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NASA NTRS1994

Physical Models of Cognition

This paper presents and discusses physical models for simulating some aspects of neural intelligence, and, in particular, the process of cognition. The main departure from the classical approach here is in utilization of a terminal version of classical dynamics introduced by the author earlier. Based upon violations of the Lipschitz condition at equilibrium points, terminal dynamics attains two new fundamental properties: it is spontaneous and nondeterministic. Special attention is focused on terminal neurodynamics as a particular architecture of terminal dynamics which is suitable for modeling of information flows. Terminal neurodynamics possesses a well-organized probabilistic structure which can be analytically predicted, prescribed, and controlled, and therefore which presents a powerful tool for modeling real-life uncertainties. Two basic phenomena associated with random behavior of neurodynamic solutions are exploited. The first one is a stochastic attractor ; a stable stationary stochastic process to which random solutions of a closed system converge. As a model of the cognition process, a stochastic attractor can be viewed as a universal tool for generalization and formation of classes of patterns. The concept of stochastic attractor is applied to model a collective brain paradigm explaining coordination between simple units of intelligence which perform a collective task without direct exchange of information. The second fundamental phenomenon discussed is terminal chaos which occurs in open systems. Applications of terminal chaos to information fusion as well as to explanation and modeling of coordination among neurons in biological systems are discussed. It should be emphasized that all the models of terminal neurodynamics are implementable in analog devices, which means that all the cognition processes discussed in the paper are reducible to the laws of Newtonian mechanics.

Zak, Michail↗

NASA NTRS2018

Internship in Augmented and Virtual Reality - Rapid Model Import Tool

The integration of virtual and augmented reality, sometimes called mixed reality, is an emerging technology which will likely skyrocket overnight much in the way smartphones did a decade ago. Kennedy Space Center's Augmented and Virtual Reality (AVR) Lab is developing a Rapid Model Import Tool (RMIT) to create a quick and efficient way to bring NASA's complex engineering 3D models into virtual and augmented environments. The long-term objective is to create a tool that will ultimately benefit KSC engineers. Its various uses within NASA can potentially span from astronaut training, to marketing, to public outreach, to name a few. Unity is a prolific cross-platform game engine that allows users to build high quality 2D and 3D games for desktop, mobile, web, and game console platforms. It is perhaps also the most widely used software for virtual reality game development. At the AVR lab, we are looking at alternative uses of Unity to build tools for NASA engineers to perform design, development, testing, and training on spacecraft, rocket delivery systems, ground support equipment, and facilities at KSC. As an intern for the RMIT project, I am charged with the task of performing research on Unity-compatible file types to develop an efficient, affordable, preservative process to bring models from CATIA 3D engineering software into the Unity environment. With a tool called the NASA Enterprise Visualization Application (NEVA), developed by the Boeing Design Visualization group at KSC, we are able to easily convert CATIA's design models to. DAE (also known as COLLADA) and .OBJ file formats. I first reduce the polygon count of the model within CATIA itself, make any necessary tweaks to reduce the model further, and then export using NEVA. The .OBJ or. DAE files that I am left with are then converted by another intern to a Unity-compatible file format using a custom Python script. I have generated extensive documentation of this process in a NEVA User Guide. By the end of this semester, we will have built a solid framework for RMIT based on a thorough understanding of virtual reality specifications and file requirements, allowing future software development teams to go forward with development on the custom tool.

Leap Motion↗

NASA NTRSDate not supplied

Examining the Relationship between Self-Reported Workload and Resilient Performance in Airline Flight Crews

Workload has long been associated with human performance in aviation. High workload is typically viewed as a system design problem. The aim of this work was to find observable behaviors that guard cognitive resource margins; thus, improve handling of perturbations that arise. Twelve commercial airline flight crews participated in an experiment at NASA Langley Research Center. We explored workload data (NASA Task Load Index) self-reported after completing flight simulation scenarios to see how different individuals, using the same technologies, cope with challenging situations. A trained observer from the same airline as the study participants assessed performance. We differentiated a lower workload crew from a higher workload crew and analyzed their workload and performance. Results indicate that the workload may be a component of pilots’ resilient performance. Initial strategies that reduced workload were classified and these types of behaviors, if implemented correctly, might assist pilots in maintaining functional workload resource margins.

