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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 649 records · Page 36

All sources NASA NTRS DOE OSTI

DOE OSTI2018

Herrick Well

Data generated at the National Energy Technology Laboratory, Morgantown WV from the Herrick 3HM well. API 34-111-2-4401-00-00 Lat/Long = 39.6572 N/80.9847W Monroe County, Ohio Core obtained by Eclipse Resources. Please see the following report for additional details: Brown, S.; Crandall, D.; Moore, J.; Mackey, P.; Carr, T.; Panetta, B. CT Scanning and Geophysical Measurements of the Utica Formation from the Herrick 3H Well; NETL-TRS-X-2018; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2018; p 94.

Computed Tomography↗

DOE OSTI2023

Citizen Green #1 Well : Sidewall Core Permeability Studies

This submission includes information on porosity & permeability measurements conducted on sidewall cores from the Citizen Green #1 well drilled on King Island, CA (API 07720688) as part of the WESTCARB partnership basin characterization effort. Cores were acquired with a rotary sidewall tool at a range of depths; only deeper cores were sufficiently consolidated for analysis. SidewallCoreFormationDepthNotes.xls provides details on available samples. Helium porosimetry, air permeability, and brine permeability measurements were conducted on all competent samples (~15). Samples were largely in the Mokelumne, H&T shale, and Starkey formations. All measurements were conducted by Core Laboratories (Bakersfield) on behalf of Lawrence Berkeley National Laboratory (LBNL) with final reports provided in November of 2012. Further measurements were conducted on some sidewalls with data in other submissions.

pemeability↗

DOE OSTI2023

IDAES-PSE 2.2.0 Release

DiagnosticsToolbox↗

DOE OSTI2023

IDAES-PSE 2.1.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.1.0 Release Highlights New IDAES Examples Repository Starting with this release, the IDAES examples are developed in the new IDAES/examples repository. Along with many content and usability improvements, the most significant changes are: To install the examples, after installing IDAES, run pip install idaes-examples The idaes get-examples command, previously used for this, has been removed The HTML version is now available at https://idaes-examples.readthedocs.io The previous URL, https://idaes.github.io/examples-pse, will not be updated and may be removed at some point in the future For more details, refer to the resources available at IDAES/examples. Removal of Non-Functional Apps A review of the code in the idaes/apps and idaes/models_extra folders was undertaken, and a number of tools were identified as being outdated or non-functional and no longer supported by their development teams. Due to this, the following tools have been removed: idaes/apps/alamopy_depr (note that the new ALAMOpy interface remains avaialble in idaes/core/surrogates) idaes/apps/helmet idaes/apps/ripe idaes/apps/roundingRegression idaes/models_extra/carbon_capture Pyomo 6.6 This version of IDAES is the first requiring Pyomo 6.6. This version of Pyomo contains multiple internal improvements and refactorings. While for the majority of cases this should have positive or no impact on solvability of IDAES models, we are aware of a small number of models that have been affected as a result of these changes. For more information, refer to the Pyomo 6.6.1 release notes. Other highlights Model Initialization A prototype API for a new approach to initializing IDAES models is now available which makes available some new techniques for initializing models. This is documented in the Initializing Models Reference Guide Modular Properties Framework Support for some transport properties Helmholtz Equation of State properties Better error checking for case where unit models are set to include phase equilibrium but the property package is set to support only a single phase Multi-Stream Contactor model: a new base model for systems involving contacting of two or more streams with mass transfer. This model is intended to be used as the foundation for models such as membrane separators, solvent extraction and other similar processes. This is documented in the Multi-Stream Contactor Reference Guide idaes/models_extra/power_generation report() methods for unit models using Helmholtz equation of state General Code Maintenance Streamlining of dependencies and creation of new optional dependency groupings to support non-core tools General linting of codebase to ensure compliance with most pylint checks Spell checking of all code and doc strings Removal of backward compatibility code for Python 2

IDAES↗

DOE OSTI2018

Preventing Run-Time Bugs at Compile-Time Using Advanced C++

When writing software, we develop algorithms that tell the computer what to do at run-time. Our solutions are easier to understand and debug when they are properly modeled using class hierarchies, enumerations, and a well-factored API. Unfortunately, even with these design tools, we end up having to debug our programs at run-time. Worse still, debugging an embedded system changes its dynamics, making it tough to find and fix concurrency issues. This paper describes techniques using C++ to detect run-time bugs *at compile time*. A concurrency library, developed at Fermilab, is used for examples in illustrating these techniques.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

BOPTest As a Platform for Building Controls and Grid-Interactive Buildings Workforce Training

