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DOE OSTI2023

Newly Released Capabilities in the Distributed-Memory SuperLU Sparse Direct Solver

We present the new features available in the recent release of SuperLU_DIST, Version 8.1.1. SuperLU_DIST is a distributed-memory parallel sparse direct solver. The new features include (1) a 3D communication-avoiding algorithm framework that trades off inter-process communication for selective memory duplication, (2) multi-GPU support for both NVIDIA GPUs and AMD GPUs, and (3) mixed-precision routines that perform single-precision LU factorization and double-precision iterative refinement. Apart from the algorithm improvements, we also modernized the software build system to use CMake and Spack package installation tools to simplify the installation procedure. Throughout the article, we describe in detail the pertinent performance-sensitive parameters associated with each new algorithmic feature, show how they are exposed to the users, and give general guidance of how to set these parameters. We illustrate that the solver’s performance both in time and memory can be greatly improved after systematic tuning of the parameters, depending on the input sparse matrix and underlying hardware.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

nrelWattileExt (SkySpark Wattile Extension) [SWR-24-73]

The NREL Wattile extension, nrelWattileExt, provides an interface between SkySpark, an energy management and analytics software, and Wattile, an NREL-developed Python package for probabilistic prediction of building energy consumption. Wattile models predict discrete quantiles of the probability distribution of a target quantity (typically energy consumption) using the historical time series data from one or more predictors (typically weather data). Within SkySpark, predictions from Wattile models can be used for measurement & verification of building performance, detection of energy anomalies, and fault detection. Related to: https://github.com/NREL/Wattile

Frank, Stephen↗

DOE OSTI2024

Distribution System Dataset Generator for AI Applications [SWR-24-75]

This software is a simple, light-weight python package to generate pytorch compatible machine learning graph dataset representing electric power distribution system. User is able to use these graph datasets to test their graph generation artificial intelligence (AI) models, link prediction AI models, graph classification AI models and so much more. This package uses grid-data-models (https://github.com/NREL-Distribution-Suites/grid-data-models) as input data format for power distribution system. NREL-Ditto (https://github.com/NREL-Distribution-Suites/ditto) tool can be leveraged to transform popular distribution system file formats such as opendss, cyme and synergi to grid-data-models.

Duwadi, Kapil↗

DOE OSTI2023

pvlib python: 2023 project update

pvlib python is a community-developed, open-source software toolbox for simulating the performance of solar photovoltaic (PV) energy components and systems. It provides reference implementations of over 100 empirical and physics-based models from the peer-reviewed scientific literature, including solar position algorithms, irradiance models, thermal models, and PV electrical models. In addition to individual low-level model implementations, pvlib python provides high-level workflows that chain these models together like building blocks to form complete “weather-to-power” photovoltaic system models. It also provides functions to fetch and import a wide variety of weather datasets useful for PV modeling. pvlib python has been developed since 2013 and follows modern best practices for open-source python software, with comprehensive automated testing, standards-based packaging, and semantic versioning. Its source code is developed openly on GitHub and releases are distributed via the Python Package Index (PyPI) and the conda-forge repository. pvlib python’s source code is made freely available under the permissive BSD-3 license. Here we (the project’s core developers) present an update on pvlib python, describing capability and community development since our 2018 publication (Holmgren, Hansen, & Mikofski, 2018).

14 SOLAR ENERGY↗

DOE OSTI2024

FY24 Advancements in PFLOTRAN Development for GDSA Framework

The Spent Fuel & Waste Science and Technology (SFWST) Campaign of the Office of Spent Fuel & Waste Disposition of U.S. Department of Energy Office of Nuclear Energy (DOE-NE) is conducting research and development on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). This report describes fiscal year 2024 accomplishments in the Geologic Disposal Safety Assessment (GDSA) PFLOTRAN Development work package, which is charged with developing subsurface simulation software for postclosure performance assessment of deep geologic disposal of SNF and HLW.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics

Liquid chromatography–high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a “primary” feature list is used as a template for matching compounds in “target” feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution’s in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

BEopt™ (Building Energy Optimization Tool) version 3.0.1 3/16/2023 [SWR-05-41]

