Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “distributed analytics”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 433 records · Page 24

Molecular interactions in short-chain perfluoroalkyl carboxylic acids and aqueous solutions

The presence of short-chain per- and polyfluoroalkyl substances in water poses a major health and environmental challenge. Here, we have performed high-energy small- and wide-angle X-ray scattering measurements on CF 3 [CF 2 ] n COOH (where n = 1, 2, 3 represents the chain length) and their aqueous solutions at 10% mole concentrations to characterize their molecular interactions at the atomic and nanometer length scales. The experimental wide-angle structure factors have been modelled using Empirical Potential Structural Refinement. The oxygen–oxygen partial X-ray pair distribution functions show that the coordination number between the hydroxyl oxygen on the acid and surrounding oxygen water molecules increases significantly with acid chain length, rising from 3.2 for n = 1 to 4.1 for n = 3. The small-angle scattering is dominated by a sharp, high-intensity peak at Q 1 ~ 0.2 Å -1 and a smaller peak at Q 2 = 1.2 Å -1 for n = 3, both of which decrease with decreasing chain length. The Q 2 peak is attributed to groups of adjacent non-bonded acid molecules, and Q 1 has contributions from both correlations between acid molecules and water–water interactions. In all cases, the models show nanoscale aggregation occurs in the form of denser channels of winding hydrogen-bonded chains, approximately 20 water molecules in length, surrounding clusters of acid molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Analytic insights into nonlocal energy transport: Steady state Fokker Planck theory in arbitrary Z plasmas

The generation of energetic electrons in laser fusion in an important issue. The electrons may either arise from a laser plasma instability, or from the uncoupled high temperature tail of a Maxwellian distribution. To study these in a laser fusion context, it is important to find a method accurate enough to be useful, and simple enough to be incorporated into a radiation hydrodynamics numerical simulation, the main workhorse for studying the laser fusion target. That is why analytic insights become important, they allow one to simplify the Fokker Planck theory so that a solution of it can be incorporated into a radiation hydrodynamic simulation. This work develops and analyzes a steady state Fokker Planck theory for plasmas of arbitrary Z. It develops a method of solving the simplified Fokker Planck method with a technique called sparse eigenfunction analysis. As a result, this method appears to work reasonably well when compared to the experimental results from the Rochester/NIF on plastic spherical targets with and without a silicon layer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Enhancing grid reliability and resilience through novel DER control, total situational awareness and integrated distribution-transmission representation

High penetration of distributed energy resources (DERs) can lead to overvoltage and thermal violations, miscoordination of protection devices and potential adverse control interactions. The challenges are exacerbated by a lack of operator situational awareness of behind-the-meter DERs, and T&D interactions. Furthermore, the large attack surface of a DER environment creates significant cyber security challenges. The motivation is to address these barriers. The main goal is to enable extreme levels of DERs in distribution systems while simultaneously enhancing their reliability and resiliency. This is achieved by a data-driven approach for operation, planning and control of distribution systems with total situational awareness and real-time DER control enabled by a network of edge intelligent devices, a cloud-based analytical platform, and secure communications.

14 SOLAR ENERGY↗

DGaaS: GPU as a Service on Distributed Computing System

In the rapidly evolving landscape of scientific computing, Graphics Processing Units (GPUs) have become indispensable for their unparalleled ability to handle parallel tasks in complex calculations, simulations, and data analysis. Their utility is further magnified in machine learning and AI applications, where they significantly accelerate model training and predictive analytics. Within this context, the Triton Inference Server emerges as a pivotal open-source tool, specializing in AI inferencing and optimizing GPU utilization across various platforms and frameworks. This paper presents an in-depth study on distributed High Throughput Computing (HTC), specifically focusing on the HTCondor framework and its resource provisioning tools, GlideinWMS and HEPCloud. These systems enable large-scale scientific experiments like CMS and DUNE to efficiently access and utilize vast computational resources. The paper explores the core architectural components of GlideinWMS, including jobs, user pools, and worker nodes, and discusses their integration with GPUs and the Triton server. The primary aim of this research is to develop a solution that optimizes GPU utilization by leveraging Glideins and containers. This approach allows computational jobs, particularly those involving AI models, to use GPUs only when essential, thereby facilitating efficient sharing of limited GPU resources. To validate this architecture, the study conducted three key tests involving custom scripts, container-based servers, and Triton server deployments. However, the study faces challenges, notably in locating the Triton server and ensuring secure remote access. To address these issues, future work will focus on developing a proxy mechanism and enhancing security protocols. In conclusion, this study offers a comprehensive roadmap for effective and efficient GPU utilization in distributed High Throughput Computing. It aims to contribute significantly to the scientific community by solving pressing problems and implementing robust solutions in collaboration with the GlideinWMS and HEPCloud teams. The research sets the stage for a more efficient, scalable, and cost-effective paradigm in scientific computing.

