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NASA NTRS2017

Multi-Organization Multi-Discipline Effort Developing a Mitigation Concept for Planetary Defense

There have been significant recent efforts in addressing mitigation approaches to neutralize Potentially Hazardous Asteroids (PHA). One such research effort was performed in 2015 by an integrated, inter-disciplinary team of asteroid scientists, energy deposition modeling scientists, payload engineers, orbital dynamist engineers, spacecraft discipline engineers, and systems architecture engineer from NASAs Goddard Space Flight Center (GSFC) and the Department of Energy (DoE) National Nuclear Security Administration (NNSA) laboratories (Los Alamos National Laboratory (LANL), Lawrence Livermore National Laboratories (LLNL) and Sandia National Laboratories). The study team collaborated with GSFCs Integrated Design Centers Mission Design Lab (MDL) which engaged a team of GSFC flight hardware discipline engineers to work with GSFC, LANL, and LLNL NEA-related subject matter experts during a one-week intensive concept formulation study in an integrated concurrent engineering environment. This team has analyzed the first of several distinct study cases for a multi-year NASA research grant. This Case 1 study references the Near-Earth Asteroid (NEA) named Bennu as the notional target due to the availability of a very detailed Design Reference Asteroid (DRA) model for its orbit and physical characteristics (courtesy of the Spectral Interpretation, Resource Identification, Security-Regolith Explorer (OSIRIS-REx) mission team). The research involved the formulation and optimization of spacecraft trajectories to intercept Bennu, overall mission and architecture concepts, and high-fidelity modeling of both kinetic impact (spacecraft collision to change a NEAs momentum and orbit) and nuclear detonation effects on Bennu, for purposes of deflecting Bennu.

Planetary Defense↗

DOE OSTI2025

Denoising Seismograms in the Time Domain Using a Deep Learning Model

Deep learning has emerged as a transformative tool for enhancing the extraction of reliable information from seismograms, addressing the increasing demand for precise and efficient seismic data analysis. We introduce an innovative encoder–decoder deep learning model, named WaveDenoiser, designed for noise reduction in the time domain, thereby eliminating the need for spectrogram computations that have been used for existing deep learning tools and significantly improving processing speed. Utilizing the benchmark dataset that is Stanford Earthquake Dataset, we developed three models of varying sizes: base, medium, and large. Notably, the large (referred to as WaveDenoiser) model demonstrated superior performance, achieving a median signal‐to‐noise ratio improvement of 8.8 dB on in‐distribution unseen data (in the same geographic region) and 7.7 dB on out‐distribution unseen data (in a new geographic region), outpacing both the base and medium models. Further evaluation of the WaveDenoiser model revealed a reduction in median arrival‐time errors by 0.02 s for P waves and 0.01 s for S waves when processing waveforms prior to phase picking using PhaseNet on in‐distribution unseen data. When tested on out‐distribution unseen data, the model also effectively reduced the P‐wave median arrival‐time error by 0.02 and 0.01 s in median arrival‐time error for S waves. Importantly, the application of WaveDenoiser resulted in a significant reduction of phase picking outliers by 1.1% to 3.6% for both P and S waves. In addition, we achieved over five times acceleration in processing speed compared with the seisBench implementation of DeepDenoiser. Our findings underscore the potential of WaveDenoiser as a powerful tool for improving seismic data analysis and processing efficiency.

P-waves↗

NASA NTRS1993

Kalman filter estimation of attitude and gyro bias with the QUEST observation model

The loss function for the Wahba attitude estimation problem employs unit vector observations with scalar weights. It is usually associated with the QUEST observation model, where the actual sensor noise is assumed the same for all components of the observed vector, regardless of the position in the field of view. The QUEST model has the great advantage of being sensor-independent except for the scalar parameter that characterizes the sensor errors. Although efficient algorithms for solving the Wahba problem exist, extending these algorithms to estimate gyro biases or sensor alignments has had mixed success. However, it is straightforward to estimate bias and alignment parameters with a Kalman filter. This paper investigates the use of an extended Kalman filter for the attitude and gyro bias that incorporates the QUEST observation model, to be referred to as the Unit Vector Filter (UVF). The UVF results are compared with those from a more conventional filter, the Real-Time Sequential Filter (RTSF), for which the residual is the two-dimensional projection of the unit vector onto the plane perpendicular to the sensor boresight. The RTSF is similar in design to that used by Multimission Modular Spacecraft for onboard attitude determination. An apparent obstacle to the use of unit vectors as measurements is their singular noise covariance matrix. Shuster has shown that this problem should not affect filter performance. The UVF and RTSF are tested using actual flight data from the Extreme Ultraviolet Explorer (EUVE). It is found that these filters generate nearly identical attitude and gyro bias estimates, thus validating the use of unit vectors and the QUEST noise model.

