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NASA NTRS2023

Plasma Activated Water: A Technology for Acid Generation and Space Crop Production

As humanity returns to the moon and onward to Mars, sustainable space travel becomes essential for cost and independence from Earth. Astronaut activities in low Earth orbit resupply consumable materials and can require alternative technologies if transportation of chemicals is deemed hazardous. Plasma technology and applications are able to generate commodities that assist in achieving Earth independence. Plasma interaction with water generates acid in-situ using on-board resources of electricity and breathable air without the need for large infrastructure. At Kennedy Space Center, researchers have shown that plasma is able to produce nitric acid, and therefore nitrates, in a plant-usable form in water. In this report, the team presents results on acid generation in g/kW-hr as well as provides results on plasma activated water for plant growth assistance. Plasma was generated utilizing DC, AC, and pulsed power supplies in order to determine optimizing parameters for generation of this useful commodity.

Plasma↗

NASA NTRSDate not supplied

Plasma Activated Water: A Technology for Acid Generation and Space Crop Production

Plasma↗

NASA NTRSDate not supplied

Airfoil Table Generation and Validation for the VR-12 and SSC-A09 Airfoils and Quadrotor Performance Prediction

As part of NASA’s Urban Air Mobility (UAM) mission, the Revolutionary Vertical Lift Technology (RVLT) project is creating rotorcraft reference designs that can be used by the rotorcraft community to develop quiet, efficient, and safer air vehicles. At NASA, this effort is made possible by the use of the RVLT toolchain software. Airfoil tables are often a critical part of the conceptual design process as they inform both comprehensive analysis and Computational Fluid Dynamics (CFD) codes of 2D aerodynamic coefficients, such as lift, drag, and moment coefficients. These airfoil tables, typically generated during experimental testing, are often proprietary and thus not widely distributable; further, there is not always clear or available reference documentation that provides information on the test conditions. The airfoil tables from these experiments are referred to in this work as Legacy airfoil tables. The scope of this study is to generate airfoil tables with the NASA OVERset grid CFD FLOW solver (OVERFLOW) coupled with the AirFoil Table Generator (AFTGen) software and validate these airfoil tables based on performance predictions calculated in Comprehensive Hierarchical Aeromechanics Rotorcraft Model (CHARM). The overall goal is to provide CFD generated airfoil tables, validate them through comprehensive analysis, and publish the airfoil tables for public distribution. The RVLT quadrotor reference model was selected as the basis for all analyses. The quadrotor uses the VR-12 and SSC-A09 airfoils for its rotor blades. Simulations were run in OVERFLOW for the VR-12 and SSC-A09 airfoils for ±20° angles of attack, Mach numbers from 0.3 to 1.0, and a Reynolds-Mach proportionality constant of 1.023x107. The C81 format airfoil tables from AFTGen were then blended with Legacy NACA 0012 airfoil table data for ±20° to ±180° angle of attack range. CHARM was used to compute thrust coefficient, power coefficient, and figure of merit for an isolated rotor in hover using proprietary legacy airfoil tables for the VR-12 and SSC-A09, as well as with the OVERFLOW generated airfoil tables for the VR-12 and SSC-A09. Overall, the OVERFLOW and Legacy performance predictions agreed well, with the best correlation between simulated and Legacy data observed with the VR-12 airfoil tables.

