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NASA NTRS1994

Toward Real Time Neural Net Flight Controllers

NASA Ames Research Center has an ongoing program in neural network control technology targeted toward real time flight demonstrations using a modified F-15 which permits direct inner loop control of actuators, rapid switching between alternative control designs, and substitutable processors. An important part of this program is the ACTIVE flight project which is examining the feasibility of using neural networks in the design, control, and system identification of new aircraft prototypes. This paper discusses two research applications initiated with this objective in mind: utilization of neural networks for wind tunnel aircraft model identification and rapid learning algorithms for on line reconfiguration and control. The first application involves the identification of aerodynamic flight characteristics from analysis of wind tunnel test data. This identification is important in the early stages of aircraft design because complete specification of control architecture's may not be possible even though concept models at varying scales are available for aerodynamic wind tunnel testing. Testing of this type is often a long and expensive process involving measurement of aircraft lift, drag, and moment of inertia at varying angles of attack and control surface configurations. This information in turn can be used in the design of the flight control systems by applying the derived lookup tables to generate piece wise linearized controllers. Thus, reduced costs in tunnel test times and the rapid transfer of wind tunnel insights into prototype controllers becomes an important factor in more efficient generation and testing of new flight systems. NASA Ames Research Center is successfully applying modular neural networks as one way of anticipating small scale aircraft model performances prior to testing, thus reducing the number of in tunnel test hours and potentially, the number of intermediate scaled models required for estimation of surface flow effects.

Jorgensen, C. C.↗

DOE OSTI2025

A Performant, Scalable Processing Pipeline for High‐Quality and FAIR Environmental Sensor Data

High-resolution environmental monitoring is necessary to record, understand, and predict biogeochemical and ecological changes particularly in coastal systems but brings significant challenges in processing and making rapidly available the resulting data. The COMPASS-FME project established a network of coastal observational sites across the Chesapeake Bay and western Lake Erie regions extensively instrumented with soil, vegetation, and weather sensors logging data every 15 min. Our data processing framework, written in R and completely open source, prioritizes rapid model-experiment iteration and makes biogeochemical data rapidly available for quality assurance/quality control, analysis, and model ingestion. This pipeline is distinguished by a standardized and modular approach to data curation, extensive metadata and documentation, and its high performance. These attributes combine to make biogeochemical data rapidly accessible across COMPASS-FME and the broader community. Flexible, powerful, and reproducible approaches to handling high-volume environmental data are crucial for accelerating biogeosciences research.

Pennington, Stephanie C. [Pacific Northwest Nation↗

DOE OSTI2021

Designer membraneless organelles sequester native factors for control of cell behavior

Subcellular compartmentalization of macromolecules increases flux and prevents inhibitory interactions to control biochemical reactions. Inspired by this functionality, we sought to build designer compartments that function as hubs to regulate the flow of information through cellular control systems. Here we report a synthetic membraneless organelle platform to control endogenous cellular activities through sequestration and insulation of native proteins. We engineer and express a disordered protein scaffold to assemble micron size condensates and recruit endogenous clients via genomic tagging with high-affinity dimerization motifs. By relocalizing up to ninety percent of a targeted enzymes to synthetic condensates, we efficiently control cellular behaviors, including proliferation, division, and cytoskeletal organization. Further, we demonstrate multiple strategies for controlled cargo release from condensates to switch cells between functional states. These synthetic organelles offer a powerful and generalizable approach to modularly control cell decision-making in a variety of model systems with broad applications for cellular engineering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Definitions, methods, and applications in interpretable machine learning

Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the predictive, descriptive, relevant (PDR) framework for discussing interpretations. The PDR framework provides 3 overarching desiderata for evaluation: predictive accuracy, descriptive accuracy, and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post hoc categories, with subgroups including sparsity, modularity, and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often underappreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

SynthNotes: A Generator Framework for High-volume, High-fidelity Synthetic Mental Health Notes

One of the key, emerging challenges that connects the "Big Data" and the AI domain is the availability of sufficient volumes of training data for AI/Machine Learning tasks. SynthNotes is a framework for generating standards-compliant, realistic mental health progress report notes at the very large, population-level scale, and in a strict privacy-preserving manner. Our framework, inspired by the needs to explore, evaluate, and train computational methods for the emerging mental health crisis in the US, is useful for benchmarking, optimization, and training of biomedical natural language processing, information extraction, and machine learning systems intended to operate at "Big Data" scale (billions of notes). The free text notes generated by SynthNotes are based on the literature and public statistical models allowing for realistic, natural language representation of a patient, and his or her mental health characteristics. Additionally, SynthNotes can partially simulate stylistic, grammatical, and expressive characteristics of a licensed mental health professional. SynthNotes is modular and flexible, allowing for representation of variety of conditions, incorporation of alternative foundational models, and parametrization of the variability of the structure, content, and size of the synthetically generated corpus. In this paper, we report on the initial use and performance characteristics of our SynthNotes framework and on the ongoing work for inclusion of content planning and deep learning-based generative methods trained on real data.

