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NASA NTRS1980

Orbit transfer vehicle engine study. Volume 3: Program costs

Budgetary and planning cost estimates are presented that were prepared for the development, production and operation and flight support phases for each of the engines proposed for OTV propulsion. The major features of each category engine are described. The development program estimates were structured to the preliminary program Work Breakdown Structures (WBS). Program costs are provided within the applicable WBS elements to Level 4 for each category engine. The production program cost estimates assume a first production lot of 50 units produced at a rate of two units per month. Cumulative average unit costs assume a 90% learning capability. The operations and flight support cost estimates are based on 15, 30 and 45 missions per year for the period 1988 through 1999. Estimated funding requirements were developed for each category and program phase. All cost estimates and funding data are presented in 1979 dollars. The assumptions and ground rules for these estimates are summarized.

Source record↗

NASA NTRS2004

In-Flight Icing Training for Pilots Using Multimedia Technology

Over the last five years, the Aircraft Icing Project of the NASA Aviation Safety Program has developed a number of in-flight icing education and training aids to support increased awareness for pilots of the hazards associated with atmospheric icing conditions. Through the development of this work, a number of new instructional design approaches and media delivery methods have been introduced to enhance the learning experience, expand user interactivity and participation, and, hopefully, increase the learner retention rates. The goal of using these multimedia techniques is to increase the effectiveness of the training materials. This paper will describe the mutlimedia technology that has been introduced and give examples of how it was used.

Burke, Kevin M.↗

NASA NTRS2004

Computation of Low Speed Cavity Noise

Over the last five years, the Aircraft Icing Project of the NASA Aviation Safety Program has developed a number of in-flight icing education and training aids to support increased awareness for pilots of the hazards associated with atmospheric icing conditions. Through the development of this work, a number of new instructional design approaches and media delivery methods have been introduced to enhance the learning experience, expand user interactivity and participation, and, hopefully, increase the learner retention rates. The goal of using these multimedia techniques is to increase the effectiveness of the training materials. This paper will describe the multimedia technology that has been introduced and give examples of how it was used.

Blech, Richard A.↗

NASA NTRS2011

Leveraging Gaming Technology to Deliver Effective Training

The best way to engage a soldier is to present them with training content consistent with their learning preference. Blended Interactive Multimedia Instruction (IMI) can be used to leach soldiers what they need to do, how to do each step, and utilize a COTS game engine to actually practices the skills learned. Blended IMI provides an enjoyable experience for the soldier, thereby increasing retention rates and motivation while decreasing the time to subject mastery. And now mobile devices have emerged as an exciting new platform, literally placing the training into the soldier's hands. In this paper, we will discuss how we leveraged commercial game engine technology, tightly integrated with the Blended IMI, to train soldiers on both laptops and mobile devices. We will provide a recent case study of how this training is being utilized, benefits and student/instructor feedback.

Cimino, James D.↗

NASA NTRS2013

Affordable Development and Qualification Strategy for Nuclear Thermal Propulsion

Nuclear Thermal Propulsion (NTP) is a concept which uses a nuclear reactor to heat a propellant to high temperatures without combustion and can achieve significantly greater specific impulse than chemical engines. NTP has been considered many times for human and cargo missions beyond low earth orbit. A lot of development and technical maturation of NTP components took place during the Rover/NERVA program of the 60's and early 70's. Other NTP programs and studies followed attempting to further mature the NTP concept and identify a champion customer willing to devote the funds and support the development schedule to a demonstration mission. Budgetary constraints require the use of an affordable development and qualification strategy that takes into account all the previous work performed on NTP to construct an existing database, and include lessons learned and past guidelines followed. Current guidelines and standards NASA uses for human rating chemical rocket engines is referenced. The long lead items for NTP development involve the fuel elements of the reactor and ground testing the engine system, subsystem, and components. Other considerations which greatly impact the development plans includes the National Space Policy, National Environmental Policy Act, Presidential Directive/National Security Council Memorandum #25 (Scientific or Technological Experiments with Possible Large-Scale Adverse Environmental Effects and Launch of Nuclear Systems into Space), and Safeguards and Security. Ground testing will utilize non-nuclear test capabilities to help down select components and subsystems before testing in a nuclear environment to save time and cost. Existing test facilities with minor modifications will be considered to the maximum extent practical. New facilities will be designed to meet minimum requirements. Engine and test facility requirements are based on the driving mission requirements with added factors of safety for better assurance and reliability. Emphasis will be placed on small engines, since the smaller the NTP engine, the easier it is to transport, assemble/disassemble, and filter the exhaust during tests. A new ground test concept using underground bore holes (modeled after the underground nuclear test program) to filter the NTP engine exhaust is being considered. The NTP engine system design, development, test, and evaluation plan includes many engine components and subsystems, which are very similar to those used in chemical engines, and can be developed in conjunction with them Other less mature NTP engine components and subsystems (e.g., reactor) will be thoroughly analyzed and tested to acceptable levels recommended by the referenced standards and guidelines. The affordable development strategy also considers a prototype flight test, as a final step in the development process. Preliminary development schedule estimates show that an aggressive development schedule (without much margin) will be required to be flight ready for a 2033 human mission to Mars.

