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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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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Towards a liana plant functional type for vegetation models

Lianas (woody climbers) are crucial components of tropical forests and they have been increasingly recognized to have profound effects on tropical forest carbon dynamics. Despite their importance, lianas' representation in vegetation models remains limited, partly due to the complexity of liana-tree dynamics and the diversity in liana life history strategies. This paper provides a comprehensive review of advances and challenges for mechanistically representing lianas in forest ecosystem models and a proposed path towards effectively representing lianas in these models. Defining a liana plant functional type is a significant challenge because of the high morphological and physiological diversity amongst liana species, and because of their structural association with trees. Here, we identify critical liana traits that likely should contribute to establishing a liana plant functional type, along with key processes to properly represent lianas in ecosystem models. Subsequently, we discuss a variety of possible liana implementation strategies with their associated strengths, limitations, computational costs and data requirements. A fundamental redesign of the tree-centric demographic vegetation models seems appropriate to accommodate the unique growth and competition strategies of lianas. We illustrate the potential of such models with a single-site case study where we disentangle putative mechanisms of liana increasing abundance. Furthermore, we underscore the critical need for comprehensive liana demographic and functional data (including long-term, physiological, and pantropical observations) for the qualitative implementation and evaluation in the proposed modeling efforts. Currently, there is a scarcity of liana data and the data that do exist have a neotropical bias. We finally introduce a new liana functional trait database that can centralize existing liana trait data, incentivize improved data gathering and thus facilitate model development and scientific analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Looking up, down, and sideways: Reconceiving cumulative effects assessment as a mindset

Despite all the effort that has gone into defining, researching and establishing best practices for cumulative effects assessment (CEA), understanding remains weak and practice wanting. At one extreme of implementation, CEA can be described as merely an irritant to the completion of a project-specific environmental assessment (EA). At the other extreme, the conceptual view is that all effects in EA should be deemed cumulative unless demonstrated otherwise. Our purpose here is to consider how we might reconceive CEA as a mindset that is at the heart of absolutely every assessment of valued ecosystem component (VEC) to ensure that we understand the relative contributions of various stressors and can decide when cumulative effects may foreclose future activities due to impacts on VECs. Conceptually, we ground the CEA mindset in the context of three lenses that must all be functioning and working together for the mindset to be operative: a technical lens; a law and policy lens; and a participatory lens. Our arguments are based on a review of the CEA, strategic effects assessment (SEA) and regional effects assessment literatures, an examination and consideration of Canadian EA and SEA case practice, and our combined professional experiences. Through using the Bay of Fundy in Canada as a case example, we establish the concept of the CEA mindset and an approach for moving forward with implementation. - Highlights: • Conceptualization of cumulative effects assessment as a mindset. • Elaboration of technical, law and policy and participation lenses critical to CEA • Coordination and integration of cumulative effects for valued ecosystem components • Application in Bay of Fundy ecosystem and terrestrial watershed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Multi-GPU immersed boundary method hemodynamics simulations

Large-scale simulations of blood flow that resolve the 3D deformation of each comprising cell are increasingly popular owing to algorithmic developments in conjunction with advances in compute capability. Among different approaches for modeling cell-resolved hemodynamics, fluid structure interaction (FSI) algorithms based on the immersed boundary method are frequently employed for coupling separate solvers for the background fluid and the cells within one framework. GPUs can accelerate these simulations; however, both current pre-exascale and future exascale CPU-GPU heterogeneous systems face communication challenges critical to performance and scalability. In this paper, we describe, to our knowledge, the largest distributed GPU-accelerated FSI simulations of high hematocrit cell-resolved flows with over 17 million red blood cells. We compare scaling on a fat node system with six GPUs per node and on a system with a single GPU per node. Through comparison between the CPU- and GPU-based implementations, we identify the costs of data movement in multiscale multi-grid FSI simulations on heterogeneous systems and show it to be the greatest performance bottleneck on the GPU.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

How fast can one resize a distributed file system?

