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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Diffusion-mediated chemical concentration variation and void evolution in ion-irradiated NiCoFeCr high-entropy alloy

High-entropy alloys (HEAs) are proposed as potential structural materials for advanced nuclear systems, but little is known about the response of matrix chemistry in HEAs upon irradiation. Here, we reveal a substantial change of matrix chemical concentration as a function of irradiation damage (depth) in equiatomic NiCoFeCr HEA irradiated by 3 MeV Ni ions. After ion irradiation, the matrix contains more Fe/Cr in depth shallower than ~900–1000 nm but more Ni/Co from ~900–1000 nm to the end of the ion-damaged region due to the preferential diffusion of vacancies through Fe/Cr. Preferential diffusion also facilitates migration of vacancies from high radiation damage region to low radiation damage region, leading to no void formation below ~900–1000 nm and void formation around the end of the ion-damaged region at a fluence of 5 × 10 16 cm -2 (~123 dpa, displacements per atom, peak dose under full cascade mode). As voids grow significantly at an increased fluence (8 × 10 16 cm -2 , 196 dpa), the matrix concentration does not change dramatically due to new voids formed below ~900–1000 nm.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Diffusion-mediated chemical concentration variation and void evolution in ion-irradiated NiCoFeCr high-entropy alloy

High-entropy alloys (HEAs) are proposed as potential structural materials for advanced nuclear systems, but little is known about the response of matrix chemistry in HEAs upon irradiation. Here, we reveal a substantial change of matrix chemical concentration as a function of irradiation damage (depth) in equiatomic NiCoFeCr HEA irradiated by 3 MeV Ni ions. After ion irradiation, the matrix contains more Fe/Cr in depth shallower than ~900–1000 nm but more Ni/Co from ~900–1000 nm to the end of the ion-damaged region due to the preferential diffusion of vacancies through Fe/Cr. Preferential diffusion also facilitates migration of vacancies from high radiation damage region to low radiation damage region, leading to no void formation below ~900–1000 nm and void formation around the end of the ion-damaged region at a fluence of 5 × 10 16 cm –2 (~123 dpa, displacements per atom, peak dose under full cascade mode). As voids grow significantly at an increased fluence (8 × 10 16 cm –2 , 196 dpa), the matrix concentration does not change dramatically due to new voids formed below ~900–1000 nm.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Reflectance spectroscopy of fresh whole leaves for the estimation of chemical concentration

Remotely sensed plant-canopy data in the visible and near-IR ranges are used to establish relations between the canopy reflectance and the chemical content of the leaves. The mathematical relation is generated by means of stepwise regression based on the derivative reflectance at certain wavelengths. Fourier filtering and sample control are used to minimize instrument noise and spectral overlap respectively, and absorption features are noted that correspond to sugar and protein. The coefficients of determination between estimated and measured concentrations are at least 0.82 for such substances as starch and chlorophyll. It is recommended in the analysis of remotly sensed canopy data that the chemicals with strong spectral overlaps with the chemical of interest be accounted for in order to estimate foliar chemical concentrations accurately.

Curran, Paul J.↗
DOE OSTI2023

2022 Results for Avian Monitoring of Inorganic Elements and Organic Chemical Concentrations in Passerine Eggs and Nestlings Collected from Technical Area 16 Burn Grounds, Technical Area 36 Minie, and Technical Area 39 Point 6 at Los Alamos National Laboratory

In 2022, nonviable avian eggs and one nestling were opportunistically collected at Los Alamos National Laboratory (LANL) near open detonation sites located at Technical Area (TA) 16 burn grounds, TA-36 Minie, and TA-39 Point 6. Samples were evaluated for inorganic elements (mostly metals) or per- and polyfluoroalkyl substances (PFAS). Three western bluebird (Sialia mexicana) egg samples were collected from TA-16 burn grounds. One deceased mountain bluebird (Sialia currucoides) nestling sample was collected from TA-36 Minie. One ash-throated flycatcher (Myiarchus cinerascens) egg sample was collected from TA-39 Point 6. Concentrations of inorganic elements (i.e., mostly metals) observed in this study were compared with the regional statistical reference level (RSRL), which is the upper-level bounds of background concentrations (mean + three standard deviations = 99% confidence interval). Several inorganic elements were not detected in avian eggs. All of the inorganic elements that were detected were below the RSRL and the lowest observable adverse effect level (LOAEL), when available. No PFAS compounds were detected in the mountain bluebird nestling sample collected from TA-36, and the majority of PFAS compounds were not detected in the western bluebird egg sample from TA-16. These data suggest that inorganic element and PFAS concentrations in eggs and nestlings are not of ecological concern. More data are needed to make a robust assessment and to evaluate trends over time.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Development of an In-Situ Leaching Technology for Extracting Residual Uranium from Remediated Soil - 20099

