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DOE OSTI2021

Stakeholder Support for Wildlife Conservation Funding Policies

State wildlife management agencies in the United States have depended on a “user-pay” funding model for conservation efforts that relies on revenue from hunting license sales and a federal excise tax on firearms, ammunition, and archery equipment. Declines in hunting participation, however, jeopardize sustainability of the current funding model. Ensuring support among stakeholders for wildlife management and conservation may require expanding sources of funding and incorporating the perspectives and values of a diversifying constituency into decision making processes. We used a web-based survey of wildlife-associated recreationists in Michigan, USA to evaluate support for a range of conservation funding policies. Respondents self-identified primarily as hunters (n = 2,558) or wildlife watchers (n = 942). We used binary logistic regression to evaluate support for four conservation funding policy options: state sales tax, lottery proceeds, extractive industry revenue, and a user-based tax on outdoor gear (i.e., “backpack tax”). Determinants of support varied by type of policy and stakeholder characteristics. We found no statistically significant differences between hunters and wildlife watchers in their support for conservation funding policies when accounting for other variables such as wildlife value orientations, engagement in stewardship behaviors, age, and gender. The industry-based policy achieved the greatest level of approval, while the backpack tax had the lowest. Respondents were mixed in their support of the sales tax and lottery proceeds options. Cluster analysis revealed three homogenous groups related to conservation funding policies: “strong support,” “mixed/opposed,” and “anti-backpack tax.” Clusters differed in their support for conservation funding policies and on psychological and demographic variables. The “strong support” and “anti-backpack tax” groups differed in their levels of stewardship engagement, knowledge of conservation funding mechanisms, and support for the backpack tax option. The “mixed-opposed” group tended to be older, less educated, and less likely to be a member of a conservation organization. Results suggest support for conservation funding differs by policy type and social and psychological characteristics of stakeholders. Based on differences in policy support revealed in this study, we suggest a multi-tiered approach to funding conservation and building on support among wildlife stakeholders to mitigate the looming funding crisis for state wildlife agencies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1994

Statistical scaling relationships and size effects in the strength and creep rupture of fibrous composites

In this presentation we discuss a new theoretical model and supporting experimental results for the strength and lifetime in creep rupture of unidirectional, carbon fiber/epoxy matrix composites at ambient conditions. First we review the 'standard' Weibull/power-law methodology that has been standard practice. Then we discuss features of a recent model which build on the statistical aspects of fiber strength, micromechanical aspects of stress transfer around fiber breaks, and time-dependent creep of the matrix. The model is applied to 'microcomposites' consisting of seven fibers in a matrix for which strength and creep-rupture data are available. The model yields Weibull distributions in an envelope format for both strength and lifetime. The respective shape, scale and power-law parameters depend on such parameters as the Weibull shape parameter for fiber strength, the exponent for matrix creep, the effective load transfer length (which grows in time due to matrix creep) and the critical cluster size for failed fibers. The experimental results are consistent with the theory, though time-dependent debonding appears to be part of the failure process.

Phoenix, S. Leigh↗

DOE OSTI2022

Creep anisotropy modeling and uncertainty quantification of an additively manufactured Ni-based superalloy

The advantages offered by additive manufacturing over traditional processes has driven a great deal of industrial and academic interest in recent years. However, the process is relatively new and requires additional investigation to become sufficiently mature for wide scale industrial adoption. Electron beam melting powder bed fusion is one technology that has shown promise for fabricating high temperature resistant materials such as nickel based superalloys. The resulting microstructures typically exhibit a strong fiber texture in the build direction giving rise to anisotropic time-dependent deformation behavior. In order to accelerate the qualification of these materials for industrial adoption accurate numerical models are needed for simulating their behavior. In this work a crystal plasticity model including non-Schmid effects is presented for capturing creep anisotropy observed in additively manufactured IN738LC. The model is calibrated via a probabilistic framework where model parameters are treated as random variables. An iterative sequential design strategy is utilized to efficiently identify the probability density of the unknown model parameters. As a case study the model is utilized to investigate the behavior of randomly oriented equiaxed grain clusters sometimes observed embedded in the additively manufactured columnar structure. A synthetic realization is simulated and uncertainty is propagated through to the full-field response. Results indicate that these features are the source of significant creep relaxation and strain accumulation which partially explains observed grain boundary decohesion at these locations.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Amphiphile conformation impacts aggregate morphology and solution structure across multiple lengthscales