Sedlak, J.↗

NASA NTRS2020

SISO Space Reference FOM - Tools and Testing

The Simulation Interoperability Standards Organization (SISO) Space Reference Federation Object Model (SpaceFOM) version 1.0 is nearing completion. Earlier papers have described the use of the High Level Architecture (HLA) in Space simulation as well as technical aspects of the SpaceFOM. This paper takes a look at different SpaceFOM tools and how they were used during the development and testing of the standard.The first organizations to develop SpaceFOM-compliant federates for SpaceFOM development and testing were NASA's Johnson Space Center (JSC), the University of Calabria (UNICAL), and Pitch Technologies.JSC is one of NASA's lead centers for human space flight. Much of the core distributed simulation technology development, specifically associated with the SpaceFOM, is done by the NASA Exploration Systems Simulations (NExSyS) team. One of NASA's principal simulation development tools is the Trick Simulation Environment. NASA's NExSyS team has been modifying and using Trick and TrickHLA to help develop and test the SpaceFOM.The System Modeling And Simulation Hub Laboratory (SMASH-Lab) at UNICAL has developed the Simulation Exploration Experience (SEE) HLA Starter kit, that has been used by most SEE teams involved in the distributed simulation of a Moon base. It is particularly useful for the development of federates that are compatible with the SpaceFOM. The HLA Starter Kit is a Java based tool that provides a well-structured framework to simplify the formulation, generation, and execution of SpaceFOM-compliant federates.Pitch Technologies, a company specializing in distributed simulation, is utilizing a number of their existing HLA tools to support development and testing of the SpaceFOM. In addition to the existing tools, Pitch has developed a few SpaceFOM specific federates: Space Master for managing the initialization, execution and pacing of any SpaceFOM federation; EarthEnvironment, a simple Root Reference Publisher; and Space Monitor, a graphical tool for monitoring reference frames and physical entities.Early testing of the SpaceFOM was carried out in the SEE university outreach program, initiated in SISO. Students were given a subset of the FOM, that was later extended. Sample federates were developed and frameworks were developed or adapted to the early FOM versions.As drafts of the standard matured, testing was performed using federates from government, industry, and academia. By mixing federates developed by different teams the standard could be tested with respect to functional correctness, robustness and clarity.These frameworks and federates have been useful when testing and verifying the design of the standard. In addition to this, they have since formed a starting point for developing SpaceFOM-compliant federations in several projects, for example for NASA, ESA as well as SEE.

Möller, Björn↗

DOE OSTI2020

SMART Mobility. Mobility Decision Science Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Mobility Decision Science Pillar. The Mobility Decision Science Pillar sought to fill gaps in existing knowledge about the human role in the mobility system including travel decision-making and technology adoption in the context of future mobility. The objective was to study how underlying preferences, needs, and contextual factors might constrain or hasten future transportation system scenarios. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

SMART Mobility. Connected and Automated Vehicles Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019 and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Connected and Automated Vehicles (CAVs) Pillar. This Pillar investigated the energy, technology, and usage implications of vehicle connectivity and automation and identified efficient CAV solutions. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant Pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