Airfoil↗

NASA NTRSDate not supplied

Simulated Multipath Using Software Generated GPS Signals

Depending on the environment, multipath can be one of the largest error sources contributing to degradation in Global Navigation Satellite System (GNSS) (e.g., GPS) performance. Multipath is a phenomenon that occurs as radio signals reflect off of surfaces, such as buildings, producing multiple copies of the original signal. When this occurs with GPS signals, it results in one or more delayed signals arriving at the receiver with or without the on-time/direct GPS signal. The receiver measures the composite of these signals which, depending on the severity of the multipath, can substantially degrade the accuracy of the receiver's calculated position. Multipath is commonly experienced in cities due to tall buildings and its mitigation is an ongoing area of study. This research demonstrates a novel approach for simulating GPS multipath through the modification of an open-source tool, GPS-SDR-SIM. The resulting additional testing capability could allow for improved development of multipath mitigating technologies. Currently, open-source tools for simulating GPS signals are available and can be used in the testing and evaluation of GPS receiver equipment. These tools can generate GPS signals that, when used by a GPS receiver, result in computation of a position solution that was pre-determined at the time of signal generation. That is, the signals produced are properly formed for the pre-determined location and result in the receiver reporting that position. This allows for a GPS receiver under test to be exposed to various simulated locations and conditions without having to be physically subjected to them. Additionally, while these signals are generated by a software simulation, they can be processed by real or software defined GPS receivers. This work utilizes the GPS-SDR-SIM software tool for GPS signal generation and while this tool does implement some sources of error that are inherent to GPS, it cannot inject multipath. GPS-SDR-SIM was modified in this effort to produce additional copies of signals with pre-determined delays. These additional delayed signals mimic multipath and represent what happens to GPS signals in the real world as they reflect off of surfaces and arrive at a receiver in place of or alongside the direct GPS signal. A successful proof of concept was prototyped and demonstrated using this modified version of GPS-SDR-SIM to produce simulated GPS signals as well as additional simulated multipath signals. The generated data was processed using a software defined GPS receiver and it was found that the introduction of simulated multipath signals successfully produced the expected characteristics of a composite multipath signal. Further maturation of this work could allow for the development of a GPS receiver testing and evaluation framework and aid in the development of multipath mitigating technologies.

GPS↗

DOE OSTI2025

Evaluating the Impact of Managed EV Charging for Reliable Operation of Bulk Power Systems with High Non-Dispatchable Generation

The growth of electric vehicles (EVs) and variable-generation (VG) sources introduces new challenges for power-system operations. This study introduces a modeling framework and evaluates five EV charging strategies under projected 2040 grid conditions in the Evergy service territory with high non-dispatchable generation. Using realistic EV behavior and generation models, their impacts on system peak demand, ramp rate, and reserve capacity are evaluated. Results show that only the peak-avoidance strategy effectively reduces system peak demand, while decentralized strategies-particularly cost based dynamic charging-can exacerbate peaks due to synchronized user behavior. However, ramp-rate minimization strategy significantly reduce the stress on dispatchable generation achieving the lowest maximum absolute ramp rate (MARR) (56.81 MW) and lowest reserve requirement (2.39 GW). In contrast, unmanaged and TOU random strategies increase the stress on dispatchable generation sources with increased MARR and reserve requirements. These findings highlight the importance of coordinated, system-aware managed charging strategies to ensure reliable and affordable grid operation in the presence of EVs and VG sources.

14 - SOLAR ENERGY↗

DOE OSTI2026

Project Development of an Electrochemical Denitration and Caustic Generation System for HLW Pretreatment at Hanford - 26350

An engineering-scale electrochemical processing skid is proposed to perform the denitration of Hanford tank waste, which would help to mitigate a key process concern with the direct feed processing of the Hanford Tank Waste Treatment and Immobilization Plant (WTP). The reduction of nitrates and organic compounds in the waste feed will directly reduce hazardous NOx and ammonia gases generated during the vitrification process, which in turn will aid in addressing potential regulatory and safety challenges associated with processing large volumes of tank waste. This paper highlights the past legacy work, project layout, accomplishments from Phase 1 and research and development envisioned for Phase 2. An innovative electrochemical denitration and caustic generation (EDCGe) process was demonstrated for the pretreatment of tank waste at the Savannah River Site (SRS) in the early 2000s. The denitration electrolyzer, off-gas abatement system, and caustic generator electrolyzer are being developed with the intent that the denitration electrolyzer will convert nitrate and nitrite anions to nitrogen gas while also yielding other gaseous byproducts, which may include N2O, NH3, VOCs, and H2. The gaseous byproducts will be managed via a tandem off-gas catalyst-bed treatment system. The caustic generation electrolyzer will recycle NaOH from the feed to produce a clean caustic stream for use within the batching tanks at Hanford, aiding in the preparation of waste for WTP. The reduction in hazardous emissions and improved waste treatment processes provides a robust solution for nuclear waste management, contributing to environmental safety and regulatory compliance. The EDCGe technology is being adapted, modified, and updated for the preparation of the Direct Feed-High Level Waste (DF-HLW) flowsheet at Hanford. Phase 1 demonstrated a bench-scale proof-of-concept for reactions involving the denitration electrolyzer and gas phase abatement of ammonia. The electrochemical technology is drawing on the scientific outcomes that were reported in the legacy work. The results from Phase 1 demonstrated the viability of the EDCGe system in reducing the nitrogen species of simple non-radioactive waste simulants. Commercially available alloys used as electrode materials and membranes are being studied for the denitration and caustic generation electrolyzers. The continuation of this project holds promise for broader applications, such as energy-efficient ammonia production, and contributes significant advancements in nuclear waste management. Additional material discovery has been investigated into ceramic Na super ion conductive (NaSICON) materials and off-gas abatement catalyst discovery. NaSICON is of interest for selective transport of Na within the electrolyzers to make a clean caustic stream. Future integration and optimization efforts, informed by Phase 1 results and ongoing research, will continue to drive advancements in nuclear waste management technology. The technology developed for the EDCGe treatment of tank waste will also have broader potential to inform other fields, such as energy-efficient ammonia production, as well as ammonia abatement catalysis through the lessons learned in electrochemical nitrate reduction. The applications and benefits of this research extend beyond Hanford and the Savannah River Site, supported by a collaborative team of scientists and engineers from national labs, academia, and industry, ensuring a comprehensive approach to solving complex waste treatment challenges. The team is leveraging advanced electrochemical technologies, machine learning, novel catalysts tailored for gaseous nitrogen species, and cutting-edge reactor systems to enhance the process efficiency and effectiveness of the denitration process.

