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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Indoor Occupant Counting by RF Backscattering

Building HVAC (heating, ventilation and air conditioning) consumes approximately 13% of all energy consumption in USA. Motion detectors, cameras and user programmable thermostats have been shown to be ineffective for HVAC controls to save energy, mostly due to the user concerns of comfort, reliability and privacy. A new HVAC control system based on real-time occupant counting that is fully automated, highly accurate, economically sensible and preserving privacy and aesthetics can thus bring forth a disruptive impact to this large energy sector. Our indoor occupant monitoring technology is based on the radio-frequency identification system (RFID), deployed in the room, not on the occupants. One reader with four antennas can be deployed on the ceiling or behind the ceiling panels for every thousand square feet in home, office and assisted living, with or without room partitions. The sticker-like passive tag, 10 cents each and maintenance-free, are profusely hidden on the wall or inside the furniture at arbitrary position, preserving privacy and aesthetics. The large number of tags can realize diverse observation points to accommodate arbitrary room layouts, which is impractical by other active units of camera, infrared, radar or lidar. With 20 tags, the system can reliably detect the number of occupants. For 100 tags, occupant posture and location can be known. The technology has been verified in the research labs and test buildings with very high accuracy. When the real-time occupant number can be accurately known without assuming devices on occupants or occupant motion, the building HVAC system can be automated to achieve building energy saving without sacrificing occupant comfort. According to our limited testing in a few types of building models and the simplified cost calculation, the RFID system has low overall cost in production, deployment, operation and maintenance. The signal processing algorithm based on machine learning requires very small number of training cases as most learning is transferrable for various layouts, and very low computational needs during operation, according to our testing in four different room sizes and layouts. In our preliminary estimate from HVAC saving alone, the RFID system can potentially pay for itself within 1.5 years, in addition to the other enhancement in building automation systems (BAS). Our commercialization strategy and business pitch deck focus on venturing this Cornell occupant monitoring technology into BAS and energy management markets. We have identified three broad BAS market segments of senior living, residential buildings, and office buildings. We have put together the minimum viable product characteristics for these identified segments, including analyses on total cost and competing technologies, as well as the fit and technical gaps for these market segments. We intend to bring the technology to market by licensing or partnering with existing BAS vendors. A list of potential collaborators and licensing partner candidates was assembled for different aspects of integrating our technology into a potential product that can be used with BAS.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2008

Best Practices for Researching and Documenting Lessons Learned

Identification, resolution, and avoidance of technical and programmatic issues are important for ensuring safe and successful space missions. Although the importance of applying lessons learned to reduce risk is frequently stressed, there is little material available to help technical and management personnel research and document lessons learned. Collecting, researching, identifying, and documenting lessons learned that will be useful to current and future management and engineering personnel is not always a straightforward task. This white paper presents lessons learned and best practices concerning the research and documentation of technical and organizational lessons learned. It is intended to enable organizations to initiate or improve lessons learned research and documentation efforts. The content of this white paper is based on four technical lessons learned projects conducted by the United Space Alliance (USA) Flight Design and Dynamics Department, in support of the NASA/Johnson Space Center (JSC) Flight Design and Dynamics Division. Each project published a report, titled as follows: (1) GPS Lessons Learned From the ISS, Space Shuttle and X-38; (2) Lessons Learned From Seven Space Shuttle Missions; (3) Space Shuttle Rendezvous and Proximity Operations Experience Report; and (4) Navigation Technical History with Lessons Learned. The four projects were different in availability of subject matter experts and primary source material, subject scope, and the level of effort required to produce the final report. However, generic lessons can be drawn from all of them. The best practices will be discussed by the phases of report research and development: (1) Defining Report Requirements, Project Organization, and Schedule; (2) Collection and Analysis of Source Material; (3) Writing and Integrating the Report; and (4) Review and Revision of the Report.

Goodman, John L.↗
NASA NTRSDate not supplied

Predicting Adverse Events and their Precursors in Aviation Using Multi-Class Multiple-Instance Learning

In recent years, there has been a rapid growth in the application of machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications. However, this paper focuses on the identification of precursors, which is a relatively newer application. Precursors are events correlated with adverse events that happen prior to the adverse event itself. Therefore, precursor mining provides many benefits including understanding the reasons behind a safety incident and the ability to identify signatures, which can be tracked throughout a flight to alert the operators of an potential upcoming adverse event. This work proposes using the multiple-instance learning (MIL) framework, a weakly supervised learning task, combined with a carefully designed Multi-Head Convolutional Neural Networks-Recurrent Neural Networks (MHCNN-RNN) architecture to predict different type of adverse events for any given flights and identify their precursors with little to no post-processing.Results obtained show that the MHCNN-RNN is able to accurately forecast high speed and high path angle events during the approach, and that it is also capable of determining the aircraft’s parameters that are correlated to these events. These parameters can be considered precursors to the events.

