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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Two-DoF Model-Informed Controller Gain Tuning for Several Floating Wind Platforms

Much effort has recently been put into the development of collective blade pitch controllers for floating offshore wind turbines, with the aim of overcoming negative damping issues that arise with traditional control methods. One proposed approach to this challenge involves using a two-degree-of-freedom model to inform the gain schedule of a nacelle velocity feedback term in an otherwise conventional proportional-integral controller. The model uses tower-top fore-aft and rotor angular displacements, and is used to calculate a nacelle velocity feedback gain that results in a specified increase in platform pitch damping. Earlier performance evaluations of this tuning method were favorable, suggesting its potential as an easy way for researchers to obtain an adequate controller. This paper expands on those previous results by examining the performance of the tuning method relative to baseline controllers for several hull configurations, and for several prescribed increases in platform pitch damping. Simulations were run in OpenFAST for several load cases above rated wind speed and show results consistent with trends in the earlier study. The tuning method is thus shown to be adaptable to many different types of hulls, making it useful for the evaluation of prototype designs.

artificial intelligence↗

NASA NTRSDate not supplied

ISRU Pilot Excavator: Bucket Drum Scaling Experimental Results

NASA’s Space Technology Mission Directorate (STMD) is funding the development of a robotic excavator called the “ISRU Pilot Excavator” which will be a technology demonstration of excavating and transporting 10 metric tons of lunar regolith on the surface of the moon with a 30k-class robotic excavator. ISRU Pilot Excavator will be the next generation of robotic excavator to use bucket drums as excavation tools. This is an evolution of the Regolith Advanced Surface Systems Operations Robot (RASSOR) developed at NASA’s Kennedy Space Center. Bucket drums are hollow cylinders with regularly spaced scoops around the perimeter. The drums rotate in one direction to collect regolith with the scoops. The regolith slides down an internal baffling system inside the drum which prevents the regolith from falling back out of the scoops. The captured regolith can then be transported while held in the drum and then deposited by rotating the drum in the opposite direction allowing the regolith to slide back down the baffling and out of the excavation scoops. Bucket drums were developed by Lockheed Martin in 2008 and used on multiple robotic excavator prototypes ever since. However the forces on a bucket drum and considerations for scaling have not been measured in detail. Bucket drums are challenging to model using classical blade\bucket equations because of their unique geometry. Therefore this experiment was performed to measure the forces on three bucket drums of the same geometry at different scales. Small: 9.4” (239mm) dia. x 8.1” (206mm) width, Medium: 11.6” (294mm) dia. x 10” (254mm) width, and Large: 17” (432mm) dia. x 14.1”(358mm) width. The test stand consisted of an actuated gantry with controlled motion in the vertical (Z) and horizontal (X) axes and a single rotation axis (R). The bucket drums were individually mounted to the rotary axis of the test stand and translated across a prepared bed of BP-1 lunar regolith simulant at a specified linear speed and cutting depth. The test stand was outfitted with a torque sensor in line with the rotation of the drum (R) and a 3 axis (X, Y, and Z) load cell. In addition to the three sizes of bucket drums the linear excavation speed and cutting depth were test variables. The results of these experiments show the relationship between the three scales of bucket drums for factors such as: excavation force, torque due to regolith rotation inside the drum, excavation energy, time to fill, etc. and will be discussed in detail in this paper. This fundamental data will be used in the design of the ISRU Pilot Excavator and can inform the design of future bucket drum excavators.