Building automation and controls are becoming increasingly complex with the emergence of Grid Integrated Efficient Buildings (GEBs) as well as new highly efficient sequences of operation and data-driven control schemes. However, there remains a significant gap in hands-on training opportunities for building operators and technicians to gain practical experience with advanced control systems in a low-risk environment. This paper presents BOPTEST (Building Optimization Performance Test) as a suitable platform for workforce training in building controls and GEB technologies. BOPTEST provides a suite of standardized building simulation test cases with a REST API, real-time control interfaces through BACnet, semantic models connecting users to building data, and built-in calculation of control metrics and performance indicators. The platform enables trainees to interact with virtual buildings using industry-standard protocols while learning how to implement and innovate control strategies. The training platform is designed to offer a structured and interactive learning experience for building engineers, helping them effectively develop, learn, and retain skills in fault identification, troubleshooting, and correction. The workflow is divided into three main phases: 1) Setup, 2) Exercise, and 3) Review, each comprising specific activities performed by either the instructor or the student. Initial pilot training sessions have yielded positive feedback from instructors and participants and demonstrates that BOPTEST effectively fills an industry need for a low-risk training resource via simulation of real building control systems, allowing trainees to gain practical experience before working in the field. The platform's ability to provide immediate performance feedback while maintaining familiar industry interfaces makes it particularly suitable for workforce development programs. This work provides a replicable model for leveraging building simulation in control education and training.

Paul, Lazlo↗

DOE OSTI2018

MaDaTS: Managing Data on Tiered Storage for Scientific Workflows

Scientific workflows are processing large amounts of data through complex simulation and analysis tasks. Meanwhile, the need to minimize I/O costs on next generation systems and the evolution of new technologies (NVRAMs, SSDs etc.) is resulting in deeper storage hierarchies on High Performance Computing (HPC) systems. A multi-tiered storage hierarchy introduces complexities in workflow and data management. There is need for simple and flexible data abstractions that can allow users to seamlessly manage workflow data and tasks on HPC systems with multiple storage tiers. MaDaTS (Managing Data on Tiered Storage for Scientific Workflows) provides an API and a command-line tool that allows users to manage their workflows and data on tiered storage (Ghoshal & Ramakrishnan (2017)).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

rabpro: global watershed boundaries, river elevation profiles, and catchment statistics

River and Basin Profiler (rabpro) is a Python package to delineate watersheds, extract river flowlines and elevation profiles, and compute watershed statistics for any location on the Earth’s surface. As fundamental hydrologically-relevant units of surface area, watersheds are areas of land that drain via aboveground pathways to the same location, or outlet. Delineations of watershed boundaries are typically performed on digital elevation models (DEMs) that represent surface elevations as gridded rasters. Depending on the resolution of the DEM and the size of the watershed, delineation may be very computationally expensive. With this in mind, we designed rabpro to provide user-friendly workflows to manage the complexity and computational expense of watershed calculations given an arbitrary coordinate pair. In addition to basic watershed delineation, rabpro will extract the elevation profile for a watershed’s mainchannel flowline. This enables the computation of river slope, which is a critical parameter in many hydrologic and geomorphologic models. Finally, rabpro provides a user-friendly wrapper around Google Earth Engine’s (GEE) Python API to enable cloud-computing of zonal watershed statistics and/or time-varying forcing data from hundreds of available datasets. Altogether, rabpro provides the ability to automate or semi-automate complex watershed analysis workflows across broad spatial extents.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Scientific Computational Imaging Code (SCICO)

Scientific Computational Imaging Code (SCICO) is a Python package for solving the inverse problems that arise in scientific imaging applications. Its primary focus is providing methods for solving ill-posed inverse problems by using an appropriate prior model of the reconstruction space. SCICO includes a growing suite of operators, cost functionals, regularizers, and optimization routines that may be combined to solve a wide range of problems, and is designed so that it is easy to add new building blocks. SCICO is built on top of JAX rather than NumPy, enabling GPU/TPU acceleration, just-in-time compilation, and automatic gradient functionality, which is used to automatically compute the adjoints of linear operators. An example of how to solve a multi-channel tomography problem with SCICO is shown in Figure 1. The SCICO source code is available from GitHub, and pre-built packages are available from PyPI. It has extensive online documentation, including API documentation and usage examples, which can be run online at Google Colab and binder.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

WATTS: Workflow and template toolkit for simulation

Modeling and simulation in many science and engineering domains often involves the execution and/or iteration of a sequence of applications, with data transfer between applications typically required. These applications often do not have a formal application programming interface (API). Instead, executing an application requires first writing a text-based input file, the format of which is typically defined in a user’s manual. While text-based input files are suitable for simple one-off calculations, they can become cumbersome if a user wants to execute the applications multiple times and systematically vary input parameters, especially when a complex workflow is involved. In this case, they must resort to either manually making changes in the input file or developing their own script that modifies the input file and executes the application. Depending on the format of the input file, writing such a script can be a non-trivial and error-prone task.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

xCDAT: A Python Package for Simple and Robust Analysis of Climate Data

xCDAT (Xarray Climate Data Analysis Tools) is an open-source Python package that extends Xarray (Hoyer & Hamman, 2017) for climate data analysis on structured grids. xCDAT streamlines analysis of climate data by exposing common climate analysis operations through a set of straightforward APIs. Some of xCDAT’s key features include spatial averaging, temporal averaging, and regridding. These features are inspired by the Community Data Analysis