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NASA NTRS2022

Remote sensing-based vegetation and soil moisture constraints reduce irrigation estimation uncertainty

Understanding the human water footprint and its impact on the hydrological cycle is essential to inform water management under climate change. Despite efforts in estimating irrigation water withdrawals in earth system models, uncertainties and discrepancies exist within and across modeling systems conditioned by model structure, irrigation parameterization, and the choice of input datasets. Achieving model reliability could be much more challenging for data-sparse regions, given limited access to ground truth for parameterization and validation. Here, we demonstrate the potential of utilizing remotely sensed vegetation and soil moisture observations in constraining irrigation estimation in the Noah-MP land surface model. Results indicate that the two constraints together can effectively reduce model sensitivity to the choice of irrigation parameterization by 7%–43%. It also improves the characterization of the spatial patterns of irrigation and its impact on evapotranspiration and surface soil moisture by correcting for vegetation conditions and irrigation timing. This study highlights the importance of utilizing remotely sensed soil moisture and vegetation measurements in detecting irrigation signals and correcting for vegetation growth. Integrating the two remote sensing datasets into the model provides an effective and less feature engineered approach to constraining the uncertainty of irrigation modeling. Such strategies can be potentially transferred to other modeling systems and applied to regions across the globe.

Wanshu Nie↗

DOE OSTI2014

Measurement of the rate of stellar tidal disruption flares

We report an observational estimate of the rate of stellar tidal disruption flares (TDFs) in inactive galaxies based on a successful search for these events among transients in galaxies using archival Sloan Digital Sky Survey (SDSS) multi-epoch imaging data (Stripe 82). This search yielded 186 nuclear flares in galaxies, 2 of which are excellent TDF candidates. Because of the systematic nature of the search, the very large number of galaxies, the long time of observation, and the fact that non-TDFs were excluded without resorting to assumptions about TDF characteristics, this study provides an unparalleled opportunity to measure the TDF rate. To compute the rate of optical stellar tidal disruption events, we simulate our entire pipeline to obtain the efficiency of detection. The rate depends on the light curves of TDFs, which are presently still poorly constrained. Using only the observed part of the SDSS light curves gives a model-independent upper limit to the optical TDF rate, N-dot <2×10{sup −4} yr{sup −1} galaxy{sup −1} (90% CL), under the assumption that the SDSS TDFs are representative examples. We develop three empirical models of the light curves based on the two SDSS light curves and two more recent and better-sampled Pan-STARRS TDF light curves, leading to our best estimate of the rate: N-dot {sub TDF}=(1.5--2.0){sub −1.3}{sup +2.7}×10{sup −5} yr{sup −1} galaxy{sup −1}. We explore the modeling uncertainties by considering two theoretically motivated light curve models, as well as two different relationships between black hole mass and galaxy luminosity, and two different treatments of the cutoff in the visibility of TDFs at large M {sub BH}. From this we conclude that these sources of uncertainty are not significantly larger than the statistical ones. Our results are applicable for galaxies hosting black holes with mass in the range of a few 10{sup 6}-10{sup 8} M {sub ☉}, and translates to a volumetric TDF rate of (4-8) × 10{sup –8±0.4} yr{sup –1} Mpc{sup –3}, with the statistical uncertainty in the exponent.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Uncertainty quantification of optical models in fission fragment deexcitation

Here, we take the first step towards incorporating compound nuclear observables at astrophysically relevant energies into the experimental evidence used to constrain optical models, by propagating the uncertainty in two global optical potentials, one phenomenological and one microscopic, to correlated fission observables using the Monte Carlo Hauser-Feshbach formalism. We compare to a wide range of historic and recent experimental fission measurements, and discuss in detail regions of disagreement. We find that the parametric optical model uncertainty in neutron-fragment correlated observables involving neutron energy is significant. On the other hand, we observe that other experimental features, particularly neutron-fragment correlations near the 132 Sn shell closure and the high energy component of neutron spectra, are unlikely to be explained by the optical potential, and will require further experimental and theoretical effort to explain.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2001

The NO(x)-HNO3 System in the Lower Stratosphere: Insights from In Situ Measurements and Implications of the J(HNO3)-[OH] Relationship

