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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
NASA NTRS1963

Nuclear Lunar Logistics Study

This document has been prepared to incorporate all presentation aid material, together with some explanatory text, used during an oral briefing on the Nuclear Lunar Logistics System given at the George C. Marshall Space Flight Center, National Aeronautics and Space Administration, on 18 July 1963. The briefing and this document are intended to present the general status of the NERVA (Nuclear Engine for Rocket Vehicle Application) nuclear rocket development, the characteristics of certain operational NERVA-class engines, and appropriate technical and schedule information. Some of the information presented herein is preliminary in nature and will be subject to further verification, checking and analysis during the remainder of the study program. In addition, more detailed information will be prepared in many areas for inclusion in a final summary report. This work has been performed by REON, a division of Aerojet-General Corporation under Subcontract 74-10039 from the Lockheed Missiles and Space Company. The presentation and this document have been prepared in partial fulfillment of the provisions of the subcontract. From the inception of the NERVA program in July 1961, the stated emphasis has centered around the demonstration of the ability of a nuclear rocket to perform safely and reliably in the space environment, with the understanding that the assignment of a mission (or missions) would place undue emphasis on performance and operational flexibility. However, all were aware that the ultimate justification for the development program must lie in the application of the nuclear propulsion system to the national space objectives.

Source record↗
DOE OSTI2023

Irradiation Effect on Noble Metal Particles in Water Using in situ Liquid Cell STEM Observation

During geologic disposal of spent nuclear fuel (SNF) in an engineered nuclear waste repository, once all other barriers have degraded, oxidizing may occur at the solid-water interface owing to a self-generated radiolytic field. The repository design includes large quantities of iron (Fe), that is anticipated to corrode under an anoxic environment, and generate hydrogen (H 2 ) gas. This H 2 gas is thought to be able to suppress the dissolution of SNF through a catalytic reaction with noble metal particles (NMP) that are pre-existing in the SNF. This interaction leads to the decomposition of the major oxidant, hydrogen peroxide (H 2 O 2 ). In conclusion, these processes are described in the Fuel Matrix Degradation (FMD) model that is being used to predict SNF degradation rates. The NMP, therefore, plays an important role within the FMD model.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2015

Nuclear Thermal Propulsion Development Risks

There are clear advantages of development of a Nuclear Thermal Propulsion (NTP) for a crewed mission to Mars. NTP for in-space propulsion enables more ambitious space missions by providing high thrust at high specific impulse ((is) approximately 900 sec) that is 2 times the best theoretical performance possible for chemical rockets. Missions can be optimized for maximum payload capability to take more payload with reduced total mass to orbit; saving cost on reduction of the number of launch vehicles needed. Or missions can be optimized to minimize trip time significantly to reduce the deep space radiation exposure to the crew. NTR propulsion technology is a game changer for space exploration to Mars and beyond. However, 'NUCLEAR' is a word that is feared and vilified by some groups and the hostility towards development of any nuclear systems can meet great opposition by the public as well as from national leaders and people in authority. The public often associates the 'nuclear' word with weapons of mass destruction. The development NTP is at risk due to unwarranted public fears and clear honest communication of nuclear safety will be critical to the success of the development of the NTP technology. Reducing cost to NTP development is critical to its acceptance and funding. In the past, highly inflated cost estimates of a full-scale development nuclear engine due to Category I nuclear security requirements and costly regulatory requirements have put the NTP technology as a low priority. Innovative approaches utilizing low enriched uranium (LEU). Even though NTP can be a small source of radiation to the crew, NTP can facilitate significant reduction of crew exposure to solar and cosmic radiation by reducing trip times by 3-4 months. Current Human Mars Mission (HMM) trajectories with conventional propulsion systems and fuel-efficient transfer orbits exceed astronaut radiation exposure limits. Utilizing extra propellant from one additional SLS launch and available energy in the NTP fuel, HMM radiation exposure can be reduced significantly.