Although the self-assembly of amphiphiles is well-studied in aqueous solutions, much less is understood about the fundamental driving forces and structure property relationships in non-polar media. In recent work [Journal of Physical Chemistry B, 2020, 124, 10822.] the authors have studied a series of malonomide-based amphiphiles that are relevant to liquid-liquid extraction. That work demonstrated that aggregation is largely driven by local dipole-dipole interactions between molecules. Here, we build upon this observation to develop a more detailed understanding of how the balance of dipole-dipole interactions (controlled by conformation) and molecular architecture influences the morphology of the aggregates across lengthscales. Using constrained molecular dynamics about key degrees of freedom, we demonstrate that the conformation of N,N-dimethyl,N,N-dioctylhexylethoxy malonamide (DMDOHEMA) and N,N-dimethyl,N,N-dibutyltetradecyl malonamide (DMDBTDMA) has a significant impact upon self-association - where appropriate conformational sampling is essential. To quantify the aggregate morphology, several graph theoretic and persistent homology based properties are determined. The former examines the patterns of intermolecular interactions within clusters, while the latter examines the 3-dimensional spatial distribution. Based upon these analyses, we find that the morphology of aggregates, particularly at higher malonamide concentration, depends on a balance of dipole alignment and alkyl tail sterics. Dipole alignment encourages linear patterns of the intermolecular interactions within aggregates, while the alkyl tail steric interactions between the malonamide result in noticeably less linear aggregates for DMDOHEMA than DMDBTDMA. This is reflected in the spatial distribution, where more holes or voids exist between extractants within the DMDOHEMA that distribute within the solution in more of a "swiss cheese" arrangement as opposed to the more filamentous distribution of DMBDTDMA. This study links conformation and molecular structure to the morphology of amphiphile assemblies, and serves as a basis for ongoing study of multicomponent amphiphile solutions with polar and other solutes, and how these impact aggregation phenomena.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Molecular architecture and functional dynamics of the pre-incision complex in nucleotide excision repair

Nucleotide excision repair (NER) is vital for genome integrity. Yet, our understanding of the complex NER protein machinery remains incomplete. Combining cryo-EM and XL-MS data with AlphaFold2 predictions, we build an integrative model of the NER pre-incision complex(PInC). Here TFIIH serves as a molecular ruler, defining the DNA bubble size and precisely positioning the XPG and XPF nucleases for incision. Using simulations and graph theoretical analyses, we unveil PInC’s assembly, global motions, and partitioning into dynamic communities. Remarkably, XPG caps XPD’s DNA-binding groove and bridges both junctions of the DNA bubble, suggesting a novel coordination mechanism of PInC’s dual incision. XPA rigging interlaces XPF/ERCC1 with RPA, XPD, XPB, and 5' ssDNA, exposing XPA’s crucial role in licensing the XPF/ERCC1 incision. Mapping disease mutations onto our models reveals clustering into distinct mechanistic classes, elucidating xeroderma pigmentosum and Cockayne syndrome disease etiology.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2015

Earth Science Data Fusion with Event Building Approach

Objectives of the NASA Information And Data System (NAIADS) project are to develop a prototype of a conceptually new middleware framework to modernize and significantly improve efficiency of the Earth Science data fusion, big data processing and analytics. The key components of the NAIADS include: Service Oriented Architecture (SOA) multi-lingual framework, multi-sensor coincident data Predictor, fast into-memory data Staging, multi-sensor data-Event Builder, complete data-Event streaming (a work flow with minimized IO), on-line data processing control and analytics services. The NAIADS project is leveraging CLARA framework, developed in Jefferson Lab, and integrated with the ZeroMQ messaging library. The science services are prototyped and incorporated into the system. Merging the SCIAMACHY Level-1 observations and MODIS/Terra Level-2 (Clouds and Aerosols) data products, and ECMWF re- analysis will be used for NAIADS demonstration and performance tests in compute Cloud and Cluster environments.

