Virtual Subject Matter Expert for Computer Aided Decision Making
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13 HYDRO ENERGY↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Applying Spatial Audio to Human Interfaces: 25 Years of NASA Experience
From the perspective of human factors engineering, the inclusion of spatial audio within a human-machine interface is advantageous from several perspectives. Demonstrated benefits include the ability to monitor multiple streams of speech and non-speech warning tones using a cocktail party advantage, and for aurally-guided visual search. Other potential benefits include the spatial coordination and interaction of multimodal events, and evaluation of new communication technologies and alerting systems using virtual simulation. Many of these technologies were developed at NASA Ames Research Center, beginning in 1985. This paper reviews examples and describes the advantages of spatial sound in NASA-related technologies, including space operations, aeronautics, and search and rescue. The work has involved hardware and software development as well as basic and applied research.
Begault, Durand R.↗
High-Reliability Waveguide Vacuum/Pressure Window
The NASA Deep Space Network (DSN) uses commercial waveguide windows on the output waveguide of Ka-band (32 GHz) low-noise amplifiers. Mechanical failure of these windows resulted in an unacceptable loss in tracking time. To address this issue, a new Ka-band WR-28 waveguide window has been designed, fabricated, and tested. The window uses a slab of low-loss, low-dielectric constant foam that is bonded into a 1/2-wave-thick waveguide/flange. The foam is a commercially available, rigid, closed-cell polymethacrylimide. It has excellent electrical properties with a dielectric constant of 1.04, and a loss tangent of 0.01. It is relatively strong with a tensile strength of 1 MPa. The material is virtually impermeable to helium. The finished window exhibits a leak rate of less than 3x10(exp -3)cu cm/s with helium. The material is also chemically resistant and can be cleaned with acetone. The window is constructed by fabricating a window body by brazing a short length of WR-28 copper waveguide into a standard rectangular flange, and machining the resulting part to a thickness of 4.6 mm. The foam is machined to a rectangular shape with a dimension of 7.06x3.53 mm. The foam is bonded into the body with a two-part epoxy. After curing, the excess glue and foam are knife-trimmed by hand. The finished window has a loss of less than 0.08 dB (2%) and a return loss of greater than 25 dB at 32 GHz. This meets the requirements for the DSN application. The window is usable for most applications over the entire 26-to-40-GHz waveguide band. The window return loss can be tuned to a required frequency by var y in g the thickness of the window slightly. Most standard waveguide windows use a thin membrane of material bonded into a recess in a waveguide flange, or sandwiched between two flanges with a polymer seal. Designs using the recessed window are prone to mechanical failure over time due to constraints on the dimensions of the recess that allow the bond to fail. Designs using the sandwich method are often permeable to helium, which prohibits the use of helium leak detection. At the time of this reporting, 40 windows have been produced. Twelve are in operation with a combined operating time of over 30,000 hours without a failure.
Britcliffe, Michael J.↗
QuTree: A tree tensor network package
Here we present QuTree, a C++ library for tree tensor network approaches. QuTree provides class structures for tensors, tensor trees, and related linear algebra functions that facilitate the fast development of tree tensor network approaches such as the multilayer multiconfigurational time-dependent Hartree approach or the density matrix renormalization group approach and its various extensions. We investigate the efficiency of relevant tensor and tensor network operations and show that the overhead for managing the network structure is negligible, even in cases with a million leaves and small tensors. QuTree focuses on providing simple, high-level routines while retaining easy access to the backend to facilitate novel developments. We demonstrate the capabilities of the package by computing the eigenstates of coupled harmonic oscillator Hamiltonians and performing random circuit simulations on a virtual quantum computer.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A comparison of helical and spur external gear machines for fluid power applications: Design and optimization
In this paper we address the problem of determining the relative performance of spur and helical external gear pumps (EGPs) for fluid power applications. Helical EGPs are commonly considered as quieter units than spur ones; however, there is no specific study reported in literature that proves such claim. Similarly, it is hard to find data indicating which design is better in terms of compactness or energy performance. To tackle this problem, a procedure is developed for virtual design optimization of both helical and spur gear EGPs. This includes addressing the two fundamental challenges of the work, parameterizing a generic EGP design and quantifying its performance. By applying this procedure, a genetic algorithm can continue improving from generation to generation, until the pareto optimal set of designs is identified for both helical and spur units. In the comparison of these, the differences between the two configurations will be identified and the fundamental question of this work can be answered; are helical gears ‘better’ than spur gears?