Kim, Tony↗
NASA NTRS2009

Grooved Fuel Rings for Nuclear Thermal Rocket Engines

An alternative design concept for nuclear thermal rocket engines for interplanetary spacecraft calls for the use of grooved-ring fuel elements. Beyond spacecraft rocket engines, this concept also has potential for the design of terrestrial and spacecraft nuclear electric-power plants. The grooved ring fuel design attempts to retain the best features of the particle bed fuel element while eliminating most of its design deficiencies. In the grooved ring design, the hydrogen propellant enters the fuel element in a manner similar to that of the Particle Bed Reactor (PBR) fuel element.

Emrich, William↗
NASA NTRS1991

The engineering of a nuclear thermal landing and ascent vehicle utilizing indigenous Martian propellant

The following paper reports on a design study of a novel space transportation concept known as a 'NIMF' (Nuclear rocket using Indigenous Martian Fuel). The NIMF is a ballistic vehicle which obtains its propellant out of the Martian air by compression and liquefaction of atmospheric CO2. This propellant is subsequently used to generate rocket thrust at a specific impulse of 264 s by being heated to high temperature (2800 K) gas in the NIMFs' nuclear thermal rocket engines. The vehicle is designed to provide surface to orbit and surface to surface transportation, as well as housing, for a crew of three astronauts. It is capable of refueling itself for a flight to its maximum orbit in less than 50 days. The ballistic NIMF has a mass of 44.7 tonnes and, with the assumed 2800 K propellant temperature, is capable of attaining highly energetic (250 km by 34,000 km elliptical) orbits. This allows it to rendezvous with interplanetary transfer vehicles which are only very loosely bound into orbit around Mars. If a propellant temperature of 2000 K is assumed, then low Mars orbit can be attained; while if 3100 K is assumed, then the ballistic NIMF is capable of injecting itself onto a minimum energy transfer orbit to Earth in a direct ascent from the Martian surface.

Zubrin, Robert M.↗
DOE OSTI2023

Adaptive, Active Learning, and Multifidelity Monte Carlo Methods in the MOOSE Stochastic Tools Module

MOOSE is an open-source computational platform for constructing multi-physics models and executing them in a massively parallel fashion. It has a stochastic tools module (STM) for forward/inverse uncertainty quantification (UQ) and surrogate modeling. This presentation details some recent developments to the STM with respect to the implementation of adaptive, active learning, and multifidelity Monte Carlo methods for forward UQ of computational models. Specifically, the adaptive Monte Carlo methods include Markov Chain Monte Carlo (MCMC)-driven algorithms like adaptive importance sampling and parallelized subset simulation for statistical QoI estimation, rare events analysis, and stochastic gradient-free optimization. The active learning methods include Gaussian Process (GP) surrogates and their training via Adam optimization, design of acquisition functions, and integration with samplers like Monte Carlo, adaptive importance, and parallelized subset simulation. These active learning methods are also designed to work in a batch mode, wherein, the required calls to the full computational model are executed in parallel whenever a user-specified batch size is met. The multifidelity methods in STM are broadly divided into two categories: hierarchical, where a defined hierarchy exists among the low-fidelity models, and peer, where all the low-fidelity models are treated equally. A GP surrogate is used to learn the differences between the low- and high-fidelity models in both multifidelity categories, and acquisition functions from the active learning classes are used to decide whether to rely on a low-fidelity model or call the expensive high-fidelity model. Alongside the software description and usage, applications are also presented to nuclear engineering computational models including a TRISO nuclear fuel particle, a reactor pressure vessel, and a heat-pipe microreactor.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multiplicity counting using organic scintillators to distinguish neutron sources: An advanced teaching laboratory

