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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Cellular Responses to Mechanical Stress Selected Contribution: A Three-Dimensional Model for Assessment of in Vitro Toxicity in Balaena Mysticetus Renal Tissue

This study established two- and three-dimensional renal proximal tubular cell cultures of the endangered species bowhead whale (Balaena mysticetus), developed SV40-transfected cultures, and cloned the 61-amino acid open reading frame for the metallothionein protein, the primary binding site for heavy metal contamination in mammals. Microgravity research, modulations in mechanical culture conditions (modeled microgravity), and shear stress have spawned innovative approaches to understanding the dynamics of cellular interactions, gene expression, and differentiation in several cellular systems. These investigations have led to the creation of ex vivo tissue models capable of serving as physiological research analogs for three-dimensional cellular interactions. These models are enabling studies in immune function, tissue modeling for basic research, and neoplasia. Three-dimensional cellular models emulate aspects of in vivo cellular architecture and physiology and may facilitate environmental toxicological studies aimed at elucidating biological functions and responses at the cellular level. Marine mammals occupy a significant ecological niche (72% of the Earth's surface is water) in terms of the potential for information on bioaccumulation and transport of terrestrial and marine environmental toxins in high-order vertebrates. Few ex vivo models of marine mammal physiology exist in vitro to accomplish the aforementioned studies. Techniques developed in this investigation, based on previous tissue modeling successes, may serve to facilitate similar research in other marine mammals.

Goodwin, T. J.↗
NASA NTRS2010

A Biomathematical Model of Lymphopoiesis and Its Application to Acute and Chronic Irradiation Assessment

After the events of September 11, 2001, there is an increasing concern of the occurrence of radiological terrorism that may result in significant casualties in densely populated areas. Much effort has been made to establish various biomarkers to rapidly assess radiation dose in mass-casualty and population-monitoring scenarios, which are demanded for effective medical management and treatment of the exposed victims. Among these the count of lymphocytes in peripheral blood and their depletion kinetics are the most important early indicators of the severity of the radiation injury. In this study, we examine a biomathematical model of lymphopoiesis which has been successfully utilized to simulate and interpret experimental data of acute and chronic irradiations on rodents [1]. With revised parameters for humans, we find this model can reproduce several sets of clinical lymphocyte data of accident victims over a wide range of absorbed doses. In addition, the absolute lymphocyte counts and the depletion rate constants calculated by this model also show good correlation with the Guskova formula and the Goans model, the two empirical tools which have been widely recognized for early estimation of the exposed dose after radiation accidents [2]. We also use the model to analyze the hematological data of the Techa River residents which were exposed to chronic low-dose irradiation during 1950-1956 [3]. This model can serve as a computational tool in radiation accident management, military operations involving nuclear warfare, radiation therapy, and space radiation risk assessment.

Hu, Shaowen↗
NASA NTRS2006

Protein Sensors Based on Optical Ring Resonators

Prototype transducers based on integrated optical ring resonators have been demonstrated to be useful for detecting the protein avidin in extremely dilute solutions. In an experiment, one of the transducers proved to be capable of indicating the presence of avidin at a concentration of as little as 300 pM in a buffer solution a detection sensitivity comparable to that achievable by previously reported protein-detection techniques. These transducers are serving as models for the further development of integrated-optics sensors for detecting small quantities of other proteins and protein-like substances. The basic principle of these transducers was described in Chemical Sensors Based on Optical Ring Resonators (NPO-40601), NASA Tech Briefs, Vol. 29, No. 10 (October 2005), page 32. The differences between the present transducers and the ones described in the cited prior article lie in details of implementation of the basic principle. As before, the resonator in a transducer of the present type is a closed-circuit dielectric optical waveguide. The outermost layer of this waveguide, analogous to the optical cladding layer on an optical fiber, consists of a layer comprising sublayers having indices of refraction lower than that of the waveguide core. The outermost sublayer absorbs the chemical of interest (in this case, avidin). The index of refraction of the outermost sublayer changes with the concentration of absorbed avidin. The resonator is designed to operate with relatively strong evanescent-wave coupling between the outer sublayer and the electromagnetic field propagating along the waveguide core. By virtue of this coupling, the chemically induced change in the index of refraction of the outermost sublayer causes a measurable change in the spectrum of the resonator output.

