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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 757 records · Page 42

PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction

Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function, many functional prediction tasks use only protein sequence. In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank in order to study the expressiveness of different structure-based prediction schemes. We present PersGNN - an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis to capture a complex set of both local and global structural features. While variations of these techniques have been successfully applied to proteins before, we demonstrate that our hybridized approach, PersGNN, outperforms either method on its own as well as a baseline neural network that learns from the same information. PersGNN achieves a 9.3% boost in area under the precision recall curve (AUPR) compared to the best individual model, as well as high F1 scores across different gene ontology categories, indicating the transferability of this approach.

Swenson, Nicolas↗

Real-Time XFEL Data Analysis at SLAC and NERSC: a Trial Run of Nascent Exascale Experimental Data Analysis

X-ray scattering experiments using Free Electron Lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID-19 viral proteins). XFEL experiments are a challenge to computing in two ways: i) due to the high cost of running XFELs, a fast turnaround time from data acquisition to data analysis is essential to make informed decisions on experimental protocols; ii) data collection rates are growing exponentially, requiring new scalable algorithms. Here we report our experiences analyzing data from two experiments at the Linac Coherent Light Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori XC40 system, using the Superfacility paradigm: our workflow automatically moves raw data between LCLS and NERSC, where it is analyzed using the software package CCTBX. We achieved real time data analysis with a turnaround time from data acquisition to full molecular reconstruction in as little as 10 min -- sufficient time for the experiment's operators to make informed decisions. By hosting the data analysis on Cori, and by automating LCLS-NERSC interoperability, we achieved a data analysis rate which matches the data acquisition rate. Furthermore, completing data analysis with 10 mins is a first for XFEL experiments and an important milestone if we are to keep up with data collection trends.

Blaschke, Johannes P.↗

XAFS Investigation of Uranium Binding by Seawater-Contacted Amidoxime Adsorbents

Extraction of uranium from seawater constitutes an important avenue for ensuring nuclear fuel availability beyond the coming century. With a predicted 100 years of uranium remaining in terrestrial ores, efficient extraction of the 4.5 billion tons of uranium dissolved in ocean water would enable access to an effectively limitless supply. More near-term benefits include establishment of a backstop technology, preventing runaway price inflation for this critical resource. The current state-of-the-art material for seawater extraction of uranium is a polymeric adsorbent composed of a robust trunk material subsequently surface functionalized by copolymerization of polyacrylonitrile and a hydrophilic comonomer. Treatment of this material with hydroxylamine results in the formation of poly-amidoxime functionalities which have been known to bind strongly with uranium since the early 1980's. Despite more than three decades of investigation, many questions remain unanswered regarding how these adsorbents bind uranium in seawater. The physical characteristics of the polymer prohibit many classical characterization techniques, while most spectroscopic approaches are impeded by the relatively low concentration of uranium compared to the polymer itself and other metals extracted by the adsorbent. The traditional approach has been to investigate small molecule analogues of uranyl - dioxouranium (VI), the form of uranium present in seawater - as bound by amidoxime or amidoxime derivatives, and assert the findings to be representative of the larger polymer system. Computational efforts have also been recently leveraged to a similar end. This approach is not without difficulty, as it has been routinely proposed that the treatment with hydroxylamine and subsequent alkaline conditioning results in formation of a cyclic imide dioxime site, which is truly responsible for uranium binding, as opposed to the 'open chain' amidoxime. Further complications ensue from considering the possibility of cooperative binding interactions from the different hydrophilic comonomers which are known to be necessary for optimizing uranium capacity. Finally, application of different surface polymerization techniques has the potential to directly affect the morphology of the surface polymer by influencing the degree of grafting, extent of polymer crosslinking, and polymer density. The impact of these morphological considerations on the uranyl binding environment cannot be easily replicated with small molecule studies or by computation. Ultimately, the precise binding environment of uranium is unknown, but constitutes critical knowledge for the design and engineering of advanced adsorbent materials capable of selective extraction of uranium from seawater. Analysis of the EXAFS fits reveals three of the four polymer morphologies appear to bind uranium in different chemical environments, none of which were predicted by crystallographic study or computational investigation, challenging numerous long-held assumptions. Both AI-8 and AN/HEA spectra possess features which could be attributable to cooperative binding of uranium by comonomers, but this cannot be definitively determined by EXAFS without further investigation. Unlike AF-1, neither AI-8 or AN/HEA require an adjacent μ{sup 2}-oxo-bridged transition metal to obtain a reasonable fit. The fit for Cyclic AF-1 is refined to almost identical parameters to the fit of the original AF-1 adsorbent, with the only difference is the contracting of the light elements in the second coordination sphere. It is thus proposed that the DMSO and heat treatment of the AF-1 fiber does very little to change the uranium binding site, and any differences in uptake are more readily explained by changes in the morphology of the graft polymer. There is no evidence for cyclic amidoxime functionality contributing significantly to the extraction of uranium from seawater, and efforts to maximize the number of these binding sites should be discontinued. While molecular interactions necessarily occur in adsorption processes, these results clearly reveal physical phenomena at the mesoscale play a critical role in adsorbent behavior, even to the extent that the binding environment differs from those predicted by small molecule studies. In contrast to the current paradigm where chemical functionality is varied, improvements in the performance of polymer-based amidoxime-functionalized adsorbents for extraction of uranium from seawater need also be pursued through interrogation and modulation of bulk morphology and improvements in polymer hydrophilicity. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Implementation and Comparison of Different Algorithms for on the fly Doppler Broadening in RMC Code