Development of the Artemis Distributed Simulation FOMs

The National Aeronautics and Space Administration (NASA) is formulating and developing the Artemis Program, a collaboration with domestic commercial and international partners that will establish a long term human presence on the Moon and extend human exploration beyond the Earth-Moon system ahead of exploring Mars. These Artemis partners are developing a portfolio of space and surface systems to support human missions to the lunar surface and beyond. The Artemis systems will provide the mobility, habitation, and logistics infrastructure that will support human exploration and foster robust scientific investigations. Each partner will contribute one or more elements to the Artemis Program with NASA having the overarching responsibility for defining the Artemis architecture and guiding the integration of this complex system of space systems. To successfully accomplish this audacious task, NASA will rely on the development and execution of many complex models and simulations. Many of these simulations will be provided by the Artemis partners. While each of these simulations will provide important insight into the characteristics and performance of an associated system, individually they will not provide insight into the integrated performance of the architecture and the system of systems working in concert to execute a given Artemis mission. To address this need, NASA is developing a distributed simulation capability called the Artemis Distributed Simulation (ADS). ADS’s distributed nature supports the complex aggregation of constituent Artemis element simulations. Artemis partner simulations will be able to join into an ADS-based distributed simulation and interact with other Artemis element simulations while limiting the exposure of proprietary designs and data. ADS is defining a distributed simulation capability built on international simulation interoperability standards, specifically the High Level Architecture (HLA) and the Space Reference Federation Object Model (SpaceFOM). While HLA and SpaceFOM provide the substantive necessary technology basis for ADS, additional common datatypes, message definitions, and execution protocols are required. These extensions constitute the ADS Federation Object Model (FOM). This paper describes the fundamental architectural elements of ADS and the FOM extensions needed to support the complex nature of the Artemis Program. This includes the examination of the ADS FOM modules, ADS base datatypes, ADS SpaceFOM Object Class extensions, new ADS Object Classes, and new ADS Interaction Classes.

HLA↗

NASA NTRSDate not supplied

Development of the Artemis Distributed Simulation FOMs

HLA↗

DOE OSTI2014

Comparative validation of the D. melanogaster modENCODE transcriptome annotation

Accurate gene model annotation of reference genomes is critical for making them useful. The modENCODE project has improved the D. melanogaster genome annotation by using deep and diverse high-throughput data. Since transcriptional activity that has been evolutionarily conserved is likely to have an advantageous function, we have performed large-scale interspecific comparisons to increase confidence in predicted annotations. To support comparative genomics, we filled in divergence gaps in the Drosophila phylogeny by generating draft genomes for eight new species. For comparative transcriptome analysis, we generated mRNA expression profiles on 81 samples from multiple tissues and developmental stages of 15 Drosophila species, and we performed cap analysis of gene expression in D. melanogaster and D. pseudoobscura. We also describe conservation of four distinct core promoter structures composed of combinations of elements at three positions. Overall, each type of genomic feature shows a characteristic divergence rate relative to neutral models, highlighting the value of multispecies alignment in annotating a target genome that should prove useful in the annotation of other high priority genomes, especially human and other mammalian genomes that are rich in noncoding sequences. We report that the vast majority of elements in the annotation are evolutionarily conserved, indicating that the annotation will be an important springboard for functional genetic testing by the Drosophila community.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2011

Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation

An emerging approach in the field of aircraft engine controls and system health management is the inclusion of real-time, onboard models for the inflight estimation of engine performance variations. This technology, typically based on Kalman-filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics, and health-management applications. A challenge that complicates this practice is the fact that an aircraft engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman-filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as health parameters to be estimated. However, in an aircraft engine, the number of sensors available is typically less than the number of health parameters, presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning parameters. The problem/objective is to optimally select the model tuning parameters to minimize Kalman-filterbased estimation error. A tuner selection technique has been developed that specifically addresses the under-determined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine that seeks to minimize the theoretical mean-squared estimation error of the Kalman filter. This approach can significantly reduce the error in onboard aircraft engine parameter estimation applications such as model-based diagnostic, controls, and life usage calculations. The advantage of the innovation is the significant reduction in estimation errors that it can provide relative to the conventional approach of selecting a subset of health parameters to serve as the model tuning parameter vector. Because this technique needs only to be performed during the system design process, it places no additional computation burden on the onboard Kalman filter implementation. The technique has been developed for aircraft engine onboard estimation applications, as this application typically presents an under-determined estimation problem. However, this generic technique could be applied to other industries using gas turbine engine technology.

