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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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DOE OSTI2026

SAM Code Enhancements for Fission Product Tracking of Noble Gases and Metals in MSRs

This report documents fiscal year 2026 enhancements to the System Analysis Module (SAM) for modeling fission product transport in liquid-fueled molten salt reactors (MSRs). The work advances three principal areas: noble gas transport, noble metal deposition, and user interface improvements. The noble gas transport capability integrates drift-flux gas transport, Henry’s law two-film interphase mass transfer with pressure-based nucleation suppression, Knudsen-regime pore diffusion into porous graphite with a conjugate salt-graphite interface constraint, built-in material properties, five Sherwood-number mass transfer correlations including three derived from high-fidelity NekRS simulations, and xenon-135 reactivity feedback through SAM’s point-kinetics model. This work also presents a comprehensive verification test suite, including new analytically verified cases for pressure-dependent onset of interphase gas transfer in a stagnant vertical pipe, a postulated FLiBe-graphite Xe extraction permeator, a gravity riser with a fission-product source, and a descending pipe with gas redissolution driven by hydrostatic pressure. A machine learning framework for bubble rise velocity prediction in molten salt systems is developed and benchmarked on molten-salt and diverse aqueous bubble datasets. The best-performing fine-tuned transfer-learning networks achieve an 82% reduction in RMSE relative to the Clift correlation, and is implemented directly in SAM. The noble metal transport capability is developed, including a liquid-wall deposition model and a gas-surface flotation mechanism that transfers insoluble particles entrained by sparging gas to wetted structures. Verification tests and demonstration cases cover the surface deposition, flotation efflux, and flotation shedding. Finally, a new [SpeciesTransport] input structure replaces positional global vectors with selfcontained, order-independent, named species blocks, simplifies the specification of multiphase species and decay chains, and remains fully compatible with existing SAM input files. Together, these developments improve the physical fidelity, verification basis, and usability of SAM for system-level analyses of fissionproduct behavior in MSRs.

Mui, Travis (ORCID:0000000303736470)↗
NASA NTRS1994

Applications Performance on NAS Intel Paragon XP/S - 15#

The Numerical Aerodynamic Simulation (NAS) Systems Division received an Intel Touchstone Sigma prototype model Paragon XP/S- 15 in February, 1993. The i860 XP microprocessor with an integrated floating point unit and operating in dual -instruction mode gives peak performance of 75 million floating point operations (NIFLOPS) per second for 64 bit floating point arithmetic. It is used in the Paragon XP/S-15 which has been installed at NAS, NASA Ames Research Center. The NAS Paragon has 208 nodes and its peak performance is 15.6 GFLOPS. Here, we will report on early experience using the Paragon XP/S- 15. We have tested its performance using both kernels and applications of interest to NAS. We have measured the performance of BLAS 1, 2 and 3 both assembly-coded and Fortran coded on NAS Paragon XP/S- 15. Furthermore, we have investigated the performance of a single node one-dimensional FFT, a distributed two-dimensional FFT and a distributed three-dimensional FFT Finally, we measured the performance of NAS Parallel Benchmarks (NPB) on the Paragon and compare it with the performance obtained on other highly parallel machines, such as CM-5, CRAY T3D, IBM SP I, etc. In particular, we investigated the following issues, which can strongly affect the performance of the Paragon: a. Impact of the operating system: Intel currently uses as a default an operating system OSF/1 AD from the Open Software Foundation. The paging of Open Software Foundation (OSF) server at 22 MB to make more memory available for the application degrades the performance. We found that when the limit of 26 NIB per node out of 32 MB available is reached, the application is paged out of main memory using virtual memory. When the application starts paging, the performance is considerably reduced. We found that dynamic memory allocation can help applications performance under certain circumstances. b. Impact of data cache on the i860/XP: We measured the performance of the BLAS both assembly coded and Fortran coded. We found that the measured performance of assembly-coded BLAS is much less than what memory bandwidth limitation would predict. The influence of data cache on different sizes of vectors is also investigated using one-dimensional FFTs. c. Impact of processor layout: There are several different ways processors can be laid out within the two-dimensional grid of processors on the Paragon. We have used the FFT example to investigate performance differences based on processors layout.