Efficient resource utilization becomes a major concern as large-scale distributed computing infrastructures keep growing in size. Malleability, the possibility for resource managers to dynamically increase or decrease the amount of resources allocated to a job, is a promising way to save energy and costs. However, state-of-the-art parallel and distributed storage systems have not been designed with malleability in mind. The reason is mainly the supposedly high cost of data transfers required by resizing operations. Nevertheless, as network and storage technologies evolve, old assumptions about potential bottlenecks can be revisited. In this study, we evaluate the viability of malleability as a design principle for a distributed storage system. We specifically model the minimal duration of the commission and decommission operations. To show how our models can be used in practice, we evaluate the performance of these operations in HDFS, a relevant state-of-the-art distributed file system. We show that the existing decommission mechanism of HDFS is good when the network is the bottleneck, but can be accelerated by up to a factor 3 when storage is the limiting factor. We also show that the commission in HDFS can be substantially accelerated. With the highlights provided by our model, we suggest improvements to speed both operations in HDFS. We discuss how the proposed models can be generalized for distributed file systems with different assumptions and what perspectives are open for the design of efficient malleable distributed file systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning

The ground state electron density — obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations — contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation, making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data, while comprehensively sampling system configurations using thermalization. Our ML models are less reliant on heuristics, and being based on Bayesian neural networks, enable uncertainty quantification. We show that our models incur significantly lower data generation costs while allowing confident — and when verifiable, accurate — predictions for a wide variety of bulk systems well beyond training, including systems with defects, different alloy compositions, and at multi-million-atom scales. Moreover, such predictions can be carried out using only modest computational resources.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2010

New optical technology for low mass intelligent trigger and readout.

New optical devices offer the potential for reductions in mass, power, and cost of data paths for on-board trigger and readout of tracking detectors. We give examples of optical modulators, MEMS beam steering devices, and optical coupling. We also present results on radiation hardness of materials as well as different approaches to using optics in triggering.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Quantifying nitrogen loss hotspots and mitigation potential for individual fields in the US Corn Belt with a metamodeling approach

The high productivity in the US Corn Belt is largely enabled by the consumption of millions of tons of manufactured fertilizer. Excessive application of nitrogen (N) fertilizer has been pervasive in this region, and the unrecovered N eventually escaped from croplands in forms of nitrous oxide (N 2 O) emission and N leaching. Mitigating these negative impacts is hindered by a lack of practical information on where to focus and how much mitigation potential to expect. At a large scale, process-based crop models are the primary tools for predicting variables required by decision making, but their applications are prohibited by expensive computational and data storage costs. To overcome these challenges, we built a series of metamodels to learn the key mechanisms regarding the carbon (C) and N cycle from a well-validated process-based biogeochemical model, ecosys. The trained metamodel captures over 98% of the variability of the ecosys simulated outputs for 99 randomly selected counties in Iowa, Illinois, and Indiana. To identify hotspots with high mitigation potential, we introduce net societal benefit (NSB) as an indicator for synthesizing the loss in yield and social benefits through emissions and pollutants avoided. Our results show that reducing N fertilizer by 10% leads to 9.8% less N 2 O emissions and 9.6% less N leaching at the cost of 4.9% more SOC depletion and 0.6% yield reduction over the study region. The estimated total annual NSB is $\$395$ M (uncertainty ranges from $\$114$ M to $\$1271$ M), including $\$334$ from social benefits (uncertainty ranges from $\$46$ M to $\$1076$ M), $\$100$ M from saving fertilizer (uncertainty ranges from $\$13$ M to $\$455$ M), and –$\$40$ M due to yield changes (uncertainty ranges from –$\$261$ M to $\$69$ M). For the median scenario, we noted that 20% of the study area accounts for nearly 50% of the NSB, and thus represent hotspot locations for targeted mitigation. Although the uncertainty range suggests that developing such a high-resolution framework is not yet settled and the scenario based estimations are not appropriate to inform the management practices for individual farmers, our efforts shed light on the new generation of analytical tools for life cycle assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Towards the usage of quarry blasts as an active seismic source for subsurface characterization and imaging

SUMMARY Active or man-made seismic sources are often used to delineate subsurface geological structures via seismic imaging techniques. Although conventional controlled seismic sources have provided high-resolution subsurface images, the high cost of data acquisition necessitates improved use of cheaper alternative seismic sources for subsurface imaging, such as traffic noise, quarry blasts and earthquakes. Here, we explore the potential use of quarry blasts as an active seismic source for subsurface imaging. The goal of our study is to demonstrate the utility of quarry blasts as seismic sources for investigating subsurface structure with a field experiment in Mill Creek, southern Oklahoma. We deployed a 7455-m-long receiver line of 72 receivers (105 m spacing) extending away from a quarry, with a recording time of 28 d. We analyse similarities between blasts, wave types, origin time and excitation location of quarry blasts, which is important information for using such signal as an active source. Given a relative homogeneous geological condition in the study area, we could not observe clear reflected waves. However, the use of P- and S-wave velocities led to the delineation of a major basement damage zone in the study area, which further demonstrates the viability of quarry blasts as a useful seismic source for subsurface geological imaging.