multiple instance learning↗
DOE OSTI2025

Multi-scale, Multi-disciplinary, and Multi-agent Explainable AI with Koopman-Undergirded Learning, Prediction, and Analysis (M3EA KULPA) (Project Closeout Report)

The goal of this project was to develop and use domain-aware machine learning formulations, based on the Koopman Operator (KO), for modelling multi-scale, multi-disciplinary (e.g., multi-physics), and/or multi-agent systems. The project developed these formulations for the following cases: • Systems with dynamics at two separate time scales, • Systems with a bi-level hierarchical control structure, • Systems with bi-level hierarchical control and dynamics at two separate time scales (the lower level controls operating at the faster time scale), and • Systems with n separate but interacting agents/disciplines (with/without control, respectively); the controls for each agent could include bi-level hierarchical control and dynamics at two separate time scales as described above. The project then defined a set of dynamical systems consisting of different nonlinear oscillators that could be used to test these different formulations and then subsequently learned the KO models for those systems. With the KO models, we were able to do the following: • Quantify system stability, including both long-term and transient behavior, • Quantify the effects of feedbacks between the different time scales and agents/disciplines in terms of those feedbacks’ effects on system stability, • Replace a standard Proportional-Integral (PI) control in the hierarchical control structure with a KO-based Linear-Quadratic Regular (LQR), a form of optimal control, • Calculate optimal supervisory control policies a) with and without time scale separated dynamics at the lower level control levels and b) with both PI and KO-based LQR lower level control policies, and • Calculate dynamic Nash equilibria for multi-agent systems where each agent makes its own control decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Protecting and Defending against Autonomous Control Systems and Digital Twin Cyber Attacks: Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Models in Nuclear Power Plants (Final)

Navigating through the complex tapestry of technological advancements, "Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Model in Nuclear Power Plants" stands at the intersection of cybersecurity and nuclear power plant operations, embarking on a journey through the intricacies of securing digital twins against malicious cyber activities. As nuclear power plants progressively integrate digital twin technology and machine learning models to optimize operations and ensure system reliability, they inadvertently expose themselves to a new spectrum of vulnerabilities, notably in the realm of hyperparameter attacks. Hyperparameters, integral in machine learning model tuning and optimal performance of digital twins, have emerged as a target for adversaries aiming to destabilize the predictive capabilities and therefore, the operational accuracy of these digital entities within critical infrastructures like nuclear plants. This paper, therefore, meticulously threads the needle through the development of a robust response strategy, poised to shield these digital reflections against calculated hyperparameter manipulations, ensuring that the digital twin can effectively and securely function as a reliable proxy for its physical counterpart. The ensuing sections delve into the orchestrated maelstrom of multi-rate time-changing intelligent coordinated hyperparameter attacks and the implementation of event-triggered predictive control, laying down a structured, predictive, and responsive framework that safeguards the nexus where the digital and physical realms of nuclear power plants coalesce. The operational integrity of digital twins in nuclear power plants depends critically on the security of machine learning hyperparameters. This study makes two different contributions. First, a decision-based idea known as a multi-rate time changing intelligent coordinated hyperparameter attack is put forth. In this attack, many hyperparameters are repeatedly changed using both random and intelligent optimal techniques by the attacker. These assaults introduce varied rates at different attack steps, compromise various amounts of hyperparameters, and improve stealth and flexibility. Second, a technique is developed for event triggered predictive control to rapidly respond to potential hyperparameter attacks. This control integrates a sliding window framework, retaining a history of previous data points and employing linear regression to predict the next data point from the current dataset. The control gain K is determined using the Lyapunov-Krasovskii method, and subsequently, an action is developed. Finally, the outcome of the simulation demonstrates the viability of the proposed method for defending nuclear power plant digital twins from hyperparameter attacks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

OPERATIONAL TECHNOLOGY BEHAVIORAL ANALYTICS (OTBA) – A DATA-CENTRIC APPROACH FOR REDUCING CYBERSECURITY RISK

This paper provides a summary of the methodology, findings, lessons learned, and insights from an investigation into the feasibility of the Operational Technology Behavioral Analytics (OTBA) cybersecurity approach. The concept was evaluated with data from the National Carbon Capture Center (NCCC) – a U.S. Department of Energy (DOE) funded facility that is managed and operated by Southern Company at Alabama Power’s E. C. Gaston generating power plant in Wilsonville, Alabama. Appropriate data sources for the post-combustion carbon capture system were identified. Infrastructure was deployed to monitor, capture and archive data for the system. Critical parameters for each subsystem were identified and analyzed. Machine-learning algorithms were used to establish and characterize normal operations and subsequently identify anomalies. This effort yielded valuable insights and formed the basis of a data-centric strategy for detecting cyber-attacks along with a coordinated response philosophy. A significant takeaway is that the OTBA cybersecurity approach is quite portable; it can be applied to other critical infrastructure beyond fossil power generation.

