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NASA NTRS2019

Development of a High-Propellant Throughput Small Spacecraft Electric Propulsion System to Enable Lower Cost NASA Science Missions

This paper describes recent progress at the NASA Glenn Research Center (GRC) in the development and demonstration of an integrated high-propellant throughput small spacecraft electric propulsion (HT-SSEP) system based on a Hall-effect thruster. A center-mounted cathode and an innovative magnetic circuit topology were implemented in the design of the Hall-effect thruster to achieve high-propellant throughput, high performance, and efficient packaging. To minimize technical risk, the HT-SSEP development approach sought to limit design features and materials to those with a clear path-to-flight. A propellant throughput capability of greater than 100 kg at a minimum thruster efficiency of 50% was targeted. The proof-of-concept NASA-H64M laboratory model (LM) thruster was designed, fabricated, and tested at GRC in fiscal year 2018. The thruster development leveraged heritage Hall-effect thruster design and manufacturing processes wherever appropriate. Recent NASA advances in Hall-effect thruster technology were also leveraged. A scalable discharge power supply (DPS) capable of powering the H64M-LM was developed, then demonstrated as part of an integrated system test. The DPS uses commercial off-the-shelf components with spaceflight equivalents. A keeper supply with DC ignitor was breadboarded, then demonstrated with a laboratory cathode. Finally, feed system trade studies were performed to ascertain what feed system architecture might be appropriate for an HT-SSEP system. This paper details the motivations for the project, the development approach, the chosen sub-system architectures, design considerations, and test results.

Benavides, Gabriel F.↗

DOE OSTI2024

Demonstration and Evaluation of Explainable and Trustworthy Predictive Technology for Condition-based Maintenance

The domestic nuclear power plant (NPP) fleet has historically relied on labor-intensive and time-consuming predictive maintenance (PdM) programs, thus driving up operation and maintenance (O&M) costs to achieve high-capacity factors. Artificial intelligence (AI) and machine-learning (ML) can help simplify complex problems such as diagnosing equipment degradation to enable more effective decision-making efforts. The benefits of AI will be felt through more efficient plant O&M, improved work processes, and better integration of people and technology. Together, these benefits hold the promise to make nuclear power more sustainable by reducing O&M costs while improving employee engagement. While AI and ML technologies hold significant promise for the nuclear industry, there are challenges or barriers to their adoption. Explainability and trustworthiness of AI are two salient challenges that need to be addressed for wider deployment of these technologies in NPPs. This research focuses specifically on addressing the explainability and trustworthiness of AI technologies to advance the human, technical, and organization (HTO) readiness levels in adopting a risk-informed PdM strategy at commercial NPPs. In addition, this approach can be adapted to enhance the acceptability of AI in other nuclear applications with a few application-specific modifications. The technical approach ensuring wider adoption of AI technologies was developed by Idaho National Laboratory (INL)—in collaboration with Public Service Enterprise Group (PSEG), Nuclear, LLC—by utilizing the circulating water system (CWS) at two PSEG-owned plant sites for demonstration. Focused user studies were performed in collaboration with subject matter experts (SMEs) from PSEG and other nuclear domains to enhance human and organization readiness by building trust in AI-informed technologies. VIsualization for PrEdictive maintenance Recommendation (VIPER)—a Battelle Energy Alliance, LLC, copyrighted software—was developed and expanded to provide a user-centric visualization by incorporating inputs from the collaborating utility, human factors engineering guidelines, and data analysts. The VIPER software enables users, who may be unfamiliar with ML in general, to be interactively engaged by asking technical questions about PdM, work orders, diagnosis results and their confidence levels, the kind of data being used, and the types of ML algorithms employed. This interactive engagement enhances explainability and builds trust. One of the enabling accomplishments was the integration of large language models (LLMs), both text-based and vision-based, in the VIPER software.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Remote Sensing of Live Fuel Moisture for Wildfires Using SMAP Satellite Observations

Live Fuel Moisture (LFM) is a critical parameter for wildfire risk assessment, traditionally measured by labor-intensive field sampling. However, sampled LFM data are influenced by site-specific factors, such as local vegetation types and plant traits, and are often collected retrospectively after wildfire events, making it difficult to obtain pre-fire data for predictive applications. Here, we evaluate the relationship between LFM and Vegetation Water Content (VWC) and Soil Moisture (SM) retrieved from SMAP L-band brightness temperature using the Maximum Entropy Production (MEP) approach. The MEP-retrieved VWC exhibited strong correlation with in situ measurements of LFM ( r > 0.6) in the Western U.S. The integration of high-resolution vegetation coverage data enhances the detection of sub-grid vegetation heterogeneity. This study demonstrates the operational potential of remote sensing derived VWC as a scalable proxy of LFM, supporting its application in regional assessment of wildfire risk.