Rodene, Dylan [Savannah River National Laboratory ↗

DOE OSTI2024

Laser pulse induced second- and third-harmonic generation of gold nanorods with real-time time-dependent density functional tight binding (RT-TDDFTB) method

In this paper, we investigate second- and third-harmonic generation processes in Au nanorod systems using the real-time time-dependent density functional tight binding method. Our study focuses on the computation of nonlinear signals based on the time dependent dipole response induced by linearly polarized laser pulses interacting with nanoparticles. We systematically explore the influence of various laser parameters, including pump intensity, duration, frequency, and polarization directions, on harmonic generation. We demonstrate all the results using Au nanorod dimer systems arranged in end-to-end configurations, and disrupting the spatial symmetry of regular single nanorod systems is crucial for second-harmonic generation processes. Furthermore, we study the impact of nanorod lengths, which lead to variable plasmon energies, on harmonic generation, and estimates of polarizabilities and hyper-polarizabilities are provided.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

Designing Future Energy Systems with Generative AI

Energy systems are experiencing various changes that impact the distribution, use, and reliability of energy. Local utilities and municipalities must respond and adapt to these changes, moving towards a future energy system with modernized infrastructure and other targeted investments and policy decisions. However, planning for and enacting these advancements requires significant effort from experts and engineers to develop strategies that ensure a reliable and secure energy future. This includes characterizing the current energy infrastructure, identifying areas for development, and engaging with local community members. Emerging generative artificial intelligence techniques can alleviate pain points and help support the development of the next generation of energy systems. Here, in this article, we highlight on-going generative AI work in the areas of atmospheric modeling, building energy management, and distribution network design, and we propose a vision for the role of generative AI that considers opportunities and identifies challenges inherent to this technology.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

The uncertainties resulting from the escalating penetration of renewable energy resources pose severe challenges to the efficient operation of modern power systems. Hydroelectricity is characterized by its flexibility, controllability, and reliability, and thus becomes one of the most ideal energy resources to hedge against such uncertainties. This paper studies the mid-term integrated generation and maintenance scheduling of a cascaded hydroelectric system (CHS) consisting of multiple cascaded reservoirs and hydroelectric units. To precisely describe the mid-term water regulation policies, the hydraulic coupling relationship and water-energy nexus of CHS are incorporated into the proposed optimization model. The uncertainties of natural water inflow and the power outputs of wind/solar energy generation are taken into consideration and captured via a stochastic process modeled by a scenario tree. A multistage stochastic optimization (MSO) approach is developed to coordinate the complementary operations of multiple energy resources, by optimizing the mid-term water resource management, generation scheduling, and maintenance scheduling of CHS. The proposed MSO model is formulated as a large-scale mixed-integer linear program that presents significant computational intractability. To address this issue, a tailored Benders decomposition algorithm is developed. Two real-world case studies are conducted to demonstrate the capability and characteristics of the proposed model and algorithm. The computational results show that the proposed MSO model can exploit the flexibility of hydroelectricity to efficiently respond to variable wind and solar power, and reserve water resources for the generation in peak months to reduce the consumption of fossil fuel. Furthermore, the proposed solution approach also exhibits promising computational efficiency when handling large-scale models.