Jason Michael Schuler↗

NASA NTRSDate not supplied

ISRU Pilot Excavator: Bucket Drum Scaling Experimental Results

NASA’s Space Technology Mission Directorate (STMD) is funding the development of a robotic excavator called the “ISRU Pilot Excavator” (IPEx) which will be a technology demonstration of excavating and transporting 10 metric tons of lunar regolith on the surface of the moon with a 30kg-class robotic excavator. IPEx will be the next generation of robotic excavators to use bucket drums as excavation tools. This is an evolution of the Regolith Advanced Surface Systems Operations Robot (RASSOR) developed at NASA’s Kennedy Space Center (KSC). Bucket drums are hollow cylinders with regularly spaced scoops around the perimeter. The drums rotate in one direction to collect regolith with the scoops. The regolith slides down an internal baffling system inside the drum which prevents the regolith from falling back out of the scoops (see Figure 1). The captured regolith can then be transported while held in the drum and then deposited by rotating the drum in the opposite direction allowing the regolith to slide back down the baffling and out of the excavation scoops. Bucket drums were developed by Lockheed Martin in 2008 and used on multiple robotic excavator prototypes ever since. However, the forces on a bucket drum and considerations for scaling have not been measured in detail. Bucket drums are challenging to model using classical blade\bucket equations because of their unique geometry. Therefore, this experiment was performed to measure the forces on three bucket drums of the same geometry at different scales. Small: 9.4” (239mm) dia. x 8.1” (206mm) width, Medium: 11.6” (294mm) dia. x 10” (254mm) width, and Large: 17” (432mm) dia. x 14.1” (358mm) width. The test stand consisted of an actuated gantry with controlled motion in the vertical (Z) and horizontal (X) axes and a single rotation axis (R). The bucket drums were individually mounted to the rotary axis of the test stand and translated across a prepared bed of BP-1 lunar regolith simulant at a specified linear speed and cutting depth. The test stand was outfitted with a torque sensor in line with the rotation of the drum (R) and a 3 axis (X, Y, and Z) load cell. In addition to the three sizes of bucket drums the linear excavation speed and cutting depth were test variables. The results of these experiments show the relationship between the three scales of bucket drums for factors such as: excavation force, torque due to regolith rotation inside the drum, excavation energy, time to fill, etc. and will be discussed in detail in this paper. This fundamental data will be used in the design of IPEx and can inform the design of future bucket drum excavators.

RASSOR↗

DOE OSTI2023

Virtual prototyping of liquid metal blanket performance in fusion pilot plant

Liquid metal blanket is a dominant design option for the next step fusion devices responsible for harvesting energy from fusion reaction, and simultaneously producing fuel for the same reaction through tritium breeding. Liquid metal blankets introduce additional complexity to the design due to fluid motion, fluid structure interaction, and magnetohydrodynamic (MHD) effects arising from the motion of the conducting fluid through the magnetic field. They are also directly affected by the plasma heat flux and neutronic fluence. PPPL is currently developing a virtual prototyping system for numerical analysis of the liquid metal blankets for future fusion devices. The system has a customized 3D computational fluid dynamics (CFD) code in its core, allowing MHD flow and conjugate heat transfer analysis in blankets fluids and solids. The code was successfully used before for dual coolant blanket analysis [A. Khodak et al., Fusion Eng. and Des. 137 (2018)]. Recently the same code was modified to allow verified simulation of MHD flows at high Hartmann numbers of several thousand typical for blanket applications. CFD code receives volumetric heat source distribution from the neutronic analysis based on MCNP code. In addition, direct tritium breeding simulation will be performed allowing optimization of the blanket performance. 2D axisymmetric version of neutronics code will be used for rapid optimization, with 3D version employed for detailed analysis. The surface heat distribution on the plasma facing wall will be defined by the software HEAT allowing 3D modeling of the heat flux based on the magnetic field distribution including gyro-orbit effects. Results of thermal analysis are imported into structural analysis code also included in the system. Finally, direct import of CAD geometry will be used for analyzing all components and as a result design option can be efficiently optimized.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Critical role of magnetic moments in heavy-fermion materials: Revisiting mysteries of SmB 6

Heavy-fermion family exhibits fascinating and often puzzling properties due to the presence of open-shell f ions and the complexity of the associated charge, orbital, and spin degrees of freedom. SmB 6 is a prototypical heavy-fermion compound that is electrically insulating but yet it displays quantum oscillations, which are a telltale signature of the metallic state. Adding to the enigma is the possibility that SmB 6 is a topological Kondo insulator. Here, by treating the spin degree of freedom on an equal footing with other degrees of freedom using the parameter-free strongly-constrained and appropriately-normed (SCAN) density functional, we explore the ground-state electronic structure of SmB6. A number of competing magnetic phases lying very closely in energy are found, indicating the key role of spin fluctuations in the material. The computed band structure, crystal-field splittings in the f-electron complex, the heavy effective electron mass at the Fermi energy, and the large specific heat are all in good agreement with the corresponding experimental results. In particular, our predicted FS explains the experimentally observed bulk quantum oscillations as well as the low electrical conductivity of SmB 6 . The topological Kondo state of SmB6 is shown to be robust regardless of its magnetic configuration. The excellent performance of SCAN in heavy-fermion systems is explained in terms of its ability to treat self-interaction errors and symmetry breaking within the framework of the density functional theory. Our study provides a new approach for modeling heavy-fermion materials