Black, Clifton↗
NASA NTRS2020

ISEE System Technical Report

The Immersive Simulations and Engineering Environment (ISEE) Laboratory at Kennedy Space Center is a tool used to assist with operation simulation, for Human Factors operation activities, assisting future missions. The simulations created are important because they ensure that simulated operations are performed for customers, to assist with requirements. Simultaneously, each simulation will allow stakeholders to determine any and all risk associated with the Human Factors involved in a specific assignment. My internship consists of multiple project elements. The first of which, was to assist the Integrations branch chief in developing a growth rate algorithm proof. The integral part of this project, involved learning to powerup and perform the operational procedures of the ISEE lab, capturing and creating characters used in the CAD model virtual environment. In doing so, this led to use the software to model and 3D print screw cap structures for mock-up. The structures will be used to manipulate weight change out used in mock-up design and construction. These structures will also be used in future mock-up models constructed in the lab.

Darryl A J Minor↗
DOE OSTI2024

Enhanced Carbon Storage Forecasting via Cross-Geology Transfer Learning

Rapid simulation of the spatiotemporal evolution of pressure & saturation for SACROC 1. Neural operator was trained on only 153 simulation runs 2. Trained to account for heterogeneity and variations/uncertainties in engineering, fluids, and geology 3. Pressure forecast has less than 1% error 4. Saturation forecast has less than 2% error 5. Traditional simulator takes 1 hour for a single scenario, while neural operator takes less than 1 minute. Rapid simulation of the spatiotemporal evolution of pressure & saturation for IBDP 1. Transfer Learning was implemented on the SACROC-based Neural Operator that was trained on only 20 simulation runs for IBDP Site 2. SACROC and IBDP Sites have several significant differences in geology and engineering parameters. 3. Pressure forecast has less than 5 psi error 4. Saturation forecast has less than 7% error 5. Traditional simulator takes 1 hour for a single scenario, while neural operator takes less than 1 minute and only 20 simulations for training/validation.

Misra, Siddharth↗
NASA NTRS2006

Logistics Lessons Learned in NASA Space Flight

The Vision for Space Exploration sets out a number of goals, involving both strategic and tactical objectives. These include returning the Space Shuttle to flight, completing the International Space Station, and conducting human expeditions to the Moon by 2020. Each of these goals has profound logistics implications. In the consideration of these objectives,a need for a study on NASA logistics lessons learned was recognized. The study endeavors to identify both needs for space exploration and challenges in the development of past logistics architectures, as well as in the design of space systems. This study may also be appropriately applied as guidance in the development of an integrated logistics architecture for future human missions to the Moon and Mars. This report first summarizes current logistics practices for the Space Shuttle Program (SSP) and the International Space Station (ISS) and examines the practices of manifesting, stowage, inventory tracking, waste disposal, and return logistics. The key findings of this examination are that while the current practices do have many positive aspects, there are also several shortcomings. These shortcomings include a high-level of excess complexity, redundancy of information/lack of a common database, and a large human-in-the-loop component. Later sections of this report describe the methodology and results of our work to systematically gather logistics lessons learned from past and current human spaceflight programs as well as validating these lessons through a survey of the opinions of current space logisticians. To consider the perspectives on logistics lessons, we searched several sources within NASA, including organizations with direct and indirect connections with the system flow in mission planning. We utilized crew debriefs, the John Commonsense lessons repository for the JSC Mission Operations Directorate, and the Skylab Lessons Learned. Additionally, we searched the public version of the Lessons Learned Information System (LLIS) and verified that we received the same result using the internal version of LLIS for our logistics lesson searches. In conducting the research, information from multiple databases was consolidated into a single spreadsheet of 300 lessons learned. Keywords were applied for the purpose of sorting and evaluation. Once the lessons had been compiled, an analysis of the resulting data was performed, first sorting it by keyword, then finding duplication and root cause, and finally sorting by root cause. The data was then distilled into the top 7 lessons learned across programs, centers, and activities.