Lukashin, C.↗

DOE OSTI2023

Visualizing the Effect of Oxidation on Magnetic Domain Behavior of Nanoscale Fe 3 GeTe 2 for Applications in Spintronics

Magnetic van der Waals (vdW) materials offer an opportunity to design heterostructures that will lead to exotic functionalities that arise from interfacial interaction. In addition to coupling to different vdW materials, the naturally oxidized surface layer of a vdW material also forms a heterostructure with its bulk film, giving rise to intriguing phenomena. Here, we directly observe the impact of oxidation on the magnetic domains, namely, magnetic stripe domain and skyrmions, in a nanoscale Fe 3 GeTe 2 flake using cryo Lorentz transmission electron microscopy. After the Fe 3 GeTe 2 is exposed to ambient conditions, partial oxidation leads to an increase in the density of skyrmions even under zero magnetic field. Complete oxidation leads to a loss of the magnetic domain structure. We observe a gradual change in Fe 3 GeTe 2 from single crystal to amorphous as the oxidation increases. The oxidized Fe 3 GeTe 2 primarily consists of iron oxide, which could be antiferromagnetic in nature. We hypothesize that the interfacial interaction between these surface antiferromagnetic oxides and the bulk ferromagnetic Fe 3 GeTe 2 , as well as the effect of interfacial roughness, leads to the increase in Néel skyrmion creation. Furthermore, this work opens a path to harness controlled oxidation as a build block to create dense skyrmion lattices without the need for an external magnetic field, leading to potential future applications in spintronic devices.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Navier: Dataflow Architecture for Computation Chemistry

Navier’s objectives were two evaluate the use of emerging technologies, especially dataflow accelerators, for high-performance computing (HPC) applications, specifically in the domain of chemistry, and to develop a prototype software stack to support such applications. Navier builds on capabilities previously developed by synergistic projects, such as PNNL Data Model Convergence (DMC) LDRD Hardware Advanced Workflows (HAW) and DuOMO, as well as DOE ARIAA. Throughout its 18 months, the Navier team developed new capabilities and artifacts at all levels of the HW/SW stack, provided a seamless way to integrate novel computing architectures (Sambanova SN10 and Xilinx Versal AI) into an existing software stack, developed chemistry workflows, data analytics tools, and HPC molecular dynamics workflows that leverage the developed stack and PNNL institutional investments in emerging architectures. Navier also explored the use of active learning to accelerate a computational chemistry workflow for organic molecules on PNNL Junction cluster (in collaboration with AMD/Xilinx). Navier developed tools, methodologies, and studies for hardware software co-design and (sparse) dataflow accelerators that are composable and can be used together or separately. These methodologies are now used in other projects, such as DOE AMAIS and HPDA. This report describes Navier’s achievement, the developed tools and methodologies, and the research findings and conclusions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

A quantum processor based on coherent transport of entangled atom arrays

The ability to engineer parallel, programmable operations between desired qubits within a quantum processor is key for building scalable quantum information systems. In most state-of-the-art approaches, qubits interact locally, constrained by the connectivity associated with their fixed spatial layout. Here we demonstrate a quantum processor with dynamic, non-local connectivity, in which entangled qubits are coherently transported in a highly parallel manner across two spatial dimensions, between layers of single- and two-qubit operations. Our approach makes use of neutral atom arrays trapped and transported by optical tweezers; hyperfine states are used for robust quantum information storage, and excitation into Rydberg states is used for entanglement generation. We use this architecture to realize programmable generation of entangled graph states, such as cluster states and a seven-qubit Steane code state. Furthermore, we shuttle entangled ancilla arrays to realize a surface code state with thirteen data and six ancillary qubits and a toric code state on a torus with sixteen data and eight ancillary qubits. Finally, we use this architecture to realize a hybrid analogue–digital evolution and use it for measuring entanglement entropy in quantum simulations, experimentally observing non-monotonic entanglement dynamics associated with quantum many-body scars. Realizing a long-standing goal, these results provide a route towards scalable quantum processing and enable applications ranging from simulation to metrology.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