Begoli, Edmon↗

DOE OSTI2025

Traffic Control via Connected and Automated Vehicles (CAVs): An Open-Road Field Experiment with 100 CAVs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. Also called “phantom jams” or “stop-and-go waves,” these instabilities are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system, referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment, the MegaVanderTest (MVT), leveraged a heterogeneous fleet of 100 longitudinally controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this article. The MegaController is a hierarchical control architecture that consists of two main layers. The upper layer is called the Speed Planner and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock onboard sensors. The Speed Planner ingests live data feeds provided by third parties as well as data from our own control vehicles and uses both to perform the speed assignment. The architecture of the Speed Planner allows for the modular use of standard control techniques, such as optimal control, model predictive control (MPC), kernel methods, and others. The architecture of the local controller allows for the flexible implementation of local controllers. Corresponding techniques include deep reinforcement learning (RL), MPC, and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers or only some. Likewise, control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars to electronic selection of adaptive cruise control (ACC) setpoints in others. The proposed architecture technically allows for the combination of all possible settings proposed previously, that is {Speed Planner algorithms} × {local Vehicle Controller algorithms} × {full or partial sensing} × {torque or speed control}. As a result, most configurations were tested throughout the ramp up to the MegaVandertest (MVT).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

The direct and indirect drivers shaping RNA viral communities in grassland soil

Recent studies have revealed diverse RNA viral communities in soils. Yet, how environmental factors influence soil RNA viruses remains largely unknown. Here, we recovered RNA viral communities from 24 metatranscriptomes sequenced from grassland soils managed under a range of environmental conditions including 1) water content: 100% and 25% water holding capacity, 2) plant presence: planted with tall wheatgrass (Thinopyrum ponticum) and bare soil, 3) cultivar type: Alkar and Jose, and 4) soil depth: 0-5 cm and 15-25 cm. The recovered RNA viral communities were novel with nearly one-third of the RNA viral contigs uniquely detected in the studied grassland. The classified RNA viral contigs are mostly known as eukaryotic RNA viruses (74.7%) belonging to Phyla Duplornaviricota, Kitrinoviricota, Lenarviricota, and Pisuviricota. Eukaryotic RNA viruses of Family Mitoviridae as well as their natural hosts, Fungi, are one of the most dominant taxa. Consistent with the results of nonmetric multidimensional scaling analysis, the four environmental conditions (water content, plant presence, cultivar, and soil depth) significantly influence the assemblages of soil RNA viral communities as suggested by the correlation analysis and the random forest algorithm. The modularity analysis of the factor network and the structural equation modeling further support the hierarchical associations among the four environmental factors and the community factors representing the co-existing eukaryotic, prokaryotic, and RNA viral communities. The soil water content, plant presence, and type of cultivar demonstrate a significant positive impact on eukaryotic RNA viral richness directly as well as indirectly on eukaryotic RNA viral abundance via influencing the co-existing eukaryotic members in this soil. Our data also provide statistical support for the negative influence of soil depth on soil eukaryotic richness and abundances resulting in its indirect impact on soil eukaryotic RNA viral communities. This study provides field-relevant information on how environmental and community factors collectively shape soil RNA communities and contribute to ecological understanding of RNA viral survival under various environmental conditions and virus-host interactions in soil.

Wu, Ruonan↗

NASA NTRS1984

Modular design attitude control system

The problem of applying modular attitude control to a rigid body-flexible suspension model of a flexible spacecraft with some state variables inaccessible was addressed by developing a sequence of single axis models and generating a series of reduced state linear observers of minimum order to reconstruct those scalar state variables that were inaccessible. The specific single axis models treated consisted of two, three and four rigid bodies, respectively, interconnected by a flexible shaft passing through the mass centers of the bodies. Reduced state linear observers of all orders up to one less than the total number of scalar state variables were generated for each of the three single axis models cited.