Evans, William A.↗
DOE OSTI2024

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators

We address the challenge of acoustic simulations in three-dimensional (3D) virtual rooms with parametric source positions, which have applications in virtual/augmented reality, game audio, and spatial computing. The wave equation can fully describe wave phenomena such as diffraction and interference. However, conventional numerical discretization methods are computationally expensive when simulating hundreds of source and receiver positions, making simulations with parametric source positions impractical. To overcome this limitation, we propose using deep operator networks to approximate linear wave-equation operators. This enables the rapid prediction of sound propagation in realistic 3D acoustic scenes with parametric source positions, achieving millisecond-scale computations. By learning a compact surrogate model, we avoid the offline calculation and storage of impulse responses for all relevant source/listener pairs. Our experiments, including various complex scene geometries, show good agreement with reference solutions, with root mean squared errors ranging from 0.02 to 0.10 Pa. Notably, our method signifies a paradigm shift as—to our knowledge—no prior machine learning approach has achieved precise predictions of complete wave fields within realistic domains.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV

The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a reactor-relevant high-performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid instability limits. High-performance experiments at TCV show statistically significant improvements in relevant metrics. A predict-first experiment, increasing plasma current by 20% from baseline, demonstrates the NSSM’s ability to make small extrapolations. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty and demonstrates the relevance of SciML for fusion experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2014

Designing an Alternate Mission Operations Control Room

The Huntsville Operations Support Center (HOSC) is a multi-project facility that is responsible for 24x7 real-time International Space Station (ISS) payload operations management, integration, and control and has the capability to support small satellite projects and will provide real-time support for SLS launches. The HOSC is a serviceoriented/ highly available operations center for ISS payloads-directly supporting science teams across the world responsible for the payloads. The HOSC is required to endure an annual 2-day power outage event for facility preventive maintenance and safety inspection of the core electro-mechanical systems. While complete system shut-downs are against the grain of a highly available sub-system, the entire facility must be powered down for a weekend for environmental and safety purposes. The consequence of this ground system outage is far reaching: any science performed on ISS during this outage weekend is lost. Engineering efforts were focused to maximize the ISS investment by engineering a suitable solution capable of continuing HOSC services while supporting safety requirements. The HOSC Power Outage Contingency (HPOC) System is a physically diversified compliment of systems capable of providing identified real-time services for the duration of a planned power outage condition from an alternate control room. HPOC was designed to maintain ISS payload operations for approximately three continuous days during planned HOSC power outages and support a local Payload Operations Team, International Partners, as well as remote users from the alternate control room located in another building. This paper presents the HPOC architecture and lessons learned during testing and the planned maiden operational commissioning. Additionally, this paper documents the necessity of an HPOC capability given the unplanned HOSC Facility power outage on April 27th, 2011, as a result of the tornado outbreak that damaged the electrical grid to such a degree that significantly inhibited the Tennessee Valley Authority's ability to transmit electricity throughout the North Alabama region.

Montgomery, Patty↗
DOE OSTI2025

Modernizing Accelerator Responsiveness and Controls in Operations

Accelerators increasingly use artificial intelligence (AI) and machine learning (ML) software and workflows for a variety of tasks, from optimization to fault detection and recovery. Efficient and sustainable application of these technologies necessitates specialized and facility-specific infrastructure commitments. Accelerator facilities also introduce unique radiation and security hazards, placing additional demands on operational infrastructure. These needs further escalate the prioritization of effective collaboration models and associated funding mechanisms and legal frameworks.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Automating Sensor Characterization with Bayesian Optimization

The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert's time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.

Cuevas-Zepeda, Julian [Chicago U., KICP; Chicago U↗
DOE OSTI2024

RuralAI in Tomato Farming: Integrated Sensor System, Distributed Computing, and Hierarchical Federated Learning for Crop Health Monitoring

Precision horticulture is evolving due to scalable sensor deployment and machine learning (ML) integration. These advancements boost the operational efficiency of individual farms, balancing the benefits of analytics with autonomy requirements. However, given concerns that affect wide geographic regions (e.g., climate change), there is a need to apply models that span farms. Federated learning (FL) has emerged as a potential solution. FL enables decentralized ML across different farms without sharing private data. Traditional FL assumes simple two-tier network topologies and, thus, falls short of operating on more complex networks found in real-world agricultural scenarios. Networks vary across crops and farms and encompass various sensor data modes, extending across jurisdictions. New hierarchical FL (HFL) approaches are needed for more efficient and context-sensitive model sharing, accommodating regulations across multiple jurisdictions. Here, we present the RuralAI architecture deployment for tomato crop monitoring, featuring sensor field units for soil, crop, and weather data collection. HFL with personalization is used to offer localized and adaptive insights. Model management, aggregation, and transfers are facilitated via a flexible approach, enabling seamless communication between local devices, edge nodes, and the cloud.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Integrated Cyber/Physical Grid Resiliency Modeling