97 MATHEMATICS AND COMPUTING↗

Data-driven occupant-behavior analytics for residential buildings

Many advances have been made in building technology to help save energy, but influencing the behavior of the occupants is still necessary to achieve low-energy use targets. One of the most practical ways to influence and change occupant behaviors is through incentives. Developing incentives for energy-saving and quantifying the impact of occupant behaviors are both active areas of research. Here, we propose a data analytics framework for detecting changes in occupant behaviors, which will help build an analytics feedback loop from behavior impact to incentive design. The framework has two major parts. The first forecasts energy consumption for each occupant, while the second determines a probability distribution for changes in energy consumption. The parts are interchangeable with other existing machine learning and statistical methods. A specific instantiation of the framework, using kernel ridge-regression for forecasting and k-means to find an empirical behavior distribution, is described in detail. An HVAC use-case with 5 different incentivized behaviors is used as an example to show that the framework can detect behavior changes induced by incentives. Furthermore, we show that some simpler behavior-change detection methods do not work, further justifying the use of advanced analytics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Metal–Organic Frameworks at the Edge of Stability: Mediating Node Distortion to Access Metastable Nanoparticle Polymorphs

Metal-organic frameworks (MOFs) are emerging as unconventional precursors for nanoparticle synthesis, with potential to leverage their tunable structures and chemistry to achieve nanomaterials with structures and compositions inaccessible via traditional synthetic routes. Here we use in situ synchrotron X-ray diffraction and pair distribution function (PDF) measurements to investigate how the dynamic structure of MOFs at the edge of stability influences their transformation into different metastable polymorphs. Our study reveals that the local structural features of metal-oxo MOF nodes at elevated temperatures are linked to the resulting nanoparticle structures formed under mild conditions. Focusing on the titanium-based MOF MIL-125, we demonstrate that manipulating the chemical environment to facilitate transformation of the Ti8 node geometry promotes formation of metastable, nanometer-scale TiO2 brookite rather than the more common anatase and rutile TiO2 polymorphs typically produced through MOF pyrolysis at high temperature. These findings highlight the potential to harness the MOF topology and chemical environment to design and control node distortions and enable access to exotic metastable nanoparticle states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Time-resolved in situ visualization of the structural response of zeolites during catalysis

Zeolites form an important class of materials that are widely used in various fields. Their unique properties come primarily from their size, shape, and connectivity of their sub-nm crystalline voids, the Si/Al ratio of the anionic framework, and the number and the nature of charge-balancing cations.The inhomogeneous distribution of the Si/Al ratio within crystals, and hence the cation density, the presence of organic residues, and their inhomogeneous distribution within crystals sensitively affect their performances because these factors influencethe intra-crystalline diffusion rates of the reactants and products. However, the information regarding these inhomogeneities in zeolite crystals cannot be obtained by conventional analytical tools because they only provide information regardingthe average properties of zeolite crystals. Here we employin situX-ray free electron laser-based time-resolved coherent X-ray diffraction imaging to investigate the internal deformations originating from the inhomogeneous Cu ion distributions in Cu-exchanged ZSM-5 zeolite crystals during the deoxygenation of nitrogen oxides with propene. We show that the interactions between the reactants and the active sites lead to an unusual strain distribution,which is confirmed by density functional theory simulations. Moreover, this elucidates the factors responsible for distinct active regions,which are essential to maximizing some applications with enhanced performances. These observations provide insights into the role of structural inhomogeneity in zeolites during a catalytic processand will assist the future design of zeolites for their applications.

36 MATERIALS SCIENCE↗

Real-Time Distribution System State Estimation with Asynchronous Measurements

We report state estimation is a fundamental task in power systems. Although distribution systems are increasingly equipped with sensing devices and smart meters, measurements are typically reported at different rates and asynchronously; these aspects pose severe strains on workhorse state estimation algorithms, which are designed to process batches of data collected in a synchronous manner from all the measurement units. In this paper, we develop a novel state estimation algorithm to continuously update the estimate of the state based on measurements received in an asynchronous manner from measurement units. The synthesis of the algorithm hinges on a proximal-point type method, implemented in an online fashion, and capable of processing measurements received sequentially from sensors. A performance analysis is presented by providing bounds on the estimation error in terms of the mean and variance that hold at each iteration and asymptotically. The scheme is also compared with a more traditional Weighted Least Squares estimator that compensates for the lack of measurement data by using, as pseudo measurements, the measurement retrieved during a certain time window. Numerical simulations on the IEEE 37-bus feeder corroborate the analytical findings.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Phoenix: A Scalable Streaming Hypergraph Analysis Framework

We present Phoenix, a scalable hypergraph analytics framework for data analytics and knowledge discovery that was implemented on the leadership class computing platforms at Oak Ridge National Laboratory (ORNL). Our software framework comprises a distributed implementation of a streaming server architecture which acts as a gateway for various hypergraph generators/external sources to connect. Phoenix has the capability to utilize diverse hypergraph generators, including HyGen, a very large-scale hypergraph generator developed by ORNL. Phoenix incorporates specific algorithms for efficient data representation by exploiting hidden structures of the hypergraphs. Our experimental results demonstrate Phoenix’s scalable and stable performance on massively parallel computing platforms. Phoenix’s superior performance is due to the merging of high-performance computing with data analytic.