Gerrish, Harold P., Jr.↗

NASA NTRS2013

Affordable Development and Qualification Strategy for Nuclear Thermal Propulsion

Gerrish, Harold P., Jr.↗

DOE OSTI2020

Million Worker Study

Radiation health issues have been an important aspect of DOE's Worker Safety and Health programs. The goal is to ensure that workers are adequately protected from the various radiological hazards associated with DOE sites and operations. Since the early 1940’s DOE has supported the conduct of epidemiologic studies of practically every DOE (AEC) facility and collected these data, now managed by the Oak Ridge Associated Universities (ORAU), in remarkable detail. In the early 2000s, the Office of Health, Safety and Security authorized access to specific DOE worker datasets. Shortly thereafter, the DOE Office of Science provided funds for a pilot study that confirmed the feasibility of the Million Worker Study (MWS), and then additional resources were provided (with other agencies) to extend the follow-up of many populations, not just DOE workers, but also atomic veterans, industrial radiographers, nuclear power plant workers and medical radiation workers (the Million Persons Study (MPS)). This proposal was a continuation of support provided by DOE, specifically to extend the follow-up of the worker populations at the Mallinckrodt Chemical Works (MCW) and Los Alamos National Laboratory (LANL). The work addresses DOE’s interest in clarifying the health risks of their workers as well as contributing knowledge on radiation risks that is relevant today with regard to compensation schemes. The findings are also important for the US public in light of the increased population exposure to medical imaging, environmental circumstances such as hydraulic fracturing, increased exposures during high altitude flights, and with regard to nuclear accidents such as Fukushima and possible terrorist events. Furthermore, it is important to consider reducing the uncertainty in current risk estimates by developing risk coefficients based on healthy American workers who are more representative of U.S. workers and the general public than 1945 Japanese survivors of the atomic bombs living in a war-torn country which experienced deprivation, malnourishment, and increased rates of infections and other diseases. But more importantly, it is important to learn whether radiation exposures received gradually over time (e.g., years) are more or less effective in causing health effects, cancer in particular, than if the radiation dose is received all at once in a fraction of a second as experienced by the Japanese atomic bomb survivors. Finally, the ability to evaluate and combine large populations with intakes of radionuclides such as uranium, radium, plutonium, americium and polonium will provide new quantitative knowledge on human health effects that hitherto has not been possible. This cost-efficient study has built on the investments made and foundations laid by investigators and government agencies, including DOE, over the past 30-40 years, which have established early worker cohorts that can now provide answers to questions on the lifetime human health risks associated with low-level radiation exposures. Collaborating institutions included: International Epidemiology Institute, Oak Ridge Associated Universities, Oak Ridge National Laboratory, Los Alamos National Laboratory, Landauer, Inc., and Vanderbilt University. Follow-up of these two populations and the integration of them with the many other cohorts (now a total of 31) in the MPS continues under a separate DOE grant (DE-AU0000046).

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2025

Domestic and international consensus standards for nuclear criticality safety – overview & status [Abstract]

The domestic and international consensus standards for nuclear criticality safety (NCS) were developed as the lessons-learned from process criticality accidents. These consensus standards were developed to reduce the rate of process criticality accidents in facilities that process, store, handle or transport fissionable materials by hand. After a significant increase in criticality accidents through the mid-1960’s, the rate of criticality accidents decreased due to these standards and the criticality accident rate is extremely low from an industrial safety perspective.