RANGE: A robust adaptive nature-inspired global explorer of potential energy surfaces

With the growing demand for realistic representations of chemical structures and the advent of exascale computing, the intelligent sampling of potential energy surfaces and efficient identification of global minima have become more essential but also more feasible. Building on prior studies demonstrating the efficiency of the Artificial Bee Colony (ABC) swarm intelligence algorithm, we report a hybrid metaheuristic framework that integrates the adaptive exploration capabilities of ABC coupled with the exploitation strengths of genetic algorithms (GA) in a scalable, Python-based implementation. The resulting tool, RANGE (Robust Adaptive Nature-inspired Global Explorer), provides seamless interfaces to multiple potential energy evaluators, either directly or via widely used Python libraries, and is designed for high-performance computing environments. We describe the implementation details of RANGE and evaluate its performance, relative to ABC- or GA-alone based algorithms, on a variety of chemical systems, including molecular clusters and heterogeneous surfaces. In conclusion, our results demonstrate RANGE’s efficiency, robustness, and broad applicability in addressing challenging global optimization problems in computational chemistry and materials science.

Algorithms and data structure↗

DOE OSTI2022

Long- and short-term temporal variability in cloud condensation nuclei spectra over a wide supersaturation range in the Southern Great Plains site

Abstract. When aerosol particles seed the formation of liquid water droplets in the atmosphere, they are called cloud condensation nuclei (CCN). Different aerosols will act as CCN under different degrees of water supersaturation (relative humidity above 100 %), depending on their size and composition. In this work, we build and analyze a best-estimate CCN spectrum product, tabulated at ∼ 45 min resolution, generated using high quality data from seven independent instruments at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The data product spans a large supersaturation range, from 0.0001 % to ∼ 30 %, and time period of 5 years, from 2009–2013, and is available on the ARM data archive. We leverage this added statistical power to examine relationships that are unclear in smaller datasets. Our analysis is performed in three main areas. First, probability distributions of many aerosol and CCN metrics are found to exhibit skewed log-normal distribution shapes. Second, clustering analyses of CCN spectra reveal that the primary drivers of CCN differences are aerosol number size distributions, rather than hygroscopicity or composition, especially at supersaturations above 0.2 %, while also allowing for a simplified understanding of seasonal and diurnal variations in CCN behavior. The predictive ability of using limited hygroscopicity data with accurate number size distributions to estimate CCN spectra is investigated, and the uncertainties of this approach are estimated. Third, the dynamics of CCN spectral clusters and concentrations are examined with cross-correlation and autocorrelation analyses. We find that CCN concentrations change rapidly on the timescale of 1–3 h, with some conservation beyond that which is greatest for the lower supersaturation region of the spectrum.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

xSDK: Building an ecosystem of highly efficient math libraries for exascale

Current efforts to build increasingly powerful computer architectures are opening up new avenues for more complex and higher fidelity simulations coupled with data analytics and learning, leading to new scientific insights and deeper understanding. At one extreme, exascale computers will be much faster than previous computer generations (performing 10 18 operations per second—that is, 1,000 times faster than petascale). To achieve these performance improvements, computer architectures are becoming increasingly complex, with deep memory hierarchies, very high node and core counts, and heterogeneous features such as graphics processing units (GPUs). Such architectural changes impact the full breadth of computing scales, as heterogeneity pervades even current-generation laptops, workstations, and moderate-sized clusters. While emerging advanced architectures provide unprecedented opportunities, they also present significant challenges for developers of scientific applications, such as multiphysics and multiscale codes, who must adapt their software to handle disruptive changes in architectures and new programming models that have not yet stabilized. Developers must consider increasing concurrency while reducing communication and synchronization, and other complexities such as the potential for using mixed precision to leverage the compute power available in low-precision tensor cores. On one hand, developers must implement new scientific capabilities, which in turn increase code complexity. On the other hand, the codes must be ported to new architectures, requiring the inclusion of new programming models and the restructuring of code to achieve good performance. Addressing these issues is beyond the capability of any single person or team—leading to the need for collaboration among many teams, who encapsulate their expertise in reusable software and work together to create sustainable software ecosystems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

