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DOE OSTI2025

Elastic Bayesian Model Calibration

Functional data are ubiquitous in scientific modeling. For instance, quantities of interest are modeled as functions of time, space, energy, density, etc. Uncertainty quantification methods for computer models with functional response have resulted in tools for emulation, sensitivity analysis, and calibration that are widely used. However, many of these tools do not perform well when the computer model’s parameters control both the amplitude variation of the functional output and its alignment (or phase variation). This paper introduces a framework for Bayesian model calibration when the model responses are misaligned functional data. The approach generates two types of data out of the misaligned functional responses: (1) aligned functions so that the amplitude variation is isolated and (2) warping functions that isolate the phase variation. These two types of data are created for the computer simulation data (both of which may be emulated) and the experimental data. The calibration approach uses both types so that it seeks to match both the amplitude and phase of the experimental data. The framework is careful to respect constraints that arise, especially when modeling phase variation, and is framed in a way that it can be done with readily available calibration software. In conclusion, we demonstrate the techniques on two simulated data examples and on two dynamic material science problems: a strength model calibration using flyer plate experiments and an equation of state model calibration using experiments performed on the Sandia National Laboratories’ Z-machine.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

HydraGNN v5.0

HydraGNN v5.0 expands the code base into a more portable, scalable, and flexible framework for scientific graph learning, with particular strength in atomistic machine-learning interatomic potentials and large-scale distributed training. The release adds Fully Sharded Data Parallel (FSDP) support alongside existing DDP and DeepSpeed paths, including FSDP-aware checkpointing and optimizer integration, and introduces a configurable multi-precision training workflow supporting FP32, BF16, and FP64 across GPUs and Intel XPUs. For atomistic modeling, HydraGNN v5.0 strengthens its MLIP capabilities through dynamic graph construction at every forward pass, energy-conserving force prediction via automatic differentiation, and per-atom energy loss formulations, while extending EGNN models to properly handle periodic boundary conditions. The release also broadens model expressiveness through graph-level attribute conditioning, adds new multi-task and model-parallel extensions such as MACE support and encoder/decoder branch optimization, and expands application coverage with integrated examples for datasets including OC25, Nabla2-DFT, QCML, Open Polymers 2026, and OPF. In parallel, HydraGNN v5.0 improves production readiness through performance optimizations for large-scale runs, stratified sampling and linear-regression preprocessing utilities, and tested installation scripts for DOE supercomputers including Frontier, Aurora, Perlmutter, and Andes. Overall, the release advances HydraGNN as a robust software platform for scalable graph neural networks across materials science, chemistry, and scientific machine learning workflows

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗

DOE OSTI2022

Quantum pixel representations and compression for N-dimensional images

Abstract We introduce a novel and uniform framework for quantum pixel representations that overarches many of the most popular representations proposed in the recent literature, such as (I)FRQI, (I)NEQR, MCRQI, and (I)NCQI. The proposed QPIXL framework results in more efficient circuit implementations and significantly reduces the gate complexity for all considered quantum pixel representations. Our method scales linearly in the number of pixels and does not use ancilla qubits. Furthermore, the circuits only consist of $$R_y$$ R y gates and $$\text {CNOT}$$ CNOT gates making them practical in the NISQ era. Additionally, we propose a circuit and image compression algorithm that is shown to be highly effective, being able to reduce the necessary gates to prepare an FRQI state for example scientific images by up to 90% without sacrificing image quality. Our algorithms are made publicly available as part of , a Quantum Image Pixel Library.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Evaluation of Precision Landing Performance Using A Generalized Aerospace Simulation in Simulink Framework