The BEopt™ (Building Energy Optimization Tool) software provides capabilities to evaluate residential building designs and identify cost-optimal efficiency packages at various levels of whole-house energy savings along the path to zero net energy. The BEopt software can be used to analyze both new construction and existing home retrofits, as well as single-family detached and multi-family buildings, through evaluation of single building designs, parametric sweeps, and cost-based optimizations. The BEopt software provides detailed simulation-based analysis based on specific house characteristics, such as size, architecture, occupancy, vintage, location, and utility rates. Discrete envelope and equipment options, reflecting realistic construction materials and practices, are evaluated. The BEopt software uses EnergyPlus®, the Department of Energy's flagship simulation engine. Simulation assumptions are based on ANSI/RESNET/ICC Standards and the Building America Housing Simulation Protocols. The sequential search optimization technique used by the BEopt software: Finds minimum-cost building designs at different target energy-savings levels Identifies multiple near-optimal designs along the path, allowing for equivalent solutions based on builder or contractor preference. Download A BEopt™ Version 3.0.1 Beta release (Mar. 16, 2023) is now available. Download the BEopt 3.0.1 Beta setup file.

Christensen, Craig↗

DOE OSTI2024

Numerical Modeling and Optimization of the iProTech Pitching Inertial Pump (PIP) Wave Energy Converter (WEC) (Cooperative Research and Development Final Report, CRADA Number: CRD-22-22968)

This work generated a first-of-its-kind automated workflow to couple time-domain simulations of wave energy converters written in one software language with a set of design generation and evaluation scripts written in another software language. This automated workflow used an existing optimization package to analyze the sensitivity of different design parameters on the power output of a specific WEC, iProTech’s Pitching Inertial Pump (PIP). Geometric, inertial, and power take-off variables were all varied and optimized to find values that produced the highest amount of power generated over varying wave conditions. The findings on these parameter sensitivity studies are used to inform future design iterations of the PIP WEC. Including more design variables in the optimizations will only increase computational run time and further software development is needed to analyze a larger optimization.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

Design and dispatch optimization of packaged ice storage systems within a connected community

The traditional implementation of cool thermal energy storage (CTES) must be reimagined within the context of a dynamic grid and smart buildings operating as connected communities. As most buildings do not operate central chillers or connect to district cooling loops, this necessitates a broader use of packaged CTES. Our objective is to begin answering the question of how such packaged CTES should be implemented within a connected community. We do so by presenting a simulation–optimization workflow employing building energy modeling software and a mixed-integer linear program to design and dispatch a packaged CTES technology to achieve minimum total annual cost. We demonstrate this methodology on a seven-building case study using current utility rates and find that total annual cooling energy costs can be reduced by 17.8% compared to baseline, after accounting for the cost of storage. We perform three parametric sensitivity studies to evaluate modeling assumptions and obtain the prioritization of storage procurement as a function of annualized life-cycle cost of storage. We find that a community optimization approach provides significantly different results than individual building optimizations and provides greater savings compared to baseline.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

A python package for analyzing Resilience of Complex Systems (pyRoCS) v.0.0

SAND2024-01040O PyRoCS software synthesizes mathematical equations from several domains—including information theory, ecology, and engineering sciences—to support resilience analysis for complex systems. Resilience is the ability of the complex system being analyzed to withstand, operate through, and recover from a disruption. The complex system can be a physical system such as an electric grid, an organization such as a company, or even a subfunction of an organization. Existing mathematical equations for resilience analysis are found within multiple domains including information theory, biological sciences, and complex systems. This package synthesizes and refactors equations from these various domains to make them more generalizable for application across different types of complex systems relevant for resilience analysis. Users will be able to apply these equations to characterize different components of complex systems based on available data. 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.

Verzi, Stephen↗

DOE OSTI2022

diffpy.mpdf : open-source software for magnetic pair distribution function analysis

The open-source Python package diffpy.mpdf , part of the DiffPy suite for diffraction and pair distribution function analysis, provides a user-friendly approach for performing magnetic pair distribution function (mPDF) analysis. The package builds on existing libraries in the DiffPy suite to allow users to create models of magnetic structures and calculate corresponding one- and three-dimensional mPDF patterns. diffpy.mpdf can be used to perform fits to mPDF data either in isolation or in combination with atomic pair distribution function data for joint refinement of the atomic and magnetic structure. Examples are given using MnO and MnTe as representative antiferromagnetic compounds and MnSb as a representative ferromagnet.

