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NASA NTRS1989

An interactive, discipline-independent data visualization system

The data visualization techniques used by National Space Science Data Center Graphics System are discussed. The system includes a self-describing data abstraction for the storage and manipulation of multidimensional data for discipline-independent scientific applications. The system's data format and graphics display are described. Consideration is given to techniques developed for the rapid display and manipulation of large, complex data sets and for manipulating algorithms with portable implementations which can operate on any data object or geometry. The processes of operating the system are given and examples are presented from applying the system to various types of data, including TOMS data, data from the International Satellite Cloud Climatology Project, and IRAS data.

Treinish, Lloyd A.↗

NASA NTRS2001

Experience and Challenges in Implementing Stratospheric Aerosol Gas Experiment on Meteor-3M Platform

Implementation of Stratospheric Aerosol Gas Experiment (SAGE) is a joint science mission between the Rosavioskosmos, also called Russian Aviation and Space Agency (RASA) and the National Aeronautics and Space Administration (NASA). Under the global collaboration agreement established by President Clinton and Yeltsin in 1995 between the United States and Russia, space was one of the major areas identified for joint scientific collaboration. There were several collaborative projects identified under space, earth, human exploration of space and aeronautics. SAGE was one of the key Earth Science instruments selected common to both countries' interests in ozone research. SAGE has a long space heritage, and four earlier versions of this instrument have flown in space for the last 15-year period. It has provided a vital ozone and aerosol data in the mid latitudes and has contributed in the overall ozone depletion research. SAGE II, the fourth instrument has been flying in space on NASA's Earth Radiation Budget Satellite (ERBS) for the last 14 years. Ball Aerospace built the instrument under Langley Research Center's (LaRC) management. SAGE III for Russian Meteor-3M mission is a third generation design with more spectral bands, elaborate data gathering and storage and intelligent terrestrial software. The Russian collaboration required a complete integration of SAGE III on the Russian Meteor-3M satellite and a launch on a Zenit-2 launch vehicle manufactured in Ukraine. The whole complex is scheduled to be launched from Baikonur cosmodrome in early 2001. This cooperative mission has presented a number of management, technical and logistical challenges on both sides. This paper makes an attempt to review and document such experiences.

Habib, Shahid↗

NASA NTRS2008

Enabling and Enhancing Space Mission Success and Reduction of Risk through the Application of an Integrated Data Architecture

The engineering phases of design, development, test, and evaluation (DDT and E) and subsequent planning, preparation, and operation (Ops) of space vehicles in a complex and distributed environment requires massive and continuous flows of information across the enterprise and across temporal stages of the vehicle lifecycle. The resulting capabilities at each subsequent stage depend in part on the capture, preparation, storage, and subsequent provision of information from prior stages. The United States National Aeronautics and Space Administration (NASA) is currently designing a fleet of new vehicles that will replace the Space Shuttle and expand space operations and exploration capabilities. This includes the 2 stage human rated lift vehicle Ares 1 and its associated crew vehicle the Orion, and a service module; the heavy lift cargo vehicle, Ares 5, and an associated cargo stage known as the Earth Departure Stage; and a Lunar Lander vehicle that contains a descent stage, and ascent stage, and a habitation module. A variety of concurrent assorted ground operations infrastructure including software and facilities are also being developed, assorted technology and assembly designs and development for equipment such as EVA suits, life support systems, command and control technologies are also in the pipeline. The development is occurring in a distributed manner, with project deliverables being contributed by a large and diverse assortment of vendors and most space faring nations. Critical information about all of the components, software, and procedures must be shared during the DDT and E phases and then made readily available to the mission operations staff for access during the planning, preparation, and operations phases, and also need to be readily available for system to system interactions. The Constellation Data Systems Project (CxDS) is identifying the needs, and designing and deploying systems and processes to support these needs. This paper details the steps and processes that NASA is applying within the Constellation Program to manage this data and information, and to insure that the correct information is available, correctly annotated, and can be provisioned digitally to enhance response times, and support engineering analysis and anomaly resolution.

