plate reactor configuration powered by high-voltage alternating power is chosen due to its simplicity and efficacy. A computational thermal-fluid model is used to evaluate the design and attain expected operational characteristics. The experimental characterization of the POWR encompassed design and operation parameters, namely electrode-water spacing, air flow rate, and voltage level. A machine learning approach is implemented to extract and quantify characteristic features of the plasma–water interaction, such plasma volume and plasma–water interface area. Experimental results revealed that the nitrate production rate varies linearly with dimensionless plasma volume. The design, fabrication, and characterization methods presented can be adapted to other POWRs and help enable on-demand nitrogen fertilizer production at low environmental and economic cost.

Physics↗

DOE OSTI2020

Multiband Quantum Criticality of Polar Metals

Motivated by recent experimental realizations of polar metals with broken inversion symmetry, we explore the emergence of strong correlations driven by criticality when the polar transition is tuned to zero. Overcoming previously discussed challenges, we illustrate a robust mechanism for coupling between the critical mode and electrons in multiband metals. Furthermore, we identify and characterize several novel interacting phases, including non-Fermi liquids, when band crossings are close to the Fermi level and present their experimental signatures for three generic types of band crossings.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Metal Halide Perovskite Solar Module Encapsulation Using Polyolefin Elastomers: The Role of Morphology in Preventing Delamination

The development of perovskite solar cells (PSCs) has ushered in a new era of solar technology, characterized by its exceptional efficiency and cost-effective production. However, the soft and fragile nature of perovskites makes module encapsulation challenging. Polyolefin elastomers (POEs) have been reported to be promising encapsulants for perovskite modules. However, little research exists on identifying criteria among different types of POEs as encapsulants. Here, two POEs with different morphologies were compared as encapsulants. The first POE crystallizes during encapsulation (crystal content ∼40%), and the resulting shrinkage or warpage leads to delamination, causing minimodule failure. In contrast, perovskite minimodules encapsulated with a mostly amorphous POE exhibited better reliability and reproducibility. The best perovskite minimodules passed the thermal cycling test for 240 cycles between −40 and 85 °C and the damp heat test for 1419 h, according to the IEC 61215 standard. This study highlights the importance of the morphology of encapsulants in achieving high-quality encapsulation. Published by the American Physical Society 2024

14 SOLAR ENERGY↗

DOE OSTI2026

Hybrid Symbolic-Numerical Modeling and Parametric Stability Analysis of DC–AC Power Systems

Hybrid DC-AC power systems integrating diverse inverter-based resources (IBRs) and multi-terminal high-voltage direct current (MTDC) networks represent a promising architecture for future power grids, while introducing challenges for modeling, stability analysis, and control design. This paper develops a hybrid symbolic-numerical modeling framework and tool to characterize the parametric small-signal stability of DC-AC coupled power systems. The proposed approach constructs parametric state-space models to enable efficient representation of system dynamics under varying control parameters and network configurations, with target parameters retained as symbolic variables and the remainder treated numerically. The stability analysis framework covers eigenvalue, sensitivity, and stability boundary and region characterization. Enhanced linear matrix inequality (LMI) techniques are proposed to directly certify small-signal stability over regions of parameter space while also reducing the conservativeness and computational burden. The resulting tools and frameworks enable rapid parametric model construction across diverse grid conditions, thereby facilitating stability-informed control and operation in complex DC–AC power systems.