Simon, Donald L.↗

NASA NTRS2011

Detection of Ionospheric Alfven Resonator Signatures Onboard C/NOFS: Implications for IRI Modeling

The 2008-2009 long-lasting solar minimum activity has been the one of its kind since the dawn of space age, offering exceptional conditions for investigating space weather in the near-Earth environment. First ever detection of Ionospheric Alfven Resonator (IAR) signatures in orbit offers new means for investigating ionospheric electrodynamics, namely MHD (MagnetoHydroDynamics) wave propagation, aeronomy processes, ionospheric dynamics, and Sun-Earth connection mechanisms at a local scale. Local and global plasma density heterogeneities in the ionosphere and magnetosphere allow for formation of waveguides and resonators where magnetosonic and shear Alfven waves propagate. The ionospheric magnetosonic waveguide results from complete magnetosonic wave reflection about the ionospheric F-region peak, where the Alfven index of refraction presents a maximum. MHD waves can also be partially trapped in the vertical direction between the lower boundary of the ionosphere and the magnetosphere, a resonance mechanism known as IAR. In this work we present C/NOFS (Communications/Navigation Outage Forecasting System) Extremely Low Frequency (ELF) electric field measurements related to IAR signatures, discuss the resonance and wave propagation mechanisms in the ionosphere, and address the electromagnetic inverse problem from which electron/ion distributions can be derived. These peculiar IAR electric field measurements provide new, complementary methodologies for inferring ionospheric electron and ion density profiles, and also contribute for the investigation of ionosphere dynamics and space weather monitoring. Specifically, IAR spectral signatures measured by C/NOFS contribute for improving the International Reference Ionosphere (IRI) model, namely electron density and ion composition.

Simoes, F.↗

DOE OSTI2020

nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications

Physics-informed neural networks (PINNs) are effective in solving inverse problems based on differential and integro-differential equations with sparse, noisy, unstructured, and multifidelity data. PINNs incorporate all available information, including governing equations (reflecting physical laws), initial-boundary conditions, and observations of quantities of interest, into a loss function to be minimized, thus recasting the original problem into an optimization problem. In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The contribution of the paper is three-fold. First, we propose a unified nonlocal Laplace operator, which converges to the classical Laplacian as one of the operator parameters, the nonlocal interaction radius δ goes to zero, and to the fractional Laplacian as δ goes to infinity. This universal operator forms a super-set of classical Laplacian and fractional Laplacian operators and, thus, has the potential to fit a broad spectrum of data sets. We provide theoretical convergence rates with respect to δ and verify them via numerical experiments. Second, we use nPINNs to estimate the two parameters, δ and α, characterizing the kernel of the unified operator. The strong non-convexity of the loss function yielding multiple (good) local minima reveals the occurrence of the operator mimicking phenomenon, that is, different pairs of estimated parameters could produce multiple solutions of comparable accuracy. Third, we propose another nonlocal operator with spatially variable order α(γ), which is more suitable for modeling turbulent Couette flow. Our results show that nPINNs can jointly infer this function as well as δ. More importantly, these parameters exhibit a universal behavior with respect to the Reynolds number, a finding that contributes to our understanding of nonlocal interactions in wall-bounded turbulence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

nPINNs: Nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications

Physics-informed neural networks (PINNs) are effective in solving inverse problems based on differential and integro-differential equations with sparse, noisy, unstructured, and multifidelity data. PINNs incorporate all available information, including governing equations (reflecting physical laws), initial-boundary conditions, and observations of quantities of interest, into a loss function to be minimized, thus recasting the original problem into an optimization problem. In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The contribution of the paper is three-fold. First, we propose a unified nonlocal Laplace operator, which converges to the classical Laplacian as one of the operator parameters, the nonlocal interaction radius $\delta$ goes to zero, and to the fractional Laplacian as $\delta$ goes to infinity. This universal operator forms a super-set of classical Laplacian and fractional Laplacian operators and, thus, has the potential to fit a broad spectrum of data sets. We also provide theoretical convergence rates with respect to $\delta$ and verify them via numerical experiments. Second, we use nPINNs to estimate the two parameters, $\delta$ and $\alpha$, characterizing the kernel of the unified operator. The strong non-convexity of the loss function yielding multiple (good) local minima reveals the occurrence of the operator mimicking phenomenon, that is, different pairs of estimated parameters could produce multiple solutions of comparable accuracy. Third, we propose another nonlocal operator with spatially variable order $\alpha(y)$, which is more suitable for modeling turbulent Couette flow. Our results show that nPINNs can jointly infer this function as well as $\delta$. More importantly, these parameters exhibit a universal behavior with respect to the Reynolds number, a finding that contributes to our understanding of nonlocal interactions in wall-bounded turbulence.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

