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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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NASA NTRSDate not supplied

AI-Enhanced Computational Tools for Entry Systems Modeling

To advance the understanding of complex atmospheric entry phenomena, NASA’s Entry Systems Modeling (ESM) team [1] has developed high-fidelity computational tools addressing multiscale challenges, from material microstructures to full-scale heatshield response. This abstract highlights a subset of ESM tools, focusing on AI integration to enhance workflows and predictive modeling. - PuMA [2] computes effective material properties from high-resolution micro-CT scans, supporting TPS analysis for NASA missions. - TomoSAM [3] automates 3D tomography dataset segmentation for PuMA using the Segment Anything Model, reducing manual effort and improving accuracy. - PATO [4] models porous reactive materials under extreme conditions, with advancements such as unified solvers, mechanical erosion, and TPS coatings for NASA missions. - arcjetCV [5] employs deep learning to analyze arc jet test footage, measuring recession rates, shape changes, and shock standoff distances, bridging simulations, and experiments to reveal TPS ablation behavior. - ARCHeS [6] simulates arc heater plasma flows, modeling turbulence, radiation, and electromagnetic interactions to optimize arc heater performance, validate TPS under extreme conditions, and serve as a foundation for developing digital twins of arc heater facilities. - SPARTA [7] simulates rarefied hypersonic flows and gas-surface interactions for planetary entry missions, leveraging GPU architectures for scalable and efficient aerothermal and ablation analyses. AI-driven solutions, such as deep learning segmentation, have streamlined workflows in ESM tools and still hold significant potential to further accelerate processes and enhance automation in entry systems modeling. [1] Haskins, J.B. (2023), [2] Ferguson, J.C. (2018), [3] Meurisse, J.B.E. (2018), [4] Semeraro, F. (2023), [5] Quintart, A. (2024) [6] Meurisse, J.B.E. (2022), [7] Plimpton, S.J. (2019)

Predictive Modeling↗
DOE OSTI2024

Characterization of the Triassic Newark Basin of New York and New Jersey for Geologic Storage of Carbon Dioxide

The project Characterization of the Triassic Newark Basin of New York and New Jersey for Geologic Storage of Carbon Dioxide is one of 9 site characterization projects that were implemented as part of ARRA (American Recovery and Reinvestment Act). Data from this project was used to improve resolution of data in NATCARB in the area of study. Data related to this study has already been incorporated in NATCARB Atlas. Sandia Technologies, LLC, and co-investigator Conrad Geoscience Corporation, examined the potential for large-scale, permanent CO2 storage in sedimentary strata within the Newark Rift Basin. The Newark Rift Basin underlies an industrialized, developed region comprising parts of New York, New Jersey, and Pennsylvania. The project characterized and investigated the suitability of Triassic age sedimentary formations for potential geologic CO2 storage. The project team drilled and cored two test wells to define the sedimentary geologic formations underlying the basin and to document or reach basement rock. With this geologic characterization phase, an integration of seismic, geologic, borehole, and formation core results provided a higher resolution assessment of CO2 storage potential. The Stockton Formation is known to be a potentially favorable geologic storage formation in the basin. In 2011, the 1-NYSTA Tandem Lot stratigraphic test well was drilled to a depth of 6,855 feet in the northern portion of the Newark Basin in southern New York State. Approximately 9 miles south-southeast on the Lamont Doherty Campus, TW-4 was drilled and cored in 2013 to a depth of 1,802 feet and contacted apparent igneous basement at a depth of 1,712 feet. Both wells penetrated the Palisades Sill ranging from 800 feet thick in the eastern well to approximately 1,800 feet in thickness at the 1-NYSTA Tandem Lot deep drill site. A diabase sill can provide an excellent seal and dense confining layer for potential CO2 storage reservoirs and flow layers that are situated beneath it within the Stockton Sandstone. The Stockton Sandstone was encountered beneath the sill in the TW-4 well on the Lamont campus, and data integration suggests that it was likely observed near total depth in the deep 1-NYSTA Tandem Lot well. The test wells confirm and define reservoirs are present beneath the sill and offer CO2 storage potential. The integration of geologic and reservoir characterization of well logs, formation cores, and formation fluids indicated Triassic age lacustrine playa lake and mudbank shales of the Upper Passaic Group can provide an effective seal for the porous and permeable underlying sandstone reservoir layers. This project acquired seismic data, drilled borehole well logs, acquired core samples, and integrated these findings to provide a better understanding of the subsurface geologic formations in the Newark Rift Basin. These findings have contributed to a higher degree of accuracy in predicting potential geologic storage opportunities, while refining geologic storage capacity estimates for the indicated reservoirs and flow units.