Accurate cross sections are of great importance to Monte Carlo particle transport calculation codes which use pointwise cross sections. It is well known that cross sections have much dependence on temperature on account of Doppler effect which is particularly vital for resonance absorption cross sections. In general, pre-prepared continuous-energy libraries are used in Monte Carlo codes, which is feasible in many applications. However, in multi-physics calculations where neutronics and thermal hydraulics are coupled, cross sections at a fine interval of temperatures are desired, which brings a great challenge to computer memory. Consequently, different methods about on the fly Doppler broadening for cross sections are proposed, which can generate cross sections at the specific temperature points during neutron simulations. These methods can be roughly classified into two categories: one is to calculate the required cross sections by different algorithms, such as interpolation using a high order function expansion, multipole representation, and different integration algorithms based on Doppler broadening equation. The other, for instance, target motion sampling (TMS), does not calculate the Doppler broadening cross sections but takes into account Doppler effect by rejection technique. In this paper, several on the fly Doppler broadening algorithms based on Doppler broadening equation are implemented in the Reactor Monte Carlo code RMC, and then the accuracy and efficiency are discussed and compared. In this paper, several algorithms for on the fly Doppler broadening based on Doppler broadening equation have been implemented in RMC and the focus falls on the comparison of the Gauss Legendre quadrature and Gauss Hermite quadrature. Gauss Legendre quadrature is more accurate than Gauss Hermite quadrature but also more time consuming. Base temperature has a significant impact on Gauss Hermite quadrature while increasing base temperature to room temperature (300 K) can give satisfactory results. In addition, the proportion of overhead of Doppler broadening treatment in total computational time tends to decrease with an increase number of nuclides in fuel. For depletion calculation, both algorithms are in good agreement with reference and the relative errors of K{sub inf} are within 5 times of standard deviation. As for total computational time, the ratio between Gauss Legendre quadrature or Gauss Hermite quadrature and reference decreases and reaches 9.6 or 3.4 at last, respectively. Four important isotopes are chosen to compare the atom density and all relative errors are within 0.25%. After comparisons of different algorithms and different base temperatures, it can be found that Gauss Hermite quadrature broadening from 300 K can perform on the fly cross section Doppler broadening with least total computational time and acceptable precision. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