Chichester, F. D.↗

NASA NTRS1989

Development of a combustor analytical design methodology for liquid rocket engines

The development of a user friendly computerized methodology for the design and analysis of liquid propellant rocket engine combustion chambers is described. An overview of the methodology, consisting of a computer program containing an appropriate modular assembly of existing industry wide performance and combustion stability models, is presented. These models are linked with an interactive front end processor enabling the user to define the performance and stability traits of an existing design (point analysis) or to create the essential design features of a combustor to meet specific performance goals and combustion stability (point design). Plans for demonstration and verification of this methodology are also presented. These plans include the creation of combustor designs using the methodology, together with predictions of the performance and combustion stability for each design. A verification test program of 26 hot fire tests with up to four designs created using this methodology is described. This testing is planned using LOX/RP-1 propellants with a thrust level of approx. 220,000 N (50,000 lbf).

Pieper, Jerry L.↗

NASA NTRS1993

Life-Support-System Analyzer

Paper describes software system, called Generic Modular Flow Schematic (GMFS), developed for use in synthesis, mathematical modeling, and analysis of life-support systems aboard spacecraft, ranging from simple open-loop configurations to complex closed-loop ones. GMFS also compares alternative systems.

Seshan, Panchalam K.↗

NASA NTRS1992

Planning for the semiconductor manufacturer of the future

Texas Instruments (TI) is currently contracted by the Air Force Wright Laboratory and the Defense Advanced Research Projects Agency (DARPA) to develop the next generation flexible semiconductor wafer fabrication system called Microelectronics Manufacturing Science & Technology (MMST). Several revolutionary concepts are being pioneered on MMST, including the following: new single-wafer rapid thermal processes, in-situ sensors, cluster equipment, and advanced Computer Integrated Manufacturing (CIM) software. The objective of the project is to develop a manufacturing system capable of achieving an order of magnitude improvement in almost all aspects of wafer fabrication. TI was awarded the contract in Oct., 1988, and will complete development with a fabrication facility demonstration in April, 1993. An important part of MMST is development of the CIM environment responsible for coordinating all parts of the system. The CIM architecture being developed is based on a distributed object oriented framework made of several cooperating subsystems. The software subsystems include the following: process control for dynamic control of factory processes; modular processing system for controlling the processing equipment; generic equipment model which provides an interface between processing equipment and the rest of the factory; specification system which maintains factory documents and product specifications; simulator for modelling the factory for analysis purposes; scheduler for scheduling work on the factory floor; and the planner for planning and monitoring of orders within the factory. This paper first outlines the division of responsibility between the planner, scheduler, and simulator subsystems. It then describes the approach to incremental planning and the way in which uncertainty is modelled within the plan representation. Finally, current status and initial results are described.

Fargher, Hugh E.↗

NASA NTRS1993

Mars Global Reference Atmospheric Model (Mars-GRAM): Release No. 2 - Overview and applications

The Mars Global Reference Atmospheric Model (Mars-GRAM), a science and engineering model for empirically parameterizing the temperature, pressure, density, and wind structure of the Martian atmosphere, is described with particular attention to the model's newest version, Mars-GRAM, Release No. 2 and to the improvements incorporated into the Release No. 2 model as compared with the Release No. 1 version. These improvements include (1) an addition of a new capability to simulate local-scale Martian dust storms and the growth and decay of these storms; (2) an addition of the Zurek and Haberle (1988) wave perturbation model, for simulating tidal perturbation effects; and (3) a new modular version of Mars-GRAM, for incorporation as a subroutine into other codes.

James, B.↗

NASA NTRS2002

Design and Fabrication of a Two-Dimensional Superconducting Bolometer Array

Large format, two dimensional arrays of close-packed bolometers will enable submillimeter cameras and spectrometers to obtain images and spectra orders of magnitude faster than present instruments. The South Pole Imaging Fabry-Perot Interferometer (SPIFI) for the AST/RO observatory and the Submillimeter and Far-InfraRed Experiment (SAFIRE) on the SOFIA airborne observatory will employ a large-format, two-dimensional, closepacked bolometer arrays. Both these instruments are imaging Fabry-Perot spectrometers operating at wavelengths between 100 micron and 700 micron. The array format is 16x32 pixels, using a 32-element multiplexer developed in part for this purpose. The low backgrounds achieved in spectroscopy require very sensitive detectors with NEPs of order 5x10(exp 18) W/square root of Hz. Superconducting detectors can be close-packed using the Pop-Up Detector (PUD) format, and SQUID multiplexers operating at the detector bas temperature can be intimately coupled to them. We have fabricated an engineering model array with this technology which features a very compact, modular approach for large format arrays.