.las↗
NASA NTRS1987

Electron dynamics and potential jump across slow mode shocks

In the de Hoffmann-Teller reference frame, the cross-shock electric field is simply the thermoelectric field responsible for preserving charge neutrality. As such, it gives information regarding the heating and dissipation occurring within the shock. The total cross-shock potential can be determined by integrating a weighted electron pressure gradient through the shock, but this requires knowledge of the density and temperature profiles. Here, a recently proposed alternative approach relying on particle dynamics is exploited to provide an independent estimate of this potential. Both determinations are applied to slow mode shocks which form the plasma sheet boundary in the deep geomagnetic tail as observed by ISEE 3. The two methods correlate well. There is no indication of the expected transition from resistive to viscous shocks, although the highest Mach number shocks show the highest potentials. The implications of these results for the electron dissipation mechanisms and turbulence at the shock are discussed.

Schwartz, Steven J.↗
DOE OSTI2026

The effect of hydroxyl spacing in diols on the solvation structure, dynamics, and transport properties of choline chloride-based deep eutectic solvents

Deep eutectic solvents (DESs) are a class of liquids that offer great potential in alleviating some of the challenges present in today's long-term energy storage methods because they have physical properties that are favorable for storable electrolyte solutions. In this work, a series of glycols (ethylene glycol, 1,3-propanediol, 1,4-butanediol, and 1,5-pentanediol) were studied as potential hydrogen bond donors (HBD) with a common choline chloride (ChCl) as the hydrogen bond acceptor (HBA). The solvation dynamics of the prepared systems were studied by measuring the solvent reorganization response using femtosecond transient absorption spectroscopy (fs-TA). Conductivity, viscosity, density, ET(30) polarity, and dynamics of the prepared DESs were analyzed, with a particular interest in determining the effect of HBD chain length on these parameters. Here, classical molecular dynamics simulations were employed to investigate how the local liquid structure, solvent dynamics, and bulk solvent properties vary with changes in glycol chain length.

Conductivity↗
DOE OSTI2025

Incentivizing Cooperative Merging Control: Insights from Multi-Agent Deep Reinforcement Learning

Cooperative driving automation enables connected and automated vehicles (CAVs) to devise cooperative merging control, introducing great potentials to alleviate traffic congestion, reduce energy consumption, and enhance safety for highway on-ramp operations. Although numerous CAV cooperative merging algorithms have been developed to improve energy and traffic performance, the agreement-seeking among CAV users and their local benefits have been understudied. This can lead to rejections of cooperative merging plans and jeopardizing CAV performance, as a cooperation may entail certain CAVs to sacrifice their local benefits to achieve a system optimum. To address this issue, the study first leverages multi-agent deep reinforcement learning (MADRL) factoring both local reward and regional reward to demonstrate the discrepancies between CAV users’ local benefits and system optimum. Next, the existence of a correlated equilibrium is proved to characterize the convergence of MADRL training. This further facilitates the incorporation of incentives (computed based on reward discrepancies) to compensate for CAV users’ local benefits and facilitate system-optimal agreements in cooperative merging operations.