Brummett, Robert C.↗

NASA NTRS2012

SWOT: The Surface Water and Ocean Topography Mission. Wide- Swath Altimetric Elevation on Earth

The elevation of the surface of the ocean and freshwater bodies on land holds key information on many important processes of the Earth System. The elevation of the ocean surface, called ocean surface topography, has been measured by conventional nadirlooking radar altimeter for the past two decades. The data collected have been used for the study of large-scale circulation and sea level change. However, the spatial resolution of the observations has limited the study to scales larger than about 200 km, leaving the smaller scales containing substantial kinetic energy of ocean circulation that is responsible for the flux of heat, dissolved gas and nutrients between the upper and the deep ocean. This flux is important to the understanding of the ocean's role in regulatingfuture climate change.The elevation of the water bodies on land is a key parameter required for the computation of storage and discharge of freshwater in rivers, lakes, and wetlands. Globally, the spatial and temporal variability of water storage and discharge is poorly known due to the lack of well-sampled observations. In situ networks measuring river flows are declining worldwide due to economic and political reasons. Conventional altimeter observations suffers from the complexity of multiple peaks caused by the reflections from water, vegetation canopy and rough topography, resulting in much less valid data over land than over the ocean. Another major limitation is the large inter track distance preventing good coverage of rivers and other water bodies.This document provides descriptions of a new measurement technique using radar interferometry to obtain wide-swath measurement of water elevation at high resolution over both the ocean and land. Making this type of measurement, which addresses the shortcomings of conventional altimetry in both oceanographic and hydrologic applications, is the objective of a mission concept called Surface Water and Ocean Topography (SWOT), which was recommended by the National Research Council's first decadal survey of NASA's Earth science program. This document provides wide-ranging examples of research opportunities in oceanography and land hydrology that would be enabled by the new type of measurement. Additional applications in many other branches of Earth System science ranging from ocean bathymetry to sea ice dynamics are also discussed. Many of the technical issues in making the measurement are discussed as well. Also presented is a preliminary design of the SWOT Mission concept, which is being jointly developed by NASA and CNES, with contributions from the Canadian Space Agency.

altimetry↗

DOE OSTI2025

Enhanced Laser-Induced Graphene Microfluidic Integrated Sensors (LIGMIS) for On-Site Biomedical and Environmental Monitoring

The convergence of microfluidic and electrochemical biosensor technologies offers significant potential for rapid, in-field diagnostics in biomedical and environmental applications. Traditional systems face challenges in cost, scalability, and operational complexity, especially in remote settings. Addressing these issues, laser-induced graphene microfluidic integrated sensors (LIGMIS) are presented as an innovative platform that integrates microfluidics and electrochemical sensors both comprised of laser-induced graphene. This study advances the LIGMIS concept by resolving issues of uneven fluid transport, increased hydrophobicity during storage, and sensor biofunctionalization challenges. Key innovations include Y-shaped reservoirs for consistent fluid flow, hydrophilic polyethyleneimine coatings to maintain wettability, and separable microfluidic and electrochemical components enabling isolated electrode nanoparticle metallization and biofunctionalization. Multiplexed electrochemical detection of the neonicotinoid imidacloprid and nitrate ions in environmental water samples yields detection limits of 707 nm and 10 -5.4 m with wide sensing ranges of 5–100 µm and 10 -5 –10 -1 m, respectively. Similarly, uric acid and calcium ions are detected in saliva, demonstrating detection limits of 217 nm and 10 -5.3 m with sensing ranges of 10–50 µm, and 10 -5 –10 -2.5 m, respectively. Overall, this biosensing demonstrates the capability of the LIGMIS platform for multiplexed detection in biologically complex solutions, with applications in environmental water quality monitoring and oral cancer screening.