42 ENGINEERING↗
Machine Learning Vacancy Formation Energy in Nickel-Based Superalloys
Thermal vacancies play a critical role in high-temperature Ni-based superalloys and influence various properties such as creep resistance, oxidation, etc. This study systematically investigates the impact of commonly used transition metals (Cr, Co, Fe), refractory metals (Nb, Ta, Mo, W) and other elements (Al, Cu, Ti, Mn) on the thermodynamic stability of 36 binary, 20 ternary, 11 quaternary, 9 quinary, and 3 senary FCC Ni-based alloys covering various elemental combinations. Density functional theory-based studies on Ni-X binary alloys show that higher concentrations of Cr, Nb, Ta, Al, and Ti introduce significant lattice distortions and broaden the distribution of vacancy formation energies (standard deviation up to 0.15 eV). These elements partially donate electrons, reducing their self-consistent chemical potentials relative to single-element reference values and lowering vacancy formation energies, while Co, Fe, Mo, and W show lower charge localization. These trends extend from 3-6 element alloys, where Cr, Nb, and Ta-rich compositions have low-energy states (~0.5 eV) that increase vacancy concentrations. Finally, graph neural network models are developed to screen over 5000 virtual alloys. Eleven leading compositions are identified with mean vacancy formation energy higher than 1.75 eV and vacancy concentration ~2 orders of magnitude lower than pure Ni at 1000 K. These results provide valuable guidelines to achieve controlled defect engineering in structural alloys.
DFT↗
PPPL Laboratory Directed Research and Development (Project Final Reports, FY2018 - FY2020)
The U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) is a collaborative national center for fusion energy science, basic sciences, and advanced technology. The Laboratory has three major missions: (1) to develop the scientific knowledge and advanced engineering to enable fusion to power the U.S. and the world; (2) to advance the science of nanoscale fabrication for technologies of tomorrow; and (3) to further the development of the scientific understanding of the plasma universe from laboratory to astrophysical scales. PPPL’s Laboratory Directed Research and Development (LDRD) program supports and encourages creativity and innovation and contributes to its long-term viability. New scientific and technical research areas emerge and are nurtured through the program. Furthermore, new capabilities are developed to enable the Laboratory to meet its and DOE’s missions. The program is used to systematically diversify the Laboratory’s programs and mission. In the last few years, the program has started projects in nanomaterial synthesis, microelectronics, advanced x-ray spectroscopy, high-energy-density physics, superconducting magnet technology, machine learning and artificial intelligence, 3D magnetic fields to optimize fusion plasmas, integration of permanent magnets with simple high-field magnets to reduce the cost of producing complex 3D magnetic fields, advanced computational methods for predictive understanding and control of fusion plasma, development of quantum computing algorithms for plasma physics, liquid metal plasma-facing components for fusion reactors, virtual engineering, and plasma-based space propulsion. The program is also the vehicle to recruit and train talented scientists and engineers with the new skills needed to perform the Laboratory’s mission. Many of the new hires through the program go on to become world-class scientists and engineers in their fields. This report provides descriptions and accomplishments of those LDRD projects that were completed during fiscal years 2018 through 2020.
36 MATERIALS SCIENCE↗
Virtual Keyboard for Hands-Free Operations
The measurement of direction of gaze (d.o.g.) has been used for clinical purposes to detect illness, such as nystagmus, unusual fixation movements and many others. It also is used to determine the points of interest in objects. In this study we employ a measurement of d.o.g. as a computer interface. The interface provides a full keyboard as well as a mouse function. Such an interface is important to computer users with paralysis or in environments where hand-free machine interface is required. The study utilizes the commercially available (ISCAN Model RK426TC) headset which consists of an InfraRed (IR) source and an IR camera to sense deflection of the illuminating beam. It also incorporates image processing package that provides the position of the pupil as well as the pupil size. The study shows the ability of implementing a full keyboard, together with some control functions, imaged on a head mounted monitor screen. This document is composed of four sections: (1) The Nature of the Equipment; (2) The Calibration Process; (3) Running Process; and (4) Conclusions.
Abou-Ali, Abdel-Latief↗
SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment
Presentation material for a paper presented at the GHGT-17 conference, Calgary, Canada, October 20-24, 2024. The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.
CO2 geologic storage↗
Simulating the metabolic pathway dynamics of an organism
Disclosed herein are systems and methods for determining metabolic pathway dynamics using time series multiomics data. In one example, after receiving time series multiomics data comprising time-series metabolomics data associated a metabolic pathway and time-series proteomics data associated with the metabolic pathway, derivatives of the time series multiomics data can be determined. A machine learning model, representing a metabolic pathway dynamics model, can be trained using the time series multiomics data and the derivatives of the time series multiomics data, wherein the metabolic pathway dynamics model relates the time-series metabolomics data and time-series proteomics data to the derivatives of the time series multiomics data. The method can include simulating a virtual strain of the organism using the metabolic pathway dynamics model.
Costello, Zachary↗
SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment
The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.
Siriwardane, Hema↗
Digital Twin Technology for Safety, Security, and Training in Spent Nuclear Fuel Handling
The increasing complexity of spent nuclear fuel handling requires significant resources to ensure safety, security, and personnel training. As nuclear facilities have continued to advance in scale and technology, the integration of digital tools has become indispensable. Among these tools, digital twins, which are virtual models of physical systems, are emerging as invaluable tools for enhancing safety protocols, security measures, and training in the nuclear sector. These models were conceptualized in the Industry 4.0 revolution. Digital twins can process data from physical systems in real time (by using sensors), include multiple code packages to enable simulations of different physics applications, and even implement artificial intelligence or machine learning techniques for advanced data processing. Despite the advantages that digital twins provide, challenges still exist regarding their widespread implementation. For instance, data used by a digital twin must be accurate to ensure that the digital twin is accurately tuned. Furthermore, if insecure digital twins are targeted by hackers, then they can pose serious risks to the security and safety of nuclear facilities.
Digital twins↗
Pioneer Odyssey: Encounter with a Giant
Ancient peoples, perhaps thousands of years ago, undoubtedly conceived the idea of "reaching out" to Jupiter, the largest and most brilliant of the "wandering stars." But for mankind to stretch across the half billion miles to the giant planet of the Solar System many advances in technical and organizational fields of human endeavor had to be made. Outreach to Jupiter did not become a serious possibility until the Pioneer F and G Project was formed by NASA early in 1968. And then man began to design an extension of his senses that would probe the environs of the giant of the Solar System, a truly pioneer odyssey into the virtually unknown regions beyond the orbit of Mars. In the ensuing year. a dedicated and cooperative effort of several thousand people in Government, university, and private industrial organizations converted the idea into a reality. Less than twelve generations after Galileo first saw the banded disc of Jupiter and the flickering dots of its large satellites in the newly invented telescope, mankind sent a machine to make observations within that Jovian system. The two Pioneer spacecraft for the mission to Jupiter each weighed only about 570 pounds, yet carried eleven highly sophisticated instruments capable of operating unattended for many year in space. The spacecraft consumes less electrical power than a standard 100 watt lamp yet is able to accept instructions from Earth to control numerous operating modes of its scientific payload, process observations from these scientific instruments and format the observations into information usable on Earth. Even more remarkable. the spacecraft transmits a radio signal of only 8 watts power - equal to a nightlight - yet the information carried by the radio signal is received back on Earth from a distance of several billion miles. The Pioneer mission could not have been a success without the special engineering, scientific and management organization created for its accomplishment. This organization was rather unique in that it first had to meet a launch date target relatively quickly and then had to function for an extremely long mission operational time, far longer than any previous mission to planets. The first task was thus to organize so that the mission could be planned and the spacecraft designed and fabricated to be ready for launch within a few weeks of a 30-month target for completion. The program also produced an organization that planned mission operations to such detail that more than 16,000 commands were transmitted flawlessly to the distant spacecraft during Jupiter encounter. And each command reached the spacecraft within one second of the planned time despite the more than 90 minutes required for the radio message to travel from Earth to the spacecraft and for the spacecraft to return a confirmation to controllers back on Earth. The organization for Pioneer also determined the required flight path from Earth to Jupiter with such precision, and controlled the launch vehicle with such accuracy, that 21 months after launch the spacecraft was able to fly behind Jupiter's satellite lo, thereby providing the first measurement that indicated the possibility of a tenuous atmosphere about this large satellite. Finally, the Pioneer organization processed and analyzed each year sufficient information from the spacecraft to fill a book having about 3 million pages and reduced this avalanche of data from space into summaries of manageable size. And all this organization depended on people, consisted of people: the people who really made this whole mission possible. Pioneer has always depended on the dedication of many individuals from many organizations throughout the world to achieve its scientific objectives, and, as evidenced by the success of the Pioneer series, this dependence is completely justified. Relatively few individuals have an opportunity during their lives to participate in such a challenging, historic, pioneering effort; and still fewer are able to enjoy the rewards of such an activity. We who have worked on Pioneer 10 and its sister spacecraft, Pioneer 11, consider ourselves fortunate to be in both classes. For the opportunity we thank the people of the United States of America, who have supported our country's space effort and its spreading of human awareness of a vast and intriguing universe in which our own unique planet Earth is only one of myriads of worlds. This volume describing the mission to Jupiter and its results is one of the many rewards for our effort which we share with you, the reader.
Fimmel, Richard O.↗
Advanced Data Science Model for Detecting Intelligent Malware
This study focused on developing a robust artificial intelligence (AI) model capable of detecting and characterizing advanced malware in Internet of Things (IoT) devices using network data. By analyzing network traffic with various machine learning (ML) models, our AI model can identify and characterize malicious activities to significantly improve malware detection accuracy and reliability as compared to traditional methods. The developed AI/ML model was trained using network data from IoT devices, leveraging classifiers such as Random Forest, Gradient Boosting, AdaBoost, and others to optimize detection performance. This project demonstrates a scalable framework for real-time malware detection and characterization in IoT networks, capable of identifying infected devices and facilitating the necessary steps to remove or isolate them, thereby preventing further infections. Although digital twin (DT) integration is not yet implemented in the current model, it represents a promising future enhancement. By creating a virtual replica of physical IoT devices, DT technology would allow for real-time monitoring and analysis without directly accessing operational technology, thus reducing the risk of compromising or reducing the performance of actual devices. This integration would further enhance the security of IoT ecosystems, combining AI technology to better flag and detect indications of malware-infected devices within a nuclear system environment.
42 ENGINEERING↗
Virtual Excited State Reference for the Discovery of Electronic Materials Database: An Open-Access Resource for Ground and Excited State Properties of Organic Molecules
This letter announces the Virtual Excited State Reference for the Discovery of Electronic Materials Database (VERDE materials DB), the first database to include downloadable excited-state structures (S 0 , S 1 , T 1 ) and photophysical properties. Furthermore, VERDE materials DB is searchable, open-access via www.verdedb.org , and focused on light-responsive π-conjugated organic molecules with applications in green chemistry, organic solar cells, and organic redox flow batteries. It includes results of our active and past virtual screening studies; to date, more than 13 000 density functional theory (DFT) calculations have been performed on 1 500 molecules to obtain frontier molecular orbitals and photophysical properties, including excitation energies, dipole moments, and redox potentials. To improve community access, we have made VERDE materials DB available via an integration with the Materials Data Facility. We are leveraging VERDE materials DB to train machine learning algorithms to identify new materials and structure–property relationships between molecular ground- and excited-states. We present a case-study involving photoaffinity labels, including predictions of new diazirine-based photoaffinity labels anticipated to have high photostabilities.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Applications of Federated Learning in Semiconductor Manufacturing [Poster]
As semiconductor manufacturers explore advanced data analytics and modeling techniques and data hungry machine learning models increase in popularity due to their accuracy in solving generalized problems and ability to learn complex relationships, federated learning emerges as a privacy preserving machine learning technique for preserving data privacy and ensuring intellectual property protection. Federated Learning is a machine learning technique focused on training models using distributed data that never needs to be centrally stored, allowing the use of advanced machine learning techniques without compromising data privacy, and in the semiconductor manufacturing industry advanced machine learning techniques can reduce cost and time, but maintaining data privacy is essential to maintaining a competitive advantage. This paper systematically reviews existing literature on applications of federated learning in the semiconductor manufacturing industry with a focus on identifying common themes, algorithms, and gaps within the literature to drive future research directions. The findings reveal five key themes, including improvements in quality assurance, virtual models, privacy preservation, reliable data practices, and emerging trends and developments. By identifying key themes in literature on federated learning and semiconductor manufacturing and analyzing gaps and discussed methodologies, this study highlights several potential future research directions to expand the application of federated learning techniques in the semiconductor manufacturing domain.
42 ENGINEERING↗
Protein-ligand binding affinity prediction using multi-instance learning with docking structures
Recent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. These methods complement physics-based computational modeling such as molecular docking for virtual high-throughput screening. Despite recent advances and improved predictive performance, most methods in this category primarily rely on utilizing co-crystal complex structures and experimentally measured binding affinities as both input and output data for model training. Nevertheless, co-crystal complex structures are not readily available and the inaccurate predicted structures from molecular docking can degrade the accuracy of the machine learning methods. We introduce a novel structure-based inference method utilizing multiple molecular docking poses for each complex entity. Our proposed method employs multi-instance learning with an attention network to predict binding affinity from a collection of docking poses. We validate our method using multiple datasets, including PDBbind and compounds targeting the main protease of SARS-CoV-2. The results demonstrate that our method leveraging docking poses is competitive with other state-of-the-art inference models that depend on co-crystal structures. This method offers binding affinity prediction without requiring co-crystal structures, thereby increasing its applicability to protein targets lacking such data.
97 MATHEMATICS AND COMPUTING↗
Virtual Contrast-enhanced Magnetic Resonance Images Synthesis for Patients With Nasopharyngeal Carcinoma Using Multimodality-guided Synergistic Neural Network
To investigate a novel deep-learning network that synthesizes virtual contrast-enhanced T1-weighted (vceT1w) magnetic resonance images (MRI) from multimodality contrast-free MRI for patients with nasopharyngeal carcinoma (NPC).
62 RADIOLOGY AND NUCLEAR MEDICINE↗