Zhou, Anye [ORNL] (ORCID:0000000301455579)↗
NASA NTRS2016

Proving Ground Potential Mission and Flight Test Objectives and Near Term Architectures

NASA is developing a Pioneering Space Strategy to expand human and robotic presence further into the solar system, not just to explore and visit, but to stay. NASA's strategy is designed to meet technical and non-technical challenges, leverage current and near-term activities, and lead to a future where humans can work, learn, operate, and thrive safely in space for an extended, and eventually indefinite, period of time. An important aspect of this strategy is the implementation of proving ground activities needed to ensure confidence in both Mars systems and deep space operations prior to embarking on the journey to the Mars. As part of the proving ground development, NASA is assessing potential mission concepts that could validate the required capabilities needed to expand human presence into the solar system. The first step identified in the proving ground is to establish human presence in the cis-lunar vicinity to enable development and testing of systems and operations required to land humans on Mars and to reach other deep space destinations. These capabilities may also be leveraged to support potential commercial and international objectives for Lunar Surface missions. This paper will discuss a series of potential proving ground mission and flight test objectives that support NASA's journey to Mars and can be leveraged for commercial and international goals. The paper will discuss how early missions will begin to satisfy these objectives, including extensibility and applicability to Mars. The initial capability provided by the launch vehicle will be described as well as planned upgrades required to support longer and more complex missions. Potential architectures and mission concepts will be examined as options to satisfy proving ground objectives. In addition, these architectures will be assessed on commercial and international participation opportunities and on how well they develop capabilities and operations applicable to Mars vicinity missions.

Smith, R. Marshall↗
DOE OSTI2025

A generative artificial intelligence framework for long-time plasma turbulence simulations

Generative deep learning techniques are employed in a novel framework for the construction of surrogate models capturing the spatiotemporal dynamics of 2D plasma turbulence. The proposed Generative Artificial Intelligence Turbulence (GAIT) framework enables the acceleration of turbulence simulations for long-time transport studies. GAIT leverages a convolutional variational auto-encoder and a recurrent neural network to generate new turbulence data from existing simulations, extending the time horizon of transport studies with minimal computational cost. The application of the GAIT framework to plasma turbulence using the Hasegawa–Wakatani (HW) model is presented, evaluating its performance via various analyses. Very good agreement is found between the GAIT and the HW models in the spatiotemporal Fourier and Proper Orthogonal Decomposition spectra, the flow topology characterized by the Okubo–Weiss parameter, and the time autocorrelation function of turbulent fluctuations. Excellent agreement has also been obtained in the probability distribution function of particle displacements and the effective turbulent diffusivity. In-depth analyses of the latent space of turbulent states, choice of hyperparameters and alternative deep learning models for the time prediction are presented. Our results highlight the potential of Artificial Intelligence-based surrogate models to overcome the computational challenges in turbulence simulation, which can be extended to other situations such as geophysical fluid dynamics.

Artificial intelligence↗
NASA NTRS2008

Extreme Helicity and Intense Convective Towers in Hurricane Bonnie

Helicity was calculated in Hurricane Bonnie (1998) using tropospheric-deep dropsonde soundings from the NASA Convection and Moisture Experiment. Large helicity existed downshear of the storm center with respect to the ambient vertical wind shear. It was associated with veering, semicircular hodographs created by strong, vortex-scale, radial-vertical flow induced by the shear. The most extreme values of helicity, among the largest ever reported in the literature, occurred in the vicinity of deep convective cells in the downshear-left quadrant. These cells reached as high as 17.5 km and displayed the temporal and spatial scales of supercells. Convective available potential energy (CAPE) averaged 861 J/kg downshear, but only about one-third as large upshear. The soundings nearest the deep cells were evaluated using two empirical supercell parameters that make use of CAPE, helicity, and/or shear. These parameters supported the possible existence of supercells as a consequence of the exceptional helicity combined with moderate but sufficient CAPE. Ambient vertical wind shear exceeded 12 m/s for 30 h, yet the hurricane maintained 50 m/s maximum winds. It is hypothesized that the long-lived convective cells enabled the storm to resist the negative impact of the shear. Supercells in large-helicity, curved-hodograph environments appear to provide a useful conceptual model for intense convection in the hurricane core. Helicity calculations might also give some insight into the behavior of vortical hot towers, which share some characteristics with supercells.