environmental monitoring↗

DOE OSTI2025

Introduction: Neuromorphic Materials

The explosive growth in data collection and the need to process it efficiently, as well as the desire to automate increasingly complex tasks in transportation, medical care, manufacturing, security and many other fields have motivated a growing interest in neuromorphic computing. Unlike the binary, transistorbased ON/OFF logic gates and separate logic and memory functionalities employed in digital computing, neuromorphic computing is inspired by animal brains that use interconnected synapses and neurons to perform processing, storage and transmission of information at the same location, while only consuming ~20 W or less of power. Motivated by the brain’s efficiency, adaptability, self-learning and resiliency qualities, neuromorphic computing can be broadly defined as an approach to processing and storing information using hardware and algorithms inspired by models of biological neural systems. Present research in neuromorphic computing encompasses approaches that vary significantly in their degree of neuro-inspiration, from systems that only incorporate features such as asynchronous, event-driven operation or use crossbar arrays of non-volatile memory (NVM) elements to accelerate deep neural networks (DNNs), to designs that embrace the extreme parallelism, sparsity, reconfigurability, adaptability, complexity and stochasticity observed in nervous systems. The term ‘neuromorphic’ computing is often credited to Carver Mead, who in the 1980s investigated Si-based analog electronics to replicate functions of the animal retina. Earlier important advances in this field include the work of Frank Rosenblatt, who proposed the concept of the perceptron, Bernard Widrow, who used this concept to build one of the first analog neural networks, the Adaline and many other researchers (see ref. 6 for an historical perspective on neuromorphic computing). With the recent increase in the use of artificial intelligence and large language models, and rising concerns over the associated energy costs, interest in neuromorphic hardware has expanded rapidly. According to some estimates, driven largely by the drastic growth in the training use of artificial intelligence (AI) models using the current computing architectures, the energy cost of computing is projected to reach the energy supply worldwide by 2045. Furthermore, while this is not a realistic outcome, it means that, if more efficient computing technologies are not developed -- soon -- the world will soon become one where demand for energy and market constraints limit the continued increase of societal access to AI and cloud services from data centers. Data centers used for training and use of these models consume hundreds of terawatt hours of electricity, already past 4% of the US electricity demand.

Circuits↗

DOE OSTI2023

Modeling, analysis, and optimization of complex nuclear processes and facilities via computational methods: The HALEU process case study

Improving and adapting industrial systems to timely meet changing programmatic and market demands is an important goal to achieve, including when operating and maintaining complex nuclear processes and facilities. However, changes to these complex systems are costly, particularly when they are already in place and bounded to stringent requirements and constraints such as when handling radioactive material and contaminated equipment. These conditions often exist when treating spent nuclear fuel remotely within shielded nuclear radiation chambers, commonly referred as hot cells, to condition nuclear material and/or fabricate products for utilization in other nuclear enterprises such as in the manufacture of advanced nuclear fuel. The illustrative case considered here is the production of high assay low enriched uranium (HALEU) products supporting the deployment of advanced nuclear reactors. For the HALEU program, resources invested were and are being systematically analyzed so that these investments are maximized in a facility that is nearly 60 years old. A methodology that has effectively enabled optimized and improvements in the Spent Fuel Treatment (SFT) program, and consequently the HALEU program, involves discrete event simulation as addressed in this article. Here, the quantification of multiple productivity metrics, including material processing rates, cycle times, bottlenecks, number of material transfers as well as equipment, workstation, and material handling utilization, has resulted in a myriad of diverse discoveries and data-informed decisions regarding process layout and constituent, labor levels and schedules, selection of new process units, storage needs, and other critical process configurations. This article describes such a computational capability being applied for decision-making, illustrates its application to an actual process and program, provides illustrative results, and argues how computational methods for the modeling, analysis, and optimization of complex processes and facilities does lead to informed decisions derived from data and not only from intuition.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Atomic structural mechanism for ferroelectric-antiferroelectric transformation in perovskite NaNbO 3