58 GEOSCIENCES↗
DOE OSTI2019

High-viscosity injector-based pink-beam serial crystallography of microcrystals at a synchrotron radiation source

Since the first successful serial crystallography (SX) experiment at a synchrotron radiation source, the popularity of this approach has continued to grow showing that third-generation synchrotrons can be viable alternatives to scarce X-ray free-electron laser sources. Synchrotron radiation flux may be increased ∼100 times by a moderate increase in the bandwidth (`pink beam' conditions) at some cost to data analysis complexity. Here, we report the first high-viscosity injector-based pink-beam SX experiments. The structures of proteinase K (PK) and A 2A adenosine receptor (A 2A AR) were determined to resolutions of 1.8 and 4.2 Å using 4 and 24 consecutive 100 ps X-ray pulse exposures, respectively. Strong PK data were processed using existing Laue approaches, while weaker A 2A AR data required an alternative data-processing strategy. This demonstration of the feasibility presents new opportunities for time-resolved experiments with microcrystals to study structural changes in real time at pink-beam synchrotron beamlines worldwide.

36 MATERIALS SCIENCE↗
DOE OSTI2025

5G Communications in Nuclear: Potential Use Cases and Security Considerations

As fifth-generation (5G) communications continues to revolutionize the future of wireless technology, there is growing demand to utilize its benefits for critical infrastructures such as nuclear power plants (NPPs). In regard to achieving full automation and control in the operation of existing and future nuclear reactors, the unique capabilities of 5G can bring several potential advantages over other wireless technologies. However, a deep investigation is needed for the availability and security of 5G communications under various NPP operational scenarios. This article examines how 5G security capabilities can be architecturally deployed in nuclear applications so as to replace existing communication infrastructures. We discuss the current use of all wireless technologies in NPPs with their key features. Consequently, we investigated several NPP use cases in which 5G offers potential advantages but entails specific security considerations. The present article covers the characteristics of 5G communications, general challenges to its application in nuclear, and the security gaps that need to be addressed. We also highlight certain 5G security-by-design features that can help addressing current stringent NPP requirements. In addition, we discuss some future research direction that can facilitate the implementation of 5G in a nuclear facility. The findings presented herein can help foster 5G deployment in NPPs, thus enabling secured data transmission, cost savings, and increased operational efficiency with enhanced reliability.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Joint tests at INL and CEA of a transient hot wire needle probe for in-pile thermal conductivity measurement

Thermal conductivity is a key property that must be known for proper design, testing, and deployment of new fuels and structural materials in nuclear reactors. Thermal conductivity is highly dependent on the physical structure, chemical composition, and the state of the material. Typically, thermal conductivity changes that occur during irradiation are currently measured out-of-pile using a 'cook and look' approach. But repeatedly removing samples from a test reactor to make measurements is expensive, has the potential to disturb phenomena of interest, and only provides understanding of the sample's end state when each measurement is made. There are also limited thermo-physical property data available for advanced fuels; and such data are needed for simulation codes, the development of next generation reactors, and advanced fuels for existing nuclear plants. Being able to quickly characterize fuel thermal conductivity during irradiation can improve the fidelity of data, reduce costs of post-irradiation examinations, increase understanding of how fuels behave under irradiation, and confirm or improve existing thermal conductivity measurement techniques. This paper discusses efforts to develop and evaluate an innovative in-pile thermal conductivity sensor based on the transient hot wire thermal conductivity method (THWM), using a single needle probe (NP) containing a line heat source and thermocouple embedded in the fuel. The sensor that has been designed and manufactured by the Idaho National Laboratory (INL) includes a unique combination of materials, geometry, and fabrication techniques that make the hot wire method suitable for in-pile applications. In particular, efforts were made to minimize the influence of the sensor and maximize fuel hot-wire heating. The probe has a thermocouple-like construction with high temperature resistant materials that remain ductile while resisting transmutation and materials interactions. THWM-NP prototypes were fabricated for both room temperature proof-of-concept evaluations and high temperature testing. Evaluations have been performed jointly by the INL and the French Alternative Energies and Atomic Energy Commission (CEA), both in Idaho Falls (USA) and in Cadarache (France), in the framework of a collaborative program for instrumentation of Material Testing Reactors. Initial tests were conducted on samples with a large range of thermal conductivities and temperatures ranging from 20 deg. C to 600 deg. C. Particularly, tests were recently performed on a sample having thermal conductivity and dimensions similar to UO{sub 2} and MOX nuclear fuels, in order to validate the ability of this sensor to operate for in-pile characterization of Light Water Reactors fuels. The results of the tests already completed at INL and CEA indicate that the Transient Hot Wire Needle Probe offers an enhanced method for in-pile detection of thermal conductivity. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Compiler-Directed Transformation for Higher-Order Stencils