High Resolution Stereoscopic PIV Measurements in a 5 x 5 Rod Bundle with Mixing Vane

Spacer grids are used in the pressurized water reactor (PWR) to fix, clamp the fuel rods, and enhance the turbulence mixing within the spaces of the rods and overall heat transfer performance. Proper understanding on the thermal-hydraulic characteristics of the turbulent mixing and heat transfer created by the space grids is important. However, spacer grids having complex design structure with dimple, mixing vane and spring have been challenges for computational fluid dynamics (CFD) turbulent models. High fidelity experimental data with spatial-temporal resolutions is essential to validate and assess the performances of CFD models. Baglietto and Ninokata applied an improved realizable anisotropic turbulence models via direct numerical simulation (DNS)-based near-wall formulation data to study the wall shear stresses and flow structures in tight bundle subchannels. These authors have shown that their models are able to capture the stress distribution and velocity field at an acceptable accuracy. The authors numerically studied the flow field characteristics at the region downstream of the spacer grids, and validated their results to particle imagery velocimetry (PIV) measurements. Dominguez and Hassan performed two-dimensional time resolved PIV (TR-PIV) measurements within a 5 x 5 PWR rod bundle with spacer-grids that utilized a matching index of refraction (MIR) approach. Their averaged data showed a rather uniform direction velocity profile after the spacer grid while the downstream illustrated a no slip wall profile. In this paper, we presents flow measurements in the MIR 5 x 5 fuel rod bundle with a spacer grid. Time resolved stereoscopic PIV (TR-SPIV) measurements were performed to obtain high spatial-temporal resolution two-dimensional three-component (2D3C) velocity vector fields in the subchannels between the rods. Flow statistics such as mean velocity, and Reynolds stresses were computed. The obtained experimental data can be used to improve the computational modeling and validate the numerical models that are currently applied to light and pressurized water reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Data-Driven Unit Commitment Refinement - a Scalable Approach for Complex Modern Power Grids

Integration of renewable generation, which is often intermittent and decentralized, substantially increases the stochasticity and complexity of power grid operations. Future power systems planning will require significant computational capability to evaluate balance between demand and supply under varying conditions, both temporally and spatially. The standard approach for generation unit commitment is to use mixed-integer linear programming to find the optimal generation schedule considering ramping and generator constraints. In the future grid this poses computational scalability challenges because generation and demand are not known with certainty due to stochasticity in weather and complexity of the grid. To address this challenge, we present a data-driven unit commitment approach that can efficiently include stochastic weather impacts and contingency considerations to improve unit commitment. Our approach uses graph-based data analytics techniques on solutions to the security constrained (and possibly stochastic) economic dispatch problem to identify potential improvements to a given unit commitment. Recent breakthroughs in fully-parallel stochastic economic dispatch software allow this approach to be scalably deployed. Simulations on synthetic South Carolina and Texas grids show this method can improve grid reliability with security constraints over a set of contingencies, while also meaningfully lowering total generation cost.

Holt, Timothy↗

Elimination LArTPC Simulation Uncertainty With Modifcations to TPC Wire Waveforms

This paper introduces a novel approach to reducing systematic uncertainties in LArTPC detector simulations, which arise from known detector effects that are challenging and computationally intensive to model. The proposed method involves modifying simulated waveforms based on observed discrepancies in ionization signals from the TPC between actual data and simulations. This approach reduces the sensitivity to detailed detector modeling and decreases the computational resources required for accurate simulations.

