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DOE OSTI2022

Reconfigurable perovskite nickelate electronics for artificial intelligence

Reconfigurable devices offer the ability to program electronic circuits on demand. Here, in this work, we demonstrated on-demand creation of artificial neurons, synapses, and memory capacitors in post-fabricated perovskite NdNiO 3 devices that can be simply reconfigured for a specific purpose by single-shot electric pulses. The sensitivity of electronic properties of perovskite nickelates to the local distribution of hydrogen ions enabled these results. With experimental data from our memory capacitors, simulation results of a reservoir computing framework showed excellent performance for tasks such as digit recognition and classification of electrocardiogram heartbeat activity. Using our reconfigurable artificial neurons and synapses, simulated dynamic networks outperformed static networks for incremental learning scenarios. The ability to fashion the building blocks of brain-inspired computers on demand opens up new directions in adaptive networks.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Toward three-dimensional DNA industrial nanorobots

Nanoscale industrial robots have potential as manufacturing platforms and are capable of automatically performing repetitive tasks to handle and produce nanomaterials with consistent precision and accuracy. We demonstrate a DNA industrial nanorobot that fabricates a three-dimensional (3D), optically active chiral structure from optically inactive parts. By making use of externally controlled temperature and ultraviolet (UV) light, our programmable robot, ~100 nanometers in size, grabs different parts, positions and aligns them so that they can be welded, releases the construct, and returns to its original configuration ready for its next operation. Our robot can also self-replicate its 3D structure and functions, surpassing single-step templating (restricted to two dimensions) by using folding to access the third dimension and more degrees of freedom. Our introduction of multiple-axis precise folding and positioning as a tool/technology for nanomanufacturing will open the door to more complex and useful nano- and microdevices.

Robotics↗

DOE OSTI2019

Harmony: A Harness Monitoring System for the Oak Ridge Leadership Computing Facility

Acceptance of a new system requires extensive testing and is often comprised of hundreds of tests. Summit, the latest flagship super-computer at the Oak Ridge Leadership Computing Facility (OLCF), and the number one system in the November 2018 Top500 list [2], completed its acceptance testing in 2018. To execute acceptance, the acceptance test (AT) team utilizes the OLCF test harness, a tool developed at the OLCF that automates the launch and verification of all acceptance tests. Acceptance requires analysis of test results and classification of all test failures. The sheer number of tests involved makes performing these tasks challenging. To complete these tasks more efficiently, in addition to lessen the personnel burden during acceptance testing, we developed a monitoring system for the OLCF test harness called Harmony.

Kuchta, Cameron↗

DOE OSTI2017

Performance Prediction Toolkit

The Performance Prediction Toolkit (PPT), is a scalable co-design tool that contains the hardware and middle-ware models, which accept proxy applications as input in runtime prediction. PPT relies on Simian, a parallel discrete event simulation engine in Python or Lua, that uses the process concept, where each computing unit (host, node, core) is a Simian entity. Processes perform their task through message exchanges to remain active, sleep, wake-up, begin and end.

Chennupati, Gopinath↗

DOE OSTI2020

Holotomography Phase-Retrieval

Holotomography is a non-destructive x-ray imaging technique which allows to visualize the interior morphology of object without damaging them. It is similar to x-ray computed tomography (CT), but unlike conventional CT, which is based on the attenuation of x-rays passing through the object, holotomography exploits the phase shift caused by the sample. This phase-contrast imaging allows to increase image contrast by recovering refractive index of materials and is proven to be more sensitive to density variations than conventional absorption-based X-ray imaging when applied to low-Z materials. In holotomography, the phase is retrieved by measuring the variations in intensity of the object at different sample to detector distances. Several algorithms exist to perform this task such as, contrast transfer function (CTF), transport of intensity equation (TIE), and a mixed approach which combine both TIE and CTF.

Forien, Jean-Baptiste↗

DOE OSTI2021

solar-data-tools

Tools for performing common tasks on solar PV data signals. These tasks include finding clear days in a data set, common data transforms, and fixing time stamp issues. These tools are designed to be automatic and require little if any input from the user. Libraries are included to help with data IO and plotting as well.