DC–AC power systems↗

DOE OSTI2023

A Graphical Model for Fusing Diverse Microbiome Data

This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high-dimensional features, in this case, transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each corresponds to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multivariate Gaussian latent space that parameterizes the set of multinomial probabilities of the transcriptome counts. The covariance matrix of the latent variables induces a covariance matrix of co-dependencies between all the transcripts, effectively fusing multiple data sources. We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model. Here, the inferred latent variables provide a common dimensionality reduction for visualizing the data and the inferred parameters provide a predictive posterior distribution. In addition to simulation studies that demonstrate the variational EM procedure, we apply our model to a bacterial microbiome dataset.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13 >C and 1 H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted 13 C and 1 H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1 H and 13 C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Comparative Assessment of U-Net-Based Deep Learning Models for Segmenting Microfractures and Pore Spaces in Digital Rocks

Segmentation of high-resolution X-ray microcomputed tomography (µCT) images is crucial in digital rock physics (DRP), affecting the characterization and analysis of microscale phenomena in the porous media. The complexity of geological structures and nonideal scanning conditions pose significant challenges to conventional image segmentation approaches. Motivated by the recent increasing popularity of deep learning (DL) techniques in image processing, this work undertakes a comparative study of DL models, specifically U-Net and its variants, for segmenting multiple targets with distinguished features in digital rocks, including discrete fracture networks (DFNs), pore spaces, and solid rock. Particularly, DFNs have a smaller volumetric fraction over others, bringing in a substantial challenge of imbalanced segmentation. The primary focus is to evaluate the architecture and feature enhancement strategies of various DL models, including U-Net, attention U-Net, residual U-Net, U-Net++, and residual U-Net++. The models were designed as 2.5D, utilizing a central 2D image and its two adjacent upper and lower 2D images as input to provide a pseudo-3D context. In addition, because the ground truth of segmentation was unknown for real-world digital rocks, we created a benchmark data set following the inverse operations of segmentation. The data synthesis started from the label images (i.e., solid rock, pore spaces, and DFNs), followed by simulating partial volume blurring, adding random background noise, and introducing ring artifacts to mimic real raw X-ray µCT images. The data set, which included various rock types (i.e., sandstone and artificial data), scanning resolution, and magnitudes of noise and artifacts, was divided into training and testing data sets with a 90% and 10% ratio, respectively. Moreover, in addition to the conventional pixel-wise evaluation metrics, the physics-based metric of the lattice-Boltzmann method (LBM) simulated permeability provided more comprehensive assessments. The results demonstrated that the residual connections, nested architectures, and redesigned skip connections contribute to the model performance and give the residual U-Net++ the highest accuracy. The improvements were mainly on the boundaries and small targets, especially the DFNs, which dominate the interconnectivity and therefore affect the permeability greatly. This study also rigorously evaluated the efficiency and generalization of each model, demonstrating that the sophisticated architectures achieved excellent practicability and maintained robust performance on completely unseen data, ensuring their suitability for diverse and challenging DRP applications.

58 GEOSCIENCES↗

DOE OSTI2020

Workshop on Improving Holdup Monitoring in the US

The characterization and quantification of nuclear deposits, or residual nuclear material retained in process equipment, generally referred to as holdup, continues to challenge nuclear processing facilities in DOE program areas such as nuclear criticality safety, material control and accountability, environmental management. The efficiency and effectiveness of nuclear operations in these facilities depends heavily on the results from nondestructive techniques designed to measure nuclear materials in situ. The traditional methods used to measure holdup were established more than 30 years ago and assume unrealistic conditions for most measurement cases. These blind, in situ measurements present unique challenges due to the following attributes: unique, non-ideal geometries; unknown deposit thicknesses; lack of representative calibration standards; large number of holdup deposit locations; and poor accessibility for many measurement locations. These difficulties and the poor assumptions made to handle them have resulted in measurement uncertainties that are often too large for many programs to effectively utilize. In an effort to address and identify solutions to the current holdup challenges facing the US nuclear industry, Oak Ridge National Laboratory hosted the Technical Workshop on Improving Holdup Monitoring in the US. The two-day workshop held August 21–22, 2019, was attended by 35 holdup measurement practitioners, program managers, stakeholders from US Department of Energy sites, the Nuclear Regulatory Commission, and representatives from commercial industry. The primary objectives were to identify critical elements of a successful and defensible holdup measurement program; identify technical challenges, needs, and potential improvements associated with measurement of holdup; and share best practices and lessons learned from recent accumulation events. Fourteen presentations were delivered on holdup topics including necessary elements for effective program management, measurement needs, emerging technologies and other measurement solutions, and lessons learned from recent events. These presentations highlighted best practices related to accumulation monitoring, considerations and proactive steps to facilitate holdup in new facility designs, imaging solutions to improve holdup assumptions, and new software developed in support of holdup measurements. Breakout sessions were also employed to determine additional needs and identify potential solutions to common holdup challenges. The primary discussions focused on causes and prevention of material accumulations, needs for uranium and plutonium measurements, and reduction of measurement uncertainty, all of which were explicitly identified as critical needs during the 2018 Workshop on Technical and Programmatic Needs for a Sustainable NDA Program for the US DOE.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Design Optimization for Printed Melt Wire Arrays Encapsulation