NASA’s science and exploration goals to return to the Moon and beyond will need to perform precision landings to place humans and cargo supplies near places of scientific interest, surface resources, or pre-established basecamps. With the maturation of new navigation technology, such as terrain relative navigation, precision landing is now feasible, enabling new exploration sites, such as the lunar poles. However, verification of precision landing performance becomes crucial since not reaching the designated landing site would have a high risk of loss of mission. Therefore, having a high-fidelity simulation platform to evaluate six degrees of freedom vehicle performance during high-risk phases of flight such landing is a fundamental part of the system verification and risk reduction. The NASA Marshall Space Flight Center has developed the GeneraLized Aerospace Simulation in Simulink® (GLASS) tool which incorporates guidance, navigation, and control algorithms, as well as vehicle and environmental models, such as gravity, vehicle mass properties, navigation sensors, propulsion, and terrain models. GLASS uses the MathWorks® Simulink® environment which provides a model-based design framework that allows the incorporation of vehicle models in a modular architecture. The Simulink® environment provides seamless integration with all the MathWorks® capabilities and toolboxes, such as control design toolboxes and Simscape™ Multibody™ dynamics toolbox. The MathWorks® environment also allows for guidance, navigation, and control algorithms to be auto coded in C language, enabling quick software and hardware in the loop testing. This paper provides an overview of GLASS capabilities for analyzing precision landing performance, including navigation trades applied to a NASA human lander reference design architecture.

Guidance↗

NASA NTRS2011

HEASARC - The High Energy Astrophysics Science Archive Research Center

The High Energy Astrophysics Science Archive Research Center (HEASARC) is NASA's archive for high-energy astrophysics and cosmic microwave background (CMB) data, supporting the broad science goals of NASA's Physics of the Cosmos theme. It provides vital scientific infrastructure to the community by standardizing science data formats and analysis programs, providing open access to NASA resources, and implementing powerful archive interfaces. Over the next five years the HEASARC will ingest observations from up to 12 operating missions, while serving data from these and over 30 archival missions to the community. The HEASARC archive presently contains over 37 TB of data, and will contain over 60 TB by the end of 2014. The HEASARC continues to secure major cost savings for NASA missions, providing a reusable mission-independent framework for reducing, analyzing, and archiving data. This approach was recognized in the NRC Portals to the Universe report (2007) as one of the HEASARC's great strengths. This poster describes the past and current activities of the HEASARC and our anticipated developments in coming years. These include preparations to support upcoming high energy missions (NuSTAR, Astro-H, GEMS) and ground-based and sub-orbital CMB experiments, as well as continued support of missions currently operating (Chandra, Fermi, RXTE, Suzaku, Swift, XMM-Newton and INTEGRAL). In 2012 the HEASARC (which now includes LAMBDA) will support the final nine-year WMAP data release. The HEASARC is also upgrading its archive querying and retrieval software with the new Xamin system in early release - and building on opportunities afforded by the growth of the Virtual Observatory and recent developments in virtual environments and cloud computing.

Smale, Alan P.↗

DOE OSTI2025

LinkML: an open data modeling framework

Background Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, nonstandardized spreadsheets, or data repositories. This lack of structure challenges interoperability, making data integration, validation, and reuse difficult. Findings LinkML (Linked Data Modeling Language) is an open framework that simplifies the process of authoring, validating, and sharing data. LinkML can describe a range of data structures, from flat, list-based models to complex, interrelated, and normalized models that utilize polymorphism and compound inheritance. It offers an approachable syntax that is not tied to any one technical architecture and can be integrated seamlessly with many existing frameworks. The LinkML syntax provides a standard way to describe schemas, classes, and relationships, allowing modelers to build well-defined, stable, and optionally ontology-aligned data structures. Once defined, LinkML schemas may be imported into other LinkML schemas. These key features make LinkML an accessible platform for interdisciplinary collaboration and a reliable way to define and share data semantics. Conclusions LinkML helps reduce heterogeneity, complexity, and the proliferation of single-use data models while simultaneously enabling compliance with FAIR (Findable, Accessible, Interoperable, and Reusable) data standards. LinkML has seen increasing adoption in various fields, including biology, chemistry, biomedicine, microbiome research, finance, electrical engineering, transportation, and commercial software development. In short, LinkML makes implicit models explicitly computable and allows data to be standardized at their origin. LinkML documentation and code are available at https://linkml.io/.

