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DOE OSTI2021

Large-scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant

We investigate the use of approximate Bayesian neural networks (BNNs) in modeling hundreds of time delay gravitational lenses for Hubble constant (H 0 ) determination. Here, our BNN was trained on synthetic Hubble Space Telescope quality images of strongly lensed active galactic nuclei with lens galaxy light included. The BNN can accurately characterize the posterior probability density functions (PDFs) of model parameters governing the elliptical power-law mass profile in an external shear field. We then propagate the BNN-inferred posterior PDFs into an ensemble H 0 inference, using simulated time delay measurements from a plausible dedicated monitoring campaign. Assuming well-measured time delays and a reasonable set of priors on the environment of the lens, we achieve a median precision of 9.3% per lens in the inferred H 0 . A simple combination of a set of 200 test lenses results in a precision of 0.5 km s –1 Mpc –1 (0.7%), with no detectable bias in this H 0 recovery test. The computation time for the entire pipeline—including the generation of the training set, BNN training and H0 inference—translates to 9 minutes per lens on average for 200 lenses and converges to 6 minutes per lens as the sample size is increased. Being fully automated and efficient, our pipeline is a promising tool for exploring ensemble-level systematics in lens modeling for H 0 inference.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Process design tools and techno-economic analysis for capacitive deionization

Capacitive deionization (CDI) devices use cyclical electrosorption on porous electrode surfaces to achieve water desalination. Process modeling and design of CDI systems requires accurate treatment of the coupling among input electrical forcing, input flow rates, and system responses including salt removal dynamics, water recovery, energy storage, and dissipation. Techno-economic analyses of CDI further require a method to calculate and compare between a produced commodity (e.g. desalted water) versus capital and operational costs of the system. In this work, we demonstrate a new modeling and analysis tool for CDI developed as an installable Matlab program that allows direct numerical simulation of CDI dynamics and calculation of key performance and cost parameters. The program is provided for free and is used to run open-source Simulink models. The Simulink environment sends information to the program and allows for a drag and drop design space where users can connect CDI cells to relevant periphery blocks such as grid energy, battery, solar panel, waste disposal, and maintenance/labor cost streams. The program allows for simulation of arbitrary current forcing and arbitrary flow rate forcing of one or more CDI cells. We employ validated well-mixed reactor formulations together with a non-linear circuit model formulation that can accommodate a variety of electric double layer sub-models (e.g. for charge efficiency). The program includes a graphical user interface (GUI) to specify CDI plant parameters, specify operating conditions, run individual tests or parameter batch-mode simulations, and plot relevant results. The techno-economic models convert among dimensional streams of species (e.g. feed, desalted water, and brine), energy, and cost and enable a variety of economic estimates including levelized water costs.

42 ENGINEERING↗

DOE OSTI2026

An adaptive model-free robotic force control strategy for hydrodynamic real-time hybrid simulation of floating offshore wind turbines

Real-time hybrid simulation (RTHS) - a cyber-physical testing approach - promises to enhance the simulation fidelity of the model-scale experiments used to prototype floating offshore wind turbines (FOWTs). In hydrodynamic RTHS (hydro-RTHS), actuators emulate aerodynamic forces on model-scale FOWT specimens subjected to physical waves in a hydrodynamic laboratory. Robotic arms are promising candidates for actuation in hydro-RTHS due to their compact multi-degree-of-freedom (DOF) capabilities. Unlike classical RTHS for seismic applications, which typically relies on displacement control, hydro-RTHS requires 6-DOF force control on newly designed floating prototypes in a model-scale setting, which presents significant challenges, including modeling uncertainties, directional asymmetry, configuration drift, bandwidth limitations, and time-varying delays. To mitigate these constraints without extensive pre-test calibration, this study proposes an adaptive model-free robotic force control strategy that combines task-space explicit force control with a secondary joint-space pose-keeping task. The Adaptive Feedforward Compensator (AFC) is integrated into the force control loop to compensate for time-varying delay. Experimental testing was conducted using a Franka Emika Panda robotic arm with a 1:50 scale FOWT specimen under operational wind and wave conditions. Results demonstrate stable and consistent 6-DOF force tracking. Effective delay compensation was observed, with low-frequency delay reductions ranging from 71.4% to 91.8% and improvements in low-frequency surge force tracking of 25.0% to 52.1%. This study enhances robotic actuation performance in hydro-RTHS and introduces a force control strategy that supports reliable robotic operation in uncertain floating environments. Future work will explore disturbance-observer mechanisms to further enhance wave rejection capabilities under extreme wind and wave conditions.