Kurte, Kuldeep↗

FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization

Annual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free-stream wind conditions. Physics-based estimates of wind farm power production typically rely on low-fidelity engineering wake models that approximate the steady-state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition's expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed-form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient-based wind farm layout optimization, in nondimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. Although the FLOWERS approach is found to predict the exact value of AEP with some error relative to the reference model (within 14% on average), it dramatically reduces computation time by an order of magnitude, produces a qualitatively similar design space at relatively low resolution, and yields comparable optimal layouts. This significant speed improvement is critical in layout optimization applications, where determining an optimal layout in an efficient manner is more important than precise AEP prediction.

17 WIND ENERGY↗

Materials data science using CRADLE: A distributed, data-centric approach

Abstract There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed. Graphical abstract

97 MATHEMATICS AND COMPUTING↗

Global Perspective of Energy Storage (2019 Annual Peer Review Report)

This report provides information about the DOE Office of Electricity Energy Storage Peer Review held in 2019 and includes posters reviewed in these categories: postdoctoral, validated reliability and safety performance, equitable regulatory environment and analytics, applied materials (Materials I), power electronics, industry acceptance, partnerships, and Materials I.

13 HYDRO ENERGY↗

Metallic Technetium Sequestration in Nickel Core/Shell Microstructure during Fe(OH)2 Transformation with Ni doping

This study investigates the impacts of Ni doping on technetium-99 (Tc) sequestration in aqueous solutions through transformation of Fe(OH)2(s) to iron spinel (magnetite) under alkaline conditions. Extensive solid characterization was performed for the mineral phases produced, as well as the Tc/Ni speciation and distribution within these phases. X-ray diffraction results show that iron spinel was the dominant mineral product without detectable Ni incorporation. The doped Ni ions mainly precipitated as fine Fe/Ni oxide/hydroxide particles, including strongly reduced nanometer?sized spheroidal Ni-rich and metallic Ni phases. High-resolution analytical scanning transmission electron microscopy using energy dispersive X-ray spectroscopy and electron energy loss spectroscopy on the produced solid samples (focused ion beam-prepared specimens) revealed three Tc distribution domains dominated by nanocrystals and, especially, a Tc-rich metallic phase. Instances of metallic Tc were specifically found in spheroidal, Ni-rich and metallic nanoparticles exhibiting a core/shell microstructure that suggests strong reduction and sequential precipitation of Ni-Tc-Ni. Mass balance analysis showed nearly 100% Tc removal from the 4.8 × 10-4 M Tc solutions. The finding of the metallic Tc encapsulation indicates that Tc sequestration through Ni-doped Fe(OH)2(s)?to?iron spinel transformation process likely provides an alternative treatment pathway for Tc removal and could be combined into further waste treatment approaches.

Wang, Guohui↗

NanoSIP: NanoSIMS Applications for Microbial Biology

High-resolution imaging with secondary ion mass spectrometry (nanoSIMS) has become a standard method in systems biology and environmental biogeochemistry and is broadly used to decipher ecophysiological traits of environmental microorganisms, metabolic processes in plant and animal tissues, and cross-kingdom symbioses. When combined with stable isotope-labeling—an approach we refer to as nanoSIP—nanoSIMS imaging offers a distinctive means to quantify net assimilation rates and stoichiometry of individual cell-sized particles in both low- and high-complexity environments. While the majority of nanoSIP studies in environmental and microbial biology have focused on nitrogen and carbon metabolism (using 15 N and 13 C tracers), multiple advances have pushed the capabilities of this approach in the past decade. The development of a high-brightness oxygen ion source has enabled high-resolution metal analyses that are easier to perform, allowing quantification of metal distribution in cells and environmental particles. New preparation methods, tools for automated data extraction from large data sets, and analytical approaches that push the limits of sensitivity and spatial resolution have allowed for more robust characterization of populations ranging from marine archaea to fungi and viruses. Further, NanoSIMS studies continue to be enhanced by correlation with orthogonal imaging and ‘omics approaches; when linked to molecular visualization methods, such as in situ hybridization and antibody labeling, these techniques enable in situ function to be linked to microbial identity and gene expression. Here we present an updated description of the primary materials, methods, and calculations used for nanoSIP, with an emphasis on recent advances in nanoSIMS applications, key methodological steps, and potential pitfalls.