AI-ready data↗

NASA NTRS2017

GEOS Atmospheric Model: Challenges at Exascale

The Goddard Earth Observing System (GEOS) model at NASA's Global Modeling and Assimilation Office (GMAO) is used to simulate the multi-scale variability of the Earth's weather and climate, and is used primarily to assimilate conventional and satellite-based observations for weather forecasting and reanalysis. In addition, assimilations coupled to an ocean model are used for longer-term forecasting (e.g., El Nino) on seasonal to interannual times-scales. The GMAO's research activities, including system development, focus on numerous time and space scales, as detailed on the GMAO website, where they are tabbed under five major themes: Weather Analysis and Prediction; Seasonal-Decadal Analysis and Prediction; Reanalysis; Global Mesoscale Modeling, and Observing System Science. A brief description of the GEOS systems can also be found at the GMAO website. GEOS executes as a collection of earth system components connected through the Earth System Modeling Framework (ESMF). The ESMF layer is supplemented with the MAPL (Modeling, Analysis, and Prediction Layer) software toolkit developed at the GMAO, which facilitates the organization of the computational components into a hierarchical architecture. GEOS systems run in parallel using a horizontal decomposition of the Earth's sphere into processing elements (PEs). Communication between PEs is primarily through a message passing framework, using the message passing interface (MPI), and through explicit use of node-level shared memory access via the SHMEM (Symmetric Hierarchical Memory access) protocol. Production GEOS weather prediction systems currently run at 12.5-kilometer horizontal resolution with 72 vertical levels decomposed into PEs associated with 5,400 MPI processes. Research GEOS systems run at resolutions as fine as 1.5 kilometers globally using as many as 30,000 MPI processes. Looking forward, these systems can be expected to see a 2 times increase in horizontal resolution every two to three years, as well as less frequent increases in vertical resolution. Coupling these resolution changes with increases in complexity, the computational demands on the GEOS production and research systems should easily increase 100-fold over the next five years. Currently, our 12.5 kilometer weather prediction system narrowly meets the time-to-solution demands of a near-real-time production system. Work is now in progress to take advantage of a hybrid MPI-OpenMP parallelism strategy, in an attempt to achieve a modest two-fold speed-up to accommodate an immediate demand due to increased scientific complexity and an increase in vertical resolution. Pursuing demands that require a 10- to 100-fold increases or more, however, would require a detailed exploration of the computational profile of GEOS, as well as targeted solutions using more advanced high-performance computing technologies. Increased computing demands of 100-fold will be required within five years based on anticipated changes in the GEOS production systems, increases of 1000-fold can be anticipated over the next ten years.

ESMF↗

DOE OSTI2020

waveSZ: A Hardware-Algorithm Co-Design of Efficient Lossy Compression for Scientific Data

Error-bounded lossy compression is critical to the success of extreme-scale scientific research because of ever-increasing volumes of data produced by today's high-performance computing (HPC) applications. Not only can error-controlled lossy compressors significantly reduce the I/O and storage burden but they can retain high data fidelity for post analysis. Existing state-of-the-art lossy compressors, however, generally suffer from relatively low compression and decompression throughput (up to hundreds of megabytes per second on a single CPU core), which considerably restrict the adoption of lossy compression by many HPC applications especially those with a fairly high data production rate. In this paper, we propose a highly efficient lossy compression approach based on field programmable gate arrays (FPGAs) under the state-of-the-art lossy compression model SZ. Our contributions are fourfold. (1) We adopt a wavefront memory layout to alleviate the data dependency during the prediction for higher-dimensional predictors, such as the Lorenzo predictor. (2) We propose a co-design framework named WAVESZ based on the wavefront memory layout and the characteristics of SZ algorithm and carefully implement it by using high-level synthesis. (3) We propose a hardware-algorithm co-optimization method to improve the performance. (4) We evaluate our proposed WAVESZ on three real-world HPC simulation datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and FPGAs. Experiments show that our WAVESZ can improve SZ's compression throughput by 6.9x similar to 8.7x over the production version running on a state-of-the-art CPU and improve the compression ratio and throughput by 2.1x and 5.8x on average, respectively, respectively, compared with the state-of-the-art FPGA design.