Molinari, John↗
DOE OSTI2025

Technology Case Study: Economic, Sustainability, and Deployment Considerations for Sustainable Aviation Fuels Produced via Lignocellulosic Sugar Catalysis

This report presents a technology case study reflecting one exemplary representative pathway for the conversion of lignocellulosic sugars to sustainable aviation fuels (SAF) via aqueous phase reforming (APR) catalysis, considered within a broader integrated biorefinery framework based on biochemical processing operations. While far from the only option for converting sugars to SAF, this pathway was selected as a case study here based on its relatively high technology maturity and simplistic processing approach (avoiding complex separations or other equipment scalability challenges), coupled with the potential for high fuel yields and favorable costs/carbon intensities with opportunities for further near-term optimization. The report considers key process integration and engineering design considerations for a modeled hypothetical, nth-plant commercial biorefinery, reflecting a number of processing options and parameters envisioned to be achievable as future goals. Resultant outputs from Aspen Plus process simulations are evaluated through techno-economic and life cycle analyses (TEA and LCA), including implications for marginal cost of CO2 abatement and inclusion of currently-applicable policy incentives. Moving beyond base case configurations, a number of alternative scenarios are also evaluated for their ability to further improve economics, greenhouse gas (GHG) emissions, and marginal cost of abatement, highlighting a path to achieve deep decarbonization goals of more than 70% GHG reduction for SAF (with the potential to reach net-negative carbon intensities in some cases) under reasonable fuel production costs. The report also highlights future opportunities and gaps for further research on this technology pathway.

09 BIOMASS FUELS↗
NASA NTRS1979

Control considerations for CCV fighters at high angles of attack

Wind tunnel and piloted simulation studies were conducted to investigate the potential high angle of attack control problems that are introduced by the use of the CCV concept of relaxed static pitch stability (RSS) on fighter aircraft. A conventional wing/aft tail design incorporating modest levels of static instability and a close-coupled canard/wing design exhibiting very high levels of instability was investigated. Two types of high angle of attack control problems can result from the use of RSS: pitch departures caused by coupling and deep stall trim. Avoidance of these problems requires that the airplane have sufficient nose-down pitch control at high angles of attack. The effectiveness of several pitch control configurations were investigated including conventional aft-mounted stabilators, wing-mounted elevators, canard-mounted flaps, and all-moveable canards. Varying the incidence of the canards was the most effective scheme; however, very large deflections may be required on highly unstable configurations to prevent pitch departure without sacrificing roll performance and to avoid deep stall trim. For situations where the high angle of attack pitch control requirement is not met, control laws were developed to inhibit the departure and to allow deep stall recovery. However, these schemes involve limiting airplane roll capability and therefore can potentially compromise maneuverability.

Nguyen, L. T.↗
NASA NTRS2016

Carbon Nanofiber Nanoelectrodes for Neural Stimulation and Chemical Detection: The Era of Smart Deep Brain Stimulation

A sensor platform based on vertically aligned carbon nanofibers (CNFs) has been developed. Their inherent nanometer scale, high conductivity, wide potential window, good biocompatibility and well-defined surface chemistry make them ideal candidates as biosensor electrodes. Here, we report two studies using vertically aligned CNF nanoelectrodes for biomedical applications. CNF arrays are investigated as neural stimulation and neurotransmitter recording electrodes for application in deep brain stimulation (DBS). Polypyrrole coated CNF nanoelectrodes have shown great promise as stimulating electrodes due to their large surface area, low impedance, biocompatibility and capacity for highly localized stimulation. CNFs embedded in SiO2 have been used as sensing electrodes for neurotransmitter detection. Our approach combines a multiplexed CNF electrode chip, developed at NASA Ames Research Center, with the Wireless Instantaneous Neurotransmitter Concentration Sensor (WINCS) system, developed at the Mayo Clinic. Preliminary results indicate that the CNF nanoelectrode arrays are easily integrated with WINCS for neurotransmitter detection in a multiplexed array format. In the future, combining CNF based stimulating and recording electrodes with WINCS may lay the foundation for an implantable "smart" therapeutic system that utilizes neurochemical feedback control while likely resulting in increased DBS application in various neuropsychiatric disorders. In total, our goal is to take advantage of the nanostructure of CNF arrays for biosensing studies requiring ultrahigh sensitivity, high-degree of miniaturization, and selective biofunctionalization.