17 WIND ENERGY↗

DOE OSTI2023

First flight performance of the Micro-X microcalorimeter X-ray sounding rocket

The flight of the Micro-X sounding rocket on July 22, 2018, marked the first operation of transition-edge sensors and their superconducting quantum interference device readouts in space. Here, the instrument combines the microcalorimeter array with an imaging mirror to take high-resolution spectra from extended X-ray sources. The first flight target was the Cassiopeia A supernova remnant. Although a rocket pointing malfunction led to no time on-target, data from the flight were used to evaluate the performance of the instrument and demonstrate the flight viability of the payload. The instrument successfully achieved a stable cryogenic environment, executed all flight operations, and observed X-rays from the on-board calibration source. The flight environment did not significantly affect the performance of the detectors compared with ground operation. The flight provided an invaluable test of the impact of external magnetic fields and the instrument configuration on detector performance. This flight provides a milestone in the flight readiness of these detector and readout technologies, both of which have been selected for future X-ray observatories.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Design of Ionization Profile Monitors at the Integrable Optics Test Accelerator Facility at Fermilab

The Integrable Optics Test Accelerator (IOTA) at Fermilab is transitioning from an electron beam facility to a proton beam facility for studies in nonlinear accelerator optics and space-charge dominated proton beams. This project involves the commissioning and fabrication of Ionization Profile Monitors (IPMs) to enable beam profile measurements at IOTA. In general, IPMs work on principle of residual gas ionization by the beam to generate beam profile. This work focuses on a mechanical design that leverages a controlled injection of noble gases, primarily Argon, as the ultra-high vacuum of the IOTA ring provides insufficient residual gas for ionization. Efforts to understand vacuum integration to ensure compatibility with the storage ring environment, the integration of real-time data acquisition systems and the commissioning of the IPMs will be discussed. This project provides a versatile diagnostic tool, supporting IOTA’s role as a testbed for larger-scale accelerator facilities and contributing to the broader understanding of beam physics in high-intensity, high-space-charge regimes.

Mwaniki, M. W. [IIT, Chicago] (ORCID:0000000169057↗

DOE OSTI2025

Designing reinforcement learning algorithms for building HVAC control: From experimental observation to simulation comparisons

Advanced supervisory-level control with reinforcement learning (RL) is regarded as a promising solution for HVAC systems to minimize energy consumption while maintaining thermal comfort and indoor air quality. However, most RL applications were conducted in the simulation environment rather than real-world HVAC systems. This paper developed a value-based RL controller termed Deep Q-Network (DQN) for a typical central HVAC system and evaluated its performance in a building test facility. By comparing DQN with a rule-based controller, the study not only demonstrated the cases where DQN could properly maintain indoor comfort but also discussed possible reasons why DQN failed in some other situations. Recognizing the limitations of value-based RL algorithms from the experimental tests, a simulation study was conducted to compare DQN with an alternative RL approach, an actor–critic algorithm termed Deep Deterministic Policy Gradient (DDPG). In scenarios with a relatively large action space, DDPG outperformed DQN by requiring fewer computational resources and achieving better thermal comfort, lower energy consumption, and more stable control actions. The findings suggest that the ability of DDPG to handle continuous control variables more effectively allows for faster convergence in training and more precise control in practice, which enhances the overall efficiency and reliability of the HVAC system.