13 HYDRO ENERGY↗

DOE OSTI2024

An electrochemical generator for the continual supply of 213 Bi from 225 Ac for use in targeted alpha therapy applications

Bismuth-213 is a radionuclide of interest for targeted alpha therapy and is supplied via a radiochemical generator system through the decay of 225 Ac. Radionuclide generators employ longer lived “parent” radionuclides to routinely supply shorter-lived “daughter” radionuclides. The traditional 225 Ac/ 213 Bi radiochemical generator relies on an organic cation exchange resin where 225 Ac binds to the resin and 213 Bi is routinely eluted. These resins degrade when they absorb large doses of ionizing radiation (>1 × 10 6 Gy/mg), which has been observed when the loading activity of 225 Ac exceeds 2.59*10 9 Bq (70 mCi). Herein we report the development of an electrochemical generator for the supply of 213Bi that has the potential to overcome this limitation. Bismuth-213 spontaneously electrodeposits onto nickel foils in 0.1 M hydrochloric acid at 70 °C. Using this method, we were able to plate an average of 73 ± 4 % of the 213 Bi in solution and obtain a final 213 Bi recovery of 65 ± 8 % in 0.1 M citrate pH 4.5 via reverse electrolysis using titanium as the cathode. The recovered 213Bi had an average radiochemical purity of >99.8 % and was successfully used to radiolabel DOTATATE with an average radiochemical yield of 85.1 % (not optimized).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI

We introduce GenAI4UQ, a software package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting. GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency. Built-in auto-tuning of hyperparameters simplifies model training, ensuring accessibility for users with varying expertise. Its versatile conditional generative framework is applicable across diverse scientific domains. While GenAI4UQ offers significant advantages in flexibility and efficiency, users should interpret its uncertainty estimates with caution in data-sparse scenarios, as the model may overestimate uncertainty—an effect common to all surrogate-based approaches including MCMC with surrogate models. Despite this, GenAI4UQ transforms inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Quantum Pair Generation in Nonlinear Metasurfaces with Mixed and Pure Photon Polarizations

Metasurfaces are highly effective at manipulating classical light in the linear regime; however, effectively controlling the polarization of nonclassical light generated from nonlinear resonant metasurfaces remains a challenge. Here, in this study, we present a solution by achieving polarization engineering of frequency-nondegenerate biphotons emitted via spontaneous parametric down-conversion in GaAs metasurfaces, utilizing quasi-bound states in the continuum (qBIC) resonances to enhance biphoton generation. Through comprehensive polarization tomography, we demonstrate that the emitted photons’ polarization directly reflects the qBIC mode’s far-field properties. Furthermore, we show that both the type of qBIC mode and the symmetry of the meta-atoms can be tailored to control each single-photon polarization state, and that the subsequent two-photon polarization states are nearly separable, offering potential applications in the heralded generation of single photons with adjustable polarization. This work provides a significant step toward utilizing metasurfaces to generate quantum light and engineer their polarization, a critical aspect for future quantum technologies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Carrier-Envelope Phase Control in Terahertz Pulse Generation Using InAs Ribbon Metasurfaces

Generation of broadband terahertz (THz) pulses with variable polarization and carrier-envelope phase can enable the tailoring of THz beam wavefronts for advanced applications in THz imaging and spectroscopy and for strong THz field optics. While metasurfaces composed of deeply subwavelength THz emitters have recently been demonstrated to define the polarization and spatial profile of the generated THz fields, precise phase control or synthesis of THz pulse waveforms remains a challenging problem. Here, we propose and demonstrate metasurfaces composed of indium arsenide (InAs) nanoscale ribbon arrays capable of generating THz pulses with variable carrier-envelope phase. We show that different THz generation mechanisms, each contributing distinct phases, can be activated in the ribbons, enabling carrier-envelope phase control spanning a range of π over a wide band of frequencies (∼1–3 THz). This is achieved solely through the ribbon array geometry using linearly polarized optical excitation of the ribbons. The arrays enable precise control of the THz phase and amplitude, opening the door to advanced structured THz wavefront synthesis using ultrathin dielectric metasurfaces.