During the 1997 Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) mission, simultaneous in situ observations of NOx and HOx radicals, their precursors, and the radiation field were obtained in the lower stratosphere. We use these observations to evaluate the primary mechanisms that control NOx-HNO3 exchange and to understand their control over the partitioning between NO2 and HNO3 in regions of continuous sunlight. We calculate NOx production (PNOx) and loss (LNOx) in a manner directly constrained by the in situ measurements and current rate constant recommendations, using approaches for representing albedo, overhead O3 and [OH] that reduce model uncertainty. We find a consistent discrepancy of 18% between modeled rates of NOx production and loss (LNOx = 1.18P(sub NOx)), which is within the measurement uncertainty of +/- 27%. The partitioning between NOx production processes is [HNO3 + OH (41 +/- 2)%; HNO3 + hv (59 +/- 2)%] and between NOx loss processes is [NO2 + OH, 90% to >97%; BrONO2 + H2O, 10% to <3%]. The steady-state description of NOx-HNO3 exchange reveals the significant influence of the tight correlation between the photolysis rate of HNO3 and [OH] established by in situ measurements throughout the lower stratosphere. Parametrizing this relationship, we find: (1) the steady-state value of [NO2](sub 24h-avg)/[HNO3] in the continuously sunlit, lower stratosphere is a function only of temperature and number density; and (2) the partitioning of NOx production between HNO3 + OH and HNO3 + hv is nearly constant throughout most of the lower stratosphere. We describe a methodology (functions of latitude, day, temperature, and pressure) for accurately predicting the steady-state value of [NO2](sub 24h-avg)/[HNO3] and the partitioning of NOx production within these regions. The results establish a metric to compare observations of [NO2](sub 24h-avg)/[HNO3] within the continuously sunlit region and provide a simple diagnostic for evaluating the accuracy of models that attempt to describe the coupled NOx-HOx photochemistry in the lower stratosphere.

Perkins, K. K.↗

DOE OSTI2021

Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification

Focal Area(s): (2)Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models. (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge- guided AI. Science Challenge: The Energy Exascale Earth System Model (E3SM) is a fully coupled, state-of-the-science Earth system model that uses code optimized for DOE's advanced computers to address the most critical scientific questions facing our nation and society (Golaz et al., 2019). The E3SM Land model (ELM) is designed to understand how the changes in terrestrial land surfaces will interact with other Earth system components and has been used to understand hydrologic cycles, biogeophysics, and ecosystem dynamics. In spite of great successes, the ELM has several known issues that restrain rapid improvements. For example, the ELM uses equilibrium models to simulate dynamic land-climate interactions and it requires long model spin-up time to identify suitable initial conditions for transient simulations. The ELM lacks built-in uncertainty mechanisms that can improve the robustness of model predictions. The ELM is a holistic, deterministic model system with a rigid design, and in many situations, it is hard to modify the ELM system to incorporate new theory/hypothesis and new data across scales to address emerging science problems (such as predicting the impacts of water cycle extremes). In addition, The ELM is technically optimized for traditional CPU-centric computers and it cannot fully utilize the current and incoming leadership computers for model simulations and uncertainty quantification (UQ). The success of artificial intelligence (AI) has inspired scientists to use AI models to discover intrinsic features from simulation data (Chattopadhyay et al., 2020) and observational data (Reichstein et al., 2019) to gain further process understanding of Earth science problems. However, autonomous AI model training through deep learning usually requires a huge amount of annotated data. To overcome the limitations from the data and computing resources, knowledge-guided AI models are necessary where human-knowledge is ingested in model construction (Banino et al., 2018) and training process (Silver et al., 2016) for efficient learning. Herein, we present a new way that leverages the process understanding from the ELM to guide AI model development for the ELM enhancement and UQ. We hope this study can inspire further Earth and environmental system model developments and transformations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Effects of error covariance structure on estimation of model averaging weights and predictive performance