Site-specific chemical solutions for extracting uranium were developed and evaluated for treating previously remediated soil at a former thorium mill tailings site, known as the Former Rare Earths Facility (REF), in West Chicago, Illinois. Prior remediation efforts had resulted in residual sand and gravel aggregate that met the soil cleanup standards. The target soil contains residual levels of uranium, which continues to leach into the site groundwater at levels exceeding groundwater protection standards (GWPS). A series of bench-scale tests were conducted to evaluate the efficacy and viability of in-situ leaching (ISL) technology to treat the target soil for the purposes of attaining uranium GWPS at the REF. Specific objectives of the bench-scale tests entailed identification and selection of optimal leaching, quantification of the extent and rate of uranium released as a function of chemical composition, chemical concentrations, chemical residence times, and obtaining critical data to estimate treatment costs. A combination of sodium bicarbonate and sodium carbonate reagent was the most effective leaching solution. In general, leaching solutions containing a higher concentration of sodium carbonate demonstrated superior uranium leaching capabilities. Removal of 50% of the uranium mass from the soil matrix was adequate for achieving compliance with the site-specific GWPS. After six to seven pore volume flushes (PVFs), the uranium leaching effectiveness of all leaching solutions was similar. The amount of initial uranium removed with each subsequent PVF was minimal, suggesting limited solubility of the remaining mass of uranium. The effects of leaching duration were significant and persisted through at least the first five consecutive PVFs. Leaching of uranium also resulted in leaching of several non-uranium constituents, many of which exceeded applicable National Pollutant Discharge Elimination System (NPDES) standards. Given historical operations and waste disposal practices at the site, leaching of non-uranium constituents was expected. Hydrochloric acid was used to stabilize treated soil. Savings of approximately $148 million in direct and indirect remediation costs are expected with an ISL alternative compared to the excavation and landfill disposal remediation option. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Comparing Transcriptomic Points of Departure to Apical Effect Concentrations For Larval Fathead Minnow Exposed to Chemicals with Four Different Modes Of Action

It is postulated that below a transcriptomic-based point of departure, adverse effects are unlikely to occur, thereby providing a chemical concentration to use in screening level hazard assessment. The present study extends previous work describing a high-throughput fathead minnow assay that can provide full transcriptomic data after exposure to a test chemical. One-day post-hatch fathead minnows were exposed to ten concentrations of three representatives of four chemical modes of action: organophosphates, ecdysone receptor agonists, plant photosystem II inhibitors, and estrogen receptor agonists for 24 h. Concentration response modeling was performed on whole body gene expression data from each exposure, using measured chemical concentrations when available. Transcriptomic points of departure in larval fathead minnow were lower than apical effect concentrations across fish species but not always lower than toxic effect concentrations in other aquatic taxa like crustaceans and insects. The point of departure was highly dependent on measured chemical concentration which were often lower than the nominal concentration. Differentially expressed genes between chemicals within modes of action were compared and often showed statistically significant overlap. In addition, reproducibility between identical exposures using a positive control chemical (CuSO 4 ) and variability associated with the transcriptomic point of departure using in silico sampling were considered. Results extend a transcriptomic-compatible fathead minnow high-throughput assay for possible use in ecological hazard screening.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Electrical Analysis of Pulsed Laser Annealed Poly-Si: Ga/SiOx Passivating Contacts