carrier-envelope phase↗

DOE OSTI2025

Extreme-ultraviolet spatiotemporal vortices via high harmonic generation

Spatiotemporal optical vortices (STOVs) are space–time structured light pulses with a unique topology that couples spatial and temporal domains and carry transverse orbital angular momentum (OAM). Up to now, their generation has been limited to the visible and infrared regions of the spectrum. During the last decade, it was shown that through the process of high-order harmonic generation, it is possible to upconvert spatial optical vortices that carry longitudinal OAM from the near-infrared into the extreme-ultraviolet (EUV), thereby producing vortices with distinct femtosecond and attosecond structure. In this work, we demonstrate theoretically and experimentally the generation of EUV spatiotemporal and spatiospectral vortices using near-infrared STOV driving laser pulses. Here, we use analytical expressions for focused STOVs to perform macroscopic calculations of high-order harmonic generation that are directly compared to the experimental results. As STOV beams are not eigenmodes of propagation, we characterize the highly charged EUV STOVs in both the near and far fields to show that they represent conjugated spatiotemporal and spatiospectral vortex pairs. Our work provides high-frequency light beams topologically coupled at the nanometre/attosecond scales domains with transverse OAM that could be suitable to explore electronic dynamics in magnetic materials, chiral media and nanostructures.

High-harmonic generation↗

DOE OSTI2025

Structural differences between human and mouse neurons and their implementation in generative AIs

Mouse and human brains have different functions that depend on their neuronal networks. We analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. We implemented these characteristics of mouse neurons in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, birds, and automobiles. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the five datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks.

generative AI↗

DOE OSTI2024

Calibrating Bayesian generative machine learning for Bayesiamplification

Recently, combinations of generative and Bayesian deep learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural networks aim to quantify the uncertainty on the generated distribution originating from limited training statistics. The interpretation of a distribution-wide uncertainty however remains ill-defined. We show a clear scheme for quantifying the calibration of Bayesian generative machine learning models. For a Continuous Normalizing Flow applied to a low-dimensional toy example, we evaluate the calibration of Bayesian uncertainties from either a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights, to gauge their behaviour on unsteady distribution edges. Well calibrated uncertainties can then be used to roughly estimate the number of uncorrelated truth samples that are equivalent to the generated sample and clearly indicate data amplification for smooth features of the distribution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Once-Through Steam Generator Model Analysis Using Python and Advanced Optimization Tools (Summer Internship Report)

This study focuses on the parametric analysis of design parameters for a once-through steam generator (OTSG) model, using python and advanced optimization tools to facilitate applications such as the flowing autoclave steam generator (FASG) test cases. Building on previous research involving another OTSG with a different design, this project aims to enhance our understanding of how steam generators (SGs) behave and how their outputs are influenced by changes in design. The reason for this design change is to allow for more precise modeling and optimization of SG performance, to provide a comparative analysis between the two designs, and to set up the model for integration with the FASG test case. The OTSG python-model is a mathematical representation (including fluid flow and heat transfer equations/models/correlations) of a steam-generating unit in a pressurized water reactor-type small modular reactor system. Design studies involve changing the model’s input design parameters to observe the resulting effects on the output of the system. By using advanced optimization tools, such as the Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory, detailed design parametric studies and model optimization were performed. Six input parameters—pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid), respectively, of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (i.e., ±1%, ±5% and ±10% relative changes) for 600 samples. The analysis provides valuable insights into SG optimization and can be used for sensor placement optimization to effectively monitor and obtain experimental data in other tests.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2026

Generative AI for Grid Operations [Slides]

In the last few years, the development and use of generative artificial intelligence (AI) and large-language models (LLMs) have changed the landscape of how AI and machine learning (ML) are being used in power systems. LLMs are built on foundational models based on large data sets that can be trained to provide information rapidly and through simple natural language prompts. Generative AI can then perform human-like tasks using ML models to identify and mimic pattens in the data sets. This presentation explores how generative AI can enhance grid operations by improving forecasts, enabling rapid contingency analyses, and offering real-time operational suggestions. By providing grid operators with valuable insights, generative AI will empower them to manage power systems more effectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