As part of the Nuclear Energy Enabling Technology (NEET) Advanced Sensor and Instrumentation (ASI) Program, Idaho National Laboratory (INL) has recently established in-house capabilities to fabricate and test new advanced-manufactured sensors for measuring peak irradiation temperature within a nuclear test reactor. Although methods of real-time temperature monitoring, such as thermocouples, may be used, the complexity of feedthroughs and attachments to collect real-time measurements greatly increases the cost of the experiment. Instead, passive monitoring techniques may be used for peak- temperature measurement that exploit the melting point of well-characterized materials (standard melt wires) to infer peak reactor temperatures. However, limited available space for instrumentation during experiments introduces an additional challenge. To accommodate this, INL has expanded its temperature- detection instrumentation capabilities to include advanced manufactured (AM) melt wires for peak irradiation temperature measurements. These melt wires can determine peak temperatures while also accommodate space limitations in irradiation experiments. In an effort to improve performance reliability of AM meltwire capabilities, a process was developed and tested to identify the significance of entrapping a high purity inert atmosphere within the packaging of printed melt wire arrays. The materials used in this study were aluminum, zinc, and tin encapsulated in high purity helium within a stainless steel (SS) 316 container. Tin, with a low melting point of approximately 230°C, Zn with a mid-melting point of approximately 420°C, and Al with a high melting point of approximately 660°C. This report describes the design, fabrication process, furnace testing and X-ray Computed Tomography (XCT) evaluation. Results show a successful outcome in creating an inert gas encapsulation and high-resolution evaluation methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Transition Core Modeling for Extended Enrichment & Accident-Tolerant Fuels Using Polaris/PARCS

Commercial light water reactor (LWR) operators and fuel vendors are currently interested in increasing the low-enriched uranium (LEU) fuel enrichments from the current limit of 5.0 $^w/_o$ $^{235}U$ up to 10 $^ w/_o$ $^{235}U$ (referred to as "LEU+") in their current fleets; they are also interested in using accident-tolerant fuel (ATF) with both LEU and LEU+ fuel. This report aims to identify modeling challenges and accuracy concerns in transition core analysis using the SCALE Polaris lattice physics code and U.S. Nuclear Regulatory Commission core simulator PARCS. At the time this study was started, no publicly available LEU+ core designs existed for boiling water reactor (BWR) or pressurized water reactor (PWR) systems. Therefore, fuel lattices were shuffled within a multi-assembly model to mimic neutronically challenging lattice combinations seen in transition cores, such as a fresh LEU+ lattice next to depleted LEU lattices. In addition to multi-assembly models, whole-core BWR transition core calculations were performed for ATF and LEU+ fuel using an existing Hatch-1 Cycle 3 core model. A whole-core BWR model was chosen due to the more heterogeneous core designs compared to those for a PWR core. Since the original core is an old checkerboard core design and no core or fuel design optimization was performed for the modeled fuel types, these core calculations were intended only to provide: (1) Comparisons of core characteristics of interest, such as the pin power distributions and peaking factors, Doppler temperature coefficients (DTCs), and control blade worths (CBWs) under challenging core designs, (2) Identification of reactor physics challenges in modeling LEU+ and ATF cores, and (3) A stress test for the Polaris/PARCS two-step modeling approach, including characterization of the relative accuracy for predicting characteristics of interest such as pin power distributions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1991