Mktchyan, Karen↗

Studying light–matter interactions and energy transfer at the nanoscale with a trapped–ion quantum computer

A grand challenge in materials sciences is to control energy transduction, transfer and dissipation at the nanoscale. Meeting this challenge requires progress in nanoscale fabrication and synthesis, but equally importantly, also progress in our understanding of how nanoscale structure influences these phenomena. Although detailed quantum mechanical models exist that capture the structure and physics of materials and molecular species, solving these models to calculate the relevant emergent phenomena, which often occur over vastly different time and energy scales, has so far proven difficult. In this project, we studied how to model energy transfer and light-matter interactions using trapped-ion quantum computers. Our studies focused on emulation of critical processes in natural systems, such as the well-known photosynthetic pigment-protein complexes responsible for capturing and transducing light to electrons. In addition, we implemented proof-of-principle experiments demonstrating that such calculations are possible on trapped-ion quantum computing hardware. These studies and experiments pave the way to investigate energy transport and transduction in a detail not accessible with classical computing technologies. We expect that our studies will allow to extract important guiding principles on how to improve the efficiency of nano-scale light-harvesting and transduction devices.

36 MATERIALS SCIENCE↗

The NASA Computational Fluid Dynamics (CFD) program - Building technology to solve future challenges

This paper presents the NASA Computational Fluid Dynamics program in terms of a strategic vision and goals as well as NASA's financial commitment and personnel levels. The paper also identifies the CFD program customers and the support to those customers. In addition, the paper discusses technical emphasis and direction of the program and some recent achievements. NASA's Ames, Langley, and Lewis Research Centers are the research hubs of the CFD program while the NASA Headquarters Office of Aeronautics represents and advocates the program.

Richardson, Pamela F.↗

Computational Complexity of Neuromorphic Algorithms

Neuromorphic computing has several characteristics that make it an extremely compelling computing paradigm for post Moore computation. Some of these characteristics include intrinsic parallelism, inherent scalability, collocated processing and memory, and event-driven computation. While these characteristics impart energy efficiency to neuromorphic systems, they do come with their own set of challenges. One of the biggest challenges in neuromorphic computing is to establish the theoretical underpinnings of the computational complexity of neuromorphic algorithms. In this paper, we take the first steps towards defining the space and time complexity of neuromorphic algorithms. Specifically, we describe a model of neuromorphic computation and state the assumptions that govern the computational complexity of neuromorphic algorithms. Next, we present a theoretical framework to define the computational complexity of a neuromorphic algorithm. We explicitly define what space and time complexities mean in the context of neuromorphic algorithms based on our model of neuromorphic computation. Finally, we leverage our approach and define the computational complexities of six neuromorphic algorithms: constant function, successor function, predecessor function, projection function, neuromorphic sorting algorithm and neighborhood subgraph extraction algorithm.

Date, Prasanna↗

Final Report from The University of Texas at Austin for DEGAS: Dynamic Global Address Space programming environments

The Dynamic, Exascale Global Address Space programming environment (DEGAS) project will develop the next generation of programming models and runtime systems to meet the challenges of Exascale computing. Our approach is to provide an efficient and scalable programming model that can be adapted to application needs through the use of dynamic runtime features and domain-specific languages for computational kernels. We address the following technical challenges: Programmability: Rich set of programming constructs based on a Hierarchical Partitioned Global Address Space (HPGAS) model, demonstrated in UPC++. Scalability: Hierarchical locality control, lightweight communication (extended GASNet), and ef- ficient synchronization mechanisms (Phasers). Performance Portability: Just-in-time specialization (SEJITS) for generating hardware-specific code and scheduling libraries for domain-specific adaptive runtimes (Habanero). Energy Efficiency: Communication-optimal code generation to optimize energy efficiency by re- ducing data movement. Resilience: Containment Domains for flexible, domain-specific resilience, using state capture mechanisms and lightweight, asynchronous recovery mechanisms. Interoperability: Runtime and language interoperability with MPI and OpenMP to encourage broad adoption.