In this advanced instructional laboratory, students explore complex detection systems and nondestructive assay techniques used in the field of nuclear physics. After setting up and calibrating a neutron detection system, students carry out timing and energy deposition analyses of radiation signals. Through the timing of prompt fission neutron signals, multiplicity counting is used to carry out a special nuclear material (SNM) nondestructive assay. Our experimental setup is comprised of eight trans-stilbene organic scintillation detectors in a well-counter configuration, and measurements are taken on a spontaneous fission source as well as two (α,n) sources. By comparing each source's measured multiplicity distribution, the resulting measurements of the (α,n) sources can be distinguished from that of the spontaneous fission source. Such comparisons prevent the spoofing, i.e., intentional imitation, of a fission source by an (α,n) neutron source. This instructional laboratory is designed for nuclear engineering and physics students interested in organic scintillators, neutron sources, and nonproliferation radiation measurement techniques.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2024

Wave Rotor Enhanced Nuclear (WREN) Propulsion: NASA Innovative Advanced Concepts (NIAC) - Phase I Final Report

Nuclear Thermal Propulsion (NTP) is identified as one of the preferred propulsion technologies for manned missions throughout the solar system (NASA MSFC).[1, 2] The state-ofthe-art NTP cycle is based on a solid core Nuclear Engine for Rocket Vehicle Application (NERVA)[3] class technology (Fig. 1) that is envisioned to provide a specific impulse of 900 seconds doubling chemical rocket performance (450 seconds). Even with this impressive increase, the NTP NERVA designs still have issues providing adequate initial to final mass fractions for high ΔV missions.[4] Nuclear Electric Propulsion (NEP) can provide extremely high Isp (2,000 to over 10,000 seconds) but with only low thrust and limits on mass to power ratios. The need for an electric power source also adds the issue of heat rejection in space where thermal energy conversion is at best 30-40% under ideal conditions. NASA Space Technology Mission Directorate (STMD) has recently expressed interest in finding advanced nuclear propulsion technology through the NASA Go:Thrust RFI.[5, 6] A novel Wave Rotor (WR) topping cycle has been proposed for our NASA NIAC concept. It promises to deliver similar thrust as NERVA class NTP propulsion, but with Isp in the 1,200-2,000 second range. Coupled with an NEP cycle, the duty cycle Isp can further be increased (1,800-4,000 seconds) with minimal addition of dry mass. This bimodal design enables fast transit trajectories for manned missions to Mars and revolutionizes the deep space exploration of our solar system.

Nuclear Thermal Propulsion↗
DOE OSTI2023

2023 SAGE-Camp Report

The 7 th Summer Physics Camp for Young Women was successfully held in person in 2023 from June 5 th to 16 th at the New Mexico School for the Arts in Santa Fe, NM at Hilo Intermediate School in Hawaii. This year’s camp was dedicated to the topic of Energy Security and was made possible thanks to the strong collaboration of Los Alamos, Sandia and Hawaii teams and the logistical and financial support of Los Alamos and Sandia National Laboratories, New Mexico Consortium, SAGE- Moore Foundation, LANL Foundation, ACS, IEE, APS four corners, N3B, Hawaii Museum of Science and Technology, New Mexico School for the Arts (NMSA) and Tech Source. The camp mobilized more than 120 volunteers who made the camp a success. The camp is free of charge to the students and included free lunch and snacks for the busy brains to have plenty of energy, also included all materials needed for the hands-on activities (like drone building, crystal structure, solar panels, wind turbine fabrication, soldering, coding etc) and also a stipend for students who attended for the full two weeks and for two educators and two student mentors in NM. The camp offered 32 high school students from New Mexico and 8 from Hawaii a unique opportunity to explore science topics and meet a broad range of role model professionals across STEM fields including astrophysics, cybersecurity, Energy fields, space science, engineering, biophysics, environmental science, robotics, computer science, nuclear engineering, radiological science, physics and chemistry. With nearly 120 volunteers who came mainly from Los Alamos National Laboratory (66%) and Sandia national laboratories (18%), two funded educators from NM, Dr. Weldon Beauchamp and Dr. Ellee Cook, and two educators from Hilo, Dr. Pascale Creek Pinner and LeAnn Ragasa, the camp was educationally sound and extremely varied. The collaboration with school educators is critical for the goal of this camp to not only impact students' lives but also improve STEM education in NM and Hawaii. The ultimate goal of the camp is to increase higher education aspirations of students, empower them to consider careers in STEM and learn more about the opportunities available to them in our local colleges and DOE National Laboratories. In addition, the camp also hired 2 past students as student mentors, Megan Odom and Elisea Jackson, who currently attend NMSA and were students at the camp in 2022 when it was virtual. The in-person camp which aims at empowering under-represented minorities in STEM in our community received more than 52 applications this year from all over NM and 8 applications from Hawaii. Our selection criteria are based on diversity, equity and inclusion, and students for whom the camp can be a life-changing opportunity are given a chance to attend the camp. During COVID, the camp was held virtually and gave the opportunity to students from remote areas in NM and Hawaii to attend from their homes. This year, fantastic families supported students everyday even when home was in remote areas in NM like Lea county, Sandoval county, Bernalillo or Mora county. The organizers hope next year they can offer a residential option for students from remote areas.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Fact Sheet - Long-length Scintillating Fibers for Radiation Detection