Joining of materials with engineered interlayers

A reliable method to join a variety of different materials is developed which uses engineered coatings to produce strong bonds by solid-state techniques at low temperatures (300-400 C). The strong bonds are capable of accommodating the strains created between Si and most substrates due to differences in thermal expansion coefficients. The approach encompasses two phases: noble metal compliant interlayers and functionally gradient interlayer bonding. Preliminary results are presented and the Ag interlayer bonds are microstructurally characterized confirming that the planar magnetron sputtered coatings/bonds are fully dense and metallurgically sound.

Challenger, Kenneth D.↗

NASA NTRS2001

Modeling the Gas Dynamics Environment in a Subscale Solid Rocket Test Motor

Subscale test motors are often used for the evaluation of solid rocket motor component materials such as internal insulation. These motors are useful for characterizing insulation performance behavior, screening insulation material candidates and obtaining material thermal and ablative property design data. One of the primary challenges associated with using subscale motors however, is the uncertainty involved when extrapolating the results to full-scale motor conditions. These uncertainties are related to differences in such phenomena as turbulent flow behavior and boundary layer development, propellant particle interactions with the wall, insulation off-gas mixing and thermochemical reactions with the bulk flow, radiation levels, material response to the local environment, and other anomalous flow conditions. In addition to the need for better understanding of physical mechanisms, there is also a need to better understand how to best simulate these phenomena using numerical modeling approaches such as computational fluid dynamics (CFD). To better understand and model interactions between major phenomena in a subscale test motor, a numerical study of the internal flow environment of a representative motor was performed. Simulation of the environment included not only gas dynamics, but two-phase flow modeling of entrained alumina particles like those found in an aluminized propellant, and offgassing from wall surfaces similar to an ablating insulation material. This work represents a starting point for establishing the internal environment of a subscale test motor using comprehensive modeling techniques, and lays the groundwork for improving the understanding of the applicability of subscale test data to full-scale motors. It was found that grid resolution, and inclusion of phenomena in addition to gas dynamics, such as two-phase and multi-component gas composition are all important factors that can effect the overall flow field predictions.

Eaton, Andrew M.↗

NASA NTRS2016

A Consistent AVHRR Visible Calibration Record Based on Multiple Methods Applicable for the NOAA Degrading Orbits: Validation - Part 2

Consistent cross-sensor Advanced Very High Resolution Radiometer (AVHRR) calibration coefficients are determined using desert, polar ice, and deep convective cloud (DCC) invariant Earth targets. The greatest AVHRR calibration challenge is the slow orbit degradation of the host satellite, which precesses toward a terminator orbit. This issue is solved by characterizing the invariant targets with NOAA-16 AVHRR observed radiances that have been referenced to the Aqua Moderate Resolution Imaging Spectrometer (MODIS) calibration using simultaneous nadir overpass (SNO) observations. Another benefit of the NOAA-16 invariant target-modeled reflectance method is that, because of the similarities among the AVHRR spectral response functions, a smaller spectral band adjustment factor is required than when establishing calibrations relative to a non-AVHRR reference instrument. The sensor- and band-specific calibration uncertainties, with respect to the calibration reference, are, on average, 2 percent and 3 percent for channels 1 and 2, respectively. The uncertainties are smaller for sensors that are in afternoon orbits, have longer records, and spend less time in terminator conditions. The multiple invariant targets referenced to Aqua MODIS (MITRAM) AVHRR calibration coefficients are evaluated for individual target consistency, compared against Aqua MODIS/AVHRR SNOs, and selected published calibration gains. The MITRAM and SNO relative calibration biases mostly agree to within 1 percent for channels 1 and 2, respectively. The individual invariant target and MITRAM sensor relative calibration biases are mostly consistent to within 1 percent and 2 percent for channels 1 and 2, respectively. The differences between the MITRAM and other published calibrations are mostly attributed to the reference instrument calibration differences.