Saini, Subhash↗
DOE OSTI2025

Development of a River Dynamical Core for E3SM to simulate compound flooding on Exascale-class heterogeneous supercomputers

Flooding events pose significant risk to human life, property, and infrastructure. Physically-consistent quantification of altered flood risks in global models requires hyper-resolution (~1 km) or fine flood simulations using two-dimensional (2D) physics schemes, both of which are unavailable in the current generation Earth System Models. Here, in this work, we have developed the River Dynamical Core (RDycore), which is an open-source, 2D shallow water equation (SWE) library for the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM). RDycore uses PETSc and libCEED libraries that allows it to run efficiently on CPUs and GPUs, as well as select a time-integration algorithm at runtime without requiring any code modifications. RDycore achieves spatial error convergence rates for problems with analytical and manufactured solutions similar to those reported previously in the literature, or consistent with the implemented first-order spatial discretization scheme. RDycore's accuracy in predicting flooding for a well-studied dam break problem is comparable to existing SWE models. For a problem with 471 million grid cells, RDycore achieves a speedup of 6.6x and 7.6x on GPUs compared to CPUs when using 320 compute nodes on DOE's Perlmutter and Frontier supercomputers, respectively. The one-way coupling of the RDycore library within E3SM is demonstrated by performing multiple 5-day flooding simulations during Hurricane Harvey driven by five precipitation datasets. The E3SM--RDycore simulations at 30 m spatial resolution accurately simulate maximum water height during the hurricane when benchmarked against a previously published study and achieve a speedup of 15x (Perlmutter) and 21x (Frontier) on GPUs relative to CPUs. The work presented here is the foundational step in providing hardware and algorithmic portability framework for simulating kilometer-scale river dynamics within E3SM.

Flood Simulation↗
DOE OSTI2022

Molecular Catalyst Synthesis Strategies to Prepare Atomically Dispersed Fe-N-C Heterogeneous Catalysts

We report a strategy to integrate atomically dispersed iron within a heterogeneous nitrogen-doped carbon (N-C) support, inspired by routes for metalation of molecular macrocyclic iron complexes. The N-C support, derived from pyrolysis of a ZIF-8 metal–organic framework, is metalated via solution-phase reaction with FeCl 2 and tributyl amine, as a Brønsted base, at 150 °C. Fe active sites are characterized by 57 Fe Mössbauer spectroscopy and aberration-corrected scanning transmission electron microscopy. The site density can be increased by selective removal of Zn 2+ ions from the N-C support prior to metalation, resembling the transmetalation strategy commonly employed for the preparation of molecular Fe-macrocycles. As a result, the utility of this approach is validated by the higher catalytic rates (per total Fe) of these materials relative to established Fe-N-C catalysts, benchmarked using an aerobic oxidation reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2010

Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight

The Integrated Health Management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. The complexity of modern aeronautic and aircraft systems (including e.g. the power distribution, flight control, solid and liquid motors) dictates employment of hybrid models and high-level reasoners for analysing mixed continuous and discrete information flow involving multiple modes of operation in uncertain environments, unknown state variables, heterogeneous software and hardware components. To provide the information link between key design/performance parameters and high-level reasoners we rely on development of multi-physics performance models, distributed sensors networks, and fault diagnostic and prognostic (FD&P) technologies in close collaboration with system designers. The main challenges of our research are related to the in-flight assessment of the structural stability, engine performance, and trajectory control. The main goal is to develop an intelligent IHM that not only enhances components and system reliability, but also provides a post-flight feedback helping to optimize design of the next generation of aerospace systems. Our efforts are concentrated on several directions of the research. One of the key components of our strategy is an innovative approach to the diagnostics/prognostics based on the real time dynamical inference (DI) technologies extended to encompass hybrid systems with hidden state trajectories. The major investments are into the multiphysics performance modelling that provides an access of the FD&P technologies to the main performance parameters of e.g. solid and liquid rocket motors and composite materials of the nozzle and case. Some of the recent results of our research are discussed in this chapter. We begin by introducing the problem of dynamical inference of stochastic nonlinear models and reviewing earlier results. Next, we present our analytical approach to the solution of this problem based on the path integral formulation. The resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. In the following Section the strengths of the algorithm are illustrated illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. Next, we discuss a number of recent results in application to the development of the IHM for aerospace system. Firstly, we apply dynamical inference approach to a solution of classical three tank problems with mixed unknown continuous and binary parameters. The problem is considered in the context of ground support system for filling fuel tanks of liquid rocket motors. It is shown that the DI algorithm is well suited for successful solution of a hybrid version of this benchmark problem even in the presence of additional periodic and stochastic perturbation of unknown strength. Secondly, we illustrate our approach by its application to an analysis of the nozzle fault in a solid rocket motor (SRM). The internal ballistics of the SRM is modelled as a set of one-dimensional partial differential equations coupled to the dynamics of the propellant regression. In this example we are specifically focussed on the inference of discrete and continuous parameters of the nozzle blocking fault and on the possibility of an application of the DI algorithm to reducing the probability of "misses" of an on-board FD&P for SRM. In the next section re-contact problem caused by first stage/upper stage separation failure is discussed. The reaction forces imposed on the nozzle of the upper stage during the re-contact and their connection to the nozzle damage and to the thrust vector control (TVC) signal are obtained. It is shown that transient impact induced torquean be modelled as a response of an effective damped oscillator. A possible application of the DI algorithm to the inference of damage parameters and predicting fault dynamics ahead of time using the actuator signal is discussed. Finally, we formulate Bayesian inferential framework for development of the IHM system for in-flight structural health monitoring (SHM) of composite materials. We consider the signal generated by piezoelectric actuator mounted on composite structure generating elastic waves in it. The signal received by the sensor is than compared with the baseline signal. The possibility of damage inference is discussed in the context of development of the SHM.

Smelyanskiy, Vadim N.↗
NASA NTRSDate not supplied

Coupled Aeropropulsive Design Optimization of a Podded Electric Propulsor

New aircraft concepts are increasingly relying on non-traditional propulsion systems to achieve lower energy consumption. These non-traditional technologies, such as boundary layer ingestion or distributed electric propulsion, require a tight integration of the propulsion sys- tem to the airframe, and therefore, a traditional design approach where the aerodynamics and propulsion disciplines are considered separately is likely to result in sub-optimal designs. As a result, we have to rely on coupled aeropropulsive design optimization, in which fully coupled aeropropulsive models are optimized to maximize the advantages of these tightly integrated propulsion systems. Despite its advantages, aeropropulsive design optimization is a relatively new field, and significant advancements are required for wide adoption of this approach, especially in the context of robustness. In this work, we introduce two fully coupled aeropropulsive design optimization approaches that are compatible with CFD models with body-force terms and boundary conditions to model the effects of the propulsion system in the flow field. To test these new approaches, we developed a podded electric fan model based on the aft-propulsor of NASA’s STARC-ABL concept. This simple design problem enables us to rapidly develop and test different aeropropulsive coupling approaches, while including the challenging physical interactions that arise from coupling CFD and propulsion models. The coupled aeropropulsive model is implemented using the MPhys library, which is built with NASA’s OpenMDAO frame- work. Using this benchmark design problem, we demonstrate the robustness of the approaches and our aeropropulsive design optimization framework by performing a sweep of optimizations for a total of 50 CFD-based aeropropulsive design optimizations. Furthermore, the new aero- propulsive coupling approaches enable multi-point design optimizations. We demonstrate this capability in a multi-point design optimization problem where we optimize cruise performance subject to a fan-face distortion constraint at rolling take-off conditions. The aeropropulsive modeling approaches we present in this work represent a significant milestone in the field of aeropropulsive design optimization. The framework we developed is extremely robust and flexible, and these developments will be crucial for future aeropropulsive design optimizations of complete turbofan engines.