Lin, Ying↗
NASA NTRS2013

A Multi-Purpose Modular Electronics Integration Node for Exploration Extravehicular Activity

As NASA works to develop an effective integrated portable life support system design for exploration Extravehicular activity (EVA), alternatives to the current system s electrical power and control architecture are needed to support new requirements for flexibility, maintainability, reliability, and reduced mass and volume. Experience with the current Extravehicular Mobility Unit (EMU) has demonstrated that the current architecture, based in a central power supply, monitoring and control unit, with dedicated analog wiring harness connections to active components in the system has a significant impact on system packaging and seriously constrains design flexibility in adapting to component obsolescence and changing system needs over time. An alternative architecture based in the use of a digital data bus offers possible wiring harness and system power savings, but risks significant penalties in component complexity and cost. A hybrid architecture that relies on a set of electronic and power interface nodes serving functional models within the Portable Life Support System (PLSS) is proposed to minimize both packaging and component level penalties. A common interface node hardware design can further reduce penalties by reducing the nonrecurring development costs, making miniaturization more practical, maximizing opportunities for maturation and reliability growth, providing enhanced fault tolerance, and providing stable design interfaces for system components and a central control. Adaptation to varying specific module requirements can be achieved with modest changes in firmware code within the module. A preliminary design effort has developed a common set of hardware interface requirements and functional capabilities for such a node based on anticipated modules comprising an exploration PLSS, and a prototype node has been designed assembled, programmed, and tested. One instance of such a node has been adapted to support testing the swingbed carbon dioxide and humidity control element in NASA s advanced PLSS 2.0 test article. This paper will describe the common interface node design concept, results of the prototype development and test effort, and plans for use in NASA PLSS 2.0 integrated tests.

Hodgson, Edward↗
NASA NTRS2012

Visible Light Responsive Catalysts Using Quantum Dot-Modified Ti02 for Air and Water Purification

The method of photocatalysis utilizing titanium dioxide, TiO2, as the catalyst has been widely studied for trace contaminant control for both air and water applications because of its low energy consumption and use of a regenerable catalyst. Titanium dioxide requires ultraviolet light for activation due to its band gap energy of 3.2 eV. Traditionally, Hg-vapor fluorescent light sources are used in PCO reactors and are a setback for the technology for space application due to the possibility of Hg contamination. The development of a visible light responsive (VLR) TiO2-based catalyst could lead to the use of solar energy in the visible region (approx.45% of the solar spectrum lies in the visible region; > 400 nm) or highly efficient LEDs (with wavelengths > 400 nm) to make PCO approaches more efficient, economical, and safe. Though VLR catalyst development has been an active area of research for the past two decades, there are few commercially available VLR catalysts; those that are available still have poor activity in the visible region compared to that in the UV region. Thus, this study was aimed at the further development of VLR catalysts by a new method - coupling of quantum dots (QD) of a narrow band gap semiconductor (e.g., CdS, CdSe, PbS, ZnSe, etc.) to the TiO2 by two preparation methods: 1) photodeposition and 2) mechanical alloying using a high-speed ball mill. A library of catalysts was developed and screened for gas and aqueous phase applications, using ethanol and 4-chlorophenol as the target contaminants, respectively. Both target compounds are well studied in photocatalytic systems serve as model contaminants for this research. Synthesized catalysts were compared in terms of preparation method, type of quantum dots, and dosage of quantum dots.