Compression Ratio↗

DOE OSTI2021

Exascale models of stellar explosions: Quintessential multi-physics simulation

The ExaStar project aims to deliver an efficient, versatile, and portable software ecosystem for multi-physics astrophysics simulations run on exascale machines. The code suite is a component-based multi-physics toolkit, built on the capabilities of current simulation codes (in particular Flash-X and Castro), and based on the massively parallel adaptive mesh refinement framework AMReX. It includes modules for hydrodynamics, advanced radiation transport, thermonuclear kinetics, and nuclear microphysics. The code will reach exascale efficiency by building upon current multi- and many-core packages integrated into an orchestration system that uses a combination of configuration tools, code translators, and a domain-specific asynchronous runtime to manage performance across a range of platform architectures. The target science includes multi-physics simulations of astrophysical explosions (such as supernovae and neutron star mergers) to understand the cosmic origin of the elements and the fundamental physics of matter and neutrinos under extreme conditions.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2011

InSAR Scientific Computing Environment

This computing environment is the next generation of geodetic image processing technology for repeat-pass Interferometric Synthetic Aperture (InSAR) sensors, identified by the community as a needed capability to provide flexibility and extensibility in reducing measurements from radar satellites and aircraft to new geophysical products. This software allows users of interferometric radar data the flexibility to process from Level 0 to Level 4 products using a variety of algorithms and for a range of available sensors. There are many radar satellites in orbit today delivering to the science community data of unprecedented quantity and quality, making possible large-scale studies in climate research, natural hazards, and the Earth's ecosystem. The proposed DESDynI mission, now under consideration by NASA for launch later in this decade, would provide time series and multiimage measurements that permit 4D models of Earth surface processes so that, for example, climate-induced changes over time would become apparent and quantifiable. This advanced data processing technology, applied to a global data set such as from the proposed DESDynI mission, enables a new class of analyses at time and spatial scales unavailable using current approaches. This software implements an accurate, extensible, and modular processing system designed to realize the full potential of InSAR data from future missions such as the proposed DESDynI, existing radar satellite data, as well as data from the NASA UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar), and other airborne platforms. The processing approach has been re-thought in order to enable multi-scene analysis by adding new algorithms and data interfaces, to permit user-reconfigurable operation and extensibility, and to capitalize on codes already developed by NASA and the science community. The framework incorporates modern programming methods based on recent research, including object-oriented scripts controlling legacy and new codes, abstraction and generalization of the data model for efficient manipulation of objects among modules, and well-designed module interfaces suitable for command- line execution or GUI-programming. The framework is designed to allow users contributions to promote maximum utility and sophistication of the code, creating an open-source community that could extend the framework into the indefinite future.

Rosen, Paul A.↗

NASA NTRS2015

Big Software for SmallSats: Adapting CFS to CubeSat Missions

Expanding capabilities and mission objectives for SmallSats and CubeSats is driving the need for reliable, reusable, and robust flight software. While missions are becoming more complicated and the scientific goals more ambitious, the level of acceptable risk has decreased. Design challenges are further compounded by budget and schedule constraints that have not kept pace. NASA's Core Flight Software System (cFS) is an open source solution which enables teams to build flagship satellite level flight software within a CubeSat schedule and budget. NASA originally developed cFS to reduce mission and schedule risk for flagship satellite missions by increasing code reuse and reliability. The Lunar Reconnaissance Orbiter, which launched in 2009, was the first of a growing list of Class B rated missions to use cFS. Large parts of cFS are now open source, which has spurred adoption outside of NASA. This paper reports on the experiences of two teams using cFS for current CubeSat missions. The performance overheads of cFS are quantified, and the reusability of code between missions is discussed. The analysis shows that cFS is well suited to use on CubeSats and demonstrates the portability and modularity of cFS code.