36 MATERIALS SCIENCE↗

DOE OSTI2026

Effects of the next-nearest-neighbor hopping on the low-dimensional Hubbard model: ferromagnetism, antiferromagnetism, and superconductivity

The Hubbard model has attracted considerable interest due to its prototypical role in describing strongly interacting electronic systems, such as high-critical-temperature superconductors as well as many novel quantum materials. By introducing next-nearest-neighbor (NNN) hoppings to the Hubbard model, the phase diagram becomes richer, and fascinating phenomena arise in both, one-dimensional chains and square lattices, such as: antiferromagnetism, ferromagnetism, superconductivity (SC), as well as charge orders, among others. Moreover, NNN hoppings play a fundamental role in understanding effects of doping on magnetism and pairing orders in strongly interacting regimes. In this article, we review the recent progress in understanding the different competing phases of this model in one and two dimensions from a computational perspective. In conclusion, we comment on the pressing technical challenges, illustrate the controversial results concerning the emergence of the SC phase, and conclude with our perspectives on future explorations.

Hubbard model↗

NASA NTRS2012

A13K-0336: Airborne Multi-Wavelength High Spectral Resolution Lidar for Process Studies and Assessment of Future Satellite Remote Sensing Concepts

NASA Langley recently developed the world's first airborne multi-wavelength high spectral resolution lidar (HSRL). This lidar employs the HSRL technique at 355 and 532 nm to make independent, unambiguous retrievals of aerosol extinction and backscatter. It also employs the standard backscatter technique at 1064 nm and is polarization-sensitive at all three wavelengths. This instrument, dubbed HSRL-2 (the secondgeneration HSRL developed by NASA Langley), is a prototype for the lidar on NASA's planned Aerosols- Clouds-Ecosystems (ACE) mission. HSRL-2 completed its first science mission in July 2012, the Two-Column Aerosol Project (TCAP) conducted by the Department of Energy (DOE) in Hyannis, MA. TCAP presents an excellent opportunity to assess some of the remote sensing concepts planned for ACE: HSRL-2 was deployed on the Langley King Air aircraft with another ACE-relevant instrument, the NASA GISS Research Scanning Polarimeter (RSP), and flights were closely coordinated with the DOE's Gulfstream-1 aircraft, which deployed a variety of in situ aerosol and trace gas instruments and the new Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR). The DOE also deployed their Atmospheric Radiation Measurement Mobile Facility and their Mobile Aerosol Observing System at a ground site located on the northeastern coast of Cape Cod for this mission. In this presentation we focus on the capabilities, data products, and applications of the new HSRL-2 instrument. Data products include aerosol extinction, backscatter, depolarization, and optical depth; aerosol type identification; mixed layer depth; and rangeresolved aerosol microphysical parameters (e.g., effective radius, index of refraction, single scatter albedo, and concentration). Applications include radiative closure studies, studies of aerosol direct and indirect effects, investigations of aerosol-cloud interactions, assessment of chemical transport models, air quality studies, present (e.g., CALIPSO) and future (e.g., EarthCARE) satellite calibration/validation, and development/assessment of advanced retrieval techniques for future satellite applications (e.g., lidar+polarimeter retrievals of aerosol and cloud properties). We will also discuss the relevance of HSRL-2 measurement capabilities to the ACE remote sensing concept.