Coutts, Janelle L.↗
NASA NTRS2014

Integrated Arrival and Departure Schedule Optimization Under Uncertainty

In terminal airspace, integrating arrivals and departures with shared waypoints provides the potential of improving operational efficiency by allowing direct routes when possible. Incorporating stochastic evaluation as a post-analysis process of deterministic optimization, and imposing a safety buffer in deterministic optimization, are two ways to learn and alleviate the impact of uncertainty and to avoid unexpected outcomes. This work presents a third and direct way to take uncertainty into consideration during the optimization. The impact of uncertainty was incorporated into cost evaluations when searching for the optimal solutions. The controller intervention count was computed using a heuristic model and served as another stochastic cost besides total delay. Costs under uncertainty were evaluated using Monte Carlo simulations. The Pareto fronts that contain a set of solutions were identified and the trade-off between delays and controller intervention count was shown. Solutions that shared similar delays but had different intervention counts were investigated. The results showed that optimization under uncertainty could identify compromise solutions on Pareto fonts, which is better than deterministic optimization with extra safety buffers. It helps decision-makers reduce controller intervention while achieving low delays.

stochastic optimization↗
NASA NTRS2014

Visible-Light-Responsive Catalysts Using Quantum Dot-Modified TiO2 for Air and Water Purification

Photocatalysis, the oxidation or reduction of contaminants by light-activated catalysts, utilizing titanium dioxide (TiO2) as the catalytic substrate has been widely studied for trace contaminant control in both air and water applications. The interest in this process is due primarily to its low energy consumption and capacity for catalyst regeneration. Titanium dioxide requires ultraviolet light for activation due to its relatively large band gap energy of 3.2 eV. Traditionally, Hg-vapor fluorescent light sources are used in PCO reactors; however, the use of mercury precludes the use of this PCO technology in a spaceflight environment due to concerns over crew Hg exposure. The development of a visible-light responsive (VLR) TiO2-based catalyst would eliminate the concerns over mercury contamination. Further, VLR development would allow for the use of ambient visible solar radiation or highly efficient LEDs, both of which would make PCO approaches more efficient, flexible, economical, and safe. Though VLR catalyst development has been an active area of research for the past two decades, there are few commercially available VLR catalysts. Those VLR catalysts that are commercially available do not have adequate catalytic activity, in the visible region, to make them competitive with those operating under UV irradiation. This study was initiated to develop more effective VLR catalysts through a novel method in which quantum dots (QD) consisting of narrow band gap semiconductors (e.g., CdS, CdSe, PbS, ZnSe, etc.) are coupled to TiO2 via two preparation methods: 1) photodeposition and 2) mechanical alloying using a high-speed ball mill. A library of catalysts was developed and screened for gas and aqueous phase applications using ethanol and 4-chlorophenol as the target contaminants, respectively. Both target compounds are well studied in photocatalytic systems and served as model contaminants for this research. Synthesized catalysts were compared in terms of preparation method, nature of the quantum dots, and dosage of quantum dots.

visible light↗
NASA NTRS2023

A Hyperspectral Inversion Framework for Estimating Absorbing Inherent Optical Properties and Biogeochemical Parameters in Inland and Coastal Waters