High concentrations of ice: Investigations using polarimetric radar observations combined with in situ measurements and cloud modeling (Final Report)

This DOE-funded joint project had the over-arching goal of understanding the reasons for observed size distributions of ice particles in cold clouds. Its approach involves the use of cloud models and field observations by radar and aircraft of real storms. The project addresses three major research objectives: (1) Utilize a novel polarimetric radar technique to retrieve size distributions and amounts of ice from radar data collected during the previous DOE ARM field campaigns; (2) Evaluate the results of ice microphysical retrievals using available in situ aircraft measurements and other remote sensors (e.g., vertically pointing cloud radars and wind profilers); (3) Improve the treatment of microphysical processes leading to cloud glaciation and ice multiplication in numerical cloud models. The study was performed by three research organizations: University of Oklahoma, The Hebrew University of Jerusalem, Israel, and Lund University, Sweden. The Israel and Swedish partners were funded by the sub-awards from the University of Oklahoma. Cloud modeling studies have been performed by the research teams at The Hebrew University of Jerusalem (HUJ) and Lund University to identify the origins of high concentrations of cloud ice in areas of high ice water content (HIWC). The Hebrew University Cloud Model (HUCM) with full spectral bin microphysics and the Lund University aerosol-cloud (AC) model with a hybrid bin / bulk microphysics scheme complementing HUCM were utilized for simulations. Both research teams had particular focus on secondary ice production (SIP) as one of the possible sources of enhanced ice concentration. The HUJ group suggested a novel concept of ice multiplication during droplet freezing. It is assumed that splintering and droplet fragmentation during droplet freezing takes place because of dendritic growth within a supercooled drop. The resulting simulations of SIP generated small ice in concentrations exceeding hundreds per liter similar to what was observed in the HIWC regions of the tropical storms. The Lund team explored the SIP mechanisms such as breakup of ice particles due to ice-ice collisions and ice sublimation that are expected to dominate the continental storms. They also quantified the impact of homogeneous nucleation of cloud droplets on the total number concentration of ice at very low temperatures near the tops of the clouds. Additionally, the Lund AC model is able to simulate the effect of aerosols of various types (including biological) on the cloud life cycle and the corresponding ice production. The HUCM / AC model was used to simulate one of the “golden” cases of the DOE MC3E campaign on 20 May 2011. The model output was converted into the fields of polarimetric radar variables using the polarimetric radar forward operator developed at the University of Oklahoma and compared with radar observations and in situ microphysical measurements onboard research aircraft. It was demonstrated that specific differential phase KDP is the best radar parameter to identify the HIWC areas and quantify the corresponding ice parameters. For the first time, the shape of the vertical profile of KDP was realistically reproduced by the cloud model with the KDP maximum in the dendritic growth layer (DGL) centered at the -15°C isotherm. The University of Oklahoma team has developed a methodology for polarimetric radar retrievals of such microphysical parameters of ice as ice water content (IWC), mean volume diameter (Dm), and total number concentration (Nt) of ice particles. These retrievals have been validated using in situ aircraft measurements during 6 field campaigns and proved to be quite robust and reliable. This allowed to build the first climatology of the vertical profiles of polarimetric radar variables and retrieved microphysical parameters for the three types of weather systems: continental MCSs, maritime MCSs, and tropical cyclones / hurricanes (Hu and Ryzhkov 2022). The data were collected by a multitude of the WSR-88D radars in 13 continental and 10 maritime MCSs and 11 landfalling hurricanes. The HIWC areas were identified within the examined storms and the corresponding “HIWC statistics” was compared with the “background” one without HIWC. An overarching conclusion of the study is that maritime tropical storms (MCSs and hurricanes) are characterized by smaller size ice in higher concentration compared to the continental MCSs. High ice water content in the HIWC areas is primarily caused by a strong jump in a number concentration of ice particles rather than the increase of their size compared to the “background” environment. This may point to the homogeneous nucleation of excessive amounts of supercooled droplets and / or secondary ice production as the possible origins of HIWC. Such a climatology provides a good observational reference for the modelers to evaluate the performance of their models. As an example, the in-depth analysis of the 20 May 2011 MC3E case shows that the advanced cloud models developed in the course of this study still tend to underestimate the number concentration of ice in the HIWC areas although they succeed in reproducing realistically looking vertical profiles of IWC and Nt. The results of the project research are summarized in 13 journal papers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Integrated Approach to Fluoride High Temperature Reactor Technology and Licensing Challenges (FHR-IRP)