bioinstrumentation↗
NASA NTRS2016

Carbon Nanofiber Nanoelectrodes for Neural Stimulation and Chemical Detection: The Era of "Smart" Deep Brain Stimulation

A sensor platform based on vertically aligned carbon nanofibers (CNFs) has been developed. Their inherent nanometer scale, high conductivity, wide potential window, good biocompatibility and well-defined surface chemistry make them ideal candidates as biosensor electrodes. Here, we report two studies using vertically aligned CNF nanoelectrodes for biomedical applications. CNF arrays are investigated as neural stimulation and neurotransmitter recording electrodes for application in deep brain stimulation (DBS). Polypyrrole coated CNF nanoelectrodes have shown great promise as stimulating electrodes due to their large surface area, low impedance, biocompatibility and capacity for highly localized stimulation. CNFs embedded in SiO2 have been used as sensing electrodes for neurotransmitter detection. Our approach combines a multiplexed CNF electrode chip, developed at NASA Ames Research Center, with the Wireless Instantaneous Neurotransmitter Concentration Sensor (WINCS) system, developed at the Mayo Clinic. Preliminary results indicate that the CNF nanoelectrode arrays are easily integrated with WINCS for neurotransmitter detection in a multiplexed array format. In the future, combining CNF based stimulating and recording electrodes with WINCS may lay the foundation for an implantable smart therapeutic system that utilizes neurochemical feedback control while likely resulting in increased DBS application in various neuropsychiatric disorders. In total, our goal is to take advantage of the nanostructure of CNF arrays for biosensing studies requiring ultrahigh sensitivity, high-degree of miniaturization, and selective biofunctionalization.

human body↗
NASA NTRS2012

The Potential for a Ka-band (32 GHz) Worldwide VLBI Network

Ka-band (32 GHz, 9mm) Very Long Baseline Interferometric (VLBI) networking has now begun and has tremendous potential for expansion over the next few years. Ka-band VLBI astrometry from NASA's Deep Space Network has already developed a catalog of 470 observable sources with highly accurate positions. Now, several antennas worldwide are planning or are considering adding Ka-band VLBI capability. Thus, there is now an opportunity to create a worldwide Ka-band network with potential for high resolution imaging and astrometry. With baselines approaching a Giga-lambda, a Ka-band network would be able to probe source structure at the nano-radian (200 as) level ( 100X better than Hubble) and thus gain insight into the astrophysics of the most compact regions of emission in active galactic nuclei. We discuss the advantages of Ka-band, show the known sources and candidates, simulate projected baseline (uv) coverage, and discuss potential radio frequency feeds. The combination of these elements demonstrates the feasibility of a worldwide Ka network within the next few years!

quasar↗
NASA NTRSDate not supplied

Supporting Crew Medical Decisions on Deep Space Missions: A Real-Time Performance Monitoring

Crewed missions into deep space will require astronauts to respond autonomously to safety- and time-critical anomalies. These and other potential issues will require the continuous monitoring and recognition of potentially subtle, but complex, anomalous data patterns. NASA continues to investigate real-time metrics for Extravehicular Operations1 and for providing feedback to improve performance2. Since it is unknown how long-term exposure to deep space will affect crew health, continuous monitoring of clinical and subclinical status is warranted. NASA is characterizing crew medical decision-support needs and identifying the recommended requirements of a Clinical Decision Support System (CDSS) within the Exploration Medical Capability Element (ExMC) of the Human Research Program3. Early detection, and continuous monitoring, of performance may provide an effective prognostic capability for assessing crew health state, and the identified requirements could be provided for an in-vehicle CDSS that acts as an assistant for delivering optimal health and performance and medical care during exploration missions. There is evidence to suggest that automatic, unobtrusive collection of keystroke features, such as alphanumeric key latencies, backspaces or typing rhythm, could be a valuable performance screening tool. Keystroke features show significant differences between control subjects and patients with overt clinical illness, such as multiple sclerosis4 and Parkinson’s disease5, as well as behavioral health and performance issues, such as mild cognitive impairment6 and depression7. This work is exploring a capability for the assessment of early task performance decline in crew as they perform daily activities. Timestamped keyboard entries are continuously collected and stored. Nominal keystroke interactions are compared to those obtained after exposure to spaceflight stressors, e.g., fatigue, to determine the methods sensitivity. This work will pave the way toward an objective detection tool that may be deployed in a spaceflight setting.