Sodium niobate (NaNbO 3 or NN) is described as “the most complex perovskite system,” which exhibits transitions between, as well as coexistence of, several ferroelectrics (FE) and antiferroelectric (AFE) phases at different temperatures. Recently, solid solutions of NN with stabilized AFE phases(s) have gained attention for energy-related applications, such as high-density energy storage and electrocaloric cooling. A better understanding of the atomic mechanisms responsible for AFE/FE phase transitions in NaNbO 3 can enable a more rational design of its solid-solution systems with tunable functional properties. In this work, we have investigated changes in the average and local atomic structure of NN using a combination of x-ray/neutron diffraction and neutron pair-distribution function (PDF) analyses. The Rietveld refinement of the x-ray/neutron-diffraction patterns indicates a coexistence of the FE Q (P2 1 ma) and AFE P (Pbma) phases in the temperature range of 300 K ≤ T ≤ 615K, while PDF analysis indicated that the local structure (r < 8Å) is better described by a P2 1 ma symmetry. Above 615 K, the average structure transitions to an AFE R phase (Pmmn or Pnma), while PDF analysis shows an increased disordering of the octahedral distortions and Na displacements at the local scale. These results indicate that the average P/Q/R phase transitions in NN can be described as a result of complex ordering of distorted octahedral tilts at the nanoscale and off-centered displacements of the Na atoms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Review of Ultrasonic Methods for Monitoring, Damage Detection, and Processing of Lithium-Ion Batteries Throughout Their Life Cycle

Lithium-ion batteries (LIBs) are the leading technology used in consumer electronics, electric vehicles, and grid-level electrochemical energy storage applications. The ever-increasing use of LIBs has highlighted a gap in understanding of their behavior throughout their life cycle. Current monitoring systems rely on electrical and sometimes temperature measurements to assess the internal state which limits information about complex electrochemical processes. In response, ultrasonic testing (UT) has shown promise for non-invasive assessment due to its ease of use and sensitivity to mechanical changes which are correlated with electrochemical changes within the battery. We summarize the research in UT methods applied to LIBs throughout their life cycle. We also discuss physics-based and data-driven modeling approaches used to interpret ultrasonic signals in the context of LIBs, with an emphasis on the existing challenge of establishing rigorous links between electrochemical behavior and elastic and poroelastic wave physics to gain insight regarding physical changes in the LIB that can be directly measured using UT. Finally, we discuss the challenges of implementing UT across the LIB life cycle and identify opportunities for further research. This review aims to provide helpful guidance to researchers and practitioners of UT in the growing field of UT for electrochemical battery systems.

25 ENERGY STORAGE↗

NASA NTRS1987

An interactive approach to surface-fitting complex geometries for flowfield applications

Numerical flowfield methods require a geometry subprogram which can calculate body coordinates, slopes, and radii of curvature for typical aircraft and spacecraft configurations. The objective of this paper is to develop a new surface-fitting technique which addresses two major problems with existing geometry packages: computer storage requirements and the time required of the user for the initial set-up of the geometry model. In the present method, coordinates of cross sections are fit in a least-squares sense using segments of general conic sections. After fitting each cross section, the next step is to blend the cross-sectional curve-fits in the longitudinal direction using general conics to fit specific meridional half-planes. For the initial setup of the geometry model, an interactive, completely menu-driven computer code has been developed to allow the user to make modifications to the initial fit for a given cross section or meridional cut. Graphic displays are provided to assist the user in the visualization of the effect of each modification. The completed model may be viewed from any angle using the code's three-dimensional graphics package. Geometry results for the modeling of the Space Shuttle and a proposed Aeroassist Flight Experiment (AFE) geometry are presented, in addition to calculated heat-transfer rates based on these models.