Guo, Fangzhou↗

DOE OSTI2022

Autonomous System Subversion Tactics: Prototypes and Recommended Countermeasures

One of the fielding requirements for Advanced and Small Modular Reactors (AR/SMR) is the ability to support remote and autonomous operations. Autonomous Control Systems (ACS) are found on platforms such as Autonomous Space Vehicles, Cruise Missiles, and advanced driver-assistance systems. Each of these ACS implementations depends upon a set of decision support subsystems responsible for supporting Autonomous Mission Managers (names vary based upon field and author preferences). These Autonomous Mission Managers receive inputs from system sensors (e.g., LIDAR collection from an automobile travelling down a street; transients from a nuclear reactor), and perform a set of classifications (e.g., Red Traffic Light; Small Pedestrian at 10m; Load Rejection; Single Coolant Pump Trip), and then use these classifications in combination with recommendation algorithms to achieve platform goals (e.g., Stop the Vehicle at the Traffic Light, Avoid the Small Pedestrian; Trip the Reactor to prevent a Safety Event). The design, implementation, and fielding of an ACS capability will alter the cyber-attack surface such that existing risk management plans will need to be updated to include how to protect and defend against data-science and decision-support-system attack classes. These attack classes would include protection of the design and training environments where algorithm selection and testing and training data would be obvious attack vectors. These attack classes would also require an informed set of detection and response procedures to identify anomalous behaviors and document best practices for anomaly assessment and vulnerability mitigation and remediation. Last year we published a Cyber Threat Assessment Methodology for Autonomous and Remote Operations for AR/SMRs along with a companion publication on Cyber Attack and Defense Use Cases. The focus of the methodology was on describing and enumerating ACS processes, components, and functions such that security engineers could: evaluate subversion options against the target; identify threat actor attributes and capabilities derived from each subversion option; and identify security controls and response countermeasures. The Use Cases document offered detailed methodology examples including an assessment of a Military Base SMR, an Autonomous System Decision Loop, and implementation of AR/SMR Machine Learning algorithms. Our proposal at the end of last year was to focus on implementation of subversion prototypes related to the last Use Case area: AR/SMR Machine Learning (ML) Algorithms. We included six attack scenarios in our Use Cases paper: a Poisoning Attack against ML functions implemented using an FPGA; a Trojaning Attack against ML classifiers exploiting the excitability of Nuclear Engineers; a Backdooring Attack against ML Training environments to ensure persistence of an attack vector; a False Positive Evasion Attack against multi-factor Access Control Systems using clever inputs; an Inference Attack against ML models by an Insider with access to the Operational environment; and an Adversarial Reprogramming Attack against a Material Access Control Video Surveillance System. At the beginning of this year these six attack scenarios were provided to our research teams at Georgia Tech and Idaho State University and each team successfully implemented a subversion attack against a ML implementation to include transient misclassifications. While this is a notable outcome from this type of research, this paper offers the reader insight into not only how to structure and execute these types of attacks, but into the thought process behind how the researcher investigated the problem space, performed initial algorithm implementation, and the trial-and-error behind arriving at the successful subversion prototypes. We include in this paper a set of associated Scenarios on how these subversion prototypes could be implemented and an initial set of guidance for AR/SMR architects, Nuclear Regulators, and Cyber Defenders to implement awareness and defense capabilities into their current operational portfolios.

42 ENGINEERING↗

DOE OSTI2024

Neutrino fast flavor oscillations with moments: Linear stability analysis and application to neutron star mergers

Providing an accurate modeling of neutrino physics in dense astrophysical environments such as binary neutron star mergers presents a challenge for hydrodynamic simulations. Nevertheless, understanding how flavor transformation can occur and affect the dynamics, the mass ejection, and the nucleosynthesis will need to be achieved in the future. Computationally expensive, large-scale simulations frequently evolve the first classical angular moments of the neutrino distributions. By promoting these quantities to matrices in flavor space, we develop a linear stability analysis of fast flavor oscillations using only the first two “quantum” moments, which notably requires generalizing the classical closure relations that appropriately truncate the hierarchy of moment equations in order to treat quantum flavor coherence. After showing the efficiency of this method on a well-understood test situation, we perform a systematic search of the occurrence of fast flavor instabilities in a neutron star merger simulation. Here, we discuss the successes and shortcomings of moment linear stability analysis, as this framework provides a time-efficient way to design and study better closure prescriptions in the future.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

IDAES-PSE 2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.0.0 Release Highlights Removal of deprecated features from IDAES v1 Update to Pyomo v6.5 – this required a number of updates to support the new NL solver writer and to address some changes in Pyomo Creation of new testing suite for backward compatibility, model robustness and verification More general implementation of the Helmholtz EoS. This brings some new features like standard property diagrams, choice of mass or mole basis, and new state variable options Standardizing names in Heat Exchanger models (breaking change from v2.0.0a2): Control Volumes named hot_side and cold_side Ports names hot_side_inlet, hot_side_outlet, cold_side_inlet and cold_side_outlet Config Blocks names hot_side_config and cold_side_config Config arguments for user provided names for each side: hot_side_name and cold_side_name. Updating Keras surrogate tool to use v1.1 of OMLT New prototype API for model initialization (idaes.core.initialization) The new API uses "Model Initializer" objects instead of class methods, allowing for the definition of multiple initialization routines for a single model A number of common, model agnostic initialization routines have also been defined, including initialization from data, block-decomposition and a general hierarchical approach equivalent to the existing method for common unit models New metadata for thermophysical properties – valid_range This can be used to record the range of values over which a property value can be trusted, such as the range of experimental data used to regress parameters A number of new utility functions have been added to check for properties with values outside the valid range and to set bounds based on this metadata Updated construction of balance expressions in Control Volumes to remove unneeded terms In the past, unneeded terms were added as a constant 0 term, however they will now be dropped entirely from the expression This was necessary due to more strict unit checking in the new Pyomo solver writer which no longer ignores 0 terms Updates to metadata for thermophysical properties to better define known properties and units of measurement This results in more strict enforcement of standard naming for thermophysical and reaction properties Users can still define custom properties, but these must be done explicitly using the define_custom_properties() method instead of being implicitly created by add_property() Updated convergence tester utility tool to support definition of benchmark files (JSON format) and comparison of performance to benchmarks Set default iteration limit for IPOPT in IDAES config to 200 iterations Update scaling of example models to work with new Pyomo NL solver writer Improve testing of extensions and examples infrastructure to avoid need for downloading files Updated distillation column to centralize common functionality and remove a number of Pyomo warnings