36 MATERIALS SCIENCE↗

DOE OSTI2025

srlife : a software tool for estimating the life of high temperature concentrating solar receivers. Part I – metallic receivers

Here, this paper introduces srlife, a tool for estimating the structural service life of concentrating solar power (CSP) receivers operating at high temperatures. Supporting both metallic and ceramic receiver designs, srlife is available as open-source software at https://github.com/applied-material-modeling/srlife and can be installed via the PyPi package manager (https://pypi.org). Given basic receiver geometry and incident heat flux, the tool performs thermohydraulic and structural analysis and estimates the life of a receiver. Designed for easy integration into a software stack, including solar field and levelized cost analysis, the tool can be utilized for optimizing receiver designs to meet service life and economic targets. This paper is Part I in a two-part series. Part I discusses the analysis process used to estimate the life of metallic receivers, along with a description of the required input data. Additionally, several heuristics applied within srlife can reduce analysis time significantly while maintaining accurate life estimations for metallic receivers when compared to full analyses. Several examples demonstrating the utility of srlife in receiver design are also discussed. Part II focuses on the life estimation of ceramic receivers, using time-dependent reliability analysis and various ceramic failure models implemented in srlife.

Creep-fatigue analysis↗

DOE OSTI2023

MILK : a Python scripting interface to MAUD for automation of Rietveld analysis

Modern diffraction experiments ( e.g. in situ parametric studies) present scientists with many diffraction patterns to analyze. Interactive analyses via graphical user interfaces tend to slow down obtaining quantitative results such as lattice parameters and phase fractions. Furthermore, Rietveld refinement strategies ( i.e. the parameter turn-on-off sequences) tend to be instrument specific or even specific to a given dataset, such that selection of strategies can become a bottleneck for efficient data analysis. Managing multi-histogram datasets such as from multi-bank neutron diffractometers or caked 2D synchrotron data presents additional challenges due to the large number of histogram-specific parameters. To overcome these challenges in the Rietveld software Material Analysis Using Diffraction ( MAUD ), the MAUD Interface Language Kit ( MILK ) is developed along with an updated text batch interface for MAUD . The open-source software MILK is computer-platform independent and is packaged as a Python library that interfaces with MAUD . Using MILK , model selection ( e.g. various texture or peak-broadening models), Rietveld parameter manipulation and distributed parallel batch computing can be performed through a high-level Python interface. A high-level interface enables analysis workflows to be easily programmed, shared and applied to large datasets, and external tools to be integrated with MAUD . Through modification to the MAUD batch interface, plot and data exports have been improved. The resulting hierarchical folders from Rietveld refinements with MILK are compatible with Cinema: Debye–Scherrer , a tool for visualizing and inspecting the results of multi-parameter analyses of large quantities of diffraction data. In this manuscript, the combined Python scripting and visualization capability of MILK is demonstrated with a quantitative texture and phase analysis of data collected at the HIPPO neutron diffractometer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

R “SHINY” GUI DEVELOPMENT FOR URANIUM ISOTOPIC ANALYSIS WITH MATRIX-ASSISTED IONIZATION MASS SPECTROMETRY

The international nuclear safeguards community continues to seek rapid, accurate, and precise characterization capabilities for the in-field measurement of uranium isotopic compositions in nuclear facilities. Mass spectrometry (MS) is considered the “gold standard” for analysis of relatively long-lived actinides such as uranium (U) and plutonium; however, conventional MS analysis often requires time consuming sample preparation and complex analytical methodologies that are difficult to perform in-field or in-facility. Matrix assisted ionization (MAI) is a novel ambient ionization MS technique (i.e., MAI-MS) that potentially addresses these challenges due to the relative simplicity of the ionization phenomenon and ruggedness of ambient MS instrumentation. Savannah River National Laboratory (SRNL, USA) has demonstrated this technique for nanogram-level 235U/238U isotope ratio measurements within seconds, with percent-level analytical uncertainties capable of discriminating depleted, natural, and low-enriched uranium. Current experimental work on developing MAI methods for uranium isotopic analysis has been enabled by parallel development of a comprehensive MAI-MS data analysis suite at SRNL. Development of this bespoke data analysis software was necessary because commercially available ambient MS software is poorly suited for uranium isotope ratio measurement. The effort leverages the power of R, a popular open-source programming language, and Shiny, an R package providing tools for graphical user interface (GUI) and web interface coding. This software allows researchers without any programming experience to harness and utilize R’s considerable data analysis/visualization power.