Open Source↗

NASA NTRSDate not supplied

Designing a Software Architecture for the Precision Assembly of Space Structures

As NASA’s space exploration and science missions expand in complexity, longevity, anddistance beyond earth’s orbit, Orbital Servicing, Assembly and Manufacturing (OSAM)technologies and concepts have become a critical area of ongoing research and innovation.Artemis’ Moon-to-Mars goals of building sustainable elements on and around the Moon andMars that allow our robots and astronauts to explore and conduct more scientific researchwill demand in situ resource utilization, construction, and maintenance to succeed. In-spaceAssembly (ISA), as a sub-component of OSAM, focuses on the on-orbit building or fabricationof mission infrastructure and payloads. One such ISA application is highlighted by the recentNASA In-Space Assembled Telescope (iSAT) study, which stated that the next generation ofspace observatories will exceed the fairing size of existing or even planned launch vehicles andISA has emerged as a viable approach for observatory assembly. Research efforts at NASALangley Research Center have led to the design of a novel TriTruss structural concept for themodular construction of large complex persistent platforms. The TriTruss design and otherdeveloping OSAM technologies enable larger and persistent space missions that would notbe possible with single-launch-sized structures. For example, 20 meter or larger telescopesor orbital platform applications. However, the increased complexity will require autonomousoperations for the construction and maintenance of long-term infrastructure to achieve missionsuccess. NASA’s Precision Assembly of Space Structures (PASS) project is focused on thestructural and autonomy capabilities required to construct an iSAT in deep space. PASSresearch efforts will develop and validate critical technologies needed for effective efficienton-orbit assembly that can be confidently adopted for future systems. PASS will utilize theTriTruss modules to demonstrate the autonomous modular assembly of a 20m-class iSAT mirrorbackbone structure including simulated mirrors and wiring harness. In this paper, we addressthe software and hardware design considerations, technologies, and challenges of designing arobust robotics framework for assembling modular space structures in support of In SpaceAssembly missions in general as well as for PASS specifically.

Benjamin N Kelley↗

DOE OSTI2024

R&D to Ensure a Scientific Basis for Qualification Tests and Standards (Final Report)

Project return on investment in a photovoltaic (PV) system depends increasingly on maintaining high energy yields, and the system lifetime is a major factor in levelized cost of electricity (LCOE). Thus, the rate of PV deployment and the success of these assets depends upon reliable long-term power generation. The overarching objective of this program is to improve photovoltaic (PV) module reliability via development of tests and standards. Where reliability problems or risk are discovered, we can design tests to ensure that these liabilities don't affect future generations of products. Customers can use these tests to understand which products are susceptible to certain degradation mechanisms, and manufacturers can use the tests to design unwanted characteristics out of their products. The work under this program identifies PV reliability needs, performs characterization that provides scientific understanding of targeted degradation mechanisms, and translates those data into practical and predictive test protocols and standards. Major accomplishments include: A model for polarization-type potential induced degradation (PID-p) was developed and validated against experimental data. NREL is currently leading a new edition of IEC 62804-1 for PID detection. PID-p can cause large losses in current and voltage for some module designs on cloudy days. Finite element modeling (FEM) and experiment was used to determine when cells crack in a module. It was shown that cells in landscape orientation are much more likely to crack than those on portrait orientation. Shortly thereafter, the first products with portrait-oriented cells were introduced. Studies of how to test for light and elevated temperature degradation (LeTID) culminated with the publication of IEC TS 63342. Software to predict the progression of LeTID was developed, validated, and made publicly available. Field validated tests and international standards for durability of PV module coatings abrasion, backsheets, and encapsulants were developed. Examples are IEC 62788-1-1, IEC 62788-2 ED2, IEC TS 62788-7-2, IEC 62788-7-3 ED1, IEC 63209-2. NREL led the development a high-temperature testing technical specification, and published guidelines that enable installers to determine whether higher-temperature testing is needed, simply based on location and mounting configuration. In a number of our case studies, variations in the bills of materials or workmanship have been associated with variations in reliability. These observations emphasize the importance of quality assurance to reliability. A framework for criticality (i.e. Pareto) analysis was developed and published. The framework helps us and other researchers determine what problems should be addressed for reliability research to have the biggest industry impact. NREL continues to participate actively in international standards development and stakeholder engagement activities, including organizing an annual PV Reliability Workshop. These activities are important for ensuring we address issues that are relevant and timely, and that we convey our results to those who may benefit.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

Biomass Harmonization and SAR Analysis with the Multi-mission Algorithm and Analysis Platform (MAAP)