As the cost of data movement increasingly dominates performance, developers of finite-volume and finite-difference solutions for partial differential equations (PDEs) are exploring novel higher-order stencils that increase numerical accuracy and computational intensity. This paper describes a new compiler reordering transformation applied to stencil operators that performs partial sums in buffers, and reuses the partial sums in computing multiple results. This optimization has multiple effect son improving stencil performance that are particularly important to higher-order stencils: exploits data reuse, reduces floating-point operations, and exposes efficient SIMD parallelism to backend compilers. We study the benefit of this optimization in the context of Geometric Multigrid (GMG), a widely used method to solvePDEs, using four different Jacobi smoothers built from 7-, 13-, 27-and 125-point stencils. We quantify performance, speedup, andnumerical accuracy, and use the Roofline model to qualify our results. Ultimately, we obtain over 4× speedup on the smoothers themselves and up to a 3× speedup on the multigrid solver. Finally, we demonstrate that high-order multigrid solvers have the potential of reducing total data movement and energy by several orders of magnitude.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Designing Future Energy Systems with Generative AI

Energy systems are experiencing various changes that impact the distribution, use, and reliability of energy. Local utilities and municipalities must respond and adapt to these changes, moving towards a future energy system with modernized infrastructure and other targeted investments and policy decisions. However, planning for and enacting these advancements requires significant effort from experts and engineers to develop strategies that ensure a reliable and secure energy future. This includes characterizing the current energy infrastructure, identifying areas for development, and engaging with local community members. Emerging generative artificial intelligence techniques can alleviate pain points and help support the development of the next generation of energy systems. Here, in this article, we highlight on-going generative AI work in the areas of atmospheric modeling, building energy management, and distribution network design, and we propose a vision for the role of generative AI that considers opportunities and identifies challenges inherent to this technology.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Structure Slicing: Extending Logical Regions with Fields, In: SC '14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Applications on modern supercomputers are increasingly limited by the cost of data movement, but mainstream programming systems have few abstractions for describing the structure of a program's data. Consequently, the burden of managing data movement, placement, and layout currently falls primarily upon the programmer. To address this problem we previously proposed a data model based on logical regions and described Legion, a programming system incorporating logical regions. In this paper, we present structure slicing, which incorporates fields into the logical region data model. We show that structure slicing enables Legion to automatically infer task parallelism from field non-interference, decouple the specification of data usage from layout, and reduce the overall amount of data moved. We demonstrate that structure slicing enables both strong and weak scaling of three Legion applications including S3D, a production combustion simulation that uses logical regions with thousands of fields, with speedups of up to 3.68X over a vectorized CPU-only Fortran implementation and 1.88X over an independently hand-tuned OpenACC code.

Computer Science↗
DOE OSTI2013

Quality-Aware Data Management for Large Scale Scientific Applications

Increasingly larger scale simulations are generating an unprecedented amount of output data, causing researchers to explore new `data staging' methods that buffer, use, and/or reduce such data online rather than simply pushing it to disk. Leveraging the capabilities of data staging, this study explores the potential for data reduction via online data compression, first using general compression techniques and then proposing use-specific methods that permit users to define simple data queries that cause only the data identified by those queries to be emitted. Using online methods for code generation and deployment, with such dynamic data queries, end users can precisely identify the quality of information (QoI) of their output data, by explicitly determining what data may be lost vs. retained, in contrast to general-purpose lossy compression methods that do not provide such levels of control. The paper also describes the key elements of a quality-aware data management system (QADMS) for high-end machines enabled by this approach. Initial experimental results demonstrate that QADMS can effectively reduce data movement cost and improve the QoS while meeting the QoI constraint stated by users.