LaBone, Elizabeth D.↗

DOE OSTI2023

UQpy v4.1: Uncertainty quantification with Python

This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured to conform with the latest Python coding conventions, refactored to simplify previous tightly coupled features, and improve its extensibility and modularity. To improve the robustness of UQpy, software engineering best practices were adopted. A new software development workflow significantly improved collaboration between team members, and continuous integration and automated testing ensured the robustness and reliability of software performance. Continuous deployment of UQpy allowed its automated packaging and distribution in system agnostic format via multiple channels, while a Docker image enables the use of the toolbox regardless of operating system limitations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Compensation of heat load deformations using adaptive optics for the ALS upgrade: a wave optics study

In this paper, a realistic wave optics simulation method has been developed to study how wavefront distortions originating from heat load deformations can be corrected using adaptive X-ray optics. Several planned soft X-ray and tender X-ray insertion-device beamlines in the Advanced Light Source upgrade rely on a common design principle. A flat, first mirror intercepts the white beam; vertical focusing is provided by a variable-line-space monochromator; and horizontal focusing comes from a single, pre-figured, adaptive mirror. A variety of scenarios to cope with thermal distortion in the first mirror are studied by finite-element analysis. The degradation of the intensity distribution at the focal plane is analyzed and the adaptive optics that correct it is modeled. The range of correctable wavefront errors across the operating range of the beamlines is reported in terms of mirror curvature and spatial frequencies. The software developed is a one-dimensional wavefront propagation package made available in the OASYS suite, an adaptable, customizable and efficient beamline modeling platform.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

FLORIS Wake Map [SWR-25-71]

The FLORIS Wake Map repository is a set of tools for creating maps of cluster wake impacts based on NREL's FLORIS software and modeling tool (https://github.com/nrel/floris). The floriswakemap package comprises two main modules: the wake_map module, dedicated to the WakeMap class, and the area_selector module, dedicated to the AreaSelector class. The WakeMap class is used to generate maps of wake impacts on both new and existing clusters, while the AreaSelector class provides selections of candidate areas for new developments based on an instantiated WakeMap object.

Sinner, Michael (Misha) [National Renewable Energy↗

DOE OSTI2024

Modeled estimates of permafrost hydrology and related fields for pan-Arctic region over the period 1980-2100

This dataset contains gridded estimates of total runoff (mm/day); subsurface runoff (mm/day); snow water equivalent (SWE, mm/day); vertical upward water flux of combined surface evaporation, evapotranspiration, and snow sublimation (mm/day); and soil temperature (C) across the pan-Arctic basin from simulations of the Permafrost Water Balance Model (PWBM v4). These data were generated to allow researchers to advance understanding of the changes unfolding in the Arctic's terrestrial water cycle under the influences of climate warming, hydrological cycle intensification, and permafrost thaw. The dataset will enable researchers to address important questions spanning multiple disciplines. For example, how will rising river flows exported to the coast impact critical zone ecosystems, near-shore ice dynamics, and ocean biogeochemical processes. Spatial resolution is 25x25 km with N=39926 grid cells spanning the domain. Three forcing datasets were used to drive simulations at an implicit daily time step over the period 1980-2100. Daily air temperature, precipitation, and wind speed data from the WATCH Forcing Data methodology applied to ERA5 reanalysis (W5E5) were used for meteorological forcings over the period 1980-2019. Simulations for the period 1980-2100 used meteorological data from two global climate models; IPSL-CM6A-LR and MPI-ESM1-2-HR. The PWBM hydrological outputs are organized by grid and month in gzipped ASCII text files within each archived tar file. Soil temperature is organized by grid cell and day of year from DOY 205 to DOY 260. No special software is required to work with these data. The package contains spatial domain and river basin information files. These data are most useful for analyses of the spatial and temporal variations in terrestrial hydrology and soil thermal dynamics. A manuscript describing the model simulations and associated analysis has been accepted for publication in The Cryosphere (Rawlins and Karmalkar, 2024).

54 ENVIRONMENTAL SCIENCES↗