Hostetler, Chris A.↗

DOE OSTI2025

Measuring the thermal conductivity of hydrogels with a bidirectional 3w method

Hydrogels are soft, water-absorbing polymer materials with diverse applications in biomedicine and agriculture. Recently, hydrogels have been proposed to encapsulate water-soluble phase change materials which store energy in their latent heat of solidification. In these applications, the thermal conductivity of these materials affects their performance. Few methods exist for measuring the thermal conductivity of small quantities of hydrogels. Here, we describe an implementation of the bidirectional 3w technique to measure the thermal conductivity of hydrogels with particular attention to their moisture content. Our implementation of the technique can probe sample volumes as little as ~20 mL and yields the thermal conductivity without requiring fitting of additional thermal parameters. We numerically simulate 3w sensor designs with frequency-domain 3-D models to quantify and reduce errors introduced by the choice of substrate and insulation layer thickness. Frequencies in the ~1−20 Hz range yield less error for the materials considered here. We verify our setup with measurements on water and report values for polyacrylamide and poly(2-acrylamido-2-methylpropane sulfonic acid) (PAMPS) hydrogels. Our swollen hydrogels exhibited thermal conductivities nearly equivalent to water, 0.6 W m-1 K-1, and we estimate thermal conductivities of 0.43 and 0.42 W m-1 K-1 for neat polyacrylamide and PAMPS, respectively. Finally, we estimate an error of ±7%, consistent with other 3ω methods, with the largest error coming from the sensor calibration. We find our implementation of the bidirectional 3w method gives reasonable results and can be employed for prototyping soft materials relevant for thermal storage.

3-omega, thermal conductivity, hydrogel, moisture ↗

DOE OSTI2026

The NREL Sensor Laboratory Detection of Hydrogen Emissions

The development of a functional hydrogen detection system is a multifaceted process that integrates hardware, deployments strategies, and analytics which can be supported by the NREL Sensor Laboratory: 1. Support of the design, validation and optimization of sensing prototypes; 2. Guide optimized sensing element development, including control electronics; 3. Laboratory testing to validate/optimize metrological performance (measurement range, detection limit, etc.); 4. Provide test sites for field deployments representative of real-world scenarios with controlled hydrogen releases; 5. Develop sensor placement and operation guidance; 6. Provide guidance on electronics to accommodate facility integration; 7. Electrical safety designs to allow for operation within restricted zones; 8. Integration into facility monitoring and control systems; 9. Guide incorporation of cyber security elements to protect facilities from malicious attacks; 10. Modeling and application of advanced analytics to detect and quantify emissions; 11. Higher Order dispersion models to guide sensor placement for reliable detection; 12. Advanced analytics for improved metrological performances, and to inform inverse modeling; 13. Market support and commercialization (national and international markets); 14. Commercial deployments in H2@SCALE markets (e.g., HUBs and other large-scale hydrogen markets); and 15. Leverage off international collaborations/partnerships (e.g., NREL is on the advisory board for the European initiative "pre-Normative Research on Hydrogen Releases Assessment"-NHyRA).

08 HYDROGEN↗

DOE OSTI2022

High-burnup Experiments in Reactivity Initiated Accidents (HERA)

High-burnup Experiments in Reactivity-initiated Accidents (HERA) is a joint experimental program (JEEP) operating within the United States (U.S.) Nuclear Energy Agency’s (NEA’s) framework for irradiation experiments (FIDES). HERA is dedicated to the understanding of light water reactor (LWR) fuel performance at high burnup under reactivity-initiated accidents (RIA). In-pile RIA experiments have been performed on high-burnup fuels (e.g., above 60 gigawatt days per metric ton of uranium [GWd/MTU]) in the CABRI reactor at the Cadarache site in southern France, and the Nuclear Safety Research Reactor (NSRR) in Japan. However, most of these experiments have taken place with heavily corroded Zircaloy claddings with pulse widths that are narrower (e.g., 5 ms – 30 ms full-width-half-max [FWHM]) than what would be likely in a commercial LWR (e.g., 30 ms – 80 ms FWHM). A few tests were performed in the CABRI facility with FWHM up to 76 ms, though only one “blistered” rod experienced cladding failure. Heavy waterside corrosion and narrow pulse widths are both known to increase the vulnerability of LWR fuel to pellet cladding mechanical interaction (PCMI). The HERA proposal is designed to: (1) quantify the impact of pulse width on fuel performance, offering new insight into the applicability of existing data; (2) generate new data on high burnup fuel under pulse conditions prototypic of LWRs; (3) quantify the additional margin provided by modern cladding alloys to PCMI failure limits; and (4) offer improved data for modelers using specially designed tests that eliminate key uncertainties in high-burnup fuel tests.