In this contribution, we examine Ga hyper-doped poly-Si/SiOx contacts realized by pulsed laser melting (PLM). Here, we use Ga as a novel p-type dopant and B as a conventional dopant to induce non-equilibrium doping using an excimer laser. We perform simulations to visualize the maximum melt depth profiles within the poly-Si, with a goal of distributing dopants close to the tunneling oxide, but at the same time preserving the passivation. Hall measurements show that sheet resistance for B is lower than Ga due to its higher solid solubility limit in Si. After comparing the Hall active dopant concentration with the chemical concentration obtained by SIMS measurement, we show nearly 100% activation B activation reaching 10^21 cm-3, while only ~20% activation for Ga. Nevertheless, we achieve active doping concentrations of Ga in poly-Si six times higher than its solid solubility limit in Si (~10^19 cm-3). We compare our Hall mobilities with values in the literature for c-Si and show that B mobilities in laser-treated poly-Si are close to that of the literature value for B, while Ga mobilities are lower, possibly due to additional scattering channels within grain boundaries and deformed lattice. We also compare our results on PLM samples with conventional furnace annealed samples, and we show much higher percent activation and mobilities. Previously, we showed a low contact resistivity of 35.5 +/- 2.4 m..omega..cm2. Here, we further confirm this result by scanning spreading resistance microscopy and Kelvin force nanoprobe microscopy. We demonstrate that our poly-Si: Ga/nCz contact exhibits large drift and diffusion currents under normal cell operating voltage, which widens the laser processing window for a good metal/poly-Si/c-Si contact.

high-efficiency solar cells↗
DOE OSTI2025

Enhancing Drinking Water Quality Modeling: Leveraging Physics Informed Neural Networks for Learning with Imperfect Reaction Models and Partial Data

Chemical kinetics models, typically formulated as systems of ordinary or partial differential equations, are valuable tools for simulating drinking water quality. However, these models often face inaccuracies due to discrepancies between the laboratory and the real-world conditions, as well as limitations in experimental analytical methods, hindering the accurate representation of the true underlying chemical mechanisms. In this study, we propose a Physics Informed Neural Network (PINN), using the eXtreme Theory of Functional Connections, to improve the prediction of chemical concentrations over time. The PINN method accounts for imperfect chemical models and incorporates partial data to improve predictions. Focusing on reactions describing water disinfection residual and disinfectant byproduct formation, which are crucial for public health and regulatory compliance, we demonstrate that the PINN model is able to accurately predict the concentrations of chemical species across various pH values. Notably, the model extends its accuracy to predict concentrations of chemical species not originally included in its training data. The developed method can be extended to a variety of chemical systems, offering a wide array of potential applications.

13 HYDRO ENERGY↗
NASA NTRS1998

Fiber-Optic Chemiluminescent Biosensors for Monitoring Aqueous Alcohols and Other Water Quality Parameters

A "reagentless" chemiluminescent biosensor and method for the determination of hydrogen peroxide, ethanol and D-glucose in water is disclosed. An aqueous stream is basified by passing it through a solid phase base bed. Luminol is then dissolved in the basified effluent at a controlled rate. Oxidation of the luminol is catalyzed by the target chemical to produce emitted light. The intensity of the emitted light is detected as a measure of the target chemical concentration in the aqueous stream. The emitted light can be transmitted by a fiber optic bundle to a remote location from the aqueous stream for a remote reading of the target chemical concentration.

Verostko, Charles E.↗
NASA NTRS1977

Flame characteristics of a NASA contra swirler module

The structure of unconfined flames stabilized on a NASA contra-swirl can has been examined. The structure depends on reference velocity and fuel-air ratio, and photographic illustrations of representative examples of each flame type are shown. A highly compact flame is obtained over a narrow band of fuel-air ratios, within the total range over which flames may be stabilized, at high reference velocity (27.8 m/s). The compact structure can only be obtained with careful fuel nozzle location. Chemical concentration profiles, temperature profiles and sodium chloride seeding experiments all confirm the visual impression of compactness. A flame with 50% greater fuel throughput has a much less compact structure, which is reflected in lower maximum temperature, much more complex chemical concentration distributions and a more diffuse recirculation boundary as observed by sodium chloride seeding than in the compact flame. Each of these flames has not only an evident axial recirculation core but also a toroidal recirculation region over the bluff region between the inner and outer swirlers. In contrast, laser Doppler velocimetry measurements carried out on a flame with lower reference velocity (5.65 m/s) do not indicate the presence of a secondary recirculation region in this flame.