Meyers, Bennet↗

DOE OSTI2023

Variational Kalman Filtering with Hinf-Based Correction v.1.0

SAND2024-01664O This software demonstrates the usage of the Variational Kalman Filtering with Hinf-Based Correction software, which can be used to calculate the mean and covariance of the estimate of a high-dimensional parameter. The software performs this task faster than the traditional Kalman filtering technique. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗

DOE OSTI2024

CMLM (Co-Optimized Machine-Learned Manifolds) [SWR-23-41]

Co-optimized Machine-Learned Manifolds (CMLM) is a data-driven approach for developing reduced-order manifold models for high-dimensional chemically reacting systems. It involves a specially designed neural network, the training of which simultaneously optimizes linear combinations of species that define the manifold, nonlinear mapping to outputs of interest such as reaction rates, and (optionally) subfilter closure for large eddy simulation. This software package provides an implementation of the CMLM approach in Python using the PyTorch machine learning library. A few example cases are included, showing how the tool can be applied to different types of data from 0D and 1D reacting simulations performed using Cantera. The neural networks can be saved in a format that is readable by the Pele suite of combustion solvers for use in reacting computational fluid dynamics simulations. This software repository contains several python scripts to perform various tasks associated with the Co-optimized Machine Learned Manifolds (CMLM) model, which is described in Perry, Henry de Frahan, and Yellapantula, CNF, 2022 (https://doi.org/10.1016/j.combustflame.2022.112286). This includes not only the code that defines the CMLM model, but also scripts to generate suitable training data, scripts to pre-process the data, scripts to train the CMLM model, and scripts to plot the output, as well as various other helper files. The scripts depend on several commonly used python libraries for data analysis and chemical reaction computations. The trained models that result from this tool are designed to work with the an interface being implemented in the Pele suite of reacting flow solvers (https://github.com/AMReX-Combustion).

Perry, Bruce↗

DOE OSTI2025

Data Agnostic Feature-Target Analysis & Ranking Machine Learning Pipeline (DAFTAR-ML) v0.1.0

DAFTAR-ML is a specialized machine-learning pipeline that identifies relevant features based on their relationship to a target variable. Many ML pipelines focus solely on prediction, and feature ranking is often absent or lacks robust statistical methods. DAFTAR-ML performs its tasks with this outcome in mind. Model training is robust, using nested cross-validation and hyperparameter tuning. Instead of relying on native feature-importance scores, it employs SHAP (SHapley Additive exPlanations) to quantify feature importance. The pipeline also produces comprehensive results, including publication-quality visualizations.

Melie, Tina [Lawrence Berkeley National Laboratory↗

DOE OSTI2020

ADEPT: a domain independent sequence alignment strategy for gpu architectures

Bioinformatic workflows frequently make use of automated genome assembly and protein clustering tools. At the core of most of these tools, a significant portion of execution time is spent in determining optimal local alignment between two sequences. This task is performed with the Smith-Waterman algorithm, which is a dynamic programming based method. With the advent of modern sequencing technologies and increasing size of both genome and protein databases, a need for faster Smith-Waterman implementations has emerged. Multiple SIMD strategies for the Smith-Waterman algorithm are available for CPUs. However, with the move of HPC facilities towards accelerator based architectures, a need for an efficient GPU accelerated strategy has emerged. Existing GPU based strategies have either been optimized for a specific type of characters (Nucleotides or Amino Acids) or for only a handful of application use-cases. In this paper, we present ADEPT, a new sequence alignment strategy for GPU architectures that is domain independent, supporting alignment of sequences from both genomes and proteins. Our proposed strategy uses GPU specific optimizations that do not rely on the nature of sequence. We demonstrate the feasibility of this strategy by implementing the Smith-Waterman algorithm and comparing it to similar CPU strategies as well as the fastest known GPU methods for each domain. ADEPT’s driver enables it to scale across multiple GPUs and allows easy integration into software pipelines which utilize large scale computational systems. We have shown that the ADEPT based Smith-Waterman algorithm demonstrates a peak performance of 360 GCUPS and 497 GCUPs for protein based and DNA based datasets respectively on a single GPU node (8 GPUs) of the Cori Supercomputer. Overall ADEPT shows 10x faster performance in a node-to-node comparison against a corresponding SIMD CPU implementation. ADEPT demonstrates a performance that is either comparable or better than existing GPU strategies. We demonstrated the efficacy of ADEPT in supporting existing bionformatics software pipelines by integrating ADEPT in MetaHipMer a high-performance denovo metagenome assembler and PASTIS a high-performance protein similarity graph construction pipeline. Our results show 10% and 30% boost of performance in MetaHipMer and PASTIS respectively.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Disordered spontaneously buckled optical gratings for improved lighting applications