Cho, Kyeungwoo [Georgia Institute of Technology, A↗

NASA NTRS2019

Development of a High-Propellant Throughput Small Spacecraft Electric Propulsion System (HT-SSEP) to Enable Lower Cost NASA Science Missions

Discusses progress at the NASA Glenn Research Center (GRC) in the development and demonstration of an integrated high-propellant throughput small spacecraft electric propulsion (HT-SSEP) system based on a Hall-effect thruster. A center-mounted cathode and an innovative magnetic circuit topology were implemented in the design of the Hall-effect thruster to achieve high-propellant throughput, high performance, and efficient packaging. To minimize technical risk, the HT-SSEP development approach sought to limit design features and materials to those with a clear path-to-flight. A propellant throughput capability of greater than 100 kilograms at a minimum thruster efficiency of 50 percent was targeted. The proof-of-concept NASA-H64M laboratory model (LM) thruster was designed, fabricated, and tested at GRC in fiscal year 2018. The thruster development leveraged heritage Hall-effect thruster design and manufacturing processes wherever appropriate. Recent NASA advances in Hall-effect thruster technology were also leveraged. A scalable discharge power supply (DPS) capable of powering the H64M-LM was developed, then demonstrated as part of an integrated system test. The DPS uses commercial off-the-shelf components with spaceflight equivalents. A keeper supply with DC ignitor was breadboarded, then demonstrated with a laboratory cathode. Finally, feed system trade studies were performed to ascertain what feed system architecture might be appropriate for an HT-SSEP system. This paper details the motivations for the project, the development approach, the chosen sub-system architectures, design considerations, and test results.

Benavides, Gabriel↗

NASA NTRS1994

Composite chronicles: A study of the lessons learned in the development, production, and service of composite structures

A study of past composite aircraft structures programs was conducted to determine the lessons learned during the programs. The study focused on finding major underlying principles and practices that experience showed have significant effects on the development process and should be recognized and understood by those responsible for using of composites. Published information on programs was reviewed and interviews were conducted with personnel associated with current and past major development programs. In all, interviews were conducted with about 56 people representing 32 organizations. Most of the people interviewed have been involved in the engineering and manufacturing development of composites for the past 20 to 25 years. Although composites technology has made great advances over the past 30 years, the effective application of composites to aircraft is still a complex problem that requires experienced personnel with special knowledge. All disciplines involved in the development process must work together in real time to minimize risk and assure total product quality and performance at acceptable costs. The most successful programs have made effective use of integrated, collocated, concurrent engineering teams, and most often used well-planned, systematic development efforts wherein the design and manufacturing processes are validated in a step-by-step or 'building block' approach. Such approaches reduce program risk and are cost effective.

Vosteen, Louis F.↗

NASA NTRS2018

Testing Enabling Technologies for Safe UAS Urban Operations

A set of more than 100 flight operations were conducted at NASA Langley Research Center using small UAS (sUAS) to demonstrate, test, and evaluate a set of technologies and an overarching air-ground system concept aimed at enabling safety. The research vehicle was tracked continuously during nominal traversal of planned flight paths while autonomously operating over moderately populated land. For selected flights, off-nominal risks were introduced, including vehicle-to-vehicle (V2V) encounters. Three contingency maneuvers were demonstrated that provide safe responses. These maneuvers made use of an integrated air/ground platform and two on-board autonomous capabilities. Flight data was monitored and recorded with multiple ground systems and was forwarded in real time to a UAS traffic management (UTM) server for airspace coordination and supervision.

Moore, Andrew J.↗

DOE OSTI2024

Potential reductions in fine particulate matter and premature mortality following implementation of air pollution controls on coal-fired power plants in India

