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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Privacy Preserving Federated Learning for Advanced Scientific Ecosystems

We present a framework to provide privacy preserving (PP) federating learning (FL) across multiple computational and experimental facilities. This work joins the compute capabilities of National Energy Research Scientific Computing Center (NERSC) and Oak Ridge National Laboratory Research Cloud (ORC) with simulated experimental data, such as those produced at the SLAC National Accelerator Laboratory and Spallation Neutron Source (SNS). We describe the software infrastructure developed to provide privacy for computational and experimental networks. We developed algorithmic privacy across the federated system by embedding database security, computation, and communication into the federation architecture, utilizing scientific tools developed by the experimental community.

Archibald, Rick [ORNL] (ORCID:0000000245389780)↗

NASA NTRS1991

White paper on science operations

Major changes are taking place in the way astronomy gets done. There are continuing advances in observational capabilities across the frequency spectrum, involving both ground-based and space-based facilities. There is also very rapid evolution of relevant computing and data management technologies. However, although the new technologies are filtering in to the astronomy community, and astronomers are looking at their computing needs in new ways, there is little coordination or coherent policy. Furthermore, although there is great awareness of the evolving technologies in the arena of operations, much of the existing operations infrastructure is ill-suited to take advantage of them. Astronomy, especially space astronomy, has often been at the cutting edge of computer use in data reduction and image analysis, but has been somewhat removed from advanced applications in operations, which have tended to be implemented by industry rather than by the end-user scientists. The purpose of this paper is threefold. First, we briefly review the background and general status of astronomy-related computing. Second, we make recommendations in three areas: data analysis; operations (directed primarily to NASA-related activities); and issues of management and policy, believing that these must be addressed to enable technological progress and to proceed through the next decade. Finally, we recommend specific NASA-related work as part of the Astrotech-21 plans, to enable better science operations in the operations of the Great Observatories and in the lunar outpost era.

Schreier, Ethan J.↗

NASA NTRS1982

Advanced flight control system study

A fly by wire flight control system architecture designed for high reliability includes spare sensor and computer elements to permit safe dispatch with failed elements, thereby reducing unscheduled maintenance. A methodology capable of demonstrating that the architecture does achieve the predicted performance characteristics consists of a hierarchy of activities ranging from analytical calculations of system reliability and formal methods of software verification to iron bird testing followed by flight evaluation. Interfacing this architecture to the Lockheed S-3A aircraft for flight test is discussed. This testbed vehicle can be expanded to support flight experiments in advanced aerodynamics, electromechanical actuators, secondary power systems, flight management, new displays, and air traffic control concepts.

Hartmann, G. L.↗

NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗

DOE OSTI2021

Advances and perspectives in discovery and functional analysis of small secreted proteins in plants

Small secreted proteins (SSPs) are less than 250 amino acids in length and are actively transported out of cells through conventional protein secretion pathways or unconventional protein secretion pathways. In plants, SSPs have been found to play important roles in various processes, including plant growth and development, plant response to abiotic and biotic stresses, and beneficial plant–microbe interactions. Over the past 10 years, substantial progress has been made in the identification and functional characterization of SSPs in several plant species relevant to agriculture, bioenergy, and horticulture. Yet, there are potentially a lot of SSPs that have not been discovered in plant genomes, which is largely due to limitations of existing computational algorithms. Recent advances in genomics, transcriptomics, and proteomics research, as well as the development of new computational algorithms based on machine learning, provide unprecedented capabilities for genome-wide discovery of novel SSPs in plants. In this review, we summarize known SSPs and their functions in various plant species. Then we provide an update on the computational and experimental approaches that can be used to discover new SSPs. Finally, we discuss strategies for elucidating the biological functions of SSPs in plants.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Deep learning at the edge enables real-time streaming ptychographic imaging