IDAES↗

DOE OSTI2021

Assessing the Energy Resilience of Office Buildings: Development and Testing of a Simplified Metric for Real Estate Stakeholders

Increasing concern over higher frequency extreme weather events is driving a push towards a more resilient built environment. In recent years there has been growing interest in understanding how to evaluate, measure, and improve building energy resilience, i.e., the ability of a building to provide energy-related services in the event of a local or regional power outage. In addition to human health and safety, many stakeholders are keenly interested in the ability of a building to allow continuity of operations and minimize business disruption. Office buildings are subject to significant economic losses when building operations are disrupted due to a power outage. We propose “occupant hours lost” (OHL) as a means to measure the business productivity lost as the result of a power outage in office buildings. OHL is determined based on indoor conditions in each space for each hour during a power outage, and then aggregated spatially and temporally to determine the whole building OHL. We used quasi-Monte Carlo parametric energy simulations to demonstrate how the OHL metric varies due to different building characteristics across different climate zones and seasons. The simulation dataset was then used to develop simple regression models for assessing the impact of ten key building characteristics on OHL. The most impactful were window-to-wall ratio and window characteristics. The regression models show promise as a simple means to assess and screen for resilience using basic building characteristics, especially for non-critical facilities where it may not be viable to conduct detailed engineering analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Mixed Nd 4+/3+ and Cluster Magnetism in Hexagonal Perovskite 12R-Ba 4 NdMn 3 O 12−γ

Hexagonal perovskite oxides with 12R stacking host well-separated face-sharing octahedral metal trimers with short metal–metal distances, leading in some cases to large degrees of magnetic frustration and cluster magnetism. Introducing magnetic ions in proximity to these trimers can influence the degree of frustration and cluster magnetism, but the extent to which the magnetism can be tuned by varying neighboring metal cations remains an open question. In this work, we test the impacts of using Nd in proximity to Mn trimers in the hexagonal perovskite 12R-Ba 4 NdMn 3 O 12−γ . Given this stoichiometry, Nd should assume the 4+ oxidation state with a spin state of S = 1, which would be the first realization of Nd 4+ in an oxide environment. Through detailed bulk magnetic, X-ray absorption spectroscopic, and powder neutron diffraction (PND) measurements, we find that Nd 4+ is realized in this material, but there is also partial reduction to Nd 3+ , which is charge-balanced by O vacancies. Magnetometry measurements indicate an antiferromagnetic ordering temperature T N ≈ 16 K, and PND measurements reveal a surprising collinear antiferromagnetic structure with magnetic space group Pc2/m (no. 10.49 in BNS notation), which has not previously been seen in this class of materials. Our results represent a comprehensive analysis of the structural, electronic, and magnetic properties of 12R-Ba 4 NdMn 3 O 12−γ , showing the first observation of partial Nd 4+ in an oxide, and demonstrating that this structural class can host a broad range of magnetic structures which are not easily predicted based on compositional trends.