clinical↗
NASA NTRS2022

TruePAL – An AI Assistant for First Responder Safety

This paper presents the development of an AI assistant, Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL), to provide real-time warning of risks of potential crashes to the first responders. The TruePAL system employs an AI and deep learning technology for saving first responders and roadside crews lives in and around active traffic. A deep neural network (DNN) and a Non-Axiomatic Reasoning System (NARS) are implemented as an AI system. A mobile app with AI interface is developed to perform verbal communication with the first responders. The TruePAL team has developed an explainable AI approach by opening up the DNN blackbox to extract the activation filters of various features and parts of the targeted objects. The combination of DNN and NARS makes the TruePAL system explainable to the users. TruePAL ingests on-board cameras, radar, and other sensor signals, analyzes the environment and traffic patterns to generate timely warning to drivers and roadside crews to avoid crashes. The TruePAL team, in collaboration with the Miami/Dade Police Dept., has designed five use cases and multiple sub-scenarios in a CARLA driving simulator to test the capability of TruePAL in timely warning to the first responder drivers in potential crash scenarios. We have successfully demonstrated its capability of timely warning in over a dozen scenarios based on the use cases. The preliminary test simulation results show that TruePAL could provide the drivers and crew members advanced warning before a crash occurs.

Chow, Edward↗
DOE OSTI2024

Trap‐Engineering the Persistent Luminescence of Ca 3 Ga 4 O 9 :Tb 3+ via Al 3+ Substitution for Optical Data Storage

Abstract Optically stimulated luminescence (OSL) materials hold great potential for optical data storage (ODS) and anticounterfeiting applications. Nevertheless, the scarcity of suitable luminescent materials with deep‐level traps remains a significant obstacle. Herein, a host substation strategy have been employed to tune the persistent luminescence (PersL) and OSL properties of Ca 3 Ga 4 O 9 :Tb 3+ by Al 3+ substitution through trap engineering and demonstrated their potential. Specifically, the photoluminescence of the Ca 2.985 (Ga 1‐y% Al y% ) 4 O 9 :0.5%Tb 3+ of Tb 3+ is first investigated due to its different occupancies of Ca 2+ . The influence of host substitution on the crystal structure, trap depth, trap density, PersL, and OSL properties have further investigated. A series of strong PersL and OSL peaks from the Ca 2.985 (Ga 1‐y% Al y% ) 4 O 9 :0.5%Tb 3+ with bluish‐green emissions have been observed. The Ca 2.985 (Ga 1‐y% Al y% ) 4 O 9 :0.5%Tb 3+ have shown controllable photon release upon thermal and optical stimuli, enhancing their performance for ODS. Thermally stimulated luminescence suggests that vacancy and defect concentrations inside the Ca 3‐x% (Ga 1‐y% Al y% ) 4 O 9 :x%Tb 3+ can be manipulated by Tb 3+ doping and Al 3+ substitution, which ultimately leads to the formation of deep traps and a broad distribution of traps with increased deep trap concentration. The work demonstrates that trap engineering through Al 3 ⁺ substitution is an effective method for tuning PersL and OSL properties of Ca 2.985 (Ga 1‐y% Al y% ) 4 O 9 :0.5%Tb 3+ for ODS.