Michael Stewart↗

NASA NTRSDate not supplied

Examining the Relationship Between Workload and Resilient Performance in Airline Flight Crews

Michael Stewart↗

DOE OSTI2016

Plasma Processing of Superconducting Radio Frequency Cavities

The development of plasma processing technology of superconducting radio frequency (SRF) cavities not only provides a chemical free and less expensive processing method, but also opens up the possibility for controlled modification of the inner surfaces of the cavity for better superconducting properties. The research was focused on the transition of plasma etching from two dimensional flat surfaces to inner surfaces of three dimensional (3D) structures. The results could be applicable to a variety of inner surfaces of 3D structures other than SRF cavities. Understanding the A r/C L plasma etching mechanism is crucial for achieving the desired modification of Nb SRF cavities. In the process of developing plasma etching technology, an apparatus was built and a method was developed to plasma etch a single cell Pill Box cavity. The plasma characterization was done with the help of optical emission spectroscopy. The Nb etch rate at various points of this cavity was measured before processing the SRF cavity. Cylindrical ring-type samples of Nb placed on the inner surface of the outer wall were used to measure the dependence of the process parameters on plasma etching. The measured etch rate dependence on the pressure, rf power, dc bias, temperature, CI2 concentration and diameter of the inner electrode was determined. The etch rate mechanism was studied by varying the temperature of the outer wall, the dc bias on the inner electrode and gas conditions. In a coaxial plasma reactor, uniform plasma etching along the cylindrical structure is a challenging task due to depletion of the active radicals along the gas flow direction. The dependence of etch rate uniformity along the cylindrical axis was determined as a function of process parameters. The formation of dc self-biases due to surface area asymmetry in this type of plasma and its variation on the pressure, rf power and gas composition was measured. Enhancing the surface area of the inner electrode to reduce the asymmetry was studied by changing the contour of the inner electrode. The optimized contour of the electrode based on these measurements was chosen for SRF cavity processing.

Upadhyay, Janardan↗

DOE OSTI2022

Autonomous aerial flight path inspection using advanced manufacturing techniques

Robotic systems have shown capabilities to perform inspection tasks in dangerous and difficult-to-access environments, such as those found in different components of power plants. However, most of the current robotic inspection technology is designed for specific components. Aerial robots, commonly termed as Drones, have raised an option to inspect a wider range of structural components. Nevertheless, current aerial inspecting technology still relies on a human pilot with limited line of sight, field of view and a reduced perception as the drone flies away, which prevents performing close-quarter inspection in intricate, structurally complex and GPS-denied environments. This work introduces offline inspection path generation methods based on robotics-integrated to manufacturing techniques. One method uses computer-aided manufacturing (CAM) techniques and the other an additive manufacturing (AM) approach to generate the flight path. That is to say, the drone would fly along the path described by a 3D (Three Dimensional) printer’s extruder or a CNC (Computer Numerical Control) machining tool, enabling to fly very close the structure even in physical structures with a complex geometry. Once the trajectories are generated, they are introduced for its validation in the Gazebo robotics simulator. Simulation results demonstrate the proper performance of the method and confirm that this approach can be used for close inspection of structural components.

42 ENGINEERING↗

DOE OSTI2015

TU-EF-204-10: A Preliminary Study Evaluating Calculation Variations of Local Noise Power Spectra for CT Simulation in Radiation Therapy

Purpose: Local noise power spectrum (NPS) properties are significantly affected by calculation variables and CT acquisition and reconstruction parameters, but a thoughtful analysis of these effects is absent. In this study, we performed a complete analysis of the effects of calculation and imaging parameters on the NPS. Methods: The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64-slice CT simulator using various scanning protocols. Images were reconstructed using both FBP and iDose4 reconstruction algorithms. From these images, local NPS were calculated for regions of interest (ROI) of varying locations and sizes, using four image background removal methods. Additionally, using a predetermined ground truth, NPS calculation accuracy for various calculation parameters was compared for computer simulated ROIs. A complete analysis of the effects of calculation, acquisition, and reconstruction parameters on the NPS was conducted. Results: The local NPS varied with ROI size and image background removal method, particularly at low spatial frequencies. The image subtraction method was the most accurate according to the computer simulation study, and was also the most effective at removing low frequency background components in the acquired data. However, first-order polynomial fitting using residual sum of squares and principle component analysis provided comparable accuracy under certain situations. Similar general trends were observed when comparing the NPS for FBP to that of iDose4 while varying other calculation and scanning parameters. However, while iDose4 reduces the noise magnitude compared to FBP, this reduction is spatial-frequency dependent, further affecting NPS variations at low spatial frequencies. Conclusion: The local NPS varies significantly depending on calculation parameters, image acquisition parameters, and reconstruction techniques. Appropriate local NPS calculation should be performed to capture spatial variations of noise; calculation methodology should be selected with consideration of image reconstruction effects and the desired purpose of CT simulation for radiotherapy tasks.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