Optimization propulsion aerodynamics↗
DOE OSTI2024

TiO 2 /TiN Interface Enables Integration of Ni 5 P 4 Electrocatalyst with a III–V Tandem Photoabsorber for Stable Unassisted Solar-Driven Water Splitting

H 2 production by direct photoelectrochemical (PEC) water splitting has remained unachievable commercially, mainly due to rapid failure at the interface between the photoabsorber(s) and catalyst(s). PEC devices made from multijunction III-V semiconductors with platinum group metal (PGM) catalysts have yielded impressive initial solar-to-H 2 (STH) efficiency >19%, which rapidly corrodes in aqueous electrolytes. Here, TiO 2 /TiN layers were fused to create a bifunctional interface between a GaInP 2 /GaAs III-V tandem photoabsorber and a polycrystalline Ni 5 P 4 HER catalyst. Here, the TiO 2 serves as a conducting corrosion barrier, while a thin layer of much denser TiN (1 nm) blocks interlayer diffusion during fabrication. This strategy allows the elevated temperatures needed to crystallize the Ni 5 P 4 nanoparticles and fuse to the TiO 2 /TiN junction to achieve minimal optical loss without damaging the sensitive photoasbsorber. The resulting photocathode exhibits an initial STH efficiency of 11.4%-13.2% in sodium phosphate electrolyte at neutral pH 7. It operated continuously for over 200 h without failure above 10% STH efficiency, exceeding all previous benchmarks. The earth-abundant Ni 5 P 4 catalyst replaces costly PGM catalysts at comparable HER activity in neutral, acidic, or basic pH electrolytes.

08 HYDROGEN↗
DOE OSTI2026

Agentic framework for programmatic crystal structure generation using a fine-tuned worker–supervisor large language model

Platinum group metals (PGMs) underpin many catalytic technologies but face severe supply constraints, motivating the search for alternative materials and computational methods to accelerate discovery. While atomistic simulation tools such as Pymatgen and ASE have streamlined structure manipulation, they require detailed inputs, limiting accessibility for experimentalists and slowing early-stage exploration. Here, in this study, we present an AI-driven agentic framework that orchestrates worker–supervisor large language models (LLMs). The worker translates natural-language prompts of varying abstraction into valid crystallographic structures using a compact LLM fine-tuned with low-rank adaptation on a curated text–code–CIF dataset, emphasizing energy-efficient training. Benchmarking against the baseline CodeGen-350M-mono model shows that fine-tuning reduces hallucination rates from 100% to as low as 5% and improves structural match accuracy to up to 82% for fully specified inputs. Accuracy declines with decreasing prompt detail but remains nontrivial even when only stoichiometry and space group are provided, underscoring the LLM’s capacity for crystallographic inference. The supervisor Claude LLM evaluates the outputs and triggers iterative refinement through the worker’s built-in structure manipulation capabilities (e.g., supercell scaling, strain, vacancy, and substitution operations). We further demonstrate use cases for technologically relevant catalysts, including IrO 2 , pyrochlore Pb 2 Ir 2 O 7 , Ni 2 FeO 4 , and Ni 3 Mo, where the framework generates physically consistent structures that can be refined via geometry optimization. This work introduces a low-energy, language-driven pathway for integrating human and machine intelligence in materials design, paving the way for AI-assisted synthesis planning and high-throughput screening of complex oxides.

AI agent↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗
DOE OSTI2017

Pilot-Scale Silicone Process for Low-Cost Carbon Dioxide Capture. Final Scientific/Technical Report