Abeywickrama, Thulitha M. [Department of Chemistry↗
DOE OSTI2024

Low‐Temperature Synthesis of Stable CaZn 2 P 2 Zintl Phosphide Thin Films as Candidate Top Absorbers

Abstract The development of tandem photovoltaics and photoelectrochemical solar cells requires new absorber materials with bandgaps in the range of ≈1.5–2.3 eV, for use in the top cell paired with a narrower‐gap bottom cell. An outstanding challenge is finding materials with suitable optoelectronic and defect properties, good operational stability, and synthesis conditions that preserve underlying device layers. This study demonstrates the Zintl phosphide compound CaZn 2 P 2 as a compelling candidate semiconductor for these applications. Phase‐pure, ≈500 nm‐thick CaZn 2 P 2 thin films are prepared using a scalable reactive sputter deposition process at growth temperatures as low as 100 °C, which is desirable for device integration. Ultraviolet‐visible spectroscopy shows that CaZn 2 P 2 films exhibit an optical absorptivity of ≈10 4 cm −1 at ≈1.95 eV direct bandgap. Room‐temperature photoluminescence (PL) measurements show near‐band‐edge optical emission, and time‐resolved microwave conductivity (TRMC) measurements indicate a photoexcited carrier lifetime of ≈30 ns. CaZn 2 P 2 is highly stable in both ambient conditions and moisture, as evidenced by PL and TRMC measurements. Experimental data are supported by first‐principles calculations, which indicate the absence of low‐formation‐energy, deep intrinsic defects. Overall, this study shall motivate future work integrating this potential top cell absorber material into tandem solar cells.

14 SOLAR ENERGY↗
DOE OSTI2026

Performance and transport in the ARC tokamak

The ARC TM tokamak, a high-field (𝐵 𝑇 = 11.4 T) fusion power plant, under development by Commonwealth Fusion Systems, is studied using a suite of integrated modelling tools to predict its fusion power generation (𝑃𝑓⁡𝑢⁢𝑠), transport and confinement properties. Analysis is based off an ARC operational point scoped first with zero-dimensional (0-D) plasma operational contour (POPCON) modelling to produce 1.13 GW of fusion power. A suite of integrated modelling tools (TRANSP, ASTRA and TORAX) were applied to predict the performance and kinetic profiles of the ARC design point, yielding a range of predicted performance spanning from ∼900 to 1300 MW in rough quantitative agreement with POPCON predictions. The sensitivity of these results to uncertain modelling inputs was probed using scans of pedestal boundary conditions around EPED-predicted values (total pressure and temperature ratios), tungsten concentration and seperatrix density around their nominal assumptions. Pedestal pressure and pedestal top (𝑇 𝑖 /𝑇 𝑒 ) play a large role in 1.5-dimensional performance predictions, able to modify the predicted 𝑃 𝑓⁡𝑢⁢𝑠 by a factor of 2 within reasonable assumptions. High-fidelity core nonlinear gyrokinetic profile predictions, performed using CGYRO (Candy et al. 2016 J. Comput. Phys., vol. 324, pp. 73–93) coupled with the PORTALS (Rodriguez-Fernandez et al. 2024 Nucl. Fusion, vol. 64, 076034; Phys. Plasmas, vol. 31, 2024, 062501) framework, yield substantially lower performance (𝑃 𝑓⁡𝑢⁢𝑠 =677 MW) compared with 0-D and medium-fidelity modelling for nominal assumptions, showing that there is non-negligible uncertainty between models and that future work on SPARC may help resolve discrepancies. Lower overall performance results from significantly reduced volume-averaged densities and temperatures, along with reduced levels of density and temperature peaking. Turbulence and transport are largely dominated by ion temperature gradient across the profile, confirmed by both linear stability and the response of the nonlinear fluxes to changes in gradients, with some impact of kinetic ballooning modes in the deep core. This work represents one of the most complete scoping of potential fusion power plant conditions performed to date. The extensive integrated modelling provides confidence in ARC performance approaching 1 GW, while nonlinear gyrokinetic modelling results in open questions into the physics of density and temperature peaking in fusion-power-plant-relevant operational space. A discussion of results and the role that the SPARC tokamak (Creely et al. 2020 J. Plasma Phys., vol. 86, 865860502) will play in informing ARC design, performance and operation is presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