Effects of vacuum and nuclear radiation on engineering materials.
Vacuum and nuclear radiation effect on space vehicle and powerplant organic materials, emphasizing accessory hardware
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Vacuum and nuclear radiation effect on space vehicle and powerplant organic materials, emphasizing accessory hardware
Vehicle design, performance, and propulsion of Nuclear Engine for Rocket Vehicle /NERVA/
Design and development of all welded and brazed instrumentation ports for nuclear engine for rocket vehicle applications reactors
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.
Developments in nuclear engine for rocket vehicle program - engine and propellant feed systems, thrust chamber, radiation effects, ground support equipment, instrumentation, and exhaust
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.
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.
Neutron Resonance Transmission Analysis (NRTA) is a spectroscopic technique which uses the resonant absorption of neutrons in the epithermal range to infer the isotopic composition of an object. This spectroscopic technique has relevance in many traditional fields of science and nuclear security. NRTA in the past made use of large, expensive accelerator facilities to achieve precise neutron beams, significantly limiting its applicability. Here, we describe a series of NRTA experiments where we use a compact, low-cost deuterium-tritium (DT) neutron generator to produce short neutron beams (2.6 m) along with a 6 Li-glass neutron detector. The time-of-flight spectral data from five elements – silver, cadmium, tungsten, indium, and 238 U – clearly show the corresponding absorption lines in the 1-30 eV range. The experiments show the applicability of NRTA in this simplified configuration, and prove the feasibility of this compact and low-cost approach. This could significantly broaden the applicability of NRTA, and make it practical and applicable in many fields, such as material science, nuclear engineering, and arms control.
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.
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.
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.
Mission analysis computer programs for evaluating nuclear engine, vehicle system, and mission parameters for nuclear propulsion system applications in 1975-1990
Nuclear fission plays an important role in fundamental and applied science, from astrophysics to nuclear engineering, yet it remains a major challenge to nuclear theory. Theoretical methods used so far to compute fission observables rely on symmetry-breaking schemes where basic information on the number of particles, angular momentum, and parity of the fissioning nucleus is lost. In this letter, we analyze the impact of restoring broken symmetries in the benchmark case of 240 Pu.
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.
Computerized simulation of control systems for nuclear light bulb engine during start-up and at nominal full power operation
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.
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.
Approximately 100 graphite-reflected highly enriched uranium (HEU, 93.14 wt % 235 U) metal annular and cylindrical critical experiments were performed in the early 1960s at the Oak Ridge Critical Experiments Facility (ORCEF). This report presents details from experiment logbooks, experimental data sheets and the author's memory for 44 HEU metal (93.14 wt % 235 U) critical assemblies with graphite reflectors varying from 10 to 19 in. thick, outside diameters varying from 7 to 15 in., inside diameters varying from 7 to 13 in. and critical HEU metal masses varying from 20.4 to 69.0 kg. The data from the 44 experiments described in this report are acceptable for use as criticality safety benchmark experiments for the International Criticality Safety Evaluation Program (ICSBEP) once the uncertainty analysis on the measured k eff is completed. Based on previous ICSBEP benchmarks with this HEU metal at ORCEF, the uncertainties in the measured k eff are expected to be as low as ±0.0004. Preparation of this report is part of an effort at Oak Ridge National Laboratory (ORNL) to document more than 15 undocumented series of critical and subcritical experiments enumerated in Critical and Subcritical NEA Benchmark Possibilities for Measurements at ORCEF and Other US DOE Facilities (Mihalzo, ORNL/TM-2019/1188, 2019) and performed by ORNL at ORCEF and other US Department of Energy critical experiments facilities. More than 500 operational days of critical facility time were used, not including setup and dismantlement time. This documentation for a part of one series of graphite reflected highly enriched uranium metal critical experiments, that used 50 operational days of ORCEF time, was performed using funding received from the DOE Office of Nuclear Energy’s Nuclear Energy University Programs at the University of Tennessee Nuclear Engineering Department. This documentation was also supported by the Nuclear Criticality, Radiation Transport, and Safety programs at ORNL.