ANS↗

DOE OSTI2025

Domestic and International Consensus Standards for Nuclear Criticality Safety - Overview & Status

The domestic and international consensus standards for nuclear criticality safety (NCS) were developed based on the lessons-learned from process criticality accidents. These consensus standards were developed to reduce the rate of process criticality accidents in facilities that process, store, handle, or transport fissionable materials by hand. After a significant increase in criticality accidents through the mid-1960s, the rate of criticality accidents decreased as a result of these standards, and the criticality accident rate is extremely low from an industrial safety perspective. The last known criticality accident inside the United States was in 1978 (nearly 47 years ago) at the Idaho Chemical Processing Plant, and outside the United States, an accident occurred at Tokai-mura, Japan, in 1999 (almost 26 years ago). The domestic consensus standards for NCS include the American National Standards (ANS) that are prepared and published by the American Nuclear Society and approved by the American National Standards Institute (ANSI). The ANS Standards Board, the NCS Consensus Committee, and the ANS-8 Subcommittee oversee the development and maintenance of these standards. There are currently 18 standards in the ANS-8 series. Nine ANS-8 standards are either in revision mode or planned for revision. A new standard for the use of nondestructive assay measurements (ANSI/ANS-8.28-2024) for NCS was approved in March of 2024. The international consensus standards for NCS calculations, procedures, and practices are maintained and developed within the International Organization for Standardization, Technical Committee 85 “Nuclear Energy,” Subcommittee 5, “Nuclear Fuel Technology,” and Working Group 8, “Nuclear Criticality Safety.” Eleven standards are currently available, four standards are proposed for revision, and four standards are at various stages of development. This paper provides the NCS community with a high-level overview and status report of domestic and international NCS consensus standards to stimulate interest and to support their continued development.

Bowen, Douglas G [ORNL] (ORCID:0000000212460026)↗

DOE OSTI2022

Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage

In this work, we present deep-learning-based surrogate models for CCUS developed with four different algorithms and a physics-framed two-phase flow problem involving displacement of water by CO 2 . The deep-learning models were trained using 3D datasets describing the pressure plume, CO 2 saturation plume, and water extraction rate generated by numerical simulation. The hyperparameters defining the architecture of the neural networks were optimized to determine the slimmest network size and training parameters that give the most efficient performance at the least training cost. To develop a robust model that closely mimics the governing physical laws, the discretized form of the two-phase fluid transport equation was used to formulate the supervised deep-learning task. The algorithms investigated in this study predicted the data to above 95% accuracy, with the multi-layer perceptron model demonstrating the best performance by balancing training speed, prediction time, and prediction accuracy with lean network capacity. Furthermore, the surrogate models simultaneously predict reservoir pressure and CO 2 saturation in every grid block, including the surface well extraction rate and bottomhole pressure, at all simulation times for a given static model realization in just a few seconds on a standard desktop computer. A key outcome of this study is that limits can be placed on network design parameters to avoid over designing neural networks, with associated efficiencies in training and prediction times. This is very useful because large volumes of data may be generated in CCUS projects and over-design of neural network architectures imposes penalties that are antithetical to the goal of near-real time forecasting.

58 GEOSCIENCES↗

DOE OSTI2020

Multiprincipal Component P2-Na 0.6 (Ti 0.2 Mn 0.2 Co 0.2 Ni 0.2 Ru 0.2 )O 2 as a High-Rate Cathode for Sodium-Ion Batteries

Mixing transition metal cations in nearly equiatomic proportions in layered oxide cathode materials is a new strategy for improving the performances of Na-ion batteries. The mixing of cations not only offers entropic stabilization of the crystal structure but also benefits the diffusion of Na ions with tuned diffusion activation energy barriers. In light of this strategy, a high-rate Na 0.6 (Ti 0.2 Mn 0.2 Co 0.2 Ni 0.2 Ru 0.2 )O 2 cathode was designed, synthesized, and investigated, combining graph-based deep learning calculations and complementary experimental characterizations. This new cathode material delivers high discharge capacities of 164 mA g –1 at 0.1 C and 68 mAh g –1 at a very high rate of 86 C, demonstrating an outstanding high rate capability. Ex situ and operando synchrotron X-ray diffraction were used to reveal the detailed structural evolution of the cathode upon cycling. Using the climbing-image nudged elastic-band calculation and Ab initio molecular dynamics simulations, we show that the optimal transition metal composition enables a percolating network of low barrier pathways for fast, macroscopic Na diffusion, resulting in the observed high rate performance.