Doelling, David R.↗

NASA NTRS2017

Where Is the Upper Altitude Boundary of Earth's Biosphere? An Upcoming Stratosphere Exploration Mission

Earths atmosphere provides a relatively thin barrier to the severe conditions of space. Terrestrial microorganisms from our planets surface gets swept up into the atmosphere at rates which are influenced by meteorology, season, and variety of other regionally-dependent conditions. Residence time of microbes in the atmosphere, dispersal patterns, and metabolic activity in clouds are topics of special interest in tropospheric aerobiology. But what about even higher altitudes? A century of ocean exploration has allowed the scientific community to characterize marine life at every depth. Such knowledge was enabled by routine access, specifically, submersible vehicles with sample return systems. The outer reaches of Earth's atmosphere probably contain trace signatures of microbial life swept up from the surface, but high-altitude exploration (from 18-50 km above sea level) has been severely constrained by a shortage of reliable affordable sample acquisition systems. Thus, attempts to establish the upper altitude limits of Earths biosphere boundary remain unresolved. Previous conclusions based on observations of microbial growth and DNA signatures from samples collected in the upper atmosphere have resulted in extraordinary claims for the tenacity of life in the stratosphere. This altitude range can be regarded as a natural laboratory for examining the edge of habitability, a topic of central important to the field of astrobiology; in fact, stratosphere conditions (high UV light and low extremes of temperature, pressure, and relative humidity) closely resemble stressors associated with the surface of Mars. Moreover, low biomass detection instruments developed exploring Earths upper atmosphere can contribute to future strategies for life detection with spacecraft exploring other worlds. While pioneering, many past stratospheric microbiology studies have been qualitative in nature and lacking rigorous contamination control measurements to verify system sterility. We have designed, constructed, and recently flown a novel system for collecting upper atmosphere bioaerosols using the NASA C20A aircraft. We will report preliminary mission results from the lower stratosphere (characterizing abundance, diversity and survivability or airborne microorganisms), describe our methods for reducing false positives, and outline the open scientific engineering challenges in an ongoing effort to better establish Earths high-altitude biosphere boundary.

Smith, David J.↗

NASA NTRSDate not supplied

Material Property Estimation in Thin Battery Components Using Guided Wave Measurement, Experimental Dispersion Curve Extraction and Finite Element Modeling

At NASA, we are investigating nondestructive evaluation (NDE) and structural health monitoring (SHM) techniques to detect precursors of thermal runaway failure in lithium metal based lithium ion batteries (LIB). The approach is centered on computational simulation models to guide inspection and aid in interpretation of results. Since lithium metal LIBs have combinations of solid and fluid-filled porous materials, obtaining accurate material properties is both challenging and critical for successful simulation of battery inspection. To this end, we investigated a multi-verification approach for material characterization of thin battery components. First, a laser Doppler vibrometer (LDV) was used to measure guided wave fields in thin (microns-thick) battery components subject to broadband excitation. The time-space wavefield data was converted to frequency-wave number data to extract guided wave dispersion curves. A data visualization and post processing graphics user interface (GUI) was developed at NASA to aid the data exploration and analysis. Due to the thinness of the samples, low frequency-thickness-product plate wave approximations allowed for the calculation of closed-form solutions for material elastic property estimation. These approximations were then verified by calculating the Lamb wave solutions using the previously obtained material properties. Finally, the estimated elastic material properties were implemented in a COMSOL simulation model, and dispersion curves were extracted from simulation results. The dispersion curves and derived material properties were then compared to the analysis results from the experimental data. These comparisons informed on the accuracy of the measured material properties and helped demonstrate the accuracy of the finite element analysis (FEA) computational models. This assessment will prove vital when we start simulating more complex multi-layer components and poroelastic media. This paper gives a brief background of the problem space, outlines the workflow for data analysis and verification, shows results from the workflow, and gives an overview of future plans for simulation of lithium metal LIB inspection.

Peter Juarez↗