Abstract Coherent imaging techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells. Driven by the construction of brighter sources and high-rate detectors, coherent imaging methods like ptychography are poised to revolutionize nanoscale materials characterization. However, these advancements are accompanied by significant increase in data and compute needs, which precludes real-time imaging, feedback and decision-making capabilities with conventional approaches. Here, we demonstrate a workflow that leverages artificial intelligence at the edge and high-performance computing to enable real-time inversion on X-ray ptychography data streamed directly from a detector at up to 2 kHz. The proposed AI-enabled workflow eliminates the oversampling constraints, allowing low-dose imaging using orders of magnitude less data than required by traditional methods.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Design improvements to the SNS ion source and diagnostics

The U.S. Spallation Neutron Source (SNS) is a state-of-the-art neutron scattering facility delivering the world's most intense pulsed-neutron beams to a wide array of instruments which are used to conduct investigations in many fields of science and engineering. The accelerator system is fed by an RF-driven, multicusp, H - ion source which nominally provides pulsed beam currents of 50-60 mA (1ms, 60Hz). This report provides a discussion of ongoing design improvements to the SNS ion source and Low Energy Beam Transport (LEBT) as well as diagnostic upgrades undertaken since the previous ICIS conference. These improvements include (i) simple mechanical modifications to the source outlet aperture which resulted in dramatically increased extracted beam current and comparable or lower emittance at similar beam currents, (ii) design improvements to the LEBT chopper target which will enable full power beam-dumping during physics studies, (iii) refinement of the SNS Allison emittance scanner that has enabled the first reliable LEBT beam measurements at full beam power (65kV, 50-100mA, 1ms, 60Hz) on the SNS ion source test stand and (iv) the implementation of a thermal imaging camera for the monitoring the LEBT electrode temperatures. (v) The design of an advanced Cs system, capable of more efficient Cs utilization with significantly lower Cs losses from the source is also presented. Mechanical details, computational simulations and experimental results are discussed within the context of these improvements.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Enhanced Models for the Simulation of Two-Phase Boiling Flow and Critical Heat Flux with the STAR-CCM+ and NEK-2P CFD Codes: Executive Summary (Final Report)

The goal of the project was to develop, verify and validate an advanced two-phase boiling flow modeling capability that enhances the existing two-phase flow modeling of the STAR-CCM+ Computational Fluid Dynamics (CFD) code. The project focused on the simulation of two phase flow and heat transfer phenomena that occur in Boiling Water Reactor (BWR) and Pressurized Water Reactor (PWR) fuel bundles under various operating conditions. The extended STAR-CCM+ two-phase flow models implemented during the project follow the approach similar to the Extended Boiling Framework [1-6] previously developed at Argonne but include more fundamental physical models of boiling phenomena. The development and validation of the extended two-phase models is supported by the U.S. DOE GAIN program and performed in collaboration with Framatome U.S. Inc.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1978

Future Computer Requirements for Computational Aerodynamics

Recent advances in computational aerodynamics are discussed as well as motivations for and potential benefits of a National Aerodynamic Simulation Facility having the capability to solve fluid dynamic equations at speeds two to three orders of magnitude faster than presently possible with general computers. Two contracted efforts to define processor architectures for such a facility are summarized.

Source record↗

NASA NTRS1991

Computational methods for structural load and resistance modeling

An automated capability for computing structural reliability considering uncertainties in both load and resistance variables is presented. The computations are carried out using an automated Advanced Mean Value iteration algorithm (AMV +) with performance functions involving load and resistance variables obtained by both explicit and implicit methods. A complete description of the procedures used is given as well as several illustrative examples, verified by Monte Carlo Analysis. In particular, the computational methods described in the paper are shown to be quite accurate and efficient for a material nonlinear structure considering material damage as a function of several primitive random variables. The results show clearly the effectiveness of the algorithms for computing the reliability of large-scale structural systems with a maximum number of resolutions.