FY 2026 Midyear Report: Seismic Monitoring of Underground Vibration Sources Using Distributed Acoustic Sensing and Seismometers

Safeguards-relevant temporal changes in underground facilities can be observed using geophysical monitoring techniques. Seismic waves, in particular, provide valuable insights into subsurface activities and can serve as an important tool for detecting anomalous events that may indicate containment breaches at geological repositories. This midyear report summarizes ongoing efforts to automatically and rapidly detect and locate anomalous vibration signals that could be indicative of potential containment breaches. Previous work during FY25 focused on compiling continuous seismic datasets from two underground sites and developing a database of continuous waveforms and ground-truth event data derived from multiple sensing modalities. Building on this foundation, we are adapting anomaly detection and geolocation algorithms to explore methods for monitoring underground activities using two relatively low-maintenance sensing technologies: a dense surface geophone array deployed at the Pleasant Gap mine in Pennsylvania, and a three-dimensional fiber-optic cable array for distributed acoustic sensing (DAS) installed in the subsurface at the Sanford Underground Research Facility (SURF) in South Dakota. This report summarizes work conducted during the first two quarters of FY26, during which we refined a dynamic power spectral density (PSD)-based detector, applied it independently to each geophone station, and then combined the per‑station detections with density-based spatial clustering of applications with noise (DBSCAN) to cluster events and produce spatial maps over a nine‑day interval. In addition, we outline plans for a field trial at the Waste Isolation Pilot Plant (WIPP) in New Mexico to compare traditional seismic monitoring approaches with DAS techniques and to evaluate the benefits of combined data analysis. Activities during the past two quarters have included the preparation and submission of a Field Test Plan to WIPP for approval, as well as submission to headquarters for review and feedback.

58 GEOSCIENCES↗

NASA NTRS1999

KNBD: A Remote Kernel Block Server for Linux

I am developing a prototype of a Linux remote disk block server whose purpose is to serve as a lower level component of a parallel file system. Parallel file systems are an important component of high performance supercomputers and clusters. Although supercomputer vendors such as SGI and IBM have their own custom solutions, there has been a void and hence a demand for such a system on Beowulf-type PC Clusters. Recently, the Parallel Virtual File System (PVFS) project at Clemson University has begun to address this need (1). Although their system provides much of the functionality of (and indeed was inspired by) the equivalent file systems in the commercial supercomputer market, their system is all in user-space. Migrating their 10 services to the kernel could provide a performance boost, by obviating the need for expensive system calls. Thanks to Pavel Machek, the Linux kernel has provided the network block device (2) with kernels 2.1.101 and later. You can configure this block device to redirect reads and writes to a remote machine's disk. This can be used as a building block for constructing a striped file system across several nodes.

Becker, Jeff↗

DOE OSTI2017

Regression-based reduced-order models to predict transient thermal output for enhanced geothermal systems