GE Global Research has developed, over the last 8 years, a platform of cost effective CO 2 capture technologies based on a non-aqueous aminosilicone solvent (GAP-1m). As demonstrated in a previous funded DOE project (DE-FE0007502), the GAP-1m solvent has increased CO 2 working capacity, lower volatility and corrosivity than the benchmark aqueous amine technology. The current report describes the cooperative program between GE Global Research (GE GRC), and the National Carbon Capture Center (NCCC) to design, construct, and operate a pilot-scale process using GAP-1m solvent to demonstrate its performance at 0.5 MWe. (i) Performance of the GAP-1m solvent was demonstrated in a 0.5 MWe pilot with real flue gas for over 900 hrs. of operation using two alternative desorption designs: a Continuous Stirred Tank Reactor (CSTR), and a Steam Stripper Column (SSC). The CSTR is a one-stage separation unit with reduced space requirements, and capital cost. The alternative is a multi-stage separation column, with improved desorption efficiency. Testing the two desorber options allowed us to identify the most cost effective, and space efficient desorber solution. (ii) CSTR Campaign: The CSTR desorber unit was designed, fabricated and integrated with the pilot solvent test unit (PSTU), replacing the PSTU Steam Stripper Column at NCCC. Solvent management and waste water special procedures were implemented to accommodate operation of the non-aqueous solvent in the PSTU. Performance of the GAP-1m solvent with the CSTR was demonstrated for over 500 hrs. while varying temperature of the desorption (230 – 265 oF), solvent circulation rate (GAP-1m : CO 2 (molar) = 1.5 – 4), and flue gas flow rates (0.2 – 0.5 MWe). Solvent carry-over in the CO 2 product was minimized by maintaining water content below 5 wt.%, and desorption pressure at 7 psig. CO 2 capture efficiency achieved was 95% at 0.25 MWe (GAP-1m : CO 2 = 4 (molar), 230 oF desorption), and 65% at 0.5 MWe (GAP-1m : CO 2 (molar) = 1.5, 248 oF). Solvent loss was dominated by thermal degradation of the rich solvent. (iii) Steam Stripper Column Campaign: Higher expected cost of the solvent vs. aqueous amines makes solvent management a top priority to maintain the low cost for the process. During the testing of the GAP-1m solvent with the CSTR, thermal degradation of the rich solvent was found to be the main mechanism in solvent loss. Small amounts of water in the working solution were found to be an effective way to enable steam stripping, thereby lowering desorption temperature, and hence reducing thermal degradation. Steam stripping also increased working capacity by 30% due to a more efficient desorption. The concept was first tested in a glass stripping column (lab scale, GE GRC), optimized in a continuous bench scale system (2 kWe, GE GRC), and demonstrated in a 0.5 MWe PSTU at NCCC. No special system modifications were required to the PSTU to accommodate the testing of the non-aqueous GAP-1 solvent with the regenerator column. SSC was found to be more robust towards solvent entrainment (H 2 O < 35 wt.%). 90 – 95% CO 2 capture efficiency was achieved under stoichiometric conditions at 0.5 MWe (235 oF desorption, 2 psig and 19 wt. % H 2 O). Both CO 2 capture efficiency and specific duty reached optimum conditions at 18 wt.% H 2 O. Low amine degradation (< 0.05 wt.%/day) was recorded over 350 hrs. of operation. Controlled water addition to GAP-1m solvent decreased the desorption temperature, thermal degradation, and improved the CO 2 working capacity due to more efficient absorption and desorption processes. Under these conditions, the GAP-1m solvent exhibited a 25% increased working capacity, and 10% reduction in specific steam duty vs. MEA, at 10 oF lower desorption temperature. (iv) Techno-economic Analysis: The pilot-scale PSTU engineering data were used to update the capture system process models, and the techno-economic analysis was performed for a 550 MW coal fired power plant. The 1st year CO 2 removal cost for the aminosilicone-based carbon-capture process was evaluated at $48/ton CO 2 using the steam stripper column. This is a 20% reduction compared to MEA, primarily due to lower overall capital cost. CO 2 cost using the CSTR desorber is dominated by the economics of the solvent make-up. The steam stripper desorber is the preferred unit operation due to a more efficient desorption, and reduced solvent make-up rate. Further reduction in CO 2 capture cost is expected by lowering the manufacturing cost of the solvent, implementing flowsheet optimization and/or implementing the next generation aminosilicone solvent with improved stability and increased CO 2 working capacity.

0.5 MW Pilot↗
DOE OSTI2025

HydroForecast Long-term: Improving hydropower’s resilience to climate change through accurate climate-scale