Technologies for directing daylight deeper within a building space are highly sought after for energy efficiency applications in order to offset artificial lighting costs and to improve workplace productivity via the use of natural light. Vertical window coatings that can perform this task by redistributing sunlight deeper into a space are especially attractive as they are significantly more straightforward to incorporate into a wide variety of architectures as well as to retrofit into existing facades as compared to roof-based skylights or bulky horizontal daylight shelf type options. The potential energy savings are even greater when one takes into account the fact that such technologies would mitigate harsh glare, allowing window shades to be open for longer portions of the day. However, low-cost and readily scalable techniques are essential for widespread adoption of these window coating technologies. Here, we describe a potentially low-cost method to create a window coating that could enhance daylight penetration, requiring only a thin film of polymeric material deposited on an elastomeric substrate. The resulting structure is a disordered, spontaneously buckled optical grating that spreads incident light without noticeable chromatic dispersion due to its stochastic patterning. The described method has the potential to improve energy efficiency and while maintaining acceptable optical clarity.

De Vetter, Brent M.↗

DOE OSTI2019

From Escherichia coli mutant 13 C labeling data to a core kinetic model: A kinetic model parameterization pipeline

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13 C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13 C-Metabolic Flux Analysis ( 13 C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13 C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Gamma-ray imaging and holdup assays of 235-F PuFF cells 1 & 2

Savannah River National Laboratory (SRNL) Nuclear Measurements (L4120) was tasked with performing enhanced characterization of the holdup in the PuFF shielded cells. Assays were performed in accordance with L16.1-ADS-2460 using two high-resolution gamma-ray detectors. The first detector, an In Situ Object Counting System (ISOCS)-characterized detector, was used in conjunction with the ISOCS Geometry Composer software to quantify grams of holdup. The second detector, a Germanium Gamma-ray Imager (GeGI), was used to visualize the location and relative intensity of the holdup in the cells. Carts and collimators were specially designed to perform optimum assays of the cells. Thick, pencil-beam tungsten collimators were fabricated to allow for extremely precise targeting of items of interest inside the cells. Carts were designed with a wide range of motion to position and align the detectors. A total of 24 measurements were made, each typically 24 hours or longer to provide sufficient statistical precision. This report presents the results of the enhanced characterization for cells 1 and 2. The measured gram values agree very well with results from the 2014 study. In addition, images were created using both the 2014 data and the new GeGI data. The GeGI images of the cells walls reveal significant Pu-238 holdup on the surface of the walls in cells 1 and 2. Additionally, holdup is visible in the two pass-throughs from cell 1 to the wing cabinets. This report documents the final element (exterior measurements coupled with gamma-ray imaging and modeling) of the enhanced characterization of cells 1-5 (East Cell Line).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2018

Energy Auditor and Quality Control Inspector Competency Model

The Energy Auditor (EA) and Quality Control Inspector (QCI) Competency model was developed to identify the soft skills, foundational competencies and define the levels of Knowledge, Skills, and Abilities (KSAs) required to successfully perform the tasks defined in the EA and QCI Job Task Analysis (JTAs), the U.S. Department of Energy (DOE) used the U.S. Department of Labor's (DOL) Competency Model Clearinghouse resources to develop a QCI and EA Competency Model. To keep the QCI and EA competency model consistent with other construction and energy management competency models, DOE and the National Renewable Energy Laboratory used the existing 'Residential Construction Competency Model' and the 'Advanced Commercial Building Competency Model' where appropriate.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

AP-105 Melter Offgas Condensate and EMF Evaporator Concentrate Raman and LIBS Qualitative Evaluation for the use of In-Line Monitoring (Rev. 0)