Coal-fired power plants (CFPPs) account for > 70% of electricity generation in India, but < 5% of facilities have installed technologies for sulfur dioxide (SO 2 ) and nitrogen oxide (NO X ) removal. Emissions of these pollutants lead to the formation of fine particulate matter (PM 2.5 ) and an increased risk of premature mortality for exposed populations. Here, we use a nested version of the GEOS-Chem global chemical transport model (0.5° × 0.625° resolution) for India to estimate reductions in PM 2.5 concentrations that could have been achieved by implementing existing emission control technologies like flue-gas desulfurization (FGD) and/or selective catalytic reduction (SCR). We quantify the associated burden of disease using the integrated exposure response (IER) and global exposure mortality model (GEMM) functions and compare the costs of premature mortality to those for FGD installation. Model simulations for 2010 suggest installation of FGD would have reduced mean annual PM 2.5 concentrations across India by 8%, compared to 3% with SCR installation, and 11% with both FGD and SCR. A 7–28% reduction in PM 2.5 was simulated for local communities closest to CFPPs (same model grid cell), leading to up to 17% reduction in annual premature mortality. Overall, more than 0.21–0.48 million premature deaths would have been avoided over a 10-year period if FGD had been implemented on all CFPPs, compared to 0.09–0.21 million with SCR and 0.22–0.72 million with both FGD and SCR. Benefits associated with such actions are approximately $\$18.1$–$\$604$ billion USD per year, which is equivalent to ~ 0.44 to 10% of India’s GDP. These results suggest that monetary benefits from avoided premature mortality far outweigh the capital and operational costs of FGD and/or SCR installation of $\$19.5$ billion and/or $\$32.8$ billion per year, respectively. This information is essential because the high costs of installation and operation are often given as reasons for delaying installation and commissioning. Finally, we conclude that policy actions to control air pollution from CFPPs are economically justifiable.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2020

Evaluation of Hybrid FPOG Applications in Regulated and Deregulated Markets Using HERON

Recent changes in the U.S. energy market, such as low natural gas prices and increased electricity production for variable renewable energy (VRE) sources, have led to an economic crisis for existing light-water reactor (LWR) nuclear power plants (NPP). Many owners and operators of LWRs have elected to decommission these plants rather than continue using them as consistent sources of clean baseload power. This has led to exploration of various possibilities to increase the economic viability of these units, including market restructuring to monetize benefits LWRs already provide to the grid through ancillary markets, load following and economic dispatch, and possible integration of secondary systems directly to the NPP for production of additional products through technologies such as hydrogen electrolysis or water desalination. Previous studies have considered the technologies associated with these Integrated Energy Systems (IES) activities, and the analysis of markets for these secondary products. To analyze the economic viability of various system configurations including IES, especially given the uncertainty surrounding load demand, electricity prices, and the availability of VRE resources, the stochastic technoeconomic analysis package HERON (Heuristic Energy Resource Optimization Network) was released earlier this year as an extension of the risk analysis framework RAVEN (Risk Analysis Virtual Environment). HERON focuses foremost on making the complex uncertainty quantification analysis tools approachable for energy systems analysts, also providing general dispatch optimization algorithms for those workflows. HERON continues to be improved and tested as a significant part of the IES viability analyses performed in this work. HERON is not a capacity expansion model. To consider market and grid energy system development in a variety of scenarios, HERON is best used in coupling with modelling tools such as US-REGEN, which sacrifice some of the uncertainty analysis and resolution of HERON's modelling for the ability to efficiently predict the change in the grid energy system's profile due to economic drivers over decades. HERON can then use this information to explore the economic viability of introducing changes to the predicted outcomes, such as the introduction of an IES. In this work, experts at EPRI using US-REGEN provide six projection scenarios for use in HERON stochastic technoeconomic analysis (STEA) in considering the options available for increasing LWR economic viability through introduction of a hydrogen-centric IES using a high-temperature steam electrolysis plant (HTSE), hydrogen storage, and a constant-rate contracted hydrogen consumer. The results obtained are differential in nature; they do not report expected profits for any configuration, but rather report on the possible increase in the NPV of a configuration with respect to a baseline no-IES configuration. Due to the uncertainty captured in the variable net load of the systems, there is likewise uncertainty in the mean values reported. We consider this viability both in terms of a regulated market, where the energy producers and IES are owned and operated by single entity, as well as a deregulated market, where the IES chooses its bid for electricity generation and is then dispatched by the grid system operator. Results indicate that for deregulated markets, the inclusion of the IES is often statistically beneficial. This is especially true in policies that are not favorable towards nuclear, as nuclear is less often dispatched and is forced to deal with frequent idle capacity. In the nominal case as well as the case of carbon tax policies, inclusion of the IES clearly benefited the economic performance of the NPP. In the regulated case, however, there was a trend towards minimizing the IES, likely due to the optimal sizing performed by US-REGEN of the NPP within the system as well as the lack of penalty for idle capacity at the NPP in the regulated market analyses.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty

Carbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO2 emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underperforming storage sites can lead to costly infrastructure modifications, inefficient pipeline routing, and economic shortfalls. To address this challenge, we propose a novel optimization workflow that is based on mixed-integer linear programming and explicitly integrates probabilistic modeling of storage uncertainty into CCS infrastructure design. This workflow generates multiple infrastructure scenarios by sampling storage capacity distributions, optimally solving each scenario using a mixed-integer linear programming model, and aggregating results into a heatmap to identify core infrastructure components that have a low likelihood of underperforming. A risk index parameter is introduced to balance trade-offs between cost, CO2 processing capacity, and risk of underperformance, allowing stakeholders to quantify and mitigate uncertainty in CCS planning. Applying this workflow to a CCS dataset from the US Department of Energy’s Carbon Utilization and Storage Partnership project reveals key insights into infrastructure resilience. Reducing the risk index from 15% to 0% is observed to lead to an 83.7% reduction in CO2 processing capacity and a 77.1% decrease in project profit, quantifying the trade-off between risk tolerance and project performance. Furthermore, our results highlight critical breakpoints, where small adjustments in the risk index produce disproportionate shifts in infrastructure performance, providing actionable guidance for decision-makers. Unlike prior approaches that aimed to cheaply repair underperforming infrastructure, our workflow constructs robust CCS networks from the ground up, ensuring cost-effective infrastructure under storage uncertainty. These findings demonstrate the practical relevance of incorporating uncertainty-aware optimization into CCS planning, equipping decision-makers with a tool to make informed project planning decisions.

Olson, Daniel↗

NASA NTRSDate not supplied

Independent Reliability Assessment of the NASA GSFC Laser Transmitter for the LISA Program

The Laser Interferometer Space Antenna (LISA) is a partnership between the European Space Agency (ESA) and NASA to build a Gravitational Wave (GW) observatory. The observatory, which consists of a three-spacecraft constellation with a nominal separation of 2.5 million km between each spacecraft, provides a tool for scientists to directly detect gravitational waves generated from various astronomical phenomena in a waveband that is not accessible from Earth. NASA is developing laser transmitters as one of the potential US contributions to LISA. The NASA laser design leverages lessons learned from previous flight missions and included the latest technologies in photonics packaging and reliability engineering to ensure a laser lifetime of >16 years covering integration and test through a possible extended mission phase. As part of the laser development process, NASA’s Goddard Space Flight Center (GSFC) requested support from the NASA Engineering and Safety Center (NESC) to independently assess the Technology Readiness Level (TRL) of the LISA Laser System (LS). The independent assessment included the following tasks: (a) assess the design for weaknesses and suggest improvements to mitigate risks, (b) assess the laser reliability plan for weaknesses and suggest improvements to mitigate risks and improve effectiveness, and (c) assess the current redundancy plan on laser subsystems for weaknesses and suggest improvements to mitigate risks and improve effectiveness. The NESC team comprised of a team of subject matter experts (SMEs) and performed a 12-month review of every aspect of the laser design. We present the assessment findings and the current development progress of the LISA laser to meet the mission requirements with a delivery of a form, fit, and functional TRL6 laser to the LSIA mission by late 2023.

LISA↗

NASA NTRS1983

Integrated Technology Rotor/Flight Research Rotor (ITR/FRR) concept definition

A program was performed to identify and evaluate a variety of candidate rotor hub configurations for the ITR/FRR. Design criteria were established for the development of the hub concepts. Five initial hub configurations were examined and two were selected for further refinement and evaluation. The selected configurations were bearingless designs. The Classic Elastic Pitch Beam (CEPB) and the Plain Elastic Pitch Beam (PEPB), both exhibit superior qualities for the criteria in the final evalution. The CEPB is favored over the PEPB primarily because it offers better capability for built in damping for stability and is judged to have a lower risk in development.

Howes, H. E.↗

DOE OSTI2025

INTEGRATION OF DATA ANALYTICS WITH SYSTEM HEALTH PROGRAMS

Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry developed and regulatory programs. However, these programs have proven to be labor intensive and expensive. There is an opportunity to significantly enhance the collection, analysis, and use of this information to provide more cost-effective plant operation. Additionally, there is an acute industry need to leverage advanced technology to reduce costs and improve operational effectiveness. The goal of this paper is to provide effective and efficient analytical methods and tools to support risk-informed decisions for the equipment reliability and asset management programs at nuclear power plants. This is accomplished by creating a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). Here we are supporting typical system engineer decisions regarding maintenance activity scheduling and component ageing management. This is performed in a risk-informed context where herein the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow. A challenge is that the structure of this workflow strongly depends on the decision that needs to be made, the type of data available, and the constraints that need to be considered. Current methods are designed to provide specific answers to specific problems; however, these methods might prove to be inadequate even when problem settings slightly change (e.g., different types of requirements, additional dependencies between system reliability and economics). We tackled this challenge by designing framework in a flexible and modular fashion such that the user can assemble and customize his/her own workflow that integrates SSC economic lifecycle models (e.g., maintenance and replacement costs), system reliability models, and optimization methods.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer Based Hydrogen Production Facility