The simultaneous remote estimation of biogeochemical parameters (BPs) and inherent optical properties (IOPs) from hyperspectral satellite imagery of globally distributed optically distinct inland and coastal waters is a complex, unsolved, non-unique inverse problem. To tackle this problem, we leverage a machine-learning model termed Mixture Density Networks (MDNs). MDNs outperform operational algorithms by calculating the covariance between the simultaneously estimated products. We train the MDNs on a large ( N = 8237) dataset of co-aligned, in situ measured, hyperspectral remote sensing reflectance (R rs ), BPs, and absorbing IOPs from globally representative optically distinct inland and coastal waters. The estimated IOPs include absorption due to phytoplankton (a ph ), chromophoric dissolved organic matter (a cdom ), and non-algal particles (a nap ). The estimated BPs include chlorophyll-a, total suspended solids, and phycocyanin (PC). MDNs dramatically reduce uncertainty in the retrievals, relative to operational algorithms, when using a 50/50 dataset split, where the MDNs are trained on a randomly selected half of the in situ dataset and validated on the other half. Our model is shown to have higher, or equivalent, generalization performance than the calculated operational algorithms available for all BPs and IOPs (except PC) via a leave-one-out cross-validation assessment. The MDNs are sensitive to uncertainties in the hyperspectral satellite R rs , resulting from instrument noise and atmospheric correction; there is a difference of ~37.4–62.8% (using median symmetric accuracy) between the MDNs’ estimates derived from co-located satellite-derived R rs and in situ R rs . Of the IOPs, a cdom and a nap are less sensitive to uncertainties in hyperspectral satellite imagery relative to a ph , with remote estimates of a ph exhibiting incorrect spectral shape and magnitude relative to in situ measured IOPs. Despite the uncertainties in satellite derived R rs , the spatial distributions of BPs and IOPs in MDN-derived product maps of Lake Erie and the Curonian Lagoon, based on imagery taken with the Hyperspectral Imager for the Coastal Ocean (HICO) and PRecursore Iper-Spettrale della Missione Applicativa (PRISMA), are confirmed via co-aligned in situ measurements and agree with the literature’s understanding of these well-studied regions. The consistency and accuracy of the model on HICO and PRISMA imagery, despite radiometric uncertainties, demonstrate its applicability to future hyperspectral missions, such as the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, where the simultaneous estimation model will serve as a key part of phytoplankton community composition analysis.

Ryan E. O'Shea↗
NASA NTRS1991

Ionospheric ion composition from satellite measurements made during 1970-1980 - Altitude profiles

Ion-mass spectrometers were carried by a number of satellites in the 1970s. The ion-composition measurements from two of these missions, the Orbiting Geophysical Observatory-6 and the Atmosphere Explorer-C, are collected into an ion composition data base to evaluate several widely used empirical and theoretical models for the species H(+), He(+), N(+), O(+), NO(+), N2(+), and O2(+). The data base covers all latitudes and local times, and the altitude range from 150 km to 1200 km, but here altitude plots are presented of the ion densities at noon and at dip latitudes of 20-40 deg N. The satellite data are compared with an early ion-density profile, with the Koehnlein and IRI-90 empirical models, and with the Utah State University theoretical ionosphere model. These comparisons serve to verify some aspects of the models, but they also reveal some outstanding differences. The solar activity dependence of H(+), He(+), N(+), and O(+) is demonstrated, although this has not been possible for the molecular ions because low altitude measurements have not been made near solar maximum.

Hoegy, W. R.↗
DOE OSTI2022

A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion

Abstract. Rivers and river habitats around the world are under sustained pressure from human activities and the changing global environment. Our ability to quantify and manage the river states in a timely manner is critical for protecting the public safety and natural resources. In recent years, vector-based river network models have enabled modeling of large river basins at increasingly fine resolutions, but are computationally demanding. This work presents a multistage, physics-guided, graph neural network (GNN) approach for basin-scale river network learning and streamflow forecasting. During training, we train a GNN model to approximate outputs of a high-resolution vector-based river network model; we then fine-tune the pretrained GNN model with streamflow observations. We further apply a graph-based, data-fusion step to correct prediction biases. The GNN-based framework is first demonstrated over a snow-dominated watershed in the western United States. A series of experiments are performed to test different training and imputation strategies. Results show that the trained GNN model can effectively serve as a surrogate of the process-based model with high accuracy, with median Kling–Gupta efficiency (KGE) greater than 0.97. Application of the graph-based data fusion further reduces mismatch between the GNN model and observations, with as much as 50 % KGE improvement over some cross-validation gages. To improve scalability, a graph-coarsening procedure is introduced and is demonstrated over a much larger basin. Results show that graph coarsening achieves comparable prediction skills at only a fraction of training cost, thus providing important insights into the degree of physical realism needed for developing large-scale GNN-based river network models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Transient Weighted Moving-Average Model of Photovoltaic Module Back-Surface Temperature