This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Nuclear Thermal Propulsion Ground Test History

Nuclear Thermal Propulsion (NTP) was started in ~1955 under the Atomic Energy Commission as project Rover and was assigned to Los Alamos National Laboratory. The Nevada Test Site was selected in 1956 and facility construction began in 1957. The KIWI-A was tested on July 1, 1959 for 5 minutes at 70MW. KIWI-A1 was tested on July 8, 1960 for 6 minutes at 85MW. KIWI-A3 was tested on October 10, 1960 for 5 minutes at 100MW. The National Aeronautics and Space Administration (NASA) was formed in 1958. On August 31, 1960 the AEC and NASA established the Space Nuclear Propulsion Office and named Harold Finger as Director. Immediately following the formation of SNPO, contracts were awarded for the Reactor In Flight Test (RIFT), master plan for the Nuclear Rocket Engine Development Station (NRDS), and the Nuclear Engine for Rocket Vehicle Application (NERVA). From December 7, 1961 to November 30, 1962, the KIWI-B1A, KIWI-B1B, and KIWI-B4A were tested at test cell A. The last two engines were only tested for several seconds before noticeable failure of the fuel elements. Harold Finger called a stop to any further hot fire testing until the problem was well understood. The KIWI-B4A cold flow test showed the problem to be related to fluid dynamics of hydrogen interstitial flow causing fuel element vibrations. President Kennedy visited the NTS one week after the KIWI-B4A failure and got to see the engine starting to be disassembled in the maintenance facility. The KIWI-B4D and KIWI-B4E were modified to not have the vibration problems and were tested in test cell C. The NERVA NRX program started testing in early 1964 with NRX-A1 cold flow test series (unfueled graphite core), NRX-A2 and NRX-A3 power test series up to 1122 MW for 13 minutes. In March 1966, the NRX-EST (Engine System Test) was the first breadboard using flight functional relationship and total operating time of 116 minutes. The NRX-EST demonstrated the feasibility of a hot bleed cycle. The NRX-A5 had multiple start-ups in May-June 1966 with 30.75 minutes accumulative operating time at or above 1GW. The NRX-A6 was tested in December 1969 and ran for 62 minutes at 1100 MW. Each engine had post-test examination and found various structure anomalies which were identified for correction and the fuel element corrosion rate was reduced. The Phoebus series of research reactors began testing at test cell C, in June 1965 with Phoebus 1A. Phoebus 1A operated for 10.5 minutes at 1100 MW before unexpected loss of propellant and leading to an engine breakdown. Phoebus 1B ran for 30 minutes in February of 1967. Phoebus 2A was the highest steady state reactor built at 5GW. Phoebus 2A ran for 12 minutes at 4100 MW demonstrating sufficient power is available. The Peewee test bed reactor was tested November- December 1968 in test cell C for 40 minutes at 500MW with overall performance close to pre-run predictions. The XE' engine was the only engine tested with close to a flight configuration and fired downward into a diffuser at the Engine Test Stand (ETS) in 1969. The XE' was 1100 MW and had ~28 start-ups. The nuclear furnace NF-1 was operated at 44 MW with multiple test runs at 90 minutes in the summer of 1972. The NF-1 was the last NTP reactor tested. The Rover/NERVA program was cancelled in 1973. However, before cancellation, a lot of other engineering work was conducted by Aerojet on a 75, 000 lbf prototype flight engine and by Los Alamos on a ~16,000 lbf "Small Engine" nuclear rocket design. The ground test history of NTP at the NRDS also offers many lessons learned on how best to setup, operate, emergency shutdown, and post-test examine NTP engines. The reactor and engine maintenance and disassembly facilities were used for assembly and inspection of radioactive engines after testing. Most reactor/ engines were run at test cell A or test cell C with open air exhaust. The Rover/NERVA program became aware of a new environmental regulation that would restrict the amount of radioactive particulates allowed to be release in open air and successfully demonstrated a scrubber concept with the NF-1. The ETS stand was the only one with a high altitude test chamber used for XE'. The ETS and other test cells showed the effects the engine's radiation had on the facility materials and instrumentation as well as side effects the ground test facility has back on the engine operation. The breakdown of Phoebus 1A at test cell C showed how the site was cleaned up and back to operation for five more engines before the program was cancelled.

Gerrish, Harold P.↗