Computer Science↗
DOE OSTI2022

Scalable Deep Learning-Based Microarchitecture Simulation on GPUs

Cycle-accurate microarchitecture simulators are essential tools for designers to architect, estimate, optimize, and manufacture new processors that meet specific design expectations. However, conventional simulators based on discrete-event methods often require an exceedingly long time-to-solution for the simulation of applications and architectures at full complexity and scale. Given the excitement around wielding the machine learning (ML) hammer to tackle various architecture problems, there have been attempts to employ ML to perform architecture simulations, such as Ithemal and SimNet. However, the direct application of existing ML approaches to architecture simulation may be even slower due to overwhelming memory traffic and stringent sequential computation logic. This work proposes the first graphics processing unit (GPU)-based microarchitecture simulator that fully unleashes the potential of GPUs to accelerate state-of-the-art ML-based simulators. First, considering the application traces are loaded from central processing unit (CPU) to GPU for simulation, we introduce various designs to reduce the data movement cost between CPUs and GPUs. Second, we propose a parallel simulation paradigm that partitions the application trace into sub-traces to simulate them in parallel with rigorous error analysis and effective error correction mechanisms. Combined, this scalable GPU-based simulator outperforms by orders of magnitude the traditional CPU-based simulators and the state-of-the-art ML-based simulators, i.e., SimNet and Ithemal.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Distributed electric field sensing using fibre optics in borehole environments

In the past decade, rapid advances in distributed optical fibre sensing technologies have made it possible to record various geophysical data (e.g. strain, temperature and pressure) continuously in both time and space along the fibre, providing an unprecedented quantity and spatial density of data compared to traditional geophysical measurements as well as reducing data acquisition cost. To date, no distributed fibre-based electromagnetic field sensing system has been implemented although electromagnetic sensing could have a broad range of applications to geophysical imaging and monitoring in borehole environments. The goal of this paper is to provide a theoretical feasibility study regarding the design and use of an electromagnetic sensing optical fibre for geophysical applications. First, we present the sensitivity analysis of a ‘hypothetical’ optical fibre coated with polyvinylidene fluoride, a polymer that provides relatively high piezoelectric properties, yet unlike ceramics, is flexible. Next, using a two-dimensional electromagnetic modelling algorithm, we simulate the earth electric-field-to-fibre-strain transfer function and estimate the theoretical sensitivity of the optical fibre to electric fields. Given the state-of-the-art distributed acoustic sensing strain sensitivities in the picometres strain range, our numerical modelling analysis suggests that a perfectly coupled polyvinylidene fluoride–coated optical fibre can measure electric field values in the mV/m to V/m amplitude range. We then apply a cylindrically symmetric modelling algorithm to simulate numerical models demonstrating the applicability of such a fibre in an oilfield environment. Scenarios investigated employ an electric field source and suggest that the measurements can be used to distinguish the oil versus water ratio with a fibre mounted inside a producing steel cased oil well as well as distinguishing between brine and hydrocarbon filled reservoir zones with a fibre located outside of the casing.

58 GEOSCIENCES↗
DOE OSTI2013

Quality assurance of asymmetric jaw alignment using 2D diode array

Purpose: A method using a 2D diode array is proposed to measure the junction gap (or overlap) and dose with high precision for routine quality assurance of the asymmetric jaw alignment.Methods: The central axis (CAX) of the radiation field was determined with a 15 × 15 cm{sup 2} photon field at four cardinal collimator angles so that the junction gap (or overlap) can be measured with respect to the CAX. Two abutting fields having a field size of 15 cm (length along the axis parallel to the junction) × 7.5 cm (width along the axis perpendicular to the junction) were used to irradiate the 2D diode array (MapCHECK2) with 100 MU delivered at the photon energy of 6 MV. The collimator was slightly rotated at 15° with respect to the beam central axis to increase the number of diodes effective on the measurement of junction gap. The junction gap and dose measured in high spatial resolution were compared to the conventional methods using an electronic portal imaging device (EPID) and radiochromic film, respectively. In addition, the reproducibility and sensitivity of the proposed method to the measurements of junction gap and dose were investigated.Results: The junction gap (or overlap) and dose measured by MapCHECK2 agreed well to those measured by the conventional methods of EPID and film (the differences ranged from −0.01 to 0 cm and from −1.34% to 0.6% for the gap and dose, respectively). No variation in the repeat measurements of the junction gap was found whereas the measurements of junction dose were found to vary in quite a small range over the days of measurement (0.21%–0.35%). While the sensitivity of the measured junction gap to the actual junction gap applied was the ideal value of 1 cm/cm as expected, the sensitivity of the junction dose to the actual junction gap increased as the junction gap (or overlap) decreased (maximum sensitivity: 201.7%/cm).Conclusions: The initial results suggest that the method is applicable for a comprehensive quality assurance of the asymmetric jaw alignment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