The Multi‐mission Algorithm and Analysis Platform (MAAP) is a collaborative effort between NASA and the European Space Agency (ESA) to support above ground biomass (AGB) research in an open science framework. MAAP brings together relevant data, algorithms, and computing capabilities in a common cloud environment to address the challenges of sharing and processing data from field, airborne and satellite measurements. MAAP was publicly released in October 2021, providing computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of data. MAAP has allowed scientists from both North America and Europe to collaborate on the generation and analysis/visualization of data derived from multiple, discipline-adjacent missions in an open, collaborative environment that has reached beyond traditional scientific investigation. MAAP has been used to support multiple scientific activities. To date, existing LiDAR data from multiple platforms has been calibrated with field measurements and combined for more comprehensive and accurate estimates of above ground biomass AGB; these LiDAR platforms include airborne (e.g. LVIS), the International Space Station (NASA’s Global Ecosystem Dynamics Investigation (GEDI), and satellites (e.g. ICESat-2). The current challenge is to effectively and seamlessly combine the aforementioned LiDAR-based data with new data sources such as P-band RADAR from ESA’s upcoming BIOMASS mission, existing ESA Sentinel-1 C-band SAR, and the 30 PB/yr of high cadence global coverage L-band SAR data from the upcoming NASA-ISRO SAR (NISAR) mission. Recent analysis using MAAP merged ICESat-2 and optical data (Harmonized Landsat Sentinel) produced the most comprehensively precise estimate of boreal-wide AGB to date. Another effort using MAAP is the production and open distribution of global comparisons of AGB map estimates, including from ICESat-2 and GEDI, to bolster stakeholder uptake for policy applications. These map estimates will feed into the Intergovernmental Panel on Climate Change (IPCC) database, likely aiding the next Global Carbon Stocktake of the UNFCCC. Furthermore, the biomass retrieval intercomparison exercise BRIX-2 could benefit from the MAAP providing standardized test cases (based on airborne campaign and spaceborne data) allowing the community to develop and apply retrieval algorithms based on these test cases, while forthcoming SAR data training curricula could also use the MAAP as a teaching and learning platform. The MAAP is meeting the challenges inherent in international, open science collaboration and large scale computing with a platform that is entirely open source and cloud native, using open standards for data access, manipulation, protocols, and formats. The MAAP data system consists of a dedicated data store whose data is indexed in an online catalog conforming to established metadata, application programmatic interfaces (APIs), and service interface standards, using an implementation of the open sourced NASA Common Metadata Repository. Federation of user identities allows users from either NASA or ESA to access and consume services from the other using a unified metadata catalog for the data utilized across the ESA and NASA MAAP platforms. Similarly, we are exploring how to increase interoperability to achieve a common approach to packaging, orchestrating and executing algorithms, with interoperable access to data for subsetting, fast browse, and cloud-optimized access, all using interoperable standards such as those from the Open Geospatial Consortium (OGC). Designed for interoperability, ESA and NASA utilize a common architecture for the software platform. It provides a cloud-based algorithm development environment (ADE) that enables scientists to develop algorithms collaboratively with access to the MAAP data catalog as well as other data archives. MAAP provides an Eclipse Che-based ADE supporting both Python and R languages, popular in this biomass community. Algorithms developed and containerized within the ADE can be deployed to run to thousands of computational nodes in the MAAP’s data processing system (DPS), dramatically speeding up processing and giving scientists a rapid, iterative turnaround of results. NASA’s implementation of the DPS is based on the Hybrid Science Data System (HySDS) framework, used by NASA flight projects to produce Earth science standard products.