97 MATHEMATICS AND COMPUTING↗

Efficient Computation Of Behavior Of Aircraft Tires

NASA technical paper discusses challenging application of computational structural mechanics to numerical simulation of responses of aircraft tires during taxing, takeoff, and landing. Presents details of three main elements of computational strategy: use of special three-field, mixed-finite-element models; use of operator splitting; and application of technique reducing substantially number of degrees of freedom. Proposed computational strategy applied to two quasi-symmetric problems: linear analysis of anisotropic tires through use of two-dimensional-shell finite elements and nonlinear analysis of orthotropic tires subjected to unsymmetric loading. Three basic types of symmetry and combinations exhibited by response of tire identified.

Tanner, John A.↗

Gradient-Enhanced Reliability Analysis of Transonic Aeroelastic Flutter

Reliability computation for transonic aeroelastic systems is a challenging task, given the complex physical mechanisms involved, and broader challenges associated with computing small probabilities of failure/flutter. This paper demonstrates the use of four different reliability estimation tools, with a particular focus on tools that can utilize gradient information: the derivative of flutter boundaries with respect to random parametric inputs. Results are shown for two test cases: the AGARD 445.6 case and the Common Research Model, and the strength and weaknesses of each UQ tool are compared in terms of cost and accuracy. Two of the UQ tools used here require gradients as part of a search process, but the other two are sample-based, and are able to leverage the existence of gradients for improved performance.

Bret K Stanford↗

The Future of Electronic Device Design: Device and Process Simulation Find Intelligence on the World Wide Web

We are on the path to meet the major challenges ahead for TCAD (technology computer aided design). The emerging computational grid will ultimately solve the challenge of limited computational power. The Modular TCAD Framework will solve the TCAD software challenge once TCAD software developers realize that there is no other way to meet industry's needs. The modular TCAD framework (MTF) also provides the ideal platform for solving the TCAD model challenge by rapid implementation of models in a partial differential solver.

Biegel, Bryan A.↗

Big Data: Next-Generation Machines for Big Science

Addressing the scientific grand challenges identified by the US Department of Energy’s (DOE’s) Office of Science’s programs alone demands a total leadership-class computing capability of 150 to 400 Pflops by the end of this decade. The successors to three of the DOE’s most powerful leadership-class machines are set to arrive in 2017 and 2018—the products of the Collaboration Oak Ridge Argonne Livermore (CORAL) initiative, a national laboratory–industry design/build approach to engineering nextgeneration petascale computers for grand challenge science. These mission-critical machines will enable discoveries in key scientific fields such as energy, biotechnology, nanotechnology, materials science, and high-performance computing, and serve as a milestone on the path to deploying exascale computing capabilities.

High-performance computing↗

Multiphysics coupling in the Exascale computing project

Multiphysics coupling presents a significant challenge in terms of both computational accuracy and performance. Achieving high performance on coupled simulations can be particularly challenging in a high-performance computing context. The US Department of Energy Exascale Computing Project has the mission to prepare mission-relevant applications for the delivery of the exascale computers starting in 2023. Many of these applications require multiphysics coupling, and the implementations must be performant on exascale hardware. In this special issue we feature six articles performing advanced multiphysics coupling that span the computational science domains in the Exascale Computing Project.

97 MATHEMATICS AND COMPUTING↗

PV Hosting Capacity Estimation: Experiences with Scalable Framework; Preprint

Hosting capacity is an indication of the amount of photovoltaics (PV) can be hosted in a distribution system. This paper presents a framework for estimating distributed PV hosting capacity at scale. We first analyze the key challenges of performing relevant large scale simulation including computational and modeling challenges. Then, we develop two python-based software tools in order to conduct repeatable distribution analyses: Distribution Integration Solution Cost Options (DISCO) for configuring and analyzing simulations, and JADE for parallelizing jobs on HPC clusters. A case study of hosting capacity estimation for SMART-DS SFO 2000+ synthetic feeders is used to demonstrate the capability of the developed framework and tools. The framework and tools can help utilities assess the overall hosting capacity of their service territory, through which the overall upgrade cost can be better planned in order to integrate more PV in the future.

distributed energy resources↗