Reduced-order modeling is a promising approach, as many phenomena can be described by a few parameters/mechanisms. An advantage and attractive aspect of a reduced-order model is that it is computational inexpensive to evaluate when compared to running a high-fidelity numerical simulation. A reduced-order model takes couple of seconds to run on a laptop while a high-fidelity simulation may take couple of hours to run on a high-performance computing cluster. The goal of this paper is to assess the utility of regression-based reduced-order models (ROMs) developed from high-fidelity numerical simulations for predicting transient thermal power output for an enhanced geothermal reservoir while explicitly accounting for uncertainties in the subsurface system and site-specific details. Numerical simulations are performed based on equally spaced values in the specified range of model parameters. Key sensitive parameters are then identified from these simulations, which are fracture zone permeability, well/skin factor, bottom hole pressure, and injection flow rate. We found the fracture zone permeability to be the most sensitive parameter. The fracture zone permeability along with time, are used to build regression-based ROMs for the thermal power output. The ROMs are trained and validated using detailed physics-based numerical simulations. Finally, predictions from the ROMs are then compared with field data. We propose three different ROMs with different levels of model parsimony, each describing key and essential features of the power production curves. The coefficients in the proposed regression-based ROMs are developed by minimizing a non-linear least-squares misfit function using the Levenberg–Marquardt algorithm. The misfit function is based on the difference between numerical simulation data and reduced-order model. ROM-1 is constructed based on polynomials up to fourth order. ROM-1 is able to accurately reproduce the power output of numerical simulations for low values of permeabilities and certain features of the field-scale data. ROM-2 is a model with more analytical functions consisting of polynomials up to order eight, exponential functions and smooth approximations of Heaviside functions, and accurately describes the field-data. At higher permeabilities, ROM-2 reproduces numerical results better than ROM-1, however, there is a considerable deviation from numerical results at low fracture zone permeabilities. ROM-3 consists of polynomials up to order ten, and is developed by taking the best aspects of ROM-1 and ROM-2. ROM-1 is relatively parsimonious than ROM-2 and ROM-3, while ROM-2 overfits the data. ROM-3 on the other hand, provides a middle ground for model parsimony. Based on R 2 -values for training, validation, and prediction data sets we found that ROM-3 is better model than ROM-2 and ROM-1. For predicting thermal drawdown in EGS applications, where high fracture zone permeabilities (typically greater than 10 –15 m 2 ) are desired, ROM-2 and ROM-3 outperform ROM-1. As per computational time, all the ROMs are 10 4 times faster when compared to running a high-fidelity numerical simulation. In conclusion, this makes the proposed regression-based ROMs attractive for real-time EGS applications because they are fast and provide reasonably good predictions for thermal power output.

58 GEOSCIENCES↗

DOE OSTI2018

Local control of defects and switching properties in ferroelectric thin films

Electric-field switching of polarization is the building block of a wide variety of ferroelectric devices. In turn, understanding the factors affecting ferroelectric switching and developing routes to control it are of great technological significance. This research provides systematic experimental evidence of the role of defects in affecting ferroelectric-polarization switching and utilizes the ability to deterministically create and spatially locate point defects in $PbZr_{0.2}Ti_{0.8}O_3$ thin films via focused-helium-ion bombardment and the subsequent defect-polarization coupling as a knob for on-demand control of ferroelectric switching (e.g., coercivity and imprint). At intermediate ion doses ($0.22-2.2×10^{14}\,ions\,cm^{-2}$), the dominant defects (isolated point defects and small clusters) show a weak interaction with domain walls (pinning potentials from $200-500\,K\,MV\,cm^{-1}$), resulting in small and symmetric changes in the coercive field. At high doses ($0.22-1×10^{15}\,ions\,cm^{-2}$), on the other hand, the dominant defects (larger defect complexes and clusters) strongly pin domain-wall motion (pinning potentials from 500 to $1600\,K\,MV\,cm^{-1}$), resulting in a large increase in the coercivity and imprint, and a reduction in the polarization. This local control of ferroelectric switching provides a route to produce novel functions; namely, tunable multiple polarization states, rewritable pre-determined 180° domain patterns, and multiple zero-field piezoresponse and permittivity states. Such an approach opens up pathways to achieve multilevel data storage and logic, nonvolatile self-sensing shape-memory devices, and nonvolatile ferroelectric field-effect transistors.