When conducting model averaging for assessing groundwater conceptual model uncertainty, the averaging weights are often evaluated using model selection criteria such as AIC, AICc, BIC, and KIC (Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Kashyap Information Criterion, respectively). However, this method often leads to an unrealistic situation in which the best model receives overwhelmingly large averaging weight (close to 100%), which cannot be justified by available data and knowledge. It was found in this study that this problem was caused by using the covariance matrix, CE, of measurement errors for estimating the negative log likelihood function common to all the model selection criteria. This problem can be resolved by using the covariance matrix, Cek, of total errors (including model errors and measurement errors) to account for the correlation between the total errors. An iterative two-stage method was developed in the context of maximum likelihood inverse modeling to iteratively infer the unknown Cek from the residuals during model calibration. The inferred Cek was then used in the evaluation of model selection criteria and model averaging weights. While this method was limited to serial data using time series techniques in this study, it can be extended to spatial data using geostatistical techniques. The method was first evaluated in a synthetic study and then applied to an experimental study, in which alternative surface complexation models were developed to simulate column experiments of uranium reactive transport. It was found that the total errors of the alternative models were temporally correlated due to the model errors. The iterative two-stage method using Cek resolved the problem that the best model receives 100% model averaging weight, and the resulting model averaging weights were supported by the calibration results and physical understanding of the alternative models. Using Cek obtained from the iterative two-stage method also improved predictive performance of the individual models and model averaging in both synthetic and experimental studies.

error correlation↗

DOE OSTI2016

A modeling comparison of deep greenhouse gas emissions reduction scenarios by 2030 in California

California aims to reduce greenhouse gas (GHG) emissions to 40% below 1990 levels by 2030. We compare six energy models that have played various roles in informing the state policymakers in setting climate policy goals and targets. These models adopt a range of modeling structures, including stock-turnover back-casting models, a least-cost optimization model, macroeconomic/macro-econometric models, and an electricity dispatch model. Results from these models provide useful insights in terms of the transformations in the energy system required, including efficiency improvements in cars, trucks, and buildings, electrification of end-uses, low- or zero-carbon electricity and fuels, aggressive adoptions of zero-emission vehicles (ZEVs), demand reduction, and large reductions of non-energy GHG emissions. Some of these studies also suggest that the direct economic costs can be fairly modest or even generate net savings, while the indirect macroeconomic benefits are large, as shifts in employment and capital investments could have higher economic returns than conventional energy expenditures. These models, however, often assume perfect markets, perfect competition, and zero transaction costs. They also do not provide specific policy guidance on how these transformative changes can be achieved. Greater emphasis on modeling uncertainty, consumer behaviors, heterogeneity of impacts, and spatial modeling would further enhance policymakers' ability to design more effective and targeted policies. Here, this paper presents an example of how policymakers, energy system modelers and stakeholders interact and work together to develop and evaluate long-term state climate policy targets. Lastly, even though this paper focuses on California, the process of dialogue and interactions, modeling results, and lessons learned can be generally adopted across different regions and scales.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Conspectus The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom. Although first-principles density functional theory (DFT) remains the workhorse of computational chemistry in mechanism deduction and property prediction, it is of limited use here. DFT is both far too computationally costly for widespread exploration of transition-metal chemical space and also prone to inaccuracies that limit its predictive performance for localized d electrons in transition-metal complexes. These challenges starkly contrast with the well-trodden regions of small-organic-molecule chemical space, where the analytical forms of molecular mechanics force fields and semiempirical theories have for decades accelerated the discovery of new molecules, accurate DFT functional performance has been demonstrated, and gold-standard methods from correlated wavefunction theory can predict experimental results to chemical accuracy. The combined promise of transition-metal chemical space exploration and lack of established tools has mandated a distinct approach. In this Account, we outline the path we charted in exploration of transition-metal chemical space starting from the first machine learning (ML) models (i.e., artificial neural network and kernel ridge regression) and representations for the prediction of open-shell transition-metal complex properties. The distinct importance of the immediate coordination environment of the metal center as well as the lack of low-level methods to accurately predict structural properties in this coordination environment first motivated and then benefited from these ML models and representations. Once developed, the recipe for prediction of geometric, spin state, and redox potential properties was straightforwardly extended to a diverse range of other properties, including in catalysis, computational “feasibility”, and the gas separation properties of periodic metal–organic frameworks. Interpretation of selected features most important for model prediction revealed new ways to encapsulate design rules and confirmed that models were robustly mapping essential structure–property relationships. Encountering the special challenge of ensuring that good model performance could generalize to new discovery targets motivated investigation of how to best carry out model uncertainty quantification. Distance-based approaches, whether in model latent space or in carefully engineered feature space, provided intuitive measures of the domain of applicability. With all of these pieces together, ML can be harnessed as an engine to tackle the large-scale exploration of transition-metal chemical space needed to satisfy multiple objectives using efficient global optimization methods. In practical terms, bringing these artificial intelligence tools to bear on the problems of transition-metal chemical space exploration has resulted in ML-model assessments of large, multimillion compound spaces in minutes and validated new design leads in weeks instead of decades.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Advances in Kriging-Based Autonomous X-Ray Scattering Experiments

Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning

A fundamental challenge in the design of LEDs is to maximise electro-luminescence efficiency at high current densities. We simulate GaN-based LED structures that delay the onset of efficiency droop by spreading carrier concentrations evenly across the active region. Statistical analysis and machine learning effectively guide the selection of the next LED structure to be examined based upon its expected efficiency as well as model uncertainty. This active learning strategy rapidly constructs a model that predicts Poisson-Schrödinger simulations of devices, and that simultaneously produces structures with higher simulated efficiencies.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2023

Uncertainty Quantification for Component Modeling Using the Discrete-Direct Approach

Threaded fastener behavior can be an important aspect of complex component and system behavior, but there is no one-size-fits-all finite element analysis technique. Proper modeling of threaded fastener joints requires careful consideration of many details, from test setup and data acquisition to constitutive modeling and uncertainty quantification approaches. This report details analysis of a “mini-radax” bolted-joint exemplar where a Discrete-Direct uncertainty quantification approach is employed to evaluate margin of the component. The mini-radax geometry is tested to failure on a drop table, and single-coupon tests of individual fasteners serve as foundational data for the analysis. Analysis predictions complement the test data well and provide additional context for engineering decision-making.

42 ENGINEERING↗

DOE OSTI2016

DIRECT DETECTION OF LYMAN CONTINUUM ESCAPE FROM LOCAL STARBURST GALAXIES WITH THE COSMIC ORIGINS SPECTROGRAPH

We report on the detection of Lyman continuum radiation in two nearby starburst galaxies. Tol 0440-381, Tol 1247-232, and Mrk 54 were observed with the Cosmic Origins Spectrograph on board the Hubble Space Telescope . The three galaxies have radial velocities of ∼13,000 km s{sup −1}, permitting a ∼35 Å window on the restframe Lyman continuum shortward of the Milky Way Lyman edge at 912 Å. The chosen instrument configuration using the G140L grating covers the spectral range from 912 to 2000 Å. We developed a dedicated background subtraction method to account for the temporal and spatial background variations of the detector, which is crucial at the low flux levels around 912 Å. This modified pipeline allowed us to significantly improve the statistical and systematic detector noise and will be made available to the community. We detect Lyman continuum in all three galaxies. However, we conservatively interpret the emission in Tol 0440-381 as an upper limit due to possible contamination by geocoronal Lyman series lines. We determined the current star formation properties from the far-ultraviolet continuum and spectral lines and used synthesis models to predict the Lyman continuum radiation emitted by the current population of hot stars. We discuss various model uncertainties such as, among others, atmospheres and evolution models. Lyman continuum escape fractions were derived from a comparison between the observed and predicted Lyman continuum fluxes. Tol 1247-232, Mrk 54, and Tol 0440-381 have absolute escape fractions of (4.5 ± 1.2)%, (2.5 ± 0.72)%, and <(7.1 ± 1.1)%, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2005

Orbital Debris Shape and Orientation Effects on Ballistic Limits

The SPHC hydrodynamic code was used to evaluate the effects of orbital debris particle shape and orientation on penetration of a typical spacecraft dual-wall shield. Impacts were simulated at near-normal obliquity at 12 km/sec. Debris cloud characteristics and damage potential are compared with those from impacts by spherical projectiles. Results of these simulations indicate the uncertainties in the predicted ballistic limits due to modeling uncertainty and to uncertainty in the impactor orientation.