A miniaturized feedstocks-to-fuels pipeline for screening the efficiency of deconstruction and microbial conversion of lignocellulosic biomass

Sustainably grown biomass is a promising alternative to produce fuels and chemicals and reduce the dependency on fossil energy sources. However, the efficient conversion of lignocellulosic biomass into biofuels and bioproducts often requires extensive testing of components and reaction conditions used in the pretreatment, saccharification, and bioconversion steps. This restriction can result in a significant and unwieldy number of combinations of biomass types, solvents, microbial strains, and operational parameters that need to be characterized, turning these efforts into a daunting and time-consuming task. Here we developed a high-throughput feedstocks-to-fuels screening platform to address these challenges. The result is a miniaturized semi-automated platform that leverages the capabilities of a solid handling robot, a liquid handling robot, analytical instruments, and a centralized data repository, adapted to operate as an ionic-liquid-based biomass conversion pipeline. The pipeline was tested by using sorghum as feedstock, the biocompatible ionic liquid cholinium phosphate as pretreatment solvent, a “one-pot” process configuration that does not require ionic liquid removal after pretreatment, and an engineered strain of the yeast Rhodosporidium toruloides that produces the jet-fuel precursor bisabolene as a conversion microbe. By the simultaneous processing of 48 samples, we show that this configuration and reaction conditions result in sugar yields (~70%) and bisabolene titers (~1500 mg/L) that are comparable to the efficiencies observed at larger scales but require only a fraction of the time. We expect that this Feedstocks-to-Fuels pipeline will become an effective tool to screen thousands of bioenergy crop and feedstock samples and assist process optimization efforts and the development of predictive deconstruction approaches.

09 BIOMASS FUELS↗

DOE OSTI2018

Creating an Interprocedural Analyst-Oriented Data Flow Representation for Binary Analysts (CIAO)

National security missions require understanding third-party software binaries, a key element of which is reasoning about how data flows through a program. However, vulnerability analysts protecting software lack adequate tools for understanding data flow in binaries. To reduce the human time burden for these analysts, we used human factors methods in a rolling discovery process to derive user-centric visual representation requirements. We encountered three main challenges: analysis projects span weeks, analysis goals significantly affect approaches and required knowledge, and analyst tools, techniques, conventions, and prioritization are based on personal preference. To address these challenges, we initially focused our human factors methods on an attack surface characterization task. We generalized our results using a two-stage modified sorting task, creating requirements for a data flow visualization. We implemented these requirements partially in manual static visualizations, which we informally evaluated, and partially in automatically generated interactive visualizations, which have yet to be integrated into workflows for evaluation. Our observations and results indicate that 1) this data flow visualization has the potential to enable novel code navigation, information presentation, and information sharing, and 2) it is an excellent time to pursue research applying human factors methods to binary analysis workflows.

42 ENGINEERING↗

DOE OSTI2024

Lifetime Energy Savings Via Advanced Manufacturing of Low Density Steels for Transportation Applications

The purpose of this “Low Density Steels for Transportation Applications” project was to develop an alloy composition and processing parameters that would result in a material suitable for use in automotive structural components at a reduced density over the current advanced high strength steel (AHSS) materials used. The project work successfully developed a robust alloy capable of exceeding project mechanical property targets at each stage of development, with an 8% density reduction over benchmark AHSS materials (7.8 g/cm3). The developed alloy has the potential to offer significant vehicle lightweighting and improved fuel economy, without sacrificing the increased passenger safety of more traditional AHSS. Through the three tasks of the project, (1) Alloy design and small-scale laboratory evaluation, (2) Laboratory development of hot rolled material and (3) Laboratory development of a cold rolled material, the laboratory work utilized advanced characterization and analytical methods on novel alloy compositions subjected to both conventional and non-conventional processing operations.