14 SOLAR ENERGY↗

DOE OSTI2021

Space to Air High-Altitude Region Adjoint Neutron Transport

Neutrons from an atmospheric nuclear explosion can be detected by sensors in orbit. Current tools for characterizing the neutron energy spectrum assume a known source and use forward transport to recreate the detector response. In realistic scenarios the true source is unknown, making this an inefficient, iterative approach. In contrast, the adjoint approach directly solves for the source spectrum, enabling source reconstruction. The time–energy fluence at the satellite and adjoint transport equation allow a Monte Carlo method to characterize the neutron source’s energy spectrum directly in a new model: the Space to High-Altitude Region Adjoint (SAHARA) model. A new adjoint source event estimator was developed in SAHARA to find feasible solutions to the neutron transport problem given the constraints of the adjoint environment. This work explores SAHARA’s development and performance for mono-energetic and continuous neutron energy sources. In general, the identified spectra were shifted towards energies approximately 5% lower than the true source spectra, but SAHARA was able to capture the correct spectral shapes. Continuous energy sources, including real-world sources Fat Man and Little Boy, resulted in identifiable spectra that could have been produced by the same distribution as the true sources as demonstrated by two-dimensional (2D) Kolmogorov–Smirnov tests.

LaMere, Zachary W.↗

DOE OSTI2026

Plasma confinement state classification in fusion power plants: Profile reflectometer and ensemble diagnostics

As Fusion Pilot Plants (FPPs) are increasingly viewed as within reach, many engineering challenges remain. Not many diagnostics are expected to be available in a reactor environment. Survivability, maintainability, and limited port space substantially restrict the number of FPP-relevant diagnostics. One remaining challenge is developing tools and devices to extract plasma state information necessary for controlling an FPP from a limited subset of diagnostics. This work is part of an overarching project to address this challenge. The specific diagnostic subset to be used in FPPs is still under debate. We take the approach of developing machine-learning-based tools for different significant plasma state parameters, using already known FPP-viable diagnostics. Previously we developed a plasma confinement mode classifier utilizing the Electron Cyclotron Emission (ECE) diagnostic. Here, we expand on this by developing a Profile Reflectometer (PR) based classifier with 97% test accuracy, and an ensemble model that combines the ECE and PR models into a single model, achieving 99% test accuracy.

Clark, Randall [Univ. of California, San Diego, CA↗

DOE OSTI2024

Fast neutron irradiation capability in existing thermal test reactors

In today’s nuclear industry, momentum towards the design, licensing, and construction of advanced nuclear demonstration plants, including fast reactors, is at a remarkably high level. However, there are currently no dedicated fast spectrum irradiation test facilities in the United States to support the development of fast spectrum technologies. As a result, a unique situation is developing where most of these plants will likely be designed by leveraging historic nuclear material technologies, but where the further optimization and advancement is impeded by the lack of fast neutron irradiation test facilities. While these circumstances present a challenge, there are some near-term opportunities that, if seized, can still help develop advanced fast reactor materials to a meaningful level of readiness to support future commercial fast reactors. Here, in this paper, we assess the feasibility of using thermal neutron filtering materials in existing experiment positions in the Advanced Test Reactor (ATR) at Idaho National Laboratory and the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory to simulate fast reactor test environments for nonfuel test specimens. Items investigated include the incident neutron flux (both fast and thermal), the total neutron fluence and cumulative atom displacements, helium production rate due to thermal neutron capture in nickel, and the potential impact that the thermal neutron filter material has on the cycle length of a given reactor. It is concluded that while HFIR provides the highest fast flux of all the options investigated, it is limited in the amount of thermal neutron filtering material that can be introduced into an experiment position without significantly affecting the operation of the reactor. Irradiation in Outboard-A positions in the ATR was found to be the most realistic near-term experiment avenue due to having ample space for several capsules in a moderately fast flux.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Oxygen and Proton Transport in Flooded Graphene Pores with N-Dopants and Defects

Reactant transport is an important consideration in the design of ideal electrode structures. For the oxygen reduction reaction catalyzed by Pt/C in proton exchange membrane fuel cell cathodes, O 2 and H + delivery to Pt surfaces and H 2 O transport away are required. Some Pt nanoparticles may only be accessible via micropores that are too small for ionomer molecules to enter, possibly requiring flooding for H + transport. Here, to test if these “buried” Pt particles can play a role in activity through this proposed transport mechanism, we have performed atomic-scale simulations based on reactive force field molecular dynamics with an emphasis on confinement below 20 Å. Diffusion coefficients as a function of the molar concentration and local environment have been evaluated in water domains confined in two-dimensional graphene nanochannels of various channel heights representing a morphological model for micropores in proton exchange fuel cell cathodes. Our study shows that local atomic-scale structures can strongly modify H + , O 2 , and H 2 O transport rates in flooded micropores less than 20 Å in size. We find that there is a critical crossover in diffusion behavior around the 20 Å spacing with larger pores having bulk-like diffusion properties and confinement below 20 Å monotonically decreases diffusion rates. As pore size decreases, we observe locally dispersed water regions that ultimately strand reactants from long-distance transport. These findings suggest that flooded micropores may in fact be viable transport pathways for relevant reactants and products if the pore walls on opposite sides remain separated by ≥10 Å separation. Furthermore, the confinement effect is so strong that N-doping and C-vacancy defects in the C pore wall have only a minimal impact on diffusion rates and their effects are counterintuitively more apparent at larger spacings. These findings provide valuable insight regarding cathode performance and the role “stranded” catalyst particles may play in fuel cell cathodes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Optical Test Bench (SYSM 5620 Final Report)