25 ENERGY STORAGE↗

DOE OSTI2023

Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models

Abstract The temperature experienced by reactants during preparation in a reactor is a key component in determining the yield and homogeneity of usable chemical products such as biomass particles. Thermocouples with sensors can be used to monitor spatial temperature gradients within reactors but these sensors are often too expensive and/or invasive. The present work proposes a strategy to identify optimal machine learning models to infer the maximum effective temperature experienced by particles during oxidative biomass torrefaction using key thermochemical combustion parameters. The maximum rate of weight loss, the corresponding temperature, and fixed carbon content on a dry‐ash‐free basis are used as literature‐based predictor variables obtained from thermogravimetric analysis. The evaluation of 24 machine‐learning models using the standard tenfold cross‐validation method suggests that the exponential Gaussian process regression (GPR) model is the most effective, followed by other GPR models. These high‐performing GPR models were also utilized to predict the effective preparation temperature distribution of reactor‐produced biomass particles under eight conditions of varying residence time and air‐to‐biomass ratio. The effective preparation temperature and residence time of individual biomass particles were then encoded into the torrefaction severity factor and used to estimate the energy yield of the reactor output as a novel quality control method. © 2023 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

09 BIOMASS FUELS↗

NASA NTRSDate not supplied

Training the Powered-Lift Evaluation Pilot

This poster describes a project to prepare pilots for a study assessing novel aircraft automation concepts for electric Vertical Takeoff and Landing (eVTOL) aircraft using NASA’s Vertical Motion Simulator (VMS). By exploring the operational and learning challenges related to transitioning between forward flight and vertical landing, we seek to establish baselines of pilot workload and aircraft handling qualities across varying atmospheric conditions and automation states. The simulated eVTOL design differentiates flight control allocations as a function of airspeed across four speed ranges as the vehicle transitions between fully thrust-borne lift and wing-borne lift. As speed increases, side stick controls command: translational ground speeds, vertical and lateral acceleration, vertical rate, vertical flight path angle, and bank angle. This novel approach to flight control allocation creates a significant learning challenge for pilots. Since initial eVTOL aircraft may have limitations on hover capabilities, automation and flight guidance cues also vary with airspeed to provide efficient landing profiles while still providing cues suitable for cruise flight. The NASA team prepared the study pilots to follow these flight guidance cues along curved Required Navigation Performance (RNP) approaches and along 6o and 12o glide paths to energy-efficient assistive-hover landing and goarounds. The pre-VMS preparation sought to prepare pilots from diverse levels of experience and background. To do this, NASA researchers designed and developed a fixed-based, large field-ofview simulator with terrain, structures, and air traffic. With one day of combined classroom learning and skill development in the fixedbase simulator, pilots were largely able to fly the simulated eVTOL in the VMS with sufficient mastery to provide handling quality assessments using the Cooper-Harper Handling Qualities Rating and workload assessments through the Bedford Workload Scale.

AAM↗

DOE OSTI2025

Prediction of Specificity of α-Conotoxins to Subtypes of Human Nicotinic Acetylcholine Receptors with Semi-supervised Machine Learning

Conotoxins are a family of highly toxic neurotoxins composed of cysteine-rich peptides produced by marine cone snails. The most lethal cone snail species to humans is Conus geographus, with fatality rates of up to ∼65% from a single sting, which is caused mostly by the activity of α-conotoxins against human nicotinic acetylcholine receptors (nAChRs). While sequence-based machine learning (ML) classifiers have been trained to identify targets of conotoxins binding voltage-gated ion channels, no ML model has been built to predict the subtype-specific nAChR targets of α-conotoxins. Here, we trained an ML model in a semi-supervised manner to predict the specificity of α-conotoxin binding toward different human nAChR subtypes to overcome the challenge of limited data in subtype-specific nAChR targets of α-conotoxins and the issue that one α-conotoxin can bind multiple nAChR subtypes with high selectivity. We considered additional features of sequences of α-conotoxins in training our ML model, including the secondary structure propensities and electrostatic properties, which resulted in better prediction capability for the ML model. Notably, we identify that most α-conotoxins bind to α3β2, α1γδ, and α7 subtypes of human nAChRs. Our findings from this study provide a framework for predicting targets of various kinds of toxins.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties

Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties.