36 MATERIALS SCIENCE↗

DOE OSTI2025

WHOLESCALE - Water & Hole Observations Leverage Effective Stress Calculations And Lessen Expenses (Final Technical Report 2020 - 2024)

The WHOLESCALE acronym stands for Water & Hole Observations Leverage Effective Stress Calculations and Lessen Expenses. The goal of the WHOLESCALE project is to simulate the spatial distribution and temporal evolution of stress in the geothermal system at San Emidio in Nevada, United States. To reach this goal, the WHOLESCALE team has developed a methodology to incorporate and interpret data from four methods of measurement into a multi-physics model that couples thermal, hydrological, and mechanical (T H-M) processes. The WHOLESCALE team has applied this methodology at the San Emidio geothermal field, located ~100 km north of Reno, Nevada in the northwestern Basin and Range province. The WHOLESCALE team includes 30 individuals working at two universities, two national laboratories, and one industry partner. Two master-degree students and five post-doctoral researchers have gained professional experience and earned partial financial support via the WHOLESCALE project. The WHOLESCALE team has taken advantage of the perturbations created by changes in pumping operations during planned shutdowns in 2016, 2021, and 2022 to infer temporal changes in the state of stress in the geothermal system at San Emidio, Nevada, U.S. The WHOLESCALE results support the working hypothesis that increasing pore-fluid pressure reduces the effective normal stress acting across fault zones. During normal operations, pumping in deep production wells decreases fluid pressures and thus increases the effective normal stresses on faults, reducing microseismicity. During planned shutdowns, the cessation of production increases pore-fluid pressure and reduces effective normal stress. The WHOLESCALE products generated during the 4-year period between 2020 and 2024 include: three articles published in the open-access, peer-reviewed scientific literature, two master’s theses, 20 presentations or papers at scientific conferences, and 17 data sets available on public repositories. The WHOLESCALE project has been completed in two phases that included three performance periods separated by two Go/No-go Stage Gate Reviews. Tasks were classified by data type (i.e., Geologic Structure, Borehole, Geodesy, Hydrology, Seismology, and Modeling). The first phase of the project started July 31, 2020 and included ongoing project coordination (Task 1), a project kickoff (Task 2), analysis of existing data (Task 3), development of the initial stress model & deployment design (Task 4), and Go/No-go Decision Point #1 (Task 5). Phase II began with implementing the 2022 deployment (Task 6), followed by Go/No-go Decision Point #2 (Task 7) The remainder of Phase II consisted of analyzing data collected during deployment (Task 8), calibration of the stress model on all observations (Task 9), and the Final Review (August 23, 2024) & Reporting (Task 10).

15 GEOTHERMAL ENERGY↗

NASA NTRS2013

HRA Aerospace Challenges

Compared to equipment designed to perform the same function over and over, humans are just not as reliable. Computers and machines perform the same action in the same way repeatedly getting the same result, unless equipment fails or a human interferes. Humans who are supposed to perform the same actions repeatedly often perform them incorrectly due to a variety of issues including: stress, fatigue, illness, lack of training, distraction, acting at the wrong time, not acting when they should, not following procedures, misinterpreting information or inattention to detail. Why not use robots and automatic controls exclusively if human error is so common? In an emergency or off normal situation that the computer, robotic element, or automatic control system is not designed to respond to, the result is failure unless a human can intervene. The human in the loop may be more likely to cause an error, but is also more likely to catch the error and correct it. When it comes to unexpected situations, or performing multiple tasks outside the defined mission parameters, humans are the only viable alternative. Human Reliability Assessments (HRA) identifies ways to improve human performance and reliability and can lead to improvements in systems designed to interact with humans. Understanding the context of the situation that can lead to human errors, which include taking the wrong action, no action or making bad decisions provides additional information to mitigate risks. With improved human reliability comes reduced risk for the overall operation or project.

DeMott, Diana↗

NASA NTRS2011

Reliability Analysis and Standardization of Spacecraft Command Generation Processes

center dot In order to reduce commanding errors that are caused by humans, we create an approach and corresponding artifacts for standardizing the command generation process and conducting risk management during the design and assurance of such processes. center dot The literature review conducted during the standardization process revealed that very few atomic level human activities are associated with even a broad set of missions. center dot Applicable human reliability metrics for performing these atomic level tasks are available. center dot The process for building a "Periodic Table" of Command and Control Functions as well as Probabilistic Risk Assessment (PRA) models is demonstrated. center dot The PRA models are executed using data from human reliability data banks. center dot The Periodic Table is related to the PRA models via Fault Links.

human errors.↗

NASA NTRSDate not supplied

A Predictive GNSS Performance Monitor for Autonomous Air Vehicles in Urban Environments

The emergence and development of advanced technologies and vehicle types has created a growing demand for the introduction of new forms of flight operations. These new and increasingly complex operational paradigms such as Advanced and Urban Air Mobility (AAM/UAM) present regulatory authorities and the aviation community with several design and implementation challenges – particularly for highly autonomous vehicles. An overarching and daunting task is finding methods to integrate these emerging operations without compromising safety or disrupting traditional airspace operations. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. This paper focus on the development and testing of a prognostic service aimed at estimating the quality of Global Navigation Satellite System (GNSS) performance for an autonomous aircraft in complex environments. The intent of this function is to proactively reduce a flight operations risk of exposure to states that may induce poor or unacceptable navigation system performance by factoring in estimates of GNSS quality into pre-flight and/or in-flight route planning. Methodologies for producing quality estimates are specified and results are provided for selected simulation and flight test cases.