cloud computing↗

DOE OSTI2022

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale (V.2.0)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Therefore, the resilience challenge for extreme-scale HPC systems requires coordination between various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. Also, due to practical limits on power consumption in future HPC systems, they are likely to embrace innovative architectures, increasing the levels of hardware and software complexities. Therefore, the techniques that seek to improve resilience must navigate the complex trade-off space between resilience and the overheads to power consumption and performance. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance & power efficiency characteristics. Additionally, few implementations of current resilience solutions are portable to newer architectures and software environments that will be deployed on future systems. We developed a new structured approach to the management of HPC resilience using the concept of resilience-based design patterns. In general, a design pattern is a repeatable solution to a commonly occurring problem. We identified the well-known solutions that are commonly used to deal with faults, errors and failures in HPC systems. In the initial design patterns specification (version 1.0), we described the various solutions, which address specific problems in the design of resilient HPC environments, in the form of patterns. Each pattern describes a problem caused by a fault, error or failure event in an HPC environment, and then describes the core of the solution of the problem in such a way that this solution may be adapted to different systems and implemented at different layers of the system stack. The catalog of these resilience design patterns provides designers with a collection of design elements. To construct complete resilience solutions using combinations of various patterns, we defined a framework that enhances HPC designers' understanding of the important constraints and the opportunities for the design patterns to be implemented and deployed at various layers of the system stack. The design framework is also useful for establishing interfaces and mechanisms to coordinate flexible fault management across hardware and software components, as well as to consider the trade-off between performance, resilience, and power consumption when constructing a solution. The resilience design patterns specification version 1.1 included more detailed explanations of the pattern solutions, the context in which the patterns are applicable, and the implications for hardware or software design. It also provided several additional examples and detailed case studies to demonstrate the use of patterns to build realistic solutions. In version 1.2 of the specification document, we have improved the pattern descriptions, including graphical representations of the pattern components. These improvements are largely based on critical comments, feedback and suggestions received from pattern experts and readers of the previous versions of the specification. The pattern classification has been modified to further clarify the relationships between pattern categories. This version of the specification also introduces a pattern language for resilience design patterns. The pattern language presents the patterns in the catalog as a network, revealing the relations among the resilience patterns. The language provides designers with the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language also enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack. The overall goal of this work is to provide hardware and software designers, as well as the users and operators of HPC systems, a systematic methodology for the design and evaluation of resilience technologies in HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types. Version 2.0 expands the resilience design pattern classification and catalog to include self-stabilization patterns and reliability, availability and performance models for each structural pattern.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2018

Flight Demonstration of Novel Atmospheric Satellite Concept

The major focus of the Phase II effort described herein is to develop and demonstrate an aircraft capable of autonomously sailing (i.e., to cruise without propulsion or external assistance), and thereby prove that the dual-aircraft platform (DAP) atmospheric satellite concept is potentially viable. This sailing mode of flight was identified as the number-1 enabling technology required for the stratospheric DAP concept (also known as Stratosat) in the NIAC (NASA Innovative Advanced Concept) Phase I effort. No scientific demonstration of this technology has ever been done or documented to our knowledge. This report describes efforts to take a major step towards the sailing mode of flight capability using a single aircraft connected by cable to a moving ground vehicle which uses sufficient crosswind to cruise without propulsion while "pulling" the ground vehicle forward (i.e., without external assistance). The development of a prototype aircraft is described in terms of novel and key hardware and software elements. A specialized prototype aircraft is described, including a novel cable release mechanism, novel "lateron" control surfaces, and a highly-accurate onboard wind measurement system. Additionally, a novel means to safely connect the aircraft to the moving ground vehicle is described involving a fishing rod/reel and integrated load cell. All of these devices were designed and developed in-house and validated in flight testing. Software is developed to provide look-up tables that give the flight condition targets (i.e., 3-D position relative to ground vehicle, forward speed, aircraft orientation, etc.), based on current wind speed and direction. These tables are successfully validated in flight simulation and used onboard the aircraft. High fidelity analysis of the aircraft aerodynamics are described - required to produce accurate target sailing flight conditions. A novel wind tunnel measurement technique is developed to accurately assess the aerodynamics of the ultra-thin cable. A new specialized flight simulator is described which is utilized to develop and verify the flight software required onboard the aircraft, and to support training of pilots for flying the aircraft while tethered to a ground vehicle. The DAP flight simulator was developed within the Matlab-Simulink framework and included detailed treatment of aircraft/cable aerodynamics, cable dynamics, experimentally-derived propeller-motor thrust curves, actuator responsiveness, and realistic air turbulence. The specialized formation flight controller algorithm, developed using this flight simulator, and onboard the aircraft is described. Finally, a novel auto-tuning software is described and verified within the flight simulator that is shown to refine the sailing flight condition targets during flight using an optimization technique involving doublet maneuvers. Virtual flights using the auto-tuning software indicate that the prototype aircraft should be able to reach and hold sailing conditions despite moderate levels of turbulence provided there is sufficient mean wind available. An overview of the flight testing program is provided. Hundreds of short flights were conducted, primarily using a “dead” short runway at Deland Municipal Airport which permitted use of a moving ground vehicle. Additional flight tests at Space Florida’s Shuttle Landing Facility are also described. First year results from these tests in which the aircraft is controlled manually, demonstrated that excessive flight testing would be required for a pilot to learn to sail with visual cues. However, second year results from autonomous flight these tests included successful demonstration of the closed-loop autonomous formation flight capability (i.e., autonomously determine, reach, and hold the required 3-D location relative to the ground vehicle required for sailing). The next step of using the auto-tune software to autonomously refine the aircraft orientation targets to finally achieve sailing remains the primary goal of future work.