Thompson, D.↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
NASA NTRS1976

Evaluation of surface water resources from machine-processing of ERTS multispectral data

The surface water resources of a large metropolitan area, Marion County (Indianapolis), Indiana, are studied in order to assess the potential value of ERTS spectral analysis to water resources problems. The results of the research indicate that all surface water bodies over 0.5 ha were identified accurately from ERTS multispectral analysis. Five distinct classes of water were identified and correlated with parameters which included: degree of water siltiness; depth of water; presence of macro and micro biotic forms in the water; and presence of various chemical concentrations in the water. The machine processing of ERTS spectral data used alone or in conjunction with conventional sources of hydrological information can lead to the monitoring of area of surface water bodies; estimated volume of selected surface water bodies; differences in degree of silt and clay suspended in water and degree of water eutrophication related to chemical concentrations.

Mausel, P. W.↗
DOE OSTI2020

Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models

Monitoring the ecological impacts of environmental pollution and the effectiveness of remediation efforts requires identifying relationships between contaminants and the disruption of biological processes in populations, communities, or ecosystems. Wildlife are useful bioindicators, but traditional comparative experimental approaches rely on a staunch and typically unverifiable assumption that, in the absence of contaminants, reference and contaminated sites would support the same densities of bioindicators, thereby inferring direct causation from indirect data. We demonstrate the utility of spatial capture-recapture (SCR) models for overcoming these issues, testing if community density of common small mammal bioindicators was directly influenced by soil chemical concentrations. By modeling density as an inhomogeneous Poisson point process, we found evidence for an inverse spatial relationship between Peromyscus density and soil mercury concentrations, but not other chemicals, such as polychlorinated biphenyls, at a site formerly occupied by a nuclear reactor. Although the coefficient point estimate supported Peromyscus density being lower where mercury concentrations were higher (β = –0.44), the 95% confidence interval overlapped zero, suggesting no effect was also compatible with our data. Estimated density from the most parsimonious model (2.88 mice/ha; 95% CI = 1.63–5.08), which did not support a density-chemical relationship, was within the range of reported densities for Peromyscus that did not inhabit contaminated sites elsewhere. Environmental pollution remains a global threat to biodiversity and ecosystem and human health, and our study provides an illustrative example of the utility of SCR models for investigating the effects that chemicals may have on wildlife bioindicator populations and communities.

36 MATERIALS SCIENCE↗
NASA NTRS1986

Preliminary results of determination of chemical element concentrations in the aerosol of Venus clouds

An X-ray radiometeric experiment is described along with the results of measurements of the elemental composition of aerosols in Venusian clouds. A preliminary analysis of the data showed that sulfur is present in the range of heights 63 to 47 km with mean content of 5.8 mg/cu m and that chlorine is present in the height range 61 t0 52 km with a mean content of 4.1 mg/cu m. The results of measurements in the range 52 to 47 km may come to an agreement if phosphorus is present in the aerosol with a mean concentration of 7.7 mg/cu m.

Andreychikov, B. M.↗
NASA NTRS2021

Extensive Characterization of Aerosol Optical Properties and Chemical Component Concentrations: Application of the GRASP/Component Approach to Long-term AERONET Measurements

A recently developed GRASP/Component approach (GRASP: Generalized Retrieval of Atmosphere and Surface Properties) was applied to AERONET (Aeronet Robotic Network) sun photometer measurements in this study. Unlike traditional aerosol component retrieval, this approach allows the inference of some information about aerosol composition directly from measured radiance, rather than indirectly through the inversion of optical parameters, and has been integrated into the GRASP algorithm. The newly developed GRASP/Component approach was applied to 13 AERONET sites for different aerosol types under the assumption of aerosol internal mixing rules to analyze the characteristics of aerosol components and their distribution patterns. The results indicate that the retrievals can characterize well the spatial and temporal variability of the component concentration for different aerosol types. A reasonable agreement between GRASP BC retrievals and MERRA-2 BC products is found for all different aerosol types. In addition, the relationships between aerosol component content and aerosol optical parameters such as aerosol optical depth (AOD), fine-mode fraction (FMF), absorption Ångström exponent (AAE), scattering Ångström exponent (SAE), and single scattering albedo (SSA) are also analyzed for indirect verifying the reliability of the component retrieval. It was demonstrated the GRASP/Component optical retrievals are in good agreement with AERONET standard products [e.g., correlation coefficient (R) of 0.93–1.0 for AOD, fine-mode AOD (AODF), coarse-mode AOD (AODC) and Ångström exponent (AE); R = ~ 0.8 for absorption AOD (AAOD) and SSA; RMSE (root mean square error) < 0.03 for AOD, AODF, AODC, AAOD and SSA]. Thus, it is demonstrated the GRASP/Component approach can provide aerosol optical products with comparable accuracy as the AERONET standard products from the ground-based sun photometer measurements as well as some additional important inside on aerosol composition.