GNSS↗

DOE OSTI2024

Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks

Abstract Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Consensus Statement on Proton Therapy in Early-Stage and Locally Advanced Non–Small Cell Lung Cancer

Radiation dose escalation has been shown to improve local control and survival in patients with non–small cell lung cancer in some studies, but randomized data have not supported this premise, possibly owing to adverse effects. Because of the physical characteristics of the Bragg peak, proton therapy (PT) delivers minimal exit dose distal to the target volume, resulting in better sparing of normal tissues in comparison to photon-based radiation therapy. This is particularly important for lung cancer given the proximity of the lung, heart, esophagus, major airways, large blood vessels, and spinal cord. However, PT is associated with more uncertainty because of the finite range of the proton beam and motion for thoracic cancers. PT is more costly than traditional photon therapy but may reduce side effects and toxicity-related hospitalization, which has its own associated cost. The cost of PT is decreasing over time because of reduced prices for the building, machine, maintenance, and overhead, as well as newer, shorter treatment programs. PT is improving rapidly as more research is performed particularly with the implementation of 4-dimensional computed tomography–based motion management and intensity modulated PT. Given these controversies, there is much debate in the oncology community about which patients with lung cancer benefit significantly from PT. The Particle Therapy Co-operative Group (PTCOG) Thoracic Subcommittee task group intends to address the issues of PT indications, advantages and limitations, cost-effectiveness, technology improvement, clinical trials, and future research directions. This consensus report can be used to guide clinical practice and indications for PT, insurance approval, and clinical or translational research directions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2021

GPU-based Image Compression for Efficient Compositing in Distributed Rendering Applications

Visualizations of large-scale data sets are often created on graphics clusters that distribute the rendering task amongst many processes. When using real-time GPU-based graphics algorithms, the most time-consuming aspect of distributed rendering is typically the com-positing phase - combining all partial images from each rendering process into the final visualization. Compo siting requires image data to be copied off the GPU and sent over a network to other processes. While compression has been utilized in existing distributed rendering compositors to reduce the data being sent over the network, this compression tends to occur after the raw images are transferred from the GPU to main memory. In this paper, we present work that leverages OpenGL / CUDA interoperability to compress raw images on the GPU prior to transferring the data to main memory. This approach can significantly reduce the device-to-host data transfer time, thus enabling more efficient compositing of images generated by distributed rendering applications.

Lipinksi, Riley↗

DOE OSTI2023

PV Stormwater Management Research and Testing (PV-SMaRT) (Final Technical Report)

The objective of the Photovoltaic Stormwater Management Research and Testing (PV-SMaRT) project was to develop and disseminate research-based, solar-specific resources for estimating stormwater runoff at ground-mounted PV facilities and detail stormwater management and water quality best practices. The intended use of stormwater management and water quality best practices and stormwater runoff estimation resources is to reduce balance of system soft costs associated with stormwater infrastructure requirements and improve water quality through research-tested best practices. Currently, stormwater regulations and guidelines vary by jurisdiction and can lead to regulatory uncertainty and increased compliance costs for managing stormwater runoff at solar sites. To address these concerns, the NREL team and its partners (University of Minnesota, Great Plains Institute, and Fresh Energy) established and engaged an advisory Water Quality Task Force (WQTF); conducted field research on stormwater infiltration and runoff at five ground-mounted PV sites; validated a 3-D hydrologic model to predict water runoff and generate stormwater runoff coefficients for a range of site conditions and PV designs; developed a stormwater management and water quality best practices document; and engaged with local jurisdictions and other stakeholders to disseminate best practices, stormwater runoff coefficients, and other tools. Key outputs of this project were a PV-SMaRT Runoff Calculator developed by University of Minnesota, a webinar detailing project outcomes and how to use the PV-SMaRT runoff calculator, and a document on Best Practices for regulators and developers regarding stormwater runoff at PV sites.

14 SOLAR ENERGY↗