Dejarnette, Fred R.↗

DOE OSTI2022

Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system

New ways to integrate energy systems to maximize efficiency are being sought to meet carbon emissions goals. Nuclear-renewable integrated energy system (NR-IES) concepts are a leading solution that couples a nuclear power plant with renewable energy, hydrogen generation plants, and energy storage systems, such that thermal and electrical power are dispatchable to fulfill grid-flexibility requirements while also producing hydrogen and maximizing revenue. Here, this paper introduces a deep reinforcement learning (DRL)-based framework to address the complex decision-making tasks for NR-IES. The objective is to maximize revenue by generating and selling hydrogen and electricity simultaneously according to their time-varying prices while keeping the energy flow in the subsystems in balance. A Python-based simulator for a NR-IES concept has been developed to integrate with OpenAI Gym and Ray/RLlib to enable an efficient and flexible computational framework for DRL research and development. Three state-of-the-art DRL algorithms have been investigated, including two-delayed deep deterministic policy gradient (TD3), soft-actor critic (SAC), proximal policy optimization (PPO), to illustrate DRL’s superiority for controlling NR-IES by comparing it with a conventional control approach, particle swarm optimization (PSO). In this effort, PPO has shown more-stable performance and also better generalization capability than SAC and TD3. Comparisons with PSO have demonstrated that, on average, PPO can achieve 13.9% more mean episode returns from the training process and 29.4% more mean episode returns from the testing process when different hydrogen-production targets are applied.

08 HYDROGEN↗

DOE OSTI2020

Temperature and Solvent Effects on H 2 Splitting and Hydricity: Ramifications on CO 2 Hydrogenation by a Rhenium Pincer Catalyst

The catalytic hydrogenation of carbon dioxide holds immense promise for applications in sustainable fuel synthesis and hydrogen storage. Mechanistic studies that connect thermodynamic parameters with the kinetics of catalysis can provide new understanding and guide predictive design of improved catalysts. Reported here are thermochemical and kinetic analyses of a new pincer-ligated rhenium complex ( tBu POCOP)Re(CO) 2 ( tBu POCOP = 2,6-bis(di-tert-butylphosphinito)phenyl) that catalyzes CO 2 hydrogenation to formate with faster rates at lower temperature. Because the catalyst follows the prototypical “outer sphere” hydrogenation mechanism, comprehensive studies of temperature and solvent effects on the H 2 splitting and hydride transfer steps are expected to be relevant to many other catalysts. Strikingly large entropy associated with cleavage of H2 results in a strong temperature dependence on the concentration of [( tBu POCOP)Re(CO) 2 H] – present during catalysis, which is further impacted by changing the solvent from toluene to tetrahydrofuran to acetonitrile. New methods for determining the hydricity of metal hydrides and formate at temperatures other than 298 K were developed, providing insight into how temperature can influence the favorability of hydride transfer during catalysis. These thermochemical insights guided the selection of conditions for CO 2 hydrogenation to formate with high activity (up to 364 h –1 at 1 atm or 3330 h –1 at 20 atm of 1:1 H 2 CO 2 ). In cases where hydride transfer is the highest individual kinetic barrier, entropic contributions to outer sphere H 2 splitting lead to a unique temperature dependence: catalytic activity increases as temperature decreases in tetrahydrofuran (200-fold increase upon cooling from 50 to 0 °C) and toluene (4-fold increase upon cooling from 100 to 50 °C). Furthermore, the ramifications on catalyst structure-function relationships are discussed, including comparisons between “outer sphere” mechanisms and metal–ligand cooperation mechanisms.

