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All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗

NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗

NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T 2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗

NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

neural networks↗

NASA NTRS2007

Space and Terrestrial Power System Integration Optimization Code BRMAPS for Gas Turbine Space Power Plants With Nuclear Reactor Heat Sources

In view of the difficult times the US and global economies are experiencing today, funds for the development of advanced fission reactors nuclear power systems for space propulsion and planetary surface applications are currently not available. However, according to the Energy Policy Act of 2005 the U.S. needs to invest in developing fission reactor technology for ground based terrestrial power plants. Such plants would make a significant contribution toward drastic reduction of worldwide greenhouse gas emissions and associated global warming. To accomplish this goal the Next Generation Nuclear Plant Project (NGNP) has been established by DOE under the Generation IV Nuclear Systems Initiative. Idaho National Laboratory (INL) was designated as the lead in the development of VHTR (Very High Temperature Reactor) and HTGR (High Temperature Gas Reactor) technology to be integrated with MMW (multi-megawatt) helium gas turbine driven electric power AC generators. However, the advantages of transmitting power in high voltage DC form over large distances are also explored in the seminar lecture series. As an attractive alternate heat source the Liquid Fluoride Reactor (LFR), pioneered at ORNL (Oak Ridge National Laboratory) in the mid 1960's, would offer much higher energy yields than current nuclear plants by using an inherently safe energy conversion scheme based on the Thorium --> U233 fuel cycle and a fission process with a negative temperature coefficient of reactivity. The power plants are to be sized to meet electric power demand during peak periods and also for providing thermal energy for hydrogen (H2) production during "off peak" periods. This approach will both supply electric power by using environmentally clean nuclear heat which does not generate green house gases, and also provide a clean fuel H2 for the future, when, due to increased global demand and the decline in discovering new deposits, our supply of liquid fossil fuels will have been used up. This is expected within the next 30 to 50 years, as predicted by the Hubbert model and confirmed by other global energy consumption prognoses. Having invested national resources into the development of NGNP, the technology and experience accumulated during the project needs to be documented clearly and in sufficient detail for young engineers coming on-board at both DOE and NASA to acquire it. Hands on training on reactor operation, test rigs of turbomachinery, and heat exchanger components, as well as computational tools will be needed. Senior scientist/engineers involved with the development of NGNP should also be encouraged to participate as lecturers, instructors, or adjunct professors at local universities having engineering (mechanical, electrical, nuclear/chemical, and/or materials) as one of their fields of study.

Juhasz, Albert J.↗

NASA NTRS2010

Micro-Ramps for External Compression Low-Boom Inlets

The application of vortex generators for flow control in an external compression, axisymmetric, low-boom concept inlet was investigated using RANS simulations with three-dimensional (3-D), structured, chimera (overset) grids and the WIND-US code. The low-boom inlet design is based on previous scale model 1- by 1-ft wind tunnel tests and features a zero-angle cowl and relaxed isentropic compression centerbody spike, resulting in defocused oblique shocks and a weak terminating normal shock. Validation of the methodology was first performed for micro-ramps in supersonic flow on a flat plate with and without oblique shocks. For the inlet configuration, simulations with several types of vortex generators were conducted for positions both upstream and downstream of the terminating normal shock. The performance parameters included incompressible axisymmetric shape factor, separation area, inlet pressure recovery, and massflow ratio. The design of experiments (DOE) methodology was used to select device size and location, analyze the resulting data, and determine the optimal choice of device geometry. The optimum upstream configuration was found to substantially reduce the post-shock separation area but did not significantly impact recovery at the aerodynamic interface plane (AIP). Downstream device placement allowed for fuller boundary layer velocity profiles and reduced distortion. This resulted in an improved pressure recovery and massflow ratio at the AIP compared to the baseline solid-wall configuration.

Rybalko, Michael↗

NASA NTRSDate not supplied

AI Foundation Models for Science: An Open Collaborative Initiative

Foundation Models (FMs), AI models designed to replace task-specific models, are increasingly being recognized for their versatility across numerous downstream applications. These models, trained using self-supervised techniques on any type of sequence data, circumvent the need for large annotated datasets, a major bottleneck in traditional AI model development. FMs can be applied to downstream tasks using few-shot learning and fine-tuning, significantly reducing the need for large labeled training datasets and computational resources. However, the development of FMs requires substantial resources, including access to data and compute power, expertise in the latest models, and specialized scientific knowledge for systematic evaluation. It is challenging for a single group to possess all these capabilities. To address this, NASA IMPACT has initiated an open collaborative effort, leveraging partnerships with the private sector and other groups within and outside NASA, to jointly build FMs. The overarching goal is to develop a consistent and collaborative approach to building FMs for high-value science datasets. This initiative has fostered collaboration within NASA and with external partners, including IBM Research, Clark University, DOE’s ORNL, ESA, and USGS. The effort focuses on identifying key datasets with a wide range of downstream applications, pretraining and building FMs using modified transformer architectures, evaluating compute infrastructure needs, and sharing models, pretraining and fine-tuning code, and data with the community. Furthermore, it aims to train the Earth science community to fine-tune these models for various downstream applications. Our initial effort resulted in the creation of a 100 million parameter HLS Geospatial Model within six months, which was released on HuggingFace. We are now expanding our scope to include data from weather and climate models and investigating multimodal models. We invite those interested in participating in this effort to join us by sharing their use cases, expertise, or data.