Biswas, Arpan↗

DOE OSTI2022

Reinforcement learning for adaptive maintenance policy optimization under imperfect knowledge of the system degradation model and partial observability of system states

Maintenance policy optimization usually is faced with challenges that arise from an imperfect knowledge of system degradation models and from the partial observability of system degradation states. Here, this paper proposes a reinforcement learning method to address these two challenges for a class of maintenance problems with Markov degradation processes. The reinforcement learning approach consists of a learning component and a planning component. Using sequentially collected observations, at each step of decision-making the learning component improves the knowledge of system degradation in terms of the probability distributions of the transition rates based on sequential Bayesian inference. Using the updated transition rates, at each step of decision-making the maintenance policy optimization problem is then formulated as a partially observable Markov decision problem, and the planning component computes the optimal maintenance policy that maximizes the expected cumulative reward. The proposed method is illustrated using a numerical example with repair and inspection maintenance actions. The result shows that as more observations are collected, the learning component progressively learns the true system degradation process, and the planning component adjusts the optimal maintenance policy accordingly as well, which leads to increased reward.

42 ENGINEERING↗

DOE OSTI2025

Computational investigation of water glasses using machine-learning potentials

The molecular origins of water’s anomalous properties have long been a subject of scientific inquiry. The liquid–liquid phase transition hypothesis, which posits the existence of distinct low-density and high-density liquid states separated by a first-order phase transition terminating at a critical point, has gained increasing experimental and computational support and offers a thermodynamically consistent framework for many of water’s anomalies. However, experimental challenges in avoiding crystallization near the postulated liquid–liquid critical point have focused attention to water’s canonical glassy states: low-density and high-density amorphous ice. Here, we use two Deep Potential machine-learning models, trained on the Strongly Constrained and Appropriately Normed density functional and the highly accurate Many-Body Polarizable potential, to conduct an investigation of water’s glassy phenomenology based on quantum mechanical calculations. Despite not being explicitly trained on amorphous ices, both models accurately capture the structure and transformation of the water glasses, including their interconversion along different thermodynamic paths. Isobaric quenching of liquid water at various pressures generates a continuum of intermediate amorphous ices and density fluctuations increase near the liquid–liquid critical pressure. The glass transition temperatures of the amorphous ices produced at different pressures exhibit two distinct branches, corresponding to low-density and high-density amorphous ice behaviors, consistent with experiment and the liquid–liquid transition hypothesis. Extrapolating transformation pressures from isothermal compressions to experimental compression rates brings our simulations into excellent agreement with data. Our findings demonstrate that machine-learning potentials trained on equilibrium phases can effectively model nonequilibrium glassy behavior and pave the way for studying long-timescale, out-of-equilibrium processes with quantum mechanical accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Using Machine Learning to Predict Future Temperature Outputs in Geothermal Systems

Optimizing the power output, and economic value, of geothermal power plants over decades of operation is a major challenge in renewable energy. Optimizing the output requires the ability to predict the mass flow rates and the output temperatures of production wells based on the inputs of injection wells, as well as the time history of the system. Machine Learning (ML) that incorporates the known physics of geothermal systems is one possible solution to this challenge. In this work, we explore the ability of ML algorithms to predict future temperature outputs based on historical data. Considering the challenges with obtaining an empirical dataset from field data that is large enough to enable reliable ML, we propose an alternate approach: developing a high-fidelity reservoir model and using computational resources to build a dataset that enables ML. As a first step towards achieving this goal, we present preliminary results from applying ML to predict the temperature timeseries of simple modeled geothermal systems. We describe the application of relevant state-of-the-art ML approaches, such as the Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to extract temporal structures in the model data. We assess the accuracy of the forecasts we obtain, compare the selected approaches, and share the lessons learned that would inform the process of training and utilizing ML algorithms for larger and more complex geothermal systems.

GEOTHERMAL ENERGY↗