14 SOLAR ENERGY↗

DOE OSTI2022

Differentiable, Learnable, Regionalized Process-Based Models With Multiphysical Outputs can Approach State-Of-The-Art Hydrologic Prediction Accuracy

Predictions of hydrologic variables across the entire water cycle have significant value for water resources management as well as downstream applications such as ecosystem and water quality modeling. Recently, purely data-driven deep learning models like long short-term memory (LSTM) showed seemingly insurmountable performance in modeling rainfall runoff and other geoscientific variables, yet they cannot predict untrained physical variables and remain challenging to interpret. Here, we show that differentiable, learnable, process-based models (called δ models here) can approach the performance level of LSTM for the intensively observed variable (streamflow) with regionalized parameterization. We use a simple hydrologic model HBV as the backbone and use embedded neural networks, which can only be trained in a differentiable programming framework, to parameterize, enhance, or replace the process-based model's modules. Without using an ensemble or post-processor, δ models can obtain a median Nash-Sutcliffe efficiency of 0.732 for 671 basins across the USA for the Daymet forcing data set, compared to 0.748 from a state-of-the-art LSTM model with the same setup. For another forcing data set, the difference is even smaller: 0.715 versus 0.722. Meanwhile, the resulting learnable process-based models can output a full set of untrained variables, for example, soil and groundwater storage, snowpack, evapotranspiration, and baseflow, and can later be constrained by their observations. Both simulated evapotranspiration and fraction of discharge from baseflow agreed decently with alternative estimates. The general framework can work with models with various process complexity and opens up the path for learning physics from big data.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Principles to adapt financing mechanisms for fully integrated hybrid energy systems

As the electricity sector evolves, and as all energy types (thermal, electric, chemical, etc.) become more coupled, there has been increased interest to develop and deploy hybrid energy systems (HES). This work focuses on fully integrated HES, where there are multiple energy sources and multiple energy products, often coupled through a storage buffer. A significant amount of the available literature on this work describes technology pathways for fully integrated HES; however, it is unclear how financial institutions should treat these systems. Fully integrated HES represent an increase in complexity from their stand-alone counterparts, but they also potentially mitigate financial risk and provide value to the energy system, which has not yet been accounted for in financing mechanisms that could help to enable such systems. This paper provides some examples of fully integrated HES and proposes principles to help adapt financing to adequately capture the value of such systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Observing and modeling the effects of production infrastructure in electromagnetic surveys

Electromagnetic (EM) methods are among the original techniques for subsurface characterization in exploration geophysics because of their particular sensitivity to the earth electrical conductivity, a physical property of rocks distinct yet complementary to density, magnetization, and strength. However, this unique ability also makes them sensitive to metallic artifacts — infrastructure such as pipes, cables, and other forms of cultural clutter — the EM footprint of which often far exceeds their diminutive stature when compared to that of bulk rock itself. In the hunt for buried treasure or unexploded ordnance, this is an advantage; in the long-term monitoring of mature oil fields after decades of production, it is quite troublesome indeed. In this study we consider the latter through the lens of an evolving energy industry landscape in which the traditional methods of EM characterization for the exploration geophysicist are applied toward emergent problems in well-casing integrity, carbon capture and storage, and overall situational awareness in the oil field. We introduce case studies from these exemplars, showing how signals from metallic artifacts can dominate those from the target itself and impose significant burdens on the requisite simulation complexity. Furthermore, we show how recent advances in numerical methods mitigate the computational explosivity of infrastructure modeling, providing feasible and real-time analysis tools for the desktop geophysicist. Lastly, we demonstrate through comparison of field data and simulation results that incorporation of infrastructure into the analysis of such geophysical data is, in a growing number of cases, a requisite but now manageable step.