Rahul Ramachandran↗

NASA NTRS1984

Salt-gradient Solar Ponds: Summary of US Department of Energy Sponsored Research

The solar pond research program conducted by the United States Department of Energy was discontinued after 1983. This document summarizes the results of the program, reviews the state of the art, and identifies the remaining outstanding issues. Solar ponds is a generic term but, in the context of this report, the term "solar pond" refers specifically to salt-gradient solar pond. Several small research solar ponds have been built and successfully tested. Procedures for filling the pond, maintaining the gradient, adjusting the zone boundaries, and extracting heat have been developed. Theories and models have also been developed and verified. The major remaining unknowns or issues involve the physical behavior of large ponds; i.e., wind mixing of the surface, lateral range or reach of horizontally injected fluids, ground thermal losses, and gradient zone boundary erosion caused by pumping fluid for heat extraction. These issues cannot be scaled and must be studied in a large outdoor solar pond. This report has been subdivided into three parts. Part One presents the results of the DOE research program. The major contributing institutions include Solar Energy Research Institute, Los Alamos National Laboratory, and Jet Propulsion Laboratory. Part Two presents related independent research conducted by universities. Part Three is a selected bibliography with abstracts.

R.L. French↗

NASA NTRS1993

Space Nuclear Thermal Propulsion (SNTP) Air Force facility

The Space Nuclear Thermal Propulsion (SNTP) Program is an initiative within the US Air Force to acquire and validate advanced technologies that could be used to sustain superior capabilities in the area or space nuclear propulsion. The SNTP Program has a specific objective of demonstrating the feasibility of the particle bed reactor (PBR) concept. The term PIPET refers to a project within the SNTP Program responsible for the design, development, construction, and operation of a test reactor facility, including all support systems, that is intended to resolve program technology issues and test goals. A nuclear test facility has been designed that meets SNTP Facility requirements. The design approach taken to meet SNTP requirements has resulted in a nuclear test facility that should encompass a wide range of nuclear thermal propulsion (NTP) test requirements that may be generated within other programs. The SNTP PIPET project is actively working with DOE and NASA to assess this possibility.

Beck, David F.↗

NASA NTRS2016

Economical Production of Pu-238: NIAC Phase I Final Report

All space exploration missions traveling beyond Jupiter must use radioisotopic power sources for electrical power. The best isotope to power these sources is plutonium-238 (Pu-238). The US supply of Pu-238 is almost exhausted and will be gone within the next decade. The Department of Energy has initiated a production program with a $10M allocation from NASA but the cost is estimated at over $100M to get to production levels. The Center for Space Nuclear Research (CSNR) has conceived of a potentially better process to produce Pu-238 earlier and for significantly less cost. Potentially, the front end capital costs could be provided by private industry such that the government only had to pay for the product produced. In the Phase I NIAC (NASA Innovative Advanced Concepts) grant, the CSNR has evaluated the feasibility of using a low power, commercially available nuclear reactor to produce 1.5 kg of Pu-238 per year. The impact on the neutronics of the reactor have been assessed, the amount of Neptunium target material estimated, and the production rates calculated. In addition, the size of the post-irradiation processing facility has been established. Finally, as the study progressed, a new method for fabricating the Pu-238 product into the form used for power sources has been identified to reduce the cost of the final product. In short, the concept appears to be viable, can produce the amount of Pu-238 needed to support the NASA missions, can be available within a few years, and will cost significantly less than the current DOE program.

Plutonium↗

NASA NTRS2007

Testing in Support of Fission Surface Power System Qualification

The strategy for qualifying a FSP system could have a significant programmatic impact. The US has not qualified a space fission power system since launch of the SNAP-10A in 1965. This paper explores cost-effective options for obtaining data that would be needed for flight qualification of a fission system. Qualification data could be obtained from both nuclear and non-nuclear testing. The ability to perform highly realistic nonnuclear testing has advanced significantly throughout the past four decades. Instrumented thermal simulators were developed during the 1970s and 1980s to assist in the development, operation, and assessment of terrestrial fission systems. Instrumented thermal simulators optimized for assisting in the development, operation, and assessment of modern FSP systems have been under development (and utilized) since 1998. These thermal simulators enable heat from fission to be closely mimicked (axial power profile, radial power profile, temperature, heat flux, etc.) and extensive data to be taken from the core region. For transient testing, pin power during a transient is calculated based on the reactivity feedback that would occur given measured values of test article temperature and/or dimensional changes. The reactivity feedback coefficients needed for the test are either calculated or measured using cold/warm zero-power criticals. In this way non-nuclear testing can be used to provide very realistic information related to nuclear operation. Non-nuclear testing can be used at all levels, including component, subsystem, and integrated system testing. FSP fuels and materials are typically chosen to ensure very high confidence in operation at design burnups, fluences, and temperatures. However, facilities exist (e.g. ATR, HFIR) for affordably performing in-pile fuel and materials irradiations, if such testing is desired. Ex-core materials and components (such as alternator materials, control drum drives, etc.) could be irradiated in university or DOE reactors to ensure adequate radiation resistance. Facilities also exist for performing warm and cold zero-power criticals.

Houts, Mike↗