Evans, Steven W.↗

NASA NTRSDate not supplied

A Framework for Evaluating Climate Model Performance Metrics

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is often used to add value to model projections: consensus projections have been shown to consistently outperform individual models. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, certain models reproduce climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables?e.g., outgoing longwave radiation and surface temperature. Metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument and surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing weighted and unweighted model ensembles. For example, one tested metric weights the ensemble by how well models reproduce the time-series probability distribution of the cloud forcing component of reflected shortwave radiation. The weighted ensemble for this metric indicates lower simulated precipitation (up to .7 mm/day) in tropical regions than the unweighted ensemble: since CMIP5 models have been shown to overproduce precipitation, this result could indicate that the metric is effective in identifying models which simulate more realistic precipitation. Ultimately, the goal of the framework is to identify performance metrics for advising better methods for ensemble averaging models and create better climate predictions.

Noel C Baker↗

NASA NTRSDate not supplied

Magnetohydrodynamics (MHD) Aerocapture System for Enabling Faster-Larger Planetary Science & Human Exploration Missions

Since our completing the NIAC Phase I NIAC on this Advanced Aerocapture System, NASA Langley Research Center has funded or supported a number of studies and code enhancements through its Center Innovation Fund (CIF) and NASA’s NSTGRO and Internship Programs to mature the analysis capabilities and quantify the merits of the MHD Aerocapture System technology. These efforts have resulted in a plug and play analysis capability for assessing MHD aerocapture system performance for arrival at many planetary bodies of interest. Our efforts have especially focused on the potential mass savings for improving the capacity for science observations at Neptune and Triton. A re-cent Forbes article published “‘Orbital mechanics is probably going to decide for us whether we go to Uranus or Neptune because we need to flyby Jupiter,’ said Kunio Sayanagi at Hampton University, Virginia, who also worked on the Neptune Odyssey proposal…. Exactly when a mission can be sent to Uranus, or Neptune, depends on the relative position of Jupiter, which can help give a spacecraft a gravitational slingshot. That drastically shortens the cruise phase.” [1] Since shortening the cruise phase is important for these science missions, any mass savings enabled by the MHD Aerocapture System could be reallocated to increasing Thermal Protection System mass to allow faster arrival speeds and/or for onboarding additional payloads such as science instruments, batteries, or propellant for conducting more science for longer durations in the desired orbits. The analysis steps and codes for conducting trades and sizing vehicles for aerocapture are as follows: Step 1 is to conduct aeroheating analysis using LAURA of the selected entry vehicle shape to identify locations on the forebody where ionization and flow velocity are sufficient for producing Lo-rentz forces. LAURA is a multiblock structured grid finite-volume CFD solver developed at the NASA Langley Research Center. [2] LAURA has been used for aerothermal analysis support of the entry, de-scent and landing (EDL) phase of interplanetary missions over the last three decades [3-7]. Step 2 is to port the LAURA results into CFDWARP to calcu-late electrical and thermal conductivities of ionized flow for sizing MHD patch system and calculating Lorentz forces needed for controls analysis. CFDWARP is a CFD code that uses advanced nu-merical methods that enable the simulation of the full coupling between the aerodynamics, the magne-tohydrodynamics, and the non-neutral plasma sheaths. CFDWARP has the unique capability to simulate efficiently the non-neutral sheaths (near the electrodes) in coupled form with the quasi-neutral bulk MHD flow [8-11]. Step 3 is to link