With hydrologic patterns and water availability across the globe shifting due to climate change, advancements in hydrologic prediction systems can help significantly reduce the uncertainties that utilities and water supply entities have in their decision making. Understanding and estimating hydrology at the climate scale is critical for managing water resources under changing climate scenarios. This project focuses on integrating state-of-the-art neural network modeling with downscaled climate projections to deliver the reliable water supply projections decades into the future to meet an urgent need from hydropower operators and water utilities. In this Phase 1 DOE SBIR proposal, we developed and validated a theory-guided neural network model, HydroForecast Long-term, for climate-scale hydrology and implemented the model within existing HydroForecast infrastructure. HydroForecast Long-term combines the most accurate streamflow modeling system with a flexible and scalable data architecture to generate water supply projections out to the year 2100. This report illustrates that we have achieved our four objectives: 1) create a prototype of HydroForecast Long-term, building the neural network prediction model, 2) build an automated data input pipeline that processes large amounts of data from the latest global temperature and precipitation climate models; 3) benchmark the accuracy of the hydrologic model over the recent two decades over a large set of diverse basins, and 4) create a set of output visuals and summary metrics informed by customer feedback that connect the data to critical decision points. This work empowers water users to make data-informed decisions supporting a resilient, renewable-powered grid and water system. The results advance the Department of Energy’s mission by addressing critical gaps in water supply planning under climate change.

13 HYDRO ENERGY↗
NASA NTRS2001

Cryogenic Propellant Long-term Storage With Zero Boil-off

Significant boil-off losses of cryogenic propellant storage systems in long-duration space mission applications result in additional propellant and large tanks. The zero boil-off (ZBO) concept consists of an active cryo-cooling system integrated with traditional passive thermal insulation. The potential mass reductions with the ZBO concept are Substantial; therefore, further exploration through technology programs has been initiated within NASA. A large-scale demonstration of the ZBO concept has been devised utilizing the Marshall Space Flight Center (MSFC) Multipurpose Hydrogen Test Bed (MHTB) along with a cryo-cooler unit. The cryo-cooler with the MHTB and spraybar recirculation/mixer system in a manner that enables thermal energy removal at a rate that equals the total tank heat leak. The liquid hydrogen is withdrawn from the tank, passed through a heat exchanger, and then the chilled liquid is sprayed back into the tank through a spraybar. The test series will be performed over a 30-40 day period. Tests will be conducted at multiple fill levels and various mixer operational cycles to demonstrate concept viability and to provide benchmark data to be used in analytical model development. In this paper. analytical models for heat flows through the MHTB tank, cryo-cooler performance. and spraybar performance will be presented.

Hedayat, A.↗
DOE OSTI2025

Chemically and mechanically recyclable polyester-based multilayer plastics

Approximately 100 million tons of multilayered plastics (MLPs) are produced each year worldwide but are not recycled due to their complex structure. Here, this work aims to design polyester-based multilayer plastics (80–100 % polyester) that provide barrier performance comparable to typical 9–12-layer commercial MLPs, while also enabling both chemical recycling (back to parent monomer) and mechanical recycling (grind-and-melt reprocessing). Such dual recyclability is not achievable with non-polyester multilayers, such as all-polyolefin systems. Furthermore, we emphasize how the multilayer architecture was tailored to balance barrier properties, mechanical integrity, and end-of-life recyclability for both flexible and rigid packaging applications. Two main categories of polyester-based MLPs are reported; in the first type, poly(butylene adipate-co-terephthalate) (PBAT)-70 % polyglycolic acid (PGA) is used as middle barrier layer, while in second type, middle barrier layer is Ethylene-vinyl alcohol (EVOH) copolymers. Polyethylene terephthalate (PET) was used as a structural layer, while either PBAT or poly(butylene succinate) (PBS) was used to enable thermal sealing and serve as the product contact layer. These MLPs are recycled by both chemical and mechanical recycling processes. Techno-economic analysis (TEA) shows that MLPs incorporating EVOH as barrier layer have similar or lower selling costs (0.32 $\$$/m 2 ) than commercial MLPs. Life cycle assessment (LCA) indicates EVOH-based MLPs have a lower carbon footprint and lower energy consumption relative to commercial MLP benchmarks. This work offers simplified MLPs that are easy to manufacture and ready to recycle, which will significantly reduce environmental impact of MLP packaging while also providing a cost-effective and practical solution for industry.