(HERA)↗

DOE OSTI2022

Real-Time Optimization Workflow Status Update

Economically optimal and safe operation of integrated energy systems (IES) requires optimization at many different time scales. A real-time optimization (RTO) workflow will attempt to maximize revenue and minimize operational costs on a time scale of minutes to hours. Such a workflow requires the use of a digital twin (DT), which is a virtual representation of a physical system. The DT is updated using real-time data from the physical system, and serves as a model in an optimization framework. The optimization results are then sent back to the physical system to complete the loop. This report details the progress made in developing building blocks for a DT/RTO framework. The Risk Analysis Virtual Environment (RAVEN) platform within the Framework for Optimization of Resources and Economics (FORCE) tool suite can perform many of the tasks required for building a DT and performing RTO. The first item of this report details RAVEN enhancements that enable RAVEN workflows to be run in various environments. Data communication between the physical system and its DT is essential for successful RTO. This includes preprocessing real-time data, loading data into a data warehouse, and querying the stored data. The second section of this report describes the progress made in implementing an adapter in Python in order for Deep Lynx to handle the data communication. Typical dispatch optimization frameworks are built on linear programming (LP). The prototype RTO workflow developed in this report uses an LP problem as a part of a receding-horizon- or economic model predictive control (EMPC) based optimization. The third section of this report details the framework of an RTO workflow in which the system consists of a simple electrical storage device. A DT can be built from a reduced-order model (ROM). Integrating a ROM into a typical LP optimization framework has been challenging because most optimization packages require the user to write algebraic expressions for the system model. The final section of this report shows how an externally built RAVEN ROM can be integrated in an RTO framework by using the Python package Pyomo. This demonstrates the RTO workflow capability from a software-only perspective and is an important step in demonstrating the capability to implement an RTO workflow for a physical system.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

IDAES-PSE 2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.0.0 Release Highlights Removal of deprecated features from IDAES v1 Update to Pyomo v6.5 – this required a number of updates to support the new NL solver writer and to address some changes in Pyomo Creation of new testing suite for backward compatibility, model robustness and verification More general implementation of the Helmholtz EoS. This brings some new features like standard property diagrams, choice of mass or mole basis, and new state variable options Standardizing names in Heat Exchanger models (breaking change from v2.0.0a2): Control Volumes named hot_side and cold_side Ports names hot_side_inlet, hot_side_outlet, cold_side_inlet and cold_side_outlet Config Blocks names hot_side_config and cold_side_config Config arguments for user provided names for each side: hot_side_name and cold_side_name. Updating Keras surrogate tool to use v1.1 of OMLT New prototype API for model initialization (idaes.core.initialization) The new API uses "Model Initializer" objects instead of class methods, allowing for the definition of multiple initialization routines for a single model A number of common, model agnostic initialization routines have also been defined, including initialization from data, block-decomposition and a general hierarchical approach equivalent to the existing method for common unit models New metadata for thermophysical properties – valid_range This can be used to record the range of values over which a property value can be trusted, such as the range of experimental data used to regress parameters A number of new utility functions have been added to check for properties with values outside the valid range and to set bounds based on this metadata Updated construction of balance expressions in Control Volumes to remove unneeded terms In the past, unneeded terms were added as a constant 0 term, however they will now be dropped entirely from the expression This was necessary due to more strict unit checking in the new Pyomo solver writer which no longer ignores 0 terms Updates to metadata for thermophysical properties to better define known properties and units of measurement This results in more strict enforcement of standard naming for thermophysical and reaction properties Users can still define custom properties, but these must be done explicitly using the define_custom_properties() method instead of being implicitly created by add_property() Updated convergence tester utility tool to support definition of benchmark files (JSON format) and comparison of performance to benchmarks Set default iteration limit for IPOPT in IDAES config to 200 iterations Update scaling of example models to work with new Pyomo NL solver writer Improve testing of extensions and examples infrastructure to avoid need for downloading files Updated distillation column to centralize common functionality and remove a number of Pyomo warnings

IDAES↗

DOE OSTI2026

Understanding Adsorption and Reactions at Aqueous Oxide Interfaces with Neural Network Potential Molecular Dynamics