Accurate modeling of photovoltaic (PV) performance requires the precise calculation of module temperature. Currently, most temperature models rely on steady-state assumptions that do not account for the transient climatic conditions and thermal mass of the module. On the other hand, complex physics-based transient models are computationally expensive and difficult to parameterize. In order to address this, a new approach to transient thermal modeling was developed, in which the steady-state predictions from previous timesteps are weighted and averaged to accurately predict the module temperature at finer time scales. This model is informed by 3-D finite-element analyses, which are used to calculate the effect of wind speed and module unit mass on module temperature. The model, in application, serves as an added filter over existing steady-state models that smooths out erroneous values that are a result of intermittency in solar resource. Validation of this moving-average model has shown that it can improve the overall PV energy performance model accuracy by as much as 0.58% over steady-state models based on mean absolute error improvements and can significantly reduce the variability between the model predictions and measured temperature times series data.

14 SOLAR ENERGY↗
NASA NTRS2012

Inspiring the Next Generation of Explorers: Scientist Involvement in the Expedition Earth and Beyond Program

Scientists, science experts, graduate and even undergraduate student researchers have a unique ability to inspire the next generation of explorers. These science, technology, engineering, and mathematics (STEM) experts can serve as role models for students and can help inspire them to consider future STEM-related careers. They have an exceptional ability to instill a sense of curiosity and fascination in the minds of students as they bring science to life in the classroom. Students and teachers are hungry for opportunities to interact with scientists. They feel honored when these experts take time out of their busy day to share their science, their expertise, and their stories. The key for teachers is to be cognizant of opportunities to connect their students with scientists. For scientists, the key is to know how to get involved, to have options for participation that involve different levels of commitment, and to work with educational specialists who can help facilitate their involvement. The Expedition Earth and Beyond (EEAB) Program, facilitated by the Astromaterials Research and Exploration Science (ARES) Directorate at the NASA Johnson Space Center, is an Earth and planetary science education program designed to inspire, engage, and educate teachers and students by getting them actively involved with NASA exploration, discovery, and the process of science. One of the main goals of the program is to facilitate student research in the classroom. The program uses astronaut photographs, provided through the ARES Crew Earth Observations (CEO) payload on the International Space Station (ISS) as the hook to help students gain an interest in a research topic. Student investigations can focus on Earth or involve comparative planetology. Student teams are encouraged to use additional imagery and data from Earth or planetary orbital spacecraft, or ground-based data collection tools, to augment the astronaut photography dataset. A second goal of the program is to provide opportunities for meaningful connections between scientists and classrooms. To do this, EEAB offers multiple opportunities for scientist involvement. One opportunity involves having scientists work as mentors for student teams conducting research. These student teams, ranging from grades 4 through 12, are able to obtain guidance, suggestions, and input from STEM experts as they conduct a research investigation. Another opportunity for scientist involvement is participation in Classroom Connection Distance Learning (DL) events. These DL events entail interactive and engaging presentations that enable STEM experts to share their expertise with students and teachers (grades 3 through 12) from all across the nation. A third opportunity for scientist involvement involves participation in virtual student team science presentations. Student teams have the opportunity to share their research and results by presenting it to science experts through the use of WebEx, an easy-to-use online conferencing tool. The impact STEM experts have on students in today s classrooms is powerful. They serve as role models to these students, and they open students eyes to a potential career path they may not have known existed otherwise. The more scientists and STEM experts we can connect with students, the greater the impact we can make as we strive to inspire and prepare our nation s next generation of explorers.