Barrier properties↗
DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Construction of Polarimetric Radar-Based Reference Rain Maps for the Iowa Flood Studies Campaign

The Global Precipitation Measurement (GPM) Mission Iowa Flood Studies (IFloodS) campaign was conducted in central and northeastern Iowa during the months of April-June, 2013. Specific science objectives for IFloodS included quantification of uncertainties in satellite and ground-based estimates of precipitation, 4-D characterization of precipitation physical processes and associated parameters (e.g., size distributions, water contents, types, structure etc.), assessment of the impact of precipitation estimation uncertainty and physical processes on hydrologic predictive skill, and refinement of field observations and data analysis approaches as they pertain to future GPM integrated hydrologic validation and related field studies. In addition to field campaign archival of raw and processed satellite data (including precipitation products), key ground-based platforms such as the NASA NPOL S-band and D3R Ka/Ku-band dual-polarimetric radars, University of Iowa X-band dual-polarimetric radars, a large network of paired rain gauge platforms, and a large network of 2D Video and Parsivel disdrometers were deployed. In something of a canonical approach, the radar (NPOL in particular), gauge and disdrometer observational assets were deployed to create a consistent high-quality distributed (time and space sampling) radar-based ground "reference" rainfall dataset, with known uncertainties, that could be used for assessing the satellite-based precipitation products at a range of space/time scales. Subsequently, the impact of uncertainties in the satellite products could be evaluated relative to the ground-benchmark in coupled weather, land-surface and distributed hydrologic modeling frameworks as related to flood prediction. Relative to establishing the ground-based "benchmark", numerous avenues were pursued in the making and verification of IFloodS "reference" dual-polarimetric radar-based rain maps, and this study documents the process and results as they pertain specifically to efforts using the NPOL radar dataset. The initial portions of the "process" involved dual-polarimetric quality control procedures which employed standard phase and correlation-based approaches to removal of clutter and non-meteorological echo. Calculation of a scale-adaptive KDP was accomplished using the method of Wang and Chandrasekar (2009; J. Atmos. Oceanic Tech.). A dual-polarimetric blockage algorithm based on Lang et al. (2009; J. Atmos. Oceanic Tech.) was then implemented to correct radar reflectivity and differential reflectivity at low elevation angles. Next, hydrometeor identification algorithms were run to identify liquid and ice hydrometeors. After the quality control and data preparation steps were completed several different dual-polarimetric rain estimation algorithms were employed to estimate rainfall rates using rainfall scans collected approximately every two to three minutes throughout the campaign. These algorithms included a polarimetrically-tuned Z-R algorithm that adjusts for drop oscillations (via Bringi et al., 2004, J. Atmos. Oceanic Tech.), and several different hybrid polarimetric variable approaches, including one that made use of parameters tuned to IFloodS 2D Video Disdrometer measurements. Finally, a hybrid scan algorithm was designed to merge the rain rate estimates from multiple low level elevation angle scans (where blockages could not be appropriately corrected) in order to create individual low-level rain maps. Individual rain maps at each time step were subsequently accumulated over multiple time scales for comparison to gauge network data. The comparison results and overall error character depended strongly on rain event type, polarimetric estimator applied, and range from the radar. We will present the outcome of these comparisons and their impact on constructing composited "reference" rainfall maps at select time and space scales.

Radar↗
DOE OSTI2023

PYK-SubstitutionOME: an integrated database containing allosteric coupling, ligand affinity and mutational, structural, pathological, bioinformatic and computational information about pyruvate kinase isozymes

Interpreting changes in patient genomes, understanding how viruses evolve and engineering novel protein function all depend on accurately predicting the functional outcomes that arise from amino acid substitutions. To that end, the development of first-generation prediction algorithms was guided by historic experimental datasets. However, these datasets were heavily biased toward substitutions at positions that have not changed much throughout evolution (i.e. conserved). Although newer datasets include substitutions at positions that span a range of evolutionary conservation scores, these data are largely derived from assays that agglomerate multiple aspects of function. To facilitate predictions from the foundational chemical properties of proteins, large substitution databases with biochemical characterizations of function are needed. We report here a database derived from mutational, biochemical, bioinformatic, structural, pathological and computational studies of a highly studied protein family—pyruvate kinase (PYK). A centerpiece of this database is the biochemical characterization—including quantitative evaluation of allosteric regulation—of the changes that accompany substitutions at positions that sample the full conservation range observed in the PYK family. We have used these data to facilitate critical advances in the foundational studies of allosteric regulation and protein evolution and as rigorous benchmarks for testing protein predictions. We trust that the collected dataset will be useful for the broader scientific community in the further development of prediction algorithms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