Chemical processes at metal oxide−water interfaces are of central importance in geochemistry, biology, and energy technologies. A better understanding of these processes would allow us to make a significant step toward optimizing and controlling them, which could in turn lead to broader impacts. Computational modeling is indispensable to accomplishing this task because complexity and disorder often make it difficult to extract atomistic information from experiments. Balancing computational cost and accuracy, simulation schemes based on efficient machine learning representations of the potential energy surface (PES) predicted by ab initio calculations have become increasingly popular over the past decade. In particular, several studies have demonstrated the ability of machine learning models to accurately reproduce the complex ab initio PESs of aqueous oxide interfaces, allowing simulations of systems and processes that are not accessible with ab initio methods. In this Account, we review our recent efforts to understand adsorption processes and reactions at aqueous oxide interfaces using deep potential molecular dynamics (DPMD), a simulation scheme employing deep neural networks (DNNs), which has proven to be quite successful in accurately describing many different systems in the condensed phase. After summarizing the DPMD methodology, we first review our work on the acid−base chemistry of oxide surfaces in contact with water, a fundamental characteristic that controls proton transfer and surface charge at the interface. We focus on the aqueous interface of rutile IrO 2 , an oxide material thus far considered the best catalyst for the oxygen evolution reaction (OER). We show that this interface is characterized by a large fraction of dissociated water and a strong Brønsted acidity of the surface sites, in good agreement with the experimentally measured value of the point of zero proton charge. In our second example, we investigate how the adsorption of organic species from ambient air or water affects the structure and wettability of the aqueous interfaces of TiO 2 , a prototypical photocatalytic material. This is a question that is relevant to understanding the UV-induced hydrophilicity of TiO 2 surfaces, a property at the basis of self-cleaning windows and related applications. Specifically focusing on formic and acetic acids, the two most common atmospheric organic acids, our simulations reveal that these acids control the wettability of TiO 2 largely through acid−base chemistry at the interface rather than chemisorption on the oxide surface, a finding that could help improve the design of self-cleaning surfaces and photocatalytic devices. Finally, we review our recent study of methanol at TiO 2 −water interfaces, a system whose interest is largely motivated by the role of methanol in enhancing photocatalytic hydrogen evolution on TiO 2 . Our simulations provide mechanistic insights into the coupled roles of the organic adsorbate and water at the TiO 2 interface, with implications for how methanol enhances the activity of H 2 evolution.

adsorption↗

DOE OSTI2023

Optimal gas cooling with minimal aero-opticaldistortion for next generation high-average-power lasers (Progress report, Year 2)

Next-generation lasers that combine high-peak-power ultrashort pulses with high repetition rates have the potential to efficiently deliver the high-average-power outputs required for inertial fusion energy generation, laser-based particle acceleration, and to generate compact secondary x- and 𝛾-ray radiation sources, among others. The combination of long gain length with high surface-to-volume ratio to remove residual heat makes gas-cooled multi-slab amplifier heads an inherently aperture- and average power scalable technology that enables this new class of solid-state lasers, and their thermal management becomes the most pressing technical challenge. As the index-of refraction depends on the gas density, heat removal from the slab surfaces unavoidably entails scattering of propagated light from regions of high temperature fluctuations. The requirement to maximize convective heat extraction from the gain medium under the constraint of minimum optical aberration of the beam renders amplifier slab cooling a technological challenge at the intersection of aero-optics, fluid-mechanics and heat transfer. The goal of this research is to leverage computational fluid dynamics and aero optics simulations of different levels of fidelity to devise a numerical modeling and optimization framework for gas-cooled multi-slab amplifiers. The project will be conducted in close collaboration with the Advanced Photon Technologies Group at Lawrence Livermore National Laboratory, where a gas-cooled test stand for laser materials and thermal management structures will be developed and installed. The final goal of the collaborative effort is to manufacture and benchmark a physical prototype of an numerically optimized amplifier head design. Measurement data on solid-state and thermo-optical distortions will be used for the validation of the numerical models. The key technical challenge in this multi-objective design problem is that of balancing optimal heat removal with minimal optical distortion. To facilitate computationally efficient design optimization and, at the same time, deepen our basic understanding of the interaction of light with turbulence, a complementary study that combines high and low-fidelity flow simulations and aero-optical models is proposed. Along the lines of these two objectives and solution strategies, the project is organized into two thrusts.