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at NREL's Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗

NASA NTRS1994

The natural space environment: Effects on spacecraft

The effects of the natural space environments on spacecraft design, development, and operation are the topic of a series of NASA Reference Publications currently being developed by the Electromagnetics and Environments Branch, Systems Analysis and Integration Laboratory, Marshall Space Flight Center. This primer provides an overview of the natural space environments and their effect on spacecraft design, development, and operations, and also highlights some of the new developments in science and technology for each space environment. It is hoped that a better understanding of the space environment and its effect on spacecraft will enable program management to more effectively minimize program risks and costs, optimize design quality, and successfully achieve mission objectives.

James, Bonnie F.↗

NASA NTRS2000

An Integrated Low-Speed Performance and Noise Prediction Methodology for Subsonic Aircraft

An integrated methodology has been assembled to compute the engine performance, takeoff and landing trajectories, and community noise levels for a subsonic commercial aircraft. Where feasible, physics-based noise analysis methods have been used to make the results more applicable to newer, revolutionary designs and to allow for a more direct evaluation of new technologies. The methodology is intended to be used with approximation methods and risk analysis techniques to allow for the analysis of a greater number of variable combinations while retaining the advantages of physics-based analysis. Details of the methodology are described and limited results are presented for a representative subsonic commercial aircraft.

Olson, E. D.↗

NASA NTRS2010

Effect of Simulant Type on the Absorptance and Emittance of Dusted Thermal Control Surfaces in a Simulated Lunar Environment

During the Apollo program the effects of lunar dust on thermal control surfaces was found to be more significant than anticipated, with several systems overheating due to deposition of dust on them. In an effort to reduce risk to future missions, a series of tests has been initiated to characterize the effects of dust on these surfaces, and then to develop technologies to mitigate that risk. Given the variations in albedo across the lunar surface, one variable that may be important is the darkness of the lunar dust, and this study was undertaken to address that concern. Three thermal control surfaces, AZ-93 white paint and AgFEP and AlFEP second surface mirrors were dusted with three different lunar dust simulants in a simulated lunar environment, and their integrated solar absorptance ( ) and thermal emittance ( ) values determined experimentally. The three simulants included JSC-1AF, a darker mare simulant, NU-LHT-1D, a light highlands simulant, and 1:1 mixture of the two. The response of AZ-93 was found to be slightly more pronounced than that of AgFEP. The increased with fractional dust coverage in both types of samples by a factor of 1.7 to 3.3, depending on the type of thermal control surface and the type of dust. The of the AZ-93 decreased by about 10 percent when fully covered by dust, while that of AgFEP increased by about 10 percent. It was found that / varied by more than a factor of two depending on the thermal control surface and the darkness of the dust. Given that the darkest simulant used in this study may be lighter than the darkest dust that could be encountered on the lunar surface, it becomes apparent that the performance degradation of thermal control surfaces due to dust on the Moon will be strongly dependent on the and of the dust in the specific locality

Gaier, James R.↗

NASA NTRSDate not supplied

Current State of NASA Continuously Rotating Detonation Cycle Engine Development

NASA is currently investigating continuous detonation cycle engines for the application of lander and interplanetary space exploration missions. The performance benefits of a detonation cycle engine may allow for a broader design trade space and more compact geometry required for future missions to the Moon and onwards towards Mars. However, the technology readiness level (TRL) within the US was found to be low with several major risk factors that require understanding prior to full engine system development. One area of uncertainty is the extreme heat loads expected during thermal steady state conditions. To achieve this, an announcement for collaborative opportunity (ACO) partnership between IN Space LLC and NASA Marshall Space Flight Center (MSFC) was established to explore integration of additive manufacturing (AM) processes and the high conductance copper-based alloys, GRCop-42 and GRCop-84. This work outlines the hot fire testing of a 7K lbf thrust class fully AM GRCop-alloy rotating detonation rocket engine (RDRE). Two annular thrust chamber configurations emulating a lander engine system were tested with LOx/GH2 and LOx/LCH4. In both configurations, select hardware was actively cooled using de-ionized water and regeneratively cooled using LCH4. All primary hardware survived long duration tests to thermal steady state up to 133 seconds in a single burn. In total, 18 starts and 802 seconds of duration were achieved with and without visual confirmation of waves present. The proportion of burned propellant, or level of complete combustion, was found to be high compared to the theoretically achievable mean chamber pressure in all cases.

Thomas Teasley↗