Xindan Zhang↗
DOE OSTI2021

Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations

In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019; Lee et al., 2020). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs could enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Standoff Detection of Chemical Plumes Using Swept-ECQCL Remote Detection Technology: Results from EMRTC Complex Terrain Dual Tracer Experiment

As part of the Complex Terrain Dual Tracer Experiments performed in support of the LYNM program, a swept-wavelength external cavity quantum cascade laser (swept-ECQCL) remote detection system was deployed to measure chemical plume propagation with high temporal resolution. The swept-ECQCL remote detection technology was initially developed at PNNL, and is now under further development at Opticslah, LLC under SBIR funding from DOE (DOE SBIR Phase I Project DE-SC0019855, Remote Detection Technologies, Program Manager Chris Ramos). The swept-ECQCL technology provides a single-frequency infrared laser source which is rapidly scanned over a large wavelength range to measure the infrared absorption of gases, liquids, or solids. Analysis of the measured infrared absorption spectrum is used to detect, identify, and quantify the chemical species which are present. While most previous laser-based techniques used small wavelength tuning ranges to measure only a few isolated spectral lines of a few species, the swept-ECQCL provides the large tuning range needed to measure multiple species simultaneously, and to detect broad absorption features from large molecules or condensed phase materials. In addition, the swept-ECQCL systems have high enough spectral resolution and sensitivity to measure narrow lines of small molecules and determine isotope ratios. The remote detection system for gas plumes is based on the swept-ECQCL technology, and consists of a swept-ECQCL source, infrared detector, control electronics, and beam transmitting and alignment optics. The remote detection system directs the infrared output of the swept-ECQCL to a target retro reflector typically located at ~10-10,000 m distance. The swept-ECQCL wavelength is varied continuously over a large wavelength range (>1000 nm) at rates up to 1000 Hz. The swept ECQCL light reflected from the remote target is collected and focused onto an infrared photodetector. By measuring the detector signal continuously as the swept-ECQCL wavelength is varied, a time-series of absorption spectra are measured for the gases along the beam line of-sight. Based on analysis and fitting of the measured absorption spectra, the time-dependent chemical concentrations of species are determined. Multiple species and mixtures can be detected simultaneously, for all species with absorption features above the sensor noise floor. The sensor output is thus a continuous, high-speed record of identified chemicals along with their concentrations and isotope ratios of interest. The high-speed operation enables low-noise operation in conditions of atmospheric turbulence and allows tracking of fluctuating concentrations in transient chemical plumes as needed for reliable remote detection applications. All components operate with minimal cooling requirements and no cryogens are needed. In addition, the output infrared light is invisible with an intensity below the maximum permissible exposure (MPE) threshold of 100 mW/cm2 for these infrared wavelengths and thus eye safe. The measurement concept for plume detection using the swept-ECQCL remote detection system is as follows. The ECQCL beam is directed toward a remote retro-reflector, which reflects the beam back to the ECQCL system where it is focused onto an infrared photodetector. The line-of-sight from the ECQCL system to the retro-reflector defines the measurement path. The detected infrared signal is recorded continuously and detects the absorption of molecular species along the measurement path. Before a plume release, the system measures absorption from atmospheric constituents, which in this LWIR band consist primarily of lines from H 2 O and CO 2 , with additional weaker absorption from N 2 O, NH 3 , and O 3 possibly contributing. The system then measures any changes in absorption due to the tracer compounds when they enter the measurement path. The shape of absorption features is used to distinguish and identify the different tracer compounds, and the strength of the absorption is used to determine the path-integrated concentration – also called the column density.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