re-sults from LAURA and CFDWARP into POST2 for calculating entry trajectories and comparing MHD control results with other aerodynamic control strategies. The Program to Optimize Simulated Tra-jectories II (POST2) is a generalized point mass, discrete parameter targeting and optimization pro-gram. POST2 provides the capability to target and optimize point mass trajectories for multiple pow-ered or un-powered vehicles near an arbitrary rotat-ing, oblate planet [12]. Step 4: TPS sizing was per-formed using the Fully Implicit Ablation and Ther-mal-response code (FIAT) tool which computes the transient one-dimensional thermal response and surface thermochemistry of a multilayer stackup of thermal protection, bonding, and structural materi-als subject to aeroheating on one surface [13]. The sizing and margining methodology used was based on the approach documented by Mahzari and Milos [14] for the dual-layered heatshield for extreme entry environment technology (DL-HEEET) TPS concept. TPS analysis utilizes trajectory information from POST2. Using this step-wise plug and play MHD Aerocapture performance assessment process, our analysis targets a Neptune aerocapture trajectory that will place the spacecraft in an observation orbit for Triton. [15]. Magnetohydrodynamic (MHD) control of a 4.5-meter diameter MSL-style capsule resulted in TPS mass savings of nearly 2000 kg when using an MHD system mass of under 200 kg. The flight path for a vehicle using the MHD control strategy has a much lower heat rate and heat load compared to the conventional aerodynamic aerocapture strategies known as bank angle con-trolled (BAC) and direct force controlled (DFC). Both BAC and DFC have heat rates significantly greater than 1500 W/cm2 typically used as an upper limit for PICA. Thus, DL-HEEET TPS concept was required for the BAC and DFC control strategies. However, considering the more benign environ-ments for the MHD case, additional TPS concepts with improved mass efficiency were also assessed. PICA was considered for the MHD controlled strat-egy since the maximum heat rate was well within the limits (<1500 W/cm2) of PICA. TPS sizing re-sulted in a significant mass reduction. The PICA layer for this sizing case was about 7.8 cm. As a point of reference, the Mars 2020 mission, which used this same PICA concept, had a PICA thickness of 3.18 cm [16]. The trajectories used for the TPS sizing originat-ed from the POST2 simulations. The current, I, to an electromagnet configuration can be manipulated to allow for active control of the vehicle. Manipula-tion of the current, I, changes the magnetic field, B, which affects the Lorentz force and therefore the MHD drag force on the vehicle. Our analysis in-cluded both open-loop and close-loop control. Closed-loop control will enable improved overall performance when taking into account mission level uncertainties, such as interplanetary delivery errors and atmospheric modeling uncertainties. The open-loop and closed-loop MHD control cases do not dip as deep into the atmosphere as the aerodynamic cases. Three types of aerodynamic-only approach-es are investigated: bank angle modulation (BAM), director force control (DFC), and Drag Modulated. BAM and DFC make use of vehicle aerodynamic angles to steer the vehicle. Thus, changing the aer-odynamic forces acting on the vehicle for control, aerodynamic drag modulated case requires a vary-ing drag area to modulate the drag force. The MHD drag modulated case modulates MHD generated drag force that adds to the aerodynamic drag. This higher atmospheric activation of drag forces by the MHD patch results in significantly less heat flux on the vehicle. The MHD technology will enable shorter cruise times and deceleration of larger payloads for increasing the capacity for science at the Ice Giants or for returning astronauts to Earth from cislunar space or from Mars. The purpose of this presentation is to provide more details about this work and to highlight plans for further research and development including a flight demonstration.

R. W. Moses↗

DOE OSTI2013

Cementitious Barriers Partnership (CBP): Using the CBP Software Toolbox to Simulate Sulfate Attack and Carbonation of Concrete Structures - 13481