42 ENGINEERING↗

DOE OSTI2025

HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling

Schedulers are critical for optimal resource utilization in high-performance computing. Traditional methods to evaluate sched- ulers are limited to post-deployment analysis, or simulators, which do not model associated infrastructure. In this work, we present the first-of-its-kind integration of scheduling and digital twins in HPC. This enables what-if studies to understand the impact of parameter configurations and scheduling decisions on the physical assets, even before deployment, or regarching changes not easily realizable in production. We (1) provide the first digital twin framework extended with scheduling capabilities, (2) integrate various top-tier HPC systems given their publicly available datasets, (3) implement extensions to integrate external scheduling simulators. Finally, we show how to (4) implement and evaluate incentive structures, as- well-as (5) evaluate machine learning based scheduling, in such novel digital-twin based meta-framework to prototype scheduling. Our work enables what-if scenarios of HPC systems to evaluate sustainability, and the impact on the simulated system.

Maiterth, Matthias [ORNL] (ORCID:000000018698460X)↗

DOE OSTI2021

Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data

Abstract. Tikhonov regularization is a tool for reducing noise amplification during data inversion. This work introduces RegularizationTools.jl, a general-purpose software package for applying Tikhonov regularization to data. The package implements well-established numerical algorithms and is suitable for systems of up to ∼ 1000 equations. Included is an abstraction to systematically categorize specific inversion configurations and their associated hyperparameters. A generic interface translates arbitrary linear forward models defined by a computer function into the corresponding design matrix. This obviates the need to explicitly write out and discretize the Fredholm integral equation, thus facilitating fast prototyping of new regularization schemes associated with measurement techniques. Example applications include the inversion involving data from scanning mobility particle sizers (SMPSs) and humidified tandem differential mobility analyzers (HTDMAs). Inversion of SMPS size distributions reported in this work builds upon the freely available software DifferentialMobilityAnalyzers.jl. The speed of inversion is improved by a factor of ∼ 200, now requiring between 2 and 5 ms per SMPS scan when using 120 size bins. Previously reported occasional failure to converge to a valid solution is reduced by switching from the L-curve method to generalized cross-validation as the metric to search for the optimal regularization parameter. Higher-order inversions resulting in smooth, denoised reconstructions of size distributions are now included in DifferentialMobilityAnalyzers.jl. This work also demonstrates that an SMPS-style matrix-based inversion can be applied to find the growth factor frequency distribution from raw HTDMA data while also accounting for multiply charged particles. The outcome of the aerosol-related inversion methods is showcased by inverting multi-week SMPS and HTDMA datasets from ground-based observations, including SMPS data obtained at Bodega Marine Laboratory during the CalWater 2/ACAPEX campaign and co-located SMPS and HTDMA data collected at the US Department of Energy observatory located at the Southern Great Plains site in Oklahoma, USA. Results show that the proposed approaches are suitable for unsupervised, nonparametric inversion of large-scale datasets as well as inversion in real time during data acquisition on low-cost reduced-instruction-set architectures used in single-board computers. The included software implementation of Tikhonov regularization is freely available, general, and domain-independent and thus can be applied to many other inverse problems arising in atmospheric measurement techniques and beyond.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2019