The National Ignition Facility (NIF) is a research facility that focuses on High Energy Density Science and Inertial Confinement Fusion. In the NIF, 192 laser beams are directed into a miniscule Target Capsule to generate an implosion – this is called a NIF Shot. Many NIF Users conduct experiments using different Targets and laser inputs to generate reactions that are of interest to the scientific community. Those reactions are monitored using various Target Diagnostic instruments, including Shot Time Photography – which provides crucial images of the Target as the implosion occurs. A subset of these NIF Users is less interested in the reaction itself and more interested in subjecting materials or devices to the intense environment that these reactions can create. Using the Target as a source of x-rays, neutrons, or blast loading for particular materials has been significant to the shock physics community and to those who work in extreme design spaces. However, the Material and Radiation Effects team – that performs these experiments on behalf of the NIF Users – has not been able to make good use of the existing Shot Time Photography Diagnostics. The STP cameras are focused on the Targets rather than the MRE Customers’ materials, the current system is not oriented to have a good view of the MRE Customers’ materials, and the cameras are limited in their capabilities (black and white, poor resolution, etc.). As MRE experiments increase in quantity and importance to the scientific community, the desire for visual monitoring of the experiments to substantiate models and inform other data is also rising.

42 ENGINEERING↗

DOE OSTI2021

Quasi-steady testing approach for high-power Hall thrusters

Hall effect thrusters operating at power levels in excess of several hundreds of kilowatts have been identified as enabling technologies for applications such as lunar tugs, large satellite orbital transfer vehicles, and solar system exploration. These large thrusters introduce significant testing challenges due to the propellant flow rate exceeding the pumping speed available in most laboratories. Even with proposed upgrades in mind, the likelihood that multiple vacuum facilities will exist in the near future to allow long duration testing of high-power Hall thrusters operating at power levels in excess of 100 kW remains extremely low. In this article, we numerically explore the feasibility of testing Hall thrusters in a quasi-steady mode defined by pulsing the mass flow rate between a nominal and a low value. Here, our simulations indicate that sub-second durations available before the chamber reaches critical pressure are sufficiently long to achieve the steady-state current and flow field distributions, allowing us to characterize thruster performance and the near plume region.

42 ENGINEERING↗

DOE OSTI2022

CubeSats for Gamma-Ray Astronomy [Book Chapter]

After many years of flying in space primarily for educational purposes, CubeSats – tiny satellites with form factors corresponding to arrangements of “1U” units, or cubes, each 10 cm on a side – have come into their own as valuable platforms for technology advancement and scientific investigations. CubeSats offer comparatively rapid, low-cost access to space for payloads that can be built, tested, and operated by relatively small teams, with substantial contributions from students and early career researchers. Continuing advances in compact, low-power detectors, readout electronics, and flight computers have now enabled X-ray and gamma-ray sensing payloads that can fit within the constraints of CubeSat missions, permitting in-orbit demonstrations of new techniques and innovative high-energy astronomy observations. Gamma-ray-sensing CubeSats are certain to make an important contribution in the new era of multi-messenger, time-domain astronomy by detecting and localizing bright transients such as gamma-ray bursts, solar flares, and terrestrial gamma-ray flashes; however, other astrophysical science areas requiring long observations in a low-background environment, including gamma-ray polarimetry, studies of nuclear lines, and measurement of diffuse backgrounds, will likely benefit as well. Here we present the primary benefits of CubeSats for high-energy astronomy, highlight the scientific areas currently or soon to be studied, and review the missions that are currently operating, under development, or proposed. A rich portfolio of CubeSats for gamma-ray astronomy already exists, and the potential for a broad range of creative and scientifically productive missions in the near future is very high.

79 ASTRONOMY AND ASTROPHYSICS↗