36 MATERIALS SCIENCE↗

NASA NTRS2002

Commercial-Off-The-Shelf Microelectromechanical Systems (MEMS) Flow-Measurement Probes Fabricated And Assembled

As an alternative to conventional tubing instrumentation for measuring airflow, designers and technicians at the NASA Glenn Research Center have been fabricating packaging components and assembling a set of unique probes using commercial-off-the-shelf microelectromechanical systems (MEMS) integrated circuits (computer chips). Using MEMS as an alternative has some compelling advantages over standard measurement devices. Sensor technologies have matured through high-production usage in industries such as automotive and aircraft manufacturers. Currently, MEMS are the choice in applications such as tire pressure monitors, altimeters, pneumatic controls, cable leak detectors, and consumer appliances. Conventional instrumentation uses tubing buried in the model aerodynamic surfaces or wind tunnel walls. The measurements are made when pressure is introduced at the tube opening. The pressure then must travel the tubing for lengths ranging from 20 to hundreds of feet before reaching an electronic signal conditioner. This condition causes a considerable amount of damping and requires measurements to be made only after the test rig has reached steady-state operation. The electronic MEMS pressure sensor is able to take readings continuously under dynamic states in nearly real time. The use of stainless steel tubing for pressure measurements requires many tubes to be cleaned, cut to length, carefully installed, and delicately deburred and spliced for use. A cluster of a few hundred 1/16-in.- (0.0625-in.-) diameter tubes (not uncommon in research testing facilities) can be several inches in diameter and may weigh enough to require two men to handle. Replacing hard tubing with electronic chips can eliminate much of the bulk. Each sensor would fit on the tip of the 1/16-in. tubing with room to spare. The P592 piezoresistive silicon pressure sensor (Lucas NovaSensor, Fremont, CA) was chosen for this project because of its cost, availability, and tolerance to extreme ambient conditions. The chip is 1 mm square by 0.6 mm thick (0.039 by 0.023 in.) with 0.12-mm (0.005-in.) wire connection tabs. Three MEMS chips were used to build the first type of flow-angularity probe. This MEMS probe will be demonstrated as an alternative to a standard tube type "Cobra Probe" now used routinely in wind tunnel and aeronautical hardware applications. The response time and accuracy would allow the probe to be translated on an actuator across a flow field, yielding precision dynamic measurements not possible with conventional instrumentation. The low profile, the minimal power requirement, the rugged construction, and the moderate cost all contribute to making MEMS sensors the enticing choice instrument in future research measurement needs. The MEMS probe efforts are a continuation of work initiated by Brian Willis, without whose foresight and efforts this project would never have been realized. This task was funded through cooperation with the NASA Electronic Parts and Packaging (NEPP) program at the Jet Propulsion Laboratory.

Redding, Chip↗

NASA NTRS2013

Scheduling Operations for Massive Heterogeneous Clusters

High-performance computing (HPC) programming has become increasingly difficult with the advent of hybrid supercomputers consisting of multicore CPUs and accelerator boards such as the GPU. Manual tuning of software to achieve high performance on this type of machine has been performed by programmers. This is needlessly difficult and prone to being invalidated by new hardware, new software, or changes in the underlying code. A system was developed for task-based representation of programs, which when coupled with a scheduler and runtime system, allows for many benefits, including higher performance and utilization of computational resources, easier programming and porting, and adaptations of code during runtime. The system consists of a method of representing computer algorithms as a series of data-dependent tasks. The series forms a graph, which can be scheduled for execution on many nodes of a supercomputer efficiently by a computer algorithm. The schedule is executed by a dispatch component, which is tailored to understand all of the hardware types that may be available within the system. The scheduler is informed by a cluster mapping tool, which generates a topology of available resources and their strengths and communication costs. Software is decoupled from its hardware, which aids in porting to future architectures. A computer algorithm schedules all operations, which for systems of high complexity (i.e., most NASA codes), cannot be performed optimally by a human. The system aids in reducing repetitive code, such as communication code, and aids in the reduction of redundant code across projects. It adds new features to code automatically, such as recovering from a lost node or the ability to modify the code while running. In this project, the innovators at the time of this reporting intend to develop two distinct technologies that build upon each other and both of which serve as building blocks for more efficient HPC usage. First is the scheduling and dynamic execution framework, and the second is scalable linear algebra libraries that are built directly on the former.

Humphrey, John↗