The Pacific Northwest National Laboratory (PNNL) was tasked to perform Raman spectroscopy and laser induced breakdown spectroscopy (LIBS) testing on the melter off-gas condensate generated during low-activity waste process testing of a sample from Hanford Site tank AP-105 and/or evaporate from evaporation of the condensate. This was a proof-of-principle demonstration that Raman spectroscopy and LIBS have the potential to be used as analytical tools for in-line process analysis of key aqueous phase analytes in radioactive process streams associated with the Hanford Waste Treatment and Immobilization Plant. The work completed under this project complements work completed for WRPS with the development of in-line monitoring systems to characterize and quantify constituents of tank waste following Cs removal and filtration. The WRPS work is currently limited to Raman sensors and utilizes chemometric models for quantification of several anionic species in the sample system. It should be noted that while this approach can be applied to off-gas condensate streams, new chemometric models are needed to accurately quantify analytes under off-gas condensate conditions. The work completed under this project makes timely use of available samples and equipment at PNNL. The project utilized the opportunity to collect off-gas samples from planned AP-105 processing work and included the direct condensate and the evaporate from the DFLAW Radioactive Waste Test Platform. Samples were characterized by instruments already in place at PNNL including a Raman spectrometer optimized for in-line monitoring of flowing and stationary samples as well as a LIBS instrument, which was optimized for static liquid droplet analysis. The ultimate objective was to determine if these techniques could be used to qualitatively characterize analytes present within the off-gas samples. The testing confirmed the use of the Raman as an in-line monitoring system was able to qualitatively observe NO 3 - , NO 2 - , aluminum (as aluminate, Al(OH) 4 - ), SO 4 2- , CrO 4 2- , CO 3 2- , and PO 4 3- in the AP-105 melter feed evaporator condensate samples. The testing also confirmed LIBS as a potential useful technique for in-line monitoring, able to qualitatively detect Na, N, Al, and tentatively Cr and Cl in a simulant stock evaporate solution.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Modeling of Pyroprocessing Hot Cell for Process Monitoring Evaluation

In this report, we examine studies undertaken to determine the applicability of applying measurements with a neutron counter within a pyroprocessing hot cell. The studies focus on the modeling of the hot cell and neutron counter in Monte Carlo N- Particle Code (MCNP) to determine expected radiation effects and applicability of neutron counters. The neutron counter of focus was the High Dose Neutron Detector (HDND) developed at Los Alamos National Laboratory (LANL). We studied the HDND to determine what would be the detector response under normal background conditions with operating process equipment as well as the background’s potential effect on counting applications involving a U/TRU ingot produced by the pyroprocessing electrorefiner. To perform this task, we were required to develop models of the pyroprocessing process cell and unit operations and generate source terms for various different times in the process. The data for the pyroprocessing materials came from SNL’s Safeguards and Security Process Model (SSPM) as part of MPACT’s advanced integration mission. This report will discuss the development of the pyroprocessing process cell and unit operations models in MCNP as well as various simulations to determine the applicability of the HDND for process monitoring efforts in the hot cell.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2017

Rubble Fire Multi-Phase Model Development

This report presents multi-phase modeling approaches that are developed for simulating rubble fire scenarios similar to a large-scale rubble pool fire test at Sandia National Laboratories using composite materials and jet fuel. The rubble pool fire test burnt oddly shaped combustible solid objects submerged in liquid fuel. As an intermediate step toward a full scale rubble fire simulation, various model improvement tasks were performed. For modeling solid decomposition, a multi-step degradation model was used for canonical verification problems and the Chemical Percolation for Devolatilization (CPD) approach was implemented. Capabilities of Lagrangian particle approach has been extended such that a group of particles may represent a solid bulk. For gas-liquid interface, the volume of fluid (VOF) technique was implemented and relevant physics were added. The developed tools offer a potential for simulating three-phase (gas, liquid, and solid) combustion applications.

42 ENGINEERING↗

DOE OSTI2020

Extent-of-Condition Review of Electrical Events Fiscal Year 2019

Sandia National Laboratories has experienced a number of electrical events in recent years. A careful examination of data shows that for the third year in a row, two of the three most frequently categorized Occurrence Reporting and Processing System (ORPS)-reportable event types were unexpected or unintended personal contact with a hazardous energy source and a failure to follow a prescribed hazardous energy control process, as defined in the occurrence reporting criteria of DOE Order 232.2A, Occurrence Reporting and Processing of Operations Information, 2D(1) and 2D(2). Sandia policy CA001.2, Identify and Manage Issues, requires an extent-of-condition review for any high-level reportable occurrence, and the analysts were tasked to perform this EOC in light of high-level reportable occurrence NA-SS-SNL-1000-2019- 0007, Contact with Electrical Energy During Marx Capacitor Troubleshooting. This extent of condition not only evaluated the 10 reportable electrical occurrences for Fiscal Year 2019, but also looked at 11 other related events: those involving control of hazardous energy and electrical events associated with less than hazardous energy.1 This Extent-of-Condition report makes several observations, conclusions, and recommendations for improvement.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