As the world looks to underground geological repositories for storing nuclear waste, technologies to safeguard the material are needed. An example of a project that developed a new technology is Tripwire. Tripwire proposes a multi-sensor system approach for geological spent nuclear fuel repositories that relies of radiation, vibration, and electromagnetic detection. Tripwire was developed by the Applied Radiation Measurements and Systems (ARMS) group at INL and sponsored by the National Nuclear Security Administration (NNSA). The ARMS group performs research and development, testing and evaluation, operational support, and training focused on applied ionizing radiation detection and measurement. Application areas include nuclear engineering for advanced reactors and fuel cycle operations, nuclear nonproliferation, nuclear counterproliferation, nuclear forensics, and arms control and disarmament.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
NASA NTRS1993

Computational modeling of nuclear thermal rockets

The topics are presented in viewgraph form and include the following: rocket engine transient simulation (ROCETS) system; ROCETS performance simulations composed of integrated component models; ROCETS system architecture significant features; ROCETS engineering nuclear thermal rocket (NTR) modules; ROCETS system easily adapts Fortran engineering modules; ROCETS NTR reactor module; ROCETS NTR turbomachinery module; detailed reactor analysis; predicted reactor power profiles; turbine bypass impact on system; and ROCETS NTR engine simulation summary.

Peery, Steven D.↗
DOE OSTI2025

An investigation on machine learning predictive accuracy improvement and uncertainty reduction using VAE-based data augmentation

The confluence of ultrafast computers with large memory, rapid progress in Machine Learning (ML) algorithms, and the availability of large datasets place multiple engineering fields at the threshold of dramatic progress. However, a unique challenge in nuclear engineering is data scarcity because experimentation on nuclear systems is usually more expensive and time-consuming than most other disciplines. One potential way to resolve the data scarcity issue is deep generative learning, which uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples that resemble the real data. In this way, one can significantly expand the dataset to train more accurate predictive ML models. In this study, our objective is to evaluate the effectiveness of data augmentation using variational autoencoder (VAE)-based deep generative models. We investigated whether the data augmentation leads to improved accuracy in the predictions of a deep neural network (DNN) model trained using the augmented data. Additionally, the DNN prediction uncertainties are quantified using Bayesian Neural Networks (BNN) and conformal prediction (CP) to assess the impact on predictive uncertainty reduction. To test the proposed methodology, we used TRACE simulations of steady-state void fraction data based on the NUPEC Boiling Water Reactor Full-size Fine-mesh Bundle Test (BFBT) benchmark. Here, we found that augmenting the training dataset using VAEs has improved the DNN model’s predictive accuracy, improved the prediction confidence intervals, and reduced the prediction uncertainties.

Bayesian neural network↗