Flight↗

NASA NTRS2003

Precision Agriculture: Changing the Face of Farming

To a large extent our work has grown out of the remote sensing technology and conceptual framework developed by geologists. For example the drive to look at the physics of reflectance and atmospheric corrections is rooted in work done in the early 1980s by the United States Geological Survey and NASA. Our work on emissivity and thermal behavior of plants pulls on research done using the Thermal Infrared Multispectral Scanner, an instrument originally conceived for geologic applications. Even our ability to geometrically map the airborne imagery onto the globe was explicitly developed because of need to map sediment flow patterns in along the coast of Louisiana. Growing from this base we have learned much in the last few years and believe our integration of geologic remote sensing with the other fields of expertise was a wise investment. Clearly none of the specialties alone could develop, let alone test, the basic approach we are now finding so powerful. This is the path that will ultimately give the information needed by the farmer. We also recognize how small a portion of the total problem has been solved. Having developed the basic logic, built proto-type tools and performed initial tests everything else remains to be done. And problems, scientific and practical, are everywhere. We have not established sensitivities. We have not robustly segregated the contributions of crop residue, soil moisture, shadows, plant and soil to the energy leaving the surface. What we do is extremely expensive and difficult. It is experimental in methodology and uses research oriented tools. We are constantly alive to the practicality of moving our results into commercial applications. We know another airborne instrument will have to be available. Atmospheric parameters will have to be measured automatically. The software will have to be re-written for speed. At times the list of problems seems endless. But the potential is also enormous. Agriculture is a huge portion of our economy. Just a one percent increase in efficiency is a $2,000,000 change. We all depend on farmers, literally for the bread we eat. No other activity of man even has an impact on the land that farming has. If application of precision agriculture can nelp farmers manage their land better, we all may benefit.

Rickman, D.↗

DOE OSTI2023

Tech-X contributions to the Center for Tokamak Transients (CTTS) Scientific Discovery through Advanced Computing (SciDAC) project (Final Report)

The Center for Tokamak Transients Simulations (CTTS) consists of computational plasma physicists, computer scientists, and applied mathematicians who are together developing and applying simulation software to improve our understanding of and ability to predict and control transient events in tokamak discharges, particularly those that lead to violent disruptions. We have major simulation activities concerning disruptions that are caused by violating ideal MHD stability criteria, vertical displacement events, resistive wall modes, and tearing modes. We are performing comprehensive modeling of disruption mitigation techniques, particularly by shattered pellet injection (SPI). The Center is built around two mature extended MHD codes: NIMROD and M3D-C1. These codes are being extended in a number of ways as required by the demands of disruption modeling, and are being optimized to work well for larger problem size and modern computer architectures as required to model tokamak plasmas with realistic parameters over the required timescales. The codes will be coupled to the FronTier code for state-of-the-art modeling of SPI. Validation with experimental data on DIII-D, NSTX, and JET is being facilitated by rigorous uncertainty quantification (UQ). The high-level objectives of the center are to: (1) Develop better understanding and improved predictive capability of when crossing a linear stability boundary will likely lead to a disruption, (2) Provide a tool for calculating vessel forces, both axisymmetric and non-axisymmetric, for worst case VDE and other disruptions in tokamaks, (3) Develop increased understanding and improved predictive capability for locked mode disruptions and how best to avoid them, (4) Develop, verify, and validate 3D models for disruption mitigation by shattered pellets that can be used to design and optimize a system for future tokamaks, and (5) Increase the efficiency and scalability of the M3D-C1 and NIMROD codes so that they can model disruptions in ITER scale devices using the most powerful DOE HPC hardware, and (6) Demonstrate code-coupling via FronTier coupling with NIMROD and M3D-C1 and by interfacing these codes within a WDM framework.

97 MATHEMATICS AND COMPUTING↗