58 GEOSCIENCES↗

DOE OSTI2022

3013 Surveillance Program Interim Summary Report (FY16-FY21)

The K-Area Documented Safety Analysis (DSA) requires the facility participate in the SRS Site Surveillance Program as a part of Shipping Package Qualification and Storage Surveillance Program. The Site Surveillance Program outlines activities for field surveillance and laboratory tests that demonstrate the 3013 containers meet the functional performance requirements described in the DSA. The SRS Surveillance Program also supports the complex-wide Integrated Surveillance Program (ISP) for 3013 containers in accordance with DOESTD-3013. The purpose of this report is to provide a summary of the SRS portion of the surveillance program activities from FY16 through FY21 and formally communicate the interpretation of these results by the Surveillance Program Authority (SPA).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Argonne Leadership Computing Facility: 2021 Operational Assessment Report

This Operational Assessment Report describes how the Argonne Leadership Computing Facility (ALCF) met or exceeded every one of its goals for calendar year (CY) 2021 as an advanced scientific computing center. In CY 2021, the ALCF operated its production resource, Theta, an Intel-based Cray XC40 system (11.7-petaflops) augmented with 24 NVIDIA DGX A100-based nodes (3.9-petaflops) that supports diverse workloads, integrating data analytics with artificial intelligence (AI) training and learning in a single platform. In 2021, we began deploying Polaris, our newest 40- petaflops system, and augmented this powerful testbed system with an additional 28 nodes to support the integration of real-time experiments and HPC resources. We also deployed our two largest storage systems yet, named Grand and Eagle, that will bring new services to our users and will power data-driven research for years to come. Last year, Theta delivered a total of 20.8 million node-hours to 16 Innovative and Novel Computational Impact on Theory and Experiment (INCITE) projects and 7.2 million node-hours to ASCR Leadership Computing Challenge (ALCC) projects (32 awarded during the 2020–2021 ALCC year and 17 awarded during the 2021–2022 ALCC year), as well as substantial support to Director’s Discretionary (DD) projects (5.5 million node-hours). As Table ES.1 shows, Theta performed exceptionally well in terms of overall availability (95.1 percent), scheduled availability (99.4 percent), and utilization (98.1 percent; Table 2.1). As of the submission date of this document, ALCF’s user community has published 249 papers in high-quality, peer-reviewed journals and technical proceedings. At the 2021 International Conference for High Performance Computing, Networking, Storage and Analysis (SC’21), Argonne researchers won two HPCwire Readers’ Choice Awards and were part of a Gordon Bell Prize finalist team recognized for developing an AI-enabled, multi-resolution simulation framework for studying complex biomolecular machines. Their framework was used to observe the SARS-CoV-2 replication-transcription machinery in action, by directly integrating experimental data. ALCF also provided a comprehensive program of high-performance computing (HPC) support services to help our community make productive use of the facility’s diverse and growing collection of resources. We are now entering the exascale era, with exascale machines being planned for national laboratories across the country, including Aurora at Argonne National Laboratory (Argonne) in 2023. ALCF researchers have been leading and guiding numerous strategic activities that will push the boundaries of what’s possible in computational science and engineering and allow us to deliver science on day one.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1994

Direct numerical simulation of sheared turbulent flow

The summer assignment to study sheared turbulent flow was divided into three phases which were: (1) literature survey, (2) computational familiarization, and (3) pilot computational studies. The governing equations of fluid dynamics or Navier-Stokes equations describe the velocity, pressure, and density as functions of position and time. In principle, when combined with conservation equations for mass, energy, and thermodynamic state of the fluid a determinate system could be obtained. In practice the Navier-Stokes equations have not been solved due to the nonlinear nature and complexity of these equations. Consequently, the importance of experiments in gaining insight for understanding the physics of the problem has been an ongoing process. Reasonable computer simulations of the problem have occured as the computational speed and storage of computers has evolved. The importance of the microstructure of the turbulence dictates the need for high resolution grids in extracting solutions which contain the physical mechanisms which are essential to a successful simulation. The recognized breakthrough occurred as a result of the pioneering work of Orzag and Patterson in which the Navier-Stokes equations were solved numerically utilizing a time saving toggling technique between physical and wave space, known as a spectral method. An equally analytically unsolvable problem, containing the same quasi-chaotic nature as turbulence, is known as the three body problem which was studied computationally as a first step this summer. This study was followed by computations of a two dimensional (2D) free shear layer.

Harris, Vascar G.↗