Thacker, B. H.↗

NASA NTRS2005

Status of the Combustion Devices Injector Technology Program at the NASA MSFC

To support the NASA Space Exploration Mission, an in-house program called Combustion Devices Injector Technology (CDIT) is being conducted at the NASA Marshall Space Flight Center (MSFC) for the fiscal year 2005. CDIT is focused on developing combustor technology and analysis tools to improve reliability and durability of upper-stage and in-space liquid propellant rocket engines. The three areas of focus include injector/chamber thermal compatibility, ignition, and combustion stability. In the compatibility and ignition areas, small-scale single- and multi-element hardware experiments will be conducted to demonstrate advanced technological concepts as well as to provide experimental data for validation of computational analysis tools. In addition, advanced analysis tools will be developed to eventually include 3-dimensional and multi- element effects and improve capability and validity to analyze heat transfer and ignition in large, multi-element injectors.

Jones, Gregg↗

NASA NTRS2006

Gauge Conditions for Moving Black Holes Without Excision

Recent demonstrations of unexcised, puncture black holes traversing freely across computational grids represent a significant advance in numerical relativity. Stable an$ accurate simulations of multiple orbits, and their radiated waves, result. This capability is critically undergirded by a careful choice of gauge. Here we present analytic considerations which suggest certain gauge choices, and numerically demonstrate their efficacy in evolving a single moving puncture.

van Meter, James↗

NASA NTRS2007

A Perspective on Computational Aerothermodynamics at NASA

The evolving role of computational aerothermodynamics (CA) within NASA over the past 20 years is reviewed. The presentation highlights contributions to understanding the Space Shuttle pitching moment anomaly observed in the first shuttle flight, prediction of a static instability for Mars Pathfinder, and the use of CA for damage assessment in post-Columbia mission support. In the view forward, several current challenges in computational fluid dynamics and aerothermodynamics for hypersonic vehicle applications are discussed. Example simulations are presented to illustrate capabilities and limitations. Opportunities to advance the state-of-art in algorithms, grid generation and adaptation, and code validation are identified.

Gnoffo, Peter A.↗

NASA NTRS2016

Developing the Next Generation of Science Data System Engineers

At Goddard, engineers and scientists with a range of experience in science data systems are needed to employ new technologies and develop advances in capabilities for supporting new Earth and Space science research. Engineers with extensive experience in science data, software engineering and computer-information architectures are needed to lead and perform these activities. The increasing types and complexity of instrument data and emerging computer technologies coupled with the current shortage of computer engineers with backgrounds in science has led the need to develop a career path for science data systems engineers and architects.The current career path, in which undergraduate students studying various disciplines such as Computer Engineering or Physical Scientist, generally begins with serving on a development team in any of the disciplines where they can work in depth on existing Goddard data systems or serve with a specific NASA science team. There they begin to understand the data, infuse technologies, and begin to know the architectures of science data systems. From here the typical career involves peermentoring, on-the-job training or graduate level studies in analytics, computational science and applied science and mathematics. At the most senior level, engineers become subject matter experts and system architect experts, leading discipline-specific data centers and large software development projects. They are recognized as a subject matter expert in a science domain, they have project management expertise, lead standards efforts and lead international projects. A long career development remains necessary not only because of the breadth of knowledge required across physical sciences and engineering disciplines, but also because of the diversity of instrument data being developed today both by NASA and international partner agencies and because multidiscipline science and practitioner communities expect to have access to all types of observational data.This paper describes an approach to defining career-path guidance for college-bound high school and undergraduate engineering students, junior and senior engineers from various disciplines.