This document constitutes progress made on this project by the Georgia Tech led Integrated Approach to Fluoride High Temperature Reactor Technology and Licensing Challenges (FHR-IRP). This is the first draft of the project final report. Chapter 3 is focused on Tritium management. Tritium management in Fluoride-salt High-temperature Reactors (FHRs) presents a major operational concern and a significant challenge. Tritium is generated via multiple pathways including various interactions in fuel and structural materials, as well as coolant (primarily interaction with lithium and beryllium), leading to a complex evolution and spatial distribution of tritium generation rate. FHR generates orders of magnitude more tritium than in LWRs, and has potential for a significant release, via permeation through structural materials (due to high temperature), and through heat exchanger(s). Therefore, accurate estimate is necessary. For that purpose relevant tritium production pathways were identified. A methodology to obtain space-time dependent tritium generation rate was developed and implemented, employing SCALE and MCNP codes. Detailed models of the reference Advanced High Temperature Reactor (AHTR) design were developed. A multi-parametric metrics was introduced to capture relevant tritium production indicators, including the instantaneous tritium production rate, total tritium production (neglecting subsequent decay), and accumulated tritium production (accounting for the decay). The obtained results will inform tritium management, including the sizing of the components, and to gain insight on the impact of the core design parameters on tritium generation rates. Research effort on Liquid Salt Impurity Removal, Redox and Corrosion Control is discussed in Chapter 4. Section 4.1 is an introduction chapter. Based on the current available knowledge and operation experience on previous MSRs and other types of nuclear reactors, the report identifies the major impurities that are related to either material compatibility, salt properties or radioactive source terms, and the potential contamination sources of these impurities, their transport in the primary coolant loop and available cleanup methodologies for a particular group of impurity. In section 4.2, the available purification methods were discussed, including the liquid/liquid extraction using liquid Bi-Li which has been applied in MSBR and electrochemical purification which is developed based on Pyroprocessing. In this section, the new multi-stage and multi-salt-component was developed and discussed. Section 4.3 discussed the redox potential control for salt chemistry and corrosion mitigation, including the control theory and the soluble/soluble buffer control method. The section also developed method on how to analyze soluble/soluble redox reaction in a molten salt system and designed an electrochemical sensor for redox potential control. Corrosion models were also reported in this section. Section 4.4 focused on how to measure the fundamental properties in a molten salt. As an example, the section focused on La fluoride in the molten salt. The section developed methods on how analyze electrochemical measurement data to obtain the fundamental properties including the redox potential, diffusion coefficient and exchange current density. All these fundamental information is important for salt purification system development. Section 4.5 focused on measurements of corrosion products in the salts. Because of the different behaviors between Cr and La in the salt, new analysis method for Cr fluorides in the salt was developed. Some results for Fe fluoride in the salt and corrosion measurements were also reported. Section 4.6 demonstrated the separation of oxide from the salt. This section also measured the solubility of La 2 O 3 in the salt as function of temperature. Section 4.7 focused on the separation of La and Ce from the molten salt. These two elements were selected because La was found to be separated by using inert electrode, while for separation of Ce, reactive electrode must be selected. Section 4.8 demonstrated the separation iodine ions from the molten salt. Two kinds of working electrode materials (metal W and graphite) were investigated. Section 4.9 focused on the fundamental data of fission products and actinides in liquid Bi. The chapter reviewed the available data, then based a CALPHAD model, the available data were broadened to the concentration-dependent cases.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2015

WE-AB-204-08: Uncertainties in Kinetic Modeling of Shortened Dynamic 18F-FMISO PET Due to Image Noise

