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At least 721 records · Page 40

Integrated System and Application Continuous Performance Monitoring and Analysis Capability (Final)

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia’s Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia’s ElectroMagnetic Plasma In Realistic Environments (EMPIRE) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia’s HPC users.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Solar gravitational redshift

Wavelengths of solar spectrum lines should be shifted toward the red by the sun's gravitational field as predicted by metric theories of gravity according to the principle of equivalence. Photographic wavelengths of 738 solar Fe I lines and their corresponding laboratory wavelengths have been studied. The measured solar wavelength minus the laboratory wavelength averaged for the strong lines agrees well with the theoretically predicted shift. Studies show that the departures depend on line strength. No dependence of the departures on wavelength was found within the existing data. By studying strong lines over a wide spectral range, velocity shifts caused by the complex motions in the solar atmosphere seem to affect the results in a minimal fashion.

Lopresto, J. C.↗

Range Process Simulation Tool

Range Process Simulation Tool (RPST) is a computer program that assists managers in rapidly predicting and quantitatively assessing the operational effects of proposed technological additions to, and/or upgrades of, complex facilities and engineering systems such as the Eastern Test Range. Originally designed for application to space transportation systems, RPST is also suitable for assessing effects of proposed changes in industrial facilities and large organizations. RPST follows a model-based approach that includes finite-capacity schedule analysis and discrete-event process simulation. A component-based, scalable, open architecture makes RPST easily and rapidly tailorable for diverse applications. Specific RPST functions include: (1) definition of analysis objectives and performance metrics; (2) selection of process templates from a processtemplate library; (3) configuration of process models for detailed simulation and schedule analysis; (4) design of operations- analysis experiments; (5) schedule and simulation-based process analysis; and (6) optimization of performance by use of genetic algorithms and simulated annealing. The main benefits afforded by RPST are provision of information that can be used to reduce costs of operation and maintenance, and the capability for affordable, accurate, and reliable prediction and exploration of the consequences of many alternative proposed decisions.

Phillips, Dave↗

Giant Dipole Moments: Remarkable Effects Mono‐, Di‐, and Tri‐ Hydrated 5,6‐Diaminobenzene‐1,2,3,4‐Tetracarbonnitrile

The molecule 5,6-diaminobenzene-1,2,3,4-tetracarbonnitrile (MOI) was first synthesized by Müllen and coworkers in 2016 and boasts an ultrastrong dipole moment of $14.1\pm 0.7$ Debye in THF. Gas phase DFT computations do not fully reflect this ultrastrong dipole moment, demonstrating the role of solvent in increasing this dipole moment. Here, we investigate the effect of solvent molecule position on the dipole moment of this species, computationally examining systems with giant dipole moments. These systems are optimized in the gas phase with the B3LYP functional, employing the aug-cc-pVTZ and def2-TZVP basis sets, as well as the B3LYP-D3BJ/aug-cc-pVTZ functional in Orca. Single point DLPNO-CCSD/aug-cc-pVDZ results were obtained from Orca and Psi4, as well as DLPNO-CCSD(T)/CBS information from Psi4. Additionally, these are compared to the dipole moments of di- and tri-hydrated systems, and the SMD models for THF and water at the B3LYP/aug-cc-pVTZ level of theory. The dissociation energies, HOMO-LUMO energy gaps, and dipole moments are presented. These metrics show the nh1nh1′ THF system boasts the largest dissociation energy and dipole moment of the singly solvated systems, due to its strong hydrogen bonding. The importance of solvent placement is highlighted and may guide the synthesis of macromolecules or organic frameworks incorporating the MOI or MOI-like subunits. Remarkably, a single solvent molecule provides a good model for the difference between the gas phase and solvated species. The predicted gas phase dipole moments computed with B3LYP/aug-cc-pVTZ for the MOI, its monohydrated complex, dihydrated complex, and its trihydrated complex are 9.6, 14.2, 16.0, and 16.8 Debye, respectively.

dipole↗

Covariance Shaping Over Riemannian Manifolds for Massive MIMO Communication

Acquiring accurate instantaneous channel state information (CSI) is a challenging aspect of massive multi-input multi-output (MIMO) communication. Utilizing statistical information, such as channel covariance matrix, to design statistical beamforming vectors is robust when compared to instantaneous CSI. In this paper, we propose a novel MIMO covariance shaping scheme over Riemannian manifolds. It serves as an effective statistical beamforming solution to a number of close proximity user equipment (UE) that are undergoing substantial channel correlation. Proposed algorithm exploits the Hermitian positive definite nature of covariance matrices lying over Riemannian manifold. We introduce Wasserstein distance function as a Riemannian metric to measure distances between channel covariance matrices. Furthermore, K-means clustering technique is utilized to effectively identify the optimal shape of effective optimal covariance matrices. Our findings suggest that maximizing the geodesic distance between covariance matrices ultimately leads to a corresponding increase in the network throughput, as determined by the beamforming vector used to shape the covariance matrices. Simulation results validate that the proposed solution converges faster than Euclidean-based state-of-the-art, while maintaining the same computational complexity. Finally, the sum rate performance asymptotically achieves full capacity for two-UE case and more than 96% of the upper bound exhaustive search benchmark for multi-UE scenario.

42 ENGINEERING↗

Hyper Parameter Tuning in Neural Optical Image Categorizer for the E-log (NOICE

The Fermilab Accelerator Division Electronic logbook (E-log) is a record of all the activities and events in the Division for the past 10 years and more. The Elog search function is a valuable resource and the institutional memory of the accelerator complex. About 300,000 files are stored in the E-log, of which the vast majority are images attached to entries and comments. The visual information contained in the images is not presently searchable. The goal of team NOICE (Neural Optical Image Categorizer for the E-log) was to design a neural network able to produce label categories for these images for use by searches. The group developed a dataset and trained a convolutional neural network (CNN) with optimized hyperparameter. Final performance metrics are presented.

Njekeu, Terence Franck↗

DeepDiagnostics: A Software Package for Streamlined Posterior Evaluation

Automated prediction techniques like simulation-based inference (SBI) are important tasks for science experiments that produce large amounts of complex, raw data. However, their development remains in its early stages because the uncertainties of these techniques lack sufficient trustworthiness and interpretability. Packages for SBI provide a growing set of diagnostics; however, the software requirements are substantial, as they are tied to the inference technology itself, and the APIs lack adaptability. We introduce the DeepDiagnostics package for diagnosing posteriors from analytic likelihood-based methods and SBI methods, such as neural posterior estimation. DeepDiagnostics produces a comprehensive set of high-quality visualizations and metrics in a highly accessible, easy-to-use, and flexible package. We address all of these goals by providing a command-line inference tool and a Python API that is controlled through a configuration file. The package includes common diagnostics, such as parity plots, corner (covariance) plots, simulation-based calibration (SBC) diagnostics (including posterior coverage and rank histograms), Lemos et al. s PQMass and TARP, Masserano et al. s WALDO, Linhart et al. s LC2ST, as well as credible region diagnostics developed by our group.

Voetberg, Maggie [Fermilab]↗

Lunar Station: The Next Logical Step in Space Development

The International Space Station (ISS) is the product of the efforts of sixteen nations over the course of several decades. It is now complete, operational, and has been continuously occupied since November of 20001. Since then the ISS has been carrying out a wide variety of research and technology development experiments, and starting to produce some pleasantly startling results. The ISS has a mass of 420 metric tons, supports a crew of six with a yearly resupply requirement of around 30 metric tons, within a pressurized volume of 916 cubic meters, and a habitable volume of 388 cubic meters. Its solar arrays produce up to 84 kilowatts of power. In the course of developing the ISS, many lessons were learned and much valuable expertise was gained. Where do we go from here? The ISS offers an existence proof of the feasibility of sustained human occupation and operations in space over decades. It also demonstrates the ability of many countries to work collaboratively on a very complex and expensive project in space over an extended period of time to achieve a common goal. By harvesting best practices and lessons learned, the ISS can also serve as a useful model for exploring architectures for beyond low-­‐ earth-­‐orbit (LEO) space development. This paper will explore the concept and feasibility for a Lunar Station. The Station concept can be implemented by either putting the equivalent capability of the ISS down on the surface of the Moon, or by developing the required capabilities through a combination of delivered materials and equipment and in situ resource utilization (ISRU). Scenarios that leverage existing technologies and capabilities as well as capabilities that are under development and are expected to be available within the next 3-­5 years, will be examined. This paper will explore how best practices and expertise gained from developing and operating the ISS and other relevant programs can be applied to effectively developing Lunar Station.

Lunar↗

NASA Efforts to Explore Additively Manufactured Thermal Protection Systems

Development of the thermal protection system (TPS) needed to protect external and internal surfaces during launch, ascent, cruise, and reentry, is a very specialized field with expertise developed over seven decades and has enabled successful robotic as well as human exploration. Well known examples are: 1) the ablative heatshield that protected Apollo Astronauts during reentry in 1960’s, 2) reusable TPS used on the Space Shuttle Orbiter, which is now sought after by commercial space industry, and 3) hot structures, such as Carbon-Carbon and other high temperature materials needed to operate control surfaces and sharp leading edges during hypervelocity flight. TPS function demands fail-safe design to ensure mission success. One TPS does not fit all, and each application requires a unique material along with manufacturing and integration approach. TPS mass is a key metric. A robust but inefficient TPS reduces payload mass. Mission- and domain-unique TPS development and flight certification efforts have been expensive with long lead times. As a result, there are limited TPS choices with high cost. For future commercial efforts to be successful, the ability to rapidly develop TPS, based on specific needs and at a lower cost, without compromising either mass efficiency or functionality is necessary. NASA is looking into this. Additive manufacturing (AM) techniques have shown their utility in making complex parts and, through automation, the potential to reduce cost and schedule in some applications. The promise of additive manufacturing to TPS, while exciting, is unexplored. In the past several years, NASA has made exploratory investments in internal development as well as in small business and universities through SBIR and STTR programs. In addition, NASA is bringing together the TPS as well as Additive Manufacturing communities to explore approaches to prioritize focus areas for broad benefits. The very first “Additively Manufactured Thermal Protection System Workshop,” to be held at NASA Johnson Space Center in Houston at the end of March of this year, is the culmination of a year-long effort to engage the research, development, and user communities involved in both TPS as well as additive manufacturing. The workshop participants include representatives from large and small commercial space industry, DoD, Federally Funded Research and Development Centers, University Researchers and NASA personnel. The proposed talk will highlight future TPS needs of both emerging commercial and Government interests, NASA’s development efforts in additively manufactured TPS, and the findings and recommendations from the first AM TPS Workshop.

Ethiraj Venkatapathy↗

Validation on the Europa Clipper Mission in the Formulation Phase

The Europa Clipper mission will perform a detailed investigation of Europa’s environment and habitability by placing a spacecraft into a looping orbit around Jupiter and performing repeated close Europa flybys. The Europa Clipper Project Verification and Validation (V&V) team is responsible for verifying that the Project System is compliant with the science and engineering requirements levied on it and validating that it is capable of meeting the mission objectives. Europa Clipper has developed a V&V framework to ensure that mission capabilities are realized at a reasonable confidence level prior to launch. The V&V team will coordinate the validation activities defined by this framework throughout the project lifecycle as the spacecraft evolves from design to implementation and operations. Validation of space missions is typically grouped into three categories: 1) requirements validation, 2) model validation, and 3) system validation. System validation, along with verification activities, are often the primary focus of V&V programs and these efforts peak in the implementation phase. However, the increasing reliance of exploration missions on models and simulations, and the complexity of the Europa Clipper mission, have dictated attention to requirements and model validation in the formulation phase. This paper discusses the development of the Europa Clipper V&V framework with emphasis on the validation of models and simulations, and of requirements. Approaches include the adaptation of institutional guidelines, such as those in the NASA-STD-7009 document, to create a Project Model V&V Plan and the use of tools such as DOORS NG to plan model validation and derive requirement quality metrics.

Srivastava, Priyanka↗

m:N Operations High Complexity Simulation

The Human Autonomy Teaming Laboratory at NASA Ames Research Center (ARC) is planning to conduct an experiment investigating system-level effects operational vertiport disruptions on Urban Air Mobility (UAM) traffic in a simulated vertiport network in Denver, CO. The simulation will involve two parties, one at NASA ARC and another at NASA Langley Research Center (LaRC), each playing the role of a UAM Operator. This study, referred to as the "High Complexity Sim," features a 3-by-2-by-2 mixed subjects design. The variables to be manipulated are the scale of the disruption (Disruption, three levels: Small, Medium, and Large, affecting 1, 2, and 3+, vertiports, respectively), m:N ratio (Ratio, two levels: 2:6 and 2:12), and the assumption of UAM corridors (Corridors, two levels: With and Without). Local and system-wide capacity (i.e., throughput), efficiency (scale and number of flight delays, filed-vs-flown flight times), and human performance metrics (workload, situation awareness, heart rate/heart rate variability, eye gaze/fixation and saccades, stress) will be measured. This presentation details the experimental design and planned timeline for the study.

multi-vehicle↗

Operational Dynamic Configuration Analysis

Sectors may combine or split within areas of specialization in response to changing traffic patterns. This method of managing capacity and controller workload could be made more flexible by dynamically modifying sector boundaries. Much work has been done on methods for dynamically creating new sector boundaries [1-5]. Many assessments of dynamic configuration methods assume the current day baseline configuration remains fixed [6-7]. A challenging question is how to select a dynamic configuration baseline to assess potential benefits of proposed dynamic configuration concepts. Bloem used operational sector reconfigurations as a baseline [8]. The main difficulty is that operational reconfiguration data is noisy. Reconfigurations often occur frequently to accommodate staff training or breaks, or to complete a more complicated reconfiguration through a rapid sequence of simpler reconfigurations. Gupta quantified a few aspects of airspace boundary changes from this data [9]. Most of these metrics are unique to sector combining operations and not applicable to more flexible dynamic configuration concepts. To better understand what sort of reconfigurations are acceptable or beneficial, more configuration change metrics should be developed and their distribution in current practice should be computed. This paper proposes a method to select a simple sequence of configurations among operational configurations to serve as a dynamic configuration baseline for future dynamic configuration concept assessments. New configuration change metrics are applied to the operational data to establish current day thresholds for these metrics. These thresholds are then corroborated, refined, or dismissed based on airspace practitioner feedback. The dynamic configuration baseline selection method uses a k-means clustering algorithm to select the sequence of configurations and trigger times from a given day of operational sector combination data. The clustering algorithm selects a simplified schedule containing k configurations based on stability score of the sector combinations among the raw operational configurations. In addition, the number of the selected configurations is determined based on balance between accuracy and assessment complexity.

Lai, Chok Fung↗

Orbit Determination Processes for the Navigation of the Cassini-Huygens Mission

Deep space navigation, particularly the Orbit Determination (OD) operations of Cassini at Saturn, cannot easily be automated due to the complex dynamical environment in which the spacecraft flies; however several sub-processes are automated. The Cassini OD operations are often faced with unique challenges that require more than routine procedures. The OD Team is staffed appropriately to meet the demanding schedules and allow some level of flexibility. This paper will discuss how the OD processes are developed and the seven-member OD team is scheduled to support efficient and accurate Cassini navigation operations. Also discussed will be the requirements of the radio-metric Doppler and range tracking data acquired via the Deep Space Network and the optical navigation images of the satellites to support the daily OD operations. Furthermore, the reliability of the OD solutions, which is ensured within the framework of the OD processes, will be explained.

Antreasian, P.G.↗

Examining the Conceptual Design Process for Future Hybrid-Electric Rotorcraft

Hybrid-electric propulsion systems introduce immense complexity and numerous design challenges not previously encountered in aircraft design. Traditional conceptual-level rotorcraft design approaches may not adequately capture the level of propulsion system detail desired for hybrid-electric vehicle conceptual design. As part of a NASA Small Business Innovative Research (SBIR) Phase II contract, Empirical Systems Aerospace (ESAero) investigated the implementation of several hybrid-electric propulsion architectures onto three rotorcraft configurations. Unique hybrid-electric variants of these configurations were compared against their conventionally-powered counterparts using typical metrics such as payload, range, and energy efficiency. The feasibility and performance of these vehicles was also investigated in the +15 and +30-year timeframes based on third-party estimations for future component performance. Using the lessons learned during this trade study, ESAero then conducted a conceptual design effort for a hybrid-electric tiltrotor demonstrator based on the XV-15. A detailed integration of the hybrid-electric propulsion system into the vehicle airframe was also performed. The hybrid-electric XV-15 concept vehicle was estimated to achieve a 10% reduction in cruise fuel consumption compared to the original NASA XV-15 at the cost of increasing the vehicle empty weight by almost 25%. The success of this design effort suggests that the design of a manned, hybrid-electric tiltrotor is technically feasible at current technology levels.

Future↗

Towards an Automated Unstructured Grid Adaptation Workflow with VULCAN

Early work is presented for an unstructured grid adaptation workflow with VULCAN and refine. Anisotropic simplex grids are iteratively adapted to match a Riemannian metric tensor field describing desired mesh spacing. The Riemannian metric tensor field is obtained from Hessians of CFD solution output scalar sensor fields; both Mach number and static temperature sensor fields are explored. In addition, we describe a Newton-method-based solver recently implemented in VULCAN utilizing Jacobian-Free-Newton-Krylov that can be used to increase flow solver automation on early grids in the adadptation process. Hypersonic flow solutions are presented on a high Reynolds number flat plate and wall heat flux is compared against a highly resolved structured solution. Additionally, complex shock boundary-layer interaction is explored in a high Mach number compression corner and complex 3D flow phenomena are evaluated on the Boundary Layer Transition (BOLT) vehicle.

Matthew O'Connell↗

Fundamental Studies of Complex Oxides and Their Interfaces

This is a final close-out report for DOE contract DE-FG02-00ER45818 entitled “Fundamental Studies of Complex Oxides and their Interfaces” at the University of Missouri, Columbia, Missouri. It funded research in Condensed Matter Physics Basic Energy Science from July 1, 2000 through Nov 30, 2022. Presented below are: (i) An overview of the research accomplishments during the entire grant period with highlights of research results from the last funding period, (ii) A list of postdocs and graduate students that received support from this grant, and (iii) A list of original papers published through the duration of the grant with impact factor metrics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Characterization of Evidence for Human System Risk Assessment

Understanding the kinds of evidence available and using the best evidence to answer a question is critical to evidenced-based decision-making, and it requires synthesis of evidence from a variety of sources. Categorization of human system risks in spaceflight, in particular, focuses on how well the integration and interpretation of all available evidence informs the risk statement that describes the relationship between spaceflight hazards and an outcome of interest. A mature understanding and categorization of these risks requires: 1) sufficient characterization of risk, 2) sufficient knowledge to determine an acceptable level of risk (i.e., a standard), 3) development of mitigations to meet the acceptable level of risk, and 4) identification of factors affecting generalizability of the evidence to different design reference missions. In the medical research community, evidence is often ranked by increasing confidence in findings gleaned from observational and experimental research (e.g., "levels of evidence"). However, an approach based solely on aspects of experimental design is problematic in assessing human system risks for spaceflight. For spaceflight, the unique challenges and opportunities include: (1) The independent variables in most evidence are the hazards of spaceflight, such as space radiation or low gravity, which cannot be entirely duplicated in terrestrial (Earth-based) analogs, (2) Evidence is drawn from multiple sources including medical and mission operations, Lifetime Surveillance of Astronaut Health (LSAH), spaceflight research (LSDA), and relevant environmental & terrestrial databases, (3) Risk metrics based primarily on LSAH data are typically derived from available prevalence or incidence data, which may limit rigorous interpretation, (4) The timeframe for obtaining adequate spaceflight sample size (n) is very long, given the small population, (5) Randomized controlled trials are unattainable in spaceflight, (6) Collection of personal and environmental data on the astronaut population may create opportunities for advanced analytics and human-environment modeling that goes beyond that achieved in isolated experimental designs; and (7) Translation of relevant research to operations is a complex, transdisciplinary enterprise in which the approach must apply across the physical, biological, behavioral, and social sciences. The approach to synthesizing evidence must address both source and fidelity of data, and reflect the most general attributes of quality of evidence in science and engineering: reliability and validity. The authors are developing a two-factor approach which includes the various kinds of evidence required to understand risks and for the integrated interpretation of all evidence that is essential to develop standards and countermeasures. A unified framework for aggregating and assessing different kinds of evidence provides a consistent, traceable, evidence-based decision-making process to translate research to operations in an environment where engineers, scientists, physicians, and managers all engage in analyzing the trade space of vehicle design, standards, requirements and solutions for spaceflight.

Steinberg, S. L.↗

Reduction of the molecular hamiltonian matrix using quantum community detection

Abstract Quantum chemistry is interested in calculating ground and excited states of molecular systems by solving the electronic Schrödinger equation. The exact numerical solution of this equation, frequently represented as an eigenvalue problem, remains unfeasible for most molecules and requires approximate methods. In this paper we introduce the use of Quantum Community Detection performed using the D-Wave quantum annealer to reduce the molecular Hamiltonian matrix in Slater determinant basis without chemical knowledge. Given a molecule represented by a matrix of Slater determinants, the connectivity between Slater determinants (as off-diagonal elements) is viewed as a graph adjacency matrix for determining multiple communities based on modularity maximization. A gauge metric based on perturbation theory is used to determine the lowest energy cluster. This cluster or sub-matrix of Slater determinants is used to calculate approximate ground state and excited state energies within chemical accuracy. The details of this method are described along with demonstrating its performance across multiple molecules of interest and bond dissociation cases. These examples provide proof-of-principle results for approximate solution of the electronic structure problem using quantum computing. This approach is general and shows potential to reduce the computational complexity of post-Hartree–Fock methods as future advances in quantum hardware become available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