HumoNet: A Framework for Realistic Modeling and Simulation of Human Mobility Network

Understanding, analyzing, and predicting human mobility and dynamics are valuable to solving pressing problems, developing effective plans, and prescribing timely remedies. As a computational approach, realistic human mobility simulations allow us to understand, analyze, and predict complex systems, including human societies. Accurate simulations rely on (1) the model that captures interactions and behaviors of myriad entities in our society and (2) the mapping of model instances to real-world entities. Taking this into account, this paper introduces the Human Mobility Network simulation framework (HumoNet), an integrated patterns of life (POL) simulation framework that leverages real-world data layers including transportation networks, points of interest, populations, popularity, and human trajectories. HumoNet is a data informed model in which agents are equipped with activities, locomotion, and planning capabilities. To simulate realistic kinematic maneuvers of individuals in transportation networks, HumoNet harnesses a microscopic traffic simulator that provides interaction among vehicles and traffic objects. In this paper, we describe the framework, outline our methodologies, and discuss the data processing and challenges of each data layer. Through experiments, we demonstrate that our simulations capture key features of human mobility by comparing them to the literature and real data using standard measures of human mobility (i.e., the radius of gyration, number of locations visited, level of exploration) and metrics scoring (i.e., Jensen-Shannon divergence). We envision that the synthetic data produced by HumoNet will serve as a benchmark for analyzing epidemics, deploying EV charging networks, and validating AI/ML tasks such as location prediction.

Kim, Joon-Seok↗
DOE OSTI2013

Extension of PENELOPE to protons: Simulation of nuclear reactions and benchmark with Geant4

Purpose: Describing the implementation of nuclear reactions in the extension of the Monte Carlo code (MC) PENELOPE to protons (PENH) and benchmarking with Geant4.Methods: PENH is based on mixed-simulation mechanics for both elastic and inelastic electromagnetic collisions (EM). The adopted differential cross sections for EM elastic collisions are calculated using the eikonal approximation with the Dirac–Hartree–Fock–Slater atomic potential. Cross sections for EM inelastic collisions are computed within the relativistic Born approximation, using the Sternheimer–Liljequist model of the generalized oscillator strength. Nuclear elastic and inelastic collisions were simulated using explicitly the scattering analysis interactive dialin database for {sup 1}H and ICRU 63 data for {sup 12}C, {sup 14}N, {sup 16}O, {sup 31}P, and {sup 40}Ca. Secondary protons, alphas, and deuterons were all simulated as protons, with the energy adapted to ensure consistent range. Prompt gamma emission can also be simulated upon user request. Simulations were performed in a water phantom with nuclear interactions switched off or on and integral depth–dose distributions were compared. Binary-cascade and precompound models were used for Geant4. Initial energies of 100 and 250 MeV were considered. For cases with no nuclear interactions simulated, additional simulations in a water phantom with tight resolution (1 mm in all directions) were performed with FLUKA. Finally, integral depth–dose distributions for a 250 MeV energy were computed with Geant4 and PENH in a homogeneous phantom with, first, ICRU striated muscle and, second, ICRU compact bone.Results: For simulations with EM collisions only, integral depth–dose distributions were within 1%/1 mm for doses higher than 10% of the Bragg-peak dose. For central-axis depth–dose and lateral profiles in a phantom with tight resolution, there are significant deviations between Geant4 and PENH (up to 60%/1 cm for depth–dose distributions). The agreement is much better with FLUKA, with deviations within 3%/3 mm. When nuclear interactions were turned on, agreement (within 6% before the Bragg-peak) between PENH and Geant4 was consistent with uncertainties on nuclear models and cross sections, whatever the material simulated (water, muscle, or bone).Conclusions: A detailed and flexible description of nuclear reactions has been implemented in the PENH extension of PENELOPE to protons, which utilizes a mixed-simulation scheme for both elastic and inelastic EM collisions, analogous to the well-established algorithm for electrons/positrons. PENH is compatible with all current main programs that use PENELOPE as the MC engine. The nuclear model of PENH is realistic enough to give dose distributions in fair agreement with those computed by Geant4.

61 RADIATION PROTECTION AND DOSIMETRY↗