Electra: A Modular-Based Expansion of NASA's Supercomputing Capability

NASA has increasingly relied on high-performance computing (HPC) re- sources for computational modeling, simulation, and data analysis to meet the science and engineering goals of its missions in space exploration, aeronautics, and Earth and space science. The NASA Advanced Supercomputing (NAS) Division at Ames Research Center in Silicon Valley, Calif., hosts NASA’s premier supercomputing resources, integral to achieving and enhancing the success of the agency’s missions. NAS provides a balanced environment, funded under the High-End Computing Capability (HECC) project, comprised of world-class supercomputers, including its flagship distributed-memory cluster, Pleiades; high-speed networking; and massive data storage facilities, along with multi-disciplinary support teams for user support, code porting and optimization, and large-scale data analysis and scientific visualization. However, as scientists have increased the fidelity of their simulations and engineers are conducting larger parameter-space studies, the requirements for supercomputing resources have been growing by leaps and bounds. With the facility housing the HECC systems reaching its power and cooling capacity, NAS undertook a prototype project to investigate an alternative approach for housing supercomputers. Modular supercomputing, or container-based computing, is an innovative concept for expanding NASA’s HPC capabilities. With modular supercomputing, additional containers—similar to portable storage pods—can be connected together as needed to accommodate the agency’s ever-increasing demand for computing resources. In addition, taking advantage of the local weather permits the use of cooling technologies that would additionally save energy and reduce annual water usage. The first stage of NASA’s Modular Supercomputing Facility (MSF) prototype, which resulted in a 1,000 square-foot module on a concrete pad with room for 16 compute racks, was completed in Fall 2016 and an SGI (now HPE) computer system, named Electra, was deployed there in early 2017. Cooling is performed via an evaporative system built into the module, and preliminary experience shows a Power Usage Effectiveness (PUE) measurement of 1.03. Electra achieved over a petaflop on the LINPACK benchmark, sufficient to rank number 96 on the November 2016 TOP500 list [14]. The system consists of 1,152 InfiniBand-connected Intel Xeon Broadwell-based nodes. Its users access their files on a facility-wide file system shared by all HECC compute assets via Mellanox MetroX InfiniBand extenders, which connect the Electra fabric to Lustre routers in the primary facility over fiber-optic links about 900 feet long. The MSF prototype has exceeded expectations and is serving as a blueprint for future expansions. In the remainder of this chapter, we detail how modular data center technology can be used to expand an existing compute resource. We begin by describing NASA’s requirements for supercomputing and how resources were provided prior to the integration of the Electra module-based system.

Biswas, Rupak↗

NASA NTRS2010

NASA's Soil Moisture Active and Passive (SMAP) Mission

The Soil Moisture Active-Passive (SMAP) Mission is one of the first Earth observation satellites being formulated by NASA in response to the 2007 National Research Council s Decadal Survey. SMAP will make global measurements of soil moisture at the Earth's land surface and its freeze-thaw state. These measurements will allow significantly improved estimates of water, energy and carbon transfers between the land and atmosphere. Soil moisture measurements are also of great importance in assessing flooding and monitoring drought. Knowledge gained from SMAP observations can help mitigate these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing over the boreal latitudes will also reduce a major uncertainty in quantifying the global carbon balance and help to resolve an apparent missing carbon sink over land. The SMAP mission concept will utilize an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna flying in a 680 km polar orbit with an 8-day exact ground track repeat aboard a 3-axis stabilized spacecraft to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these surface observations with advanced modeling and data assimilation to provide estimates of deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP recently completed its Phase A Mission Concept Study Phase for NASA and transitioned into Phase B (Formulation and Detailed Design). A number of significant accomplishments occurred during this initial phase of mission development. The SMAP project held several open meetings to solicit community feedback on possible science algorithms, prepared preliminary draft Algorithm Theoretical Basis Documents (ATBDs) for each mission science product, and established a prototype algorithm testbed to enable testing and evaluation of the performance of candidate algorithms. SMAP conducted an Applications Workshop in September 2009 to coordinate with potential application users interested in the mission data. A draft Applications Plan describing the Project s planned outreach to potential applications users has been prepared and will be updated during Phase B. SMAP made a significant evaluation of the potential terrestrial radio frequency interference (RFI) source environment and established radiometer and radar flight hardware and ground processing mitigation approaches. SMAP finalized its science orbit and orbit injection approach to optimize launch mass and prepared launch and commissioning scenarios and timeline. A science data communications approach was developed to maximize available science data volume to improve science margins while maintaining moderately short data product latencies to support many potential applications using existing ground assets and with minimum impact to the flight system. SMAP developed rigid multi-body and flexible body dynamics and control models and system designs for the 6-meter rotating instrument reflector-boom assembly (RBA) and flight system to confirm pointing and control performance, and devised strategies to efficiently implement on-orbit balancing if needed. Industry partners were selected for the spin mechanism assembly (SMA) and RBA. Preliminary designs for the radar and radiometer were initiated, including constructing breadboards of key assemblies.

Kellogg, Kent↗