The Cementitious Barriers Partnership (CBP) Project is a multi-disciplinary, multi-institutional collaboration supported by the U.S. Department of Energy Office of Tank Waste Management. The CBP project has developed a set of integrated modeling tools and leaching test methods to help improve understanding and prediction of the long-term hydraulic and chemical performance of cementitious materials used in nuclear applications. State-of-the-art modeling tools, including LeachXS{sup TM}/ORCHESTRA and STADIUM{sup R}, were selected for their demonstrated abilities to simulate reactive transport and degradation in cementitious materials. The new U.S. Environmental Protection Agency leaching test methods based on the Leaching Environmental Assessment Framework (LEAF), now adopted as part of the SW-846 RCRA methods, have been used to help make the link between modeling and experiment. Although each of the CBP tools has demonstrated utility as a standalone product, coupling the models over relevant spatial and temporal solution domains can provide more accurate predictions of cementitious materials behavior over relevant periods of performance. The LeachXS{sup TM}/ORCHESTRA and STADIUM{sup R} models were first linked to the GoldSim Monte Carlo simulator to better and more easily characterize model uncertainties and as a means to coupling the models allowing linking to broader performance assessment evaluations that use CBP results for a source term. Two important degradation scenarios were selected for initial demonstration: sulfate ingress / attack and carbonation of cementitious materials. When sufficient sulfate is present in the pore solution external to a concrete barrier, sulfate can diffuse into the concrete, react with the concrete solid phases, and cause cracking that significantly changes the transport and structural properties of the concrete. The penetration of gaseous carbon dioxide within partially saturated concrete usually initiates a series of carbonation reactions with both dissolved ions and the hydrated cement paste. The carbonation process itself does not have a negative effect, per se, on the paste physical properties and can even result in reduced porosity and can help form a protective layer at the surface of concrete. However, carbonation has been shown to increase leaching of some constituents and can potentially have a detrimental effect on reinforced concrete structures by lowering pH to ca. 9 and de-passivating embedded steel (e.g. rebar) and accelerating corrosion, which are important processes related to high-level waste tank integrity and closure evaluations. The use of the CBP Software Toolbox to simulate these important degradation phenomena for both concrete vaults and high-level waste tanks are demonstrated in this paper. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

IceCube's convex all-sky neutrino spectrum consistent with the magnetically powered corona scenario for active galactic nuclei

High-energy multimessenger background analyses over the past decade have provided evidence for a population of hidden neutrino sources that are opaque to GeV-TeV gamma rays, a picture bolstered by recent observations of the nearby active galaxy NGC 1068. The coronal regions in the hearts of active galactic nuclei (AGNs) have been proposed as the most promising sites for such hidden nonthermal particle production, and NGC 1068 is expected to be the most neutrino-active galaxy for IceCube. We demonstrate that the latest all-sky neutrino spectrum, exhibiting a spectral bend around 3-30 TeV, is consistent with predictions of the magnetically powered corona scenario, and the models for the all-sky neutrino flux can simultaneously explain the multimessenger data from NGC 1068 within observational and modeling uncertainties. We further show, in a largely model-independent way, that the contribution from NGC 1068-like sources does not overshoot the observed medium-energy neutrino flux. Finally, we highlight the key role of the Eddington ratio, which can drive substantial variations in the predicted neutrino fluxes of nearby AGNs, and we encourage systematic multimessenger searches for the neutrino-brightest AGNs.

Murase, Kohta [Penn State U.; Penn State U., Unive↗

DOE OSTI2022

Quantifying subsurface parameter and transport uncertainty using surrogate modelling and environmental tracers

Here, we combine physics-based groundwater reactive transport modelling with machine-learning techniques to quantify hydrogeological model and solute transport predictive uncertainties. We train an artificial neural network (ANN) on a dataset of groundwater hydraulic heads and 3 H concentrations generated using a high-fidelity groundwater reactive transport model. Using the trained ANN as a surrogate model to reproduce the input–output response of the high-fidelity reactive transport model, we quantify the posterior distributions of hydrogeological parameters and hydraulic forcing conditions using Markov chain Monte Carlo calibration against field observations of groundwater hydraulic heads and 3 H concentrations. We demonstrate the methodology with a model application that predicts Chlorofluorocarbon-12 (CFC-12) solute transport at a contaminated field site in Wyoming, United States. Our results show that including 3 H observations in the calibration dataset reduced the uncertainty in the estimated permeability field and infiltration rates, compared to calibration against hydraulic heads alone. However, predictive uncertainty quantification shows that CFC-12 transport predictions conditioned to the parameter posterior distributions cannot reproduce the field measurements. We found that calibrating the model to hydraulic head and 3 H observations results in groundwater mean ages that are too large to explain the observed CFC-12 concentrations. The coupling of the physics-based reactive transport model with the machine-learning surrogate model allows us to efficiently quantify model parameter and predictive uncertainties, which is typically computationally intractable using reactive transport models alone.

58 GEOSCIENCES↗