07 ISOTOPE AND RADIATION SOURCES↗

A scoping study of far-SOL main-wall protection limiters for steady-state operation of compact pilot plant tokamaks

We present a novel method for handling steady-state heat fluxes incident on the main wall of pilot plant-scale magnetic fusion devices, based on the utilization of protection limiters in the far scrape-off layer (SOL). This method helps avoid large plasma-wall gaps, without excessively compromising blanket performance. We present an optimization algorithm for determining the appropriate size and scale of these protection limiters given (1) probability distributions of SOL plasma parameters and (2) assumed risk tolerance. As part of this optimization, we have developed an analytic description of parallel heat fluxes across limiter shadows, and an objective cost function (the ‘Far-SOL Marginal Cost’) to quantify the impact that different main-wall thermal management design choices have on reactor capital cost. Applying the model to a midscale fusion pilot plant concept shows that making use of far-SOL protection limiters can reduce capital costs on the order of $500 M, relative to naively increasing the plasma-wall gap. Our analysis demonstrates that the far-SOL power decay length is the highest-leverage plasma assumption for thermal loading of the first wall, and the primary cost driver for main wall thermal management. The relative cost efficiency of protection limiters increases as assumptions on the far-SOL heat flux become more pessimistic. The concepts described in this paper motivate the further development of far-SOL protection limiters as part of larger efforts to design economical core-edge-wall compatible solutions for a fusion pilot plant.

Design under uncertainty↗

Leptodermic corrections to the TOV equations and nuclear astrophysics within the effective surface approximation

The macroscopic model for a neutron star (NS) as a liquid drop at the equilibrium is used to extend the Tolman-Oppenheimer-Volkoff (TOV) equations taking into account the gradient terms responsible for the system surface. The parameters of the Schwarzschild metric in the spherical case are found with these surface corrections to the known leading (zero) order of the leptodermic approximation a/R << 1, where a is the NS effective-surface (ES) thickness, and R is the effective NS radius. The energy density $\mathscr{E}$ is considered in a general form including the functions of the particle number density and of its gradient terms. The macroscopic gravitational component $Φ$(ρ) of the energy density is taken into account in the simplest form as expansion in powers of $ρ$ – $\overline{ρ}$, where $\overline{ρ}$ is the saturation density, up to second order, in terms of its contributions to the separation particle energy and incompressibility. Density distributions ρ across the NS ES in the normal direction to the ES, which are derived in the simple analytical form at the same leading approximation, was used for the derivation of the modified TOV (MTOV) equations by accounting for their NS surface corrections. As a result, the MTOV equations are analytically solved at first order and the results are compared with the standard TOV approach of the zero order.

Magner, A. G. [Institute for Nuclear Research, Kyi↗

Hot electrons between cold walls

We consider electron cooling in a collisionless plasma slab between two cold and freely emitting walls. Numerical calculations suggest a counterintuitive behavior of this system: the cooling rate slows down and eventually stops, leaving the system with a significant fraction of its initial thermal energy. Analytical treatment within the Vlasov–Poisson model reveals a set of steady-states with a two-component distribution of electrons: the primary electrons trapped within the potential wells and the secondary electrons forming the counterstreaming beams. We show that such steady-states are linearly stable with respect to one-dimensional perturbations. Establishment of a particular steady-state depends on initial conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Magnetic tunnel junction random number generators applied to dynamically tuned probability trees driven by spin orbit torque

Abstract Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNGs) can consume orders of magnitude less energy per bit than CMOS pseudo-RNGs. Here, we numerically investigate with a macrospin Landau–Lifshitz-Gilbert equation solver the use of pMTJs driven by spin–orbit torque to directly sample numbers from arbitrary probability distributions with the help of a tunable probability tree. The tree operates by dynamically biasing sequences of pMTJ relaxation events, called ‘coinflips’, via an additional applied spin-transfer-torque current. Specifically, using a single, ideal pMTJ device we successfully draw integer samples on the interval [0, 255] from an exponential distribution based on p -value distribution analysis. In order to investigate device-to-device variations, the thermal stability of the pMTJs are varied based on manufactured device data. It is found that while repeatedly using a varied device inhibits ability to recover the probability distribution, the device variations average out when considering the entire set of devices as a ‘bucket’ to agnostically draw random numbers from. Further, it is noted that the device variations most significantly impact the highest level of the probability tree, with diminishing errors at lower levels. The devices are then used to draw both uniformly and exponentially distributed numbers for the Monte Carlo computation of a problem from particle transport, showing excellent data fit with the analytical solution. Finally, the devices are benchmarked against CMOS and memristor RNGs, showing faster bit generation and significantly lower energy use.

77 NANOSCIENCE AND NANOTECHNOLOGY↗