Benford, Dominic↗

NASA NTRS2003

Surface Time-Variable Gravity Signals and Possible Sources Including Core Mass Flow

Over two decades of geodetic satellite-laser-ranging (SLR) data show that the variation of the Earth's oblateness parameter J2 has a clear seasonal signal of amplitude of about 3e-10 and a secular decrease of about -2.8e-11/year, superimposed on some interesting interannual fluctuations. Physically, any change in mass distribution or/inside the Earth will be reflected in the time-variable gravity signal obtained outside the Earth, according to Newton s gravitational law. Therefore, such signal contains contributions from all geophysical sources that redistribute mass, on all temporal and spatial scales, including those from the core. Besides Earth rotation and geomagnetic field variations, the time-variable gravity also contains information linking Earth surface observations with internal core dynamical processes. The time scales of the gravity signal are critical in helping differentiate different contributions. The atmosphere and hydrosphere are responsible for the seasonal and much of the interannual and intraseasoanl fluctuations, while the secular trend is due mainly to the post-glacial rebound but possibly core mass flow. To estimate the latter effect, we use our MoSST (Modular, Scalable, Self-consistent, Three-dimensional) core dynamics model to forward simulate the core flow, and density variation due to the core convection. Our results suggest that, when upward continued to the surface, the J2 component of the core mass redistribution can reach an overall amplitude of e-11/year, approaching the SLR detectability and significant in geophysical terms. We also find a general westward drift of the mass flow, with a speed comparable to that of the geomagnetic westward drift.

Chao, Benjamin F.↗

NASA NTRS2004

Design and Fabrication of a Two-Dimensional Superconducting Pop-up Bolometer Array

We have been developing an architecture for producing large format, two dimensional arrays of close-packed bolometers, which will enable submillimeter cameras and spectrometers to obtain images and spectra orders of magnitude faster than present instruments. The low backgrounds achieved in these instruments require very sensitive detectors with NEPs of order 5 x 10(exp -18) W/square root of Hz. Superconducting transition edge sensor bolometers can be close-packed using the Pop-up Detector (PUD) format, and SQUID multiplexers operating at the detector base temperature can be intimately coupled to them. The array unit cell is 8 x 32 pixels, using 32- element detector and multiplexer components. We have fabricated an engineering model array with this technology which features a very compact, modular approach for large format arrays. We report on the production of the 32-element components for the arrays. Planned instruments using this array architecture include the Submillimeter and Far-InfraRed Experiment (SAFIRE) on the SOFIA airborne observatory, the South Pole Imaging Fabry-Perot Interferometer (SPIFI) for the AST/RO observatory, the Millimeter Bolometer Camera for the Atacama Cosmology Telescope (MBC/ACT), and the Redshift (Z) Early Universe Spectrometer (ZEUS j.

Benford, Dominic J.↗

NASA NTRSDate not supplied

TPSAS-NF1676L-17560-DND

There has been much work in the past few years updating direct simulation Monte Carlo (DSMC) algorithms and phenomenological models. Algorithmic advances[1] have allowed simulations to be run more efficiently reducing the time to solution as well as allowing simulations to be run at lower altitudes because of the increased efficiency. Improved phenomenological models have focused on chemistry[2, 3] and internal energy modes[4, 5] and have had a profound effect on DSMC simulations by way of expanded physical realism[6] and computational speed. The DAC software[7, 8] has been widely adopted and performs well the jobs it was designed to do, but upgrading DAC to take advantage of these modeling advances has proven difficult due to a lack of modularity in the code base. Agency investment in DSMC code development has been almost non-existent for the past two decades. DAC was developed in the 1990?s at LaRC and has seen little improvement since. However, NASA is currently (under the Hypersonics Entry, Descent and Landing program) investing in the possibility of a stand-alone code to incorporate recent developments to expand physical realism, time to solution, and expand the range of usefulness of DSMC (both in velocity space and altitude).

Derek S Liechty↗

NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden C. Szeto↗

NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

Aiden Szeto↗