Purpose: To assess uncertainties in voxel-wise kinetic modeling (KM) of dynamic 18F-FMISO PET (dPET) due to image noise for different lengths of dPET acquisitions. Methods: 12 tumor time activity curves (TACs) deduced from 6 head and neck cancer patient dPET datasets (45min dPET + 10min frames at ∼95min and ∼160min respectively) were modeled using an irreversible two-tissue compartment model to estimate kinetic rate constants (KRCs) k3, vB, K1, and K1/k2 (reference standard). For each modeled TAC, 1000 noisy TACs were simulated by adding Gaussian noise with standard deviation equivalent to that observed on a voxel level in GE DSTE PET-CT. KM was conducted for each set of noisy TACs using (i) full dataset (170min), and repeated using (ii) 105min and (iii) 45min shortened subsets, with an input function that was image-derived from the dPET dataset of matching length. Absolute value of percent difference between KRCs from noisy TACs as estimated from either of 3 datasets and reference standard KRCs was used to represent bias. Results: For all KRCs, bias was higher for shortened datasets. For k3, bias was lower when actual k3 was higher: from 7%±5% (170min), 9%±7% (105min), and 14%±11% (45min) (actual k3=0.0098), to 32%±25% (170min), 45%±34% (95min), and 79%±48% (45min) (actual k3=0.0008). Similar relationship was observed for vB, with bias being between ∼7% (actual vB=0.28) and ∼26–34% (actual vB=0.08). For K1 [K1/k2], bias was ∼6–10% [∼2–3%] (dependence on actual value of K1 [K1/k2] was not observed). Conclusion: Uncertainties in voxel-wise KM of shortened 18F-FMISO dPET due to image noisy only are larger for shorter acquisitions and, for k3 [vB], the bias was found to be inversely correlated with the actual values of k3 [vB]. Using 45min subset, k3, vB, K1, and K1/k2 could be estimated to within ∼15–80%, ∼10–35%, ∼10%, and ∼3%, respectively.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Climate change is intensifying rainfall erosivity and soil erosion in West Africa

Soil erosion is a critical environmental challenge with significant implications for agriculture, water quality, and ecosystem stability. Understanding its dynamics is essential for sustainable environmental management and societal welfare. Here, we analyze rainfall erosivity and erosion patterns across West Africa (WAF) during the historical (1982–2014), near future (2028–2060), and far future (2068–2100) periods under Shared Socioeconomic Pathways (SSPs 370 and 585). Using bias-corrected-downscaled (BCD) climate models validated against reference data, we ensure an accurate representation of rainfall—a key driver of erosivity (R-factor) and soil erosion. We compare Renard's approach and the Modified Fournier Index (MFI) to calculate the R-factor and note a strong correlation. However, Renard's method shows slightly lower accuracy in Sierra Leone, Guinea, and The Gambia, likely due to its inability to capture high-intensity, short-duration rainfall events. In contrast, the MFI, utilizing continuous rain gauge data, proves more reliable for these regions. We also attribute fluctuations in erosivity, such as those seen during the 2003 West Africa floods, to synoptic weather patterns influenced by multiple climate processes. Furthermore, our analysis reveals regions where future soil erosion could exceed 20 t/ha/yr due to climate change. Under the SSP 370 scenario, soil erosion in WAF is projected to rise by 14.84 % in the near future and 18.65 % in the far future, increasing further under SSP 585 to 19.86 % and 23.49 %, respectively. The most severe increases are expected in Benin and Nigeria, with Nigeria potentially facing a 66.41 % rise in erosion by the far future under SSP 585. These findings highlight the region's exposure to intensified climatic conditions and underscore the urgent need for targeted soil management and climate adaptation strategies to mitigate erosion's ecological and socioeconomic impacts.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Applicability of Sentinel-1 Terrain Observation by Progressive Scans multitemporal interferometry for monitoring slow ground motions in the San Francisco Bay Area

Data from the Sentinel-1 satellite have already proven useful for investigating seismic and volcanic events since its launch in April 2014. The requirement of ultrahigh coregistration accuracy and the current relatively short time of Sentinel-1 acquisitions make its application challenging for studying slow deformation processes, such as fault creep and land subsidence. Here in this paper we analyze a set of 14 SAR images over the San Francisco Bay Area spanning 1 year from early 2015 to 2016. We show that implementing an existing Enhanced Spectral Diversity algorithm or using precise orbits together with a reference digital elevation model both yield the required coregistration accuracy for making use of the phase measurements in time series analysis of ground deformation. Following a thorough validation test, we update our estimates of Hayward Fault creep rate and confirm uplift due to recharge of the Santa Clara Valley aquifer system during the final summer of 4 year drought.

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