Graff, Paige↗
DOE OSTI2025

Multiphysics Modeling of Microreactors with NEAMS codes, and Validation Based on KRUSTY Reactivity Insertion

The NEAMS Multiphysics Applications team continues to assess code usability and functionality for microreactor design and safety analyses, while demonstrating that NEAMS tools capture both steady-state and transient behavior across distinct microreactor concepts. In FY2025, the team advanced full-core, high-fidelity, multiphysics models that solve more complex problems and strengthen verification/validation for several microreactor systems: heat-pipe microreactor (HPMR), gas-cooled microreactor (GCMR), and the KRUSTY experiment. These models employ the MOOSE MultiApp/Transfers architecture with Griffin for neutronics, BISON for heat conduction/thermomechanics, Sockeye for heat pipes, SAM/THM for coolant channels and loops, and SWIFT for hydride behavior, with meshes generated via the MOOSE Reactor Module. The graphite models available in the Grizzly code were also investigated for future analyses. For the HPMR, a Na-HPMR variant was constructed to align with recently validated heat-pipe experiments and Sockeye’s LCVF capability, enabling mechanistic heat-pipe transients and startup modeling. The Na-HPMR will serve as the primary model for HPMR investigations in upcoming tasks. The load-following and single heat-pipe failure scenarios (Griffin/BISON/Sockeye), which were previously modeled for the K-HPMR, were replicated for the Na-HPMR, showing strong negative temperature feedback and highly localized thermal effects, respectively, while the startup case captured vapor-front progression and heat-removal activation. Solid mechanics was added to the previously built K-HPMR full-core model in BISON, showing minimal impact on steady-state reactivity yet enabling stress-field predictions that prepare the path for full-core TRISO performance analyses. For the GCMR, automated steady-state and four transient scenarios were executed using Griffin/BISON/SAM/SWIFT. Results confirm robust inherent safety: power collapses promptly in loss-of-cooling events, the inlet-temperature drop settles to a new equilibrium, and a single-channel blockage yields only a ~30 K local fuel-temperature rise with <0.4% power decrease. SWIFT-predicted hydrogen redistribution affects reactivity during both steady-state and transient conditions, underscoring its importance. A Brayton-cycle balance of plant (BOP) model in SAM/THM demonstrated stable startup behavior, and xenon-driven reactivity during load following was analyzed. To improve TRISO-compact temperature fidelity, a fast multiscale Heat Source Decomposition (HSD) treatment was implemented. Against heterogeneous benchmarks, HSD reduces underprediction of kernel temperatures and lowers predicted peak powers in reactivity-insertion transients compared to previous homogenized models. KRUSTY warm-critical validation progressed from FY2024 baselines: the 15Ȼ insertion shows excellent agreement in peak power (~2% high) and temperature trends, and the 30Ȼ case was automated via a feedback controller that maintained power near 3 kW for ~150 s with close agreement to data. The successful modeling of the warm critical tests has laid a strong foundation for simulating more complex nuclear system tests in the years ahead. Throughout FY2025, developer feedback was provided (e.g., MOOSE batch mesh generation, distributed pre-split meshes, Griffin sweeper on displaced meshes), several new models were contributed to the Virtual Test Bed, and an OECD-NEA WPRS multiphysics benchmark based on the HPMR was initiated to enable broader cross-comparison and best-practice development with the nuclear community at large.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1992

Creative-Dynamics Approach To Neural Intelligence

Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

Zak, Michail A.↗
NASA NTRS2011

Results From a Pressure Sensitive Paint Test Conducted at the National Transonic Facility on Test 197: The Common Research Model

This report will serve to present results of a test of the pressure sensitive paint (PSP) technique on the Common Research Model (CRM). This test was conducted at the National Transonic Facility (NTF) at NASA Langley Research Center. PSP data was collected on several surfaces with the tunnel operating in both cryogenic mode and standard air mode. This report will also outline lessons learned from the test as well as possible approaches to challenges faced in the test that can be applied to later entries.

Watkins, A. Neal↗
NASA NTRS2017

UAV Trajectory Modeling Using Neural Networks

Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural network should be able to predict the vehicle's future states at next time step. A complete 4-D trajectory are then generated step by step using the trained neural network. Experiments in this work show that the neural network can approximate the sUAV's model and predict the trajectory accurately.

Neural Networks↗
DOE OSTI2025

Advanced Computational Modeling of High-Level Waste Vitrification at the Hanford Site

The U.S. Department of Energy (DOE) has selected vitrification for stabilizing legacy tank waste at the Hanford site, where radioactive waste from plutonium production was historically stored in underground tanks. This waste will be separated into low-activity waste (LAW) and high-level waste (HLW) fractions and processed at the Waste Treatment and Immobilization Plant (WTP). At WTP, glass melters are used for the vitrification of radioactive tank waste, transforming it into a stable borosilicate glass form for safe long-term storage. The melter vessel is constructed from highly durable and heat-resistant materials, where the vitrification process occurs. The main regions that are modeled are the melt pool, plenum, cold cap, riser/discharge chamber, and surrounding structure with insulation layers. Forced convection induced by air bubblers at the base of the melter ensure uniform temperature distribution and provide heat to the cold cap layer. The cold cap is a region of reacting batch feed that floats on top of the molten glass and is where the batch-to-glass reactions occur. Joule heating provided by electrodes mounted along the vertical walls of the melter and immersed directly in the glass, generates the necessary heat for the net endothermic conversion processes that occur in the cold cap. The high temperatures, radioactivity, and opaque nature of the glass prevent direct observation inside the melters. Therefore, computational models are essential for providing insight into factors that affect melter throughput. Thermocouples in the plenum provide operators with plenum temperature measurements. Operational adjustments include bubbling rate, voltage supplied to the electrodes, feed adjustments, and glass removal rate. Different computational fluid dynamics (CFD) models have been developed, each serving a specific purpose. There are CFD models of different scale melters, as well as models that capture the two-phase flow interfaces of rising bubbles in the molten glass or models with a simplified molten glass region so that the surrounding structure and plenum can be feasibly incorporated. Pilot-scale melter models have been developed to serve as validation of the methods employed in the simulation of the full-scale WTP melters. Models incorporating resolved bubbling are used to develop momentum source terms to implement into a single phase, multi-region, steady-state flow model that is being validated by measured process parameters such as glass production rate, voltage, input power, plenum temperatures, etc. The resolved bubbling model uses the multiphase volume of fluid approach to model the system with a high-resolution interface capturing scheme to maintain sharp interfaces between the molten glass and the air phase. The suite of CFD models is continually being improved to incorporate more realistic physics and achieve faster turnaround time. For example, an incremental controller is implemented to automatically adjust electrode voltage within the simulation to a molten glass set point temperature of 1150°C. Newer models feature improved meshes to ensure conformal meshes between regions and eliminate unnecessary mesh refinement in areas that are not of interest (such as boundary layers in offgas ports). Instead of explicitly modeling the structural, refractory, and insulation layers of the melter, a thermal resistance approach is used with published correlations used for boundary conditions. The development of robust and efficient CFD models will be instrumental in enabling the WTP to successfully fulfill its mission of safely stabilizing legacy nuclear waste.

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗
DOE OSTI2024

Benchmarking the exponential ansatz for the Holstein model

Polarons are quasiparticles formed as a result of lattice distortions induced by charge carriers. The single-electron Holstein model captures the fundamentals of single polaron physics. We examine the power of the exponential ansatz for the polaron ground-state wavefunction in its coupled cluster, canonical transformation, and (canonically transformed) perturbative variants across the parameter space of the Holstein model. Our benchmark serves to guide future developments of polaron wavefunctions beyond the single-electron Holstein model.

Chemistry↗