Next Generation of Science↗

NASA NTRS2018

Scale-Resolving Simulations of Low-Pressure Turbine Cascades with Wall Roughness Using A Spectral-Element Method

The accurate prediction of wall-roughness effects in turbomachinery is becoming critical as turbine designers address airfoil surface quality and degradation concerns arising from the shift to advanced ceramic matrix composite (CMC) or additively-manufactured airfoils operating in higher temperature environments. In this paper, a recently developed computational capability for accurate and efficient scale-resolving simulations of turbomachinery is extended to analyze the boundary- layer separation and transition characteristics in a rough-wall low-pressure turbine (LPT) cascade. The computational capability is based on an entropy-stable discontinuous-Galerkin spectral-element approach that extends to arbitrarily high orders of spatial and temporal accuracy, and is implemented in an efficient manner for a modern high performance computer architecture. Results from the scale-resolving simulations of both smooth and rough airfoil cascades are presented and compared to previous experiments and numerical simulations. The results show that the suction surface boundary layer undergoes laminar separation, transition, and turbulent reattachment for the smooth airfoil cascade, while in the presence of roughness the separation and transition behavior of the suction surface boundary layer is substantially modified. The differences between the smooth and rough airfoil cascades are then highlighted by a detailed analysis of their respective turbulent flow fields.

Spectral-Element↗

NASA NTRS2024

Prediction of Concentrated Vortex Aerodynamics: Current CFD Capability Survey

Concentrated vortex flows contribute to the aerodynamic performance of aircraft at elevated load conditions. For military interests, the vortex flows are exploited at maneuver conditions of combat aircraft and missiles. For transport interests, the vortex flows are exploited at takeoff and landing conditions as well as at select transonic conditions. Aircraft applications of these vortex flows are reviewed with a historical perspective followed by a discussion of the underlying physics of a concentrated vortex flow. A hierarchy of computational fluid dynamics simulation technology is then presented followed by findings from a capability survey for predicting concentrated vortex flows with computational fluid dynamics. Results are focused on military and civil aircraft; only limited results are included for missiles. Opportunities for predictive capability advancement are then reported with comments related to digital transformation interests. A hierarchical approach that merges a physics-based perspective of the concentrated vortex flows with a systems engineering viewpoint of the air vehicle is also used to frame much of the discussion.

vortex flow↗

NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗

DOE OSTI2025

Computational Algorithms for Unit Commitment with AC Power Flows (Final Report)

Security-constrained unit commitment (SCUC) is a key component in power system operations. When AC power flow constraints are considered in the SCUC model (AC-SCUC), the problem becomes extremely difficult due to its discrete and non-convex nature, as described in “Grid Optimization Competition Challenge 3 Problem Formulation (GOCC)”. There are four main challenges: (i) Discrete decisions regarding unit online/offline status and start-up/shut-down procedures for every single unit. The number of discrete decision variables increases considerably when a system integrates multiple generators; (ii) Configuration-based combined-cycle formulations, and multi-commodity models that include ramping products, spin/non-spin products, and regulation up/down products. The combined-cycle units introduce additional discrete decision variables and auxiliary service products further complicate the model by connecting multi-commodity products’ continuous and discrete variables; (iii) SCUC models with AC power flow constraints are far more complex due to massive bilinear terms in the large-scale nonlinear power balance equations. The nonlinear power balance equations are further complicated by the discrete step control variables of shunts; (iv) N − 1 contingency analysis. The size of the model increases linearly with the number of contingencies considered, greatly increasing the size of the optimization model. Accordingly, there is an emergent need to develop a robust algorithm capable of deriving a high-quality solution in a short time and passing through contingency tests simultaneously. In this project, we explore innovative techniques to address this challenging problem by integrating advanced polyhedral theory, approximation methods, relaxation strategies, decomposition techniques, and parallel computing. Each technique approaches the problem from a different perspective, leveraging its specific strengths to tackle distinct challenges. Each individual method has demonstrated its effectiveness in the PI’s previous research. Their integration is expected to significantly reduce the computational time required to solve the proposed complex problem. Successful completion of this project has the potential to transform the industry by enhancing optimization solvers capable of handling large-scale day-ahead energy market clearing models within strict time constraints, while incorporating AC power flow constraints. This advancement will lead to reduced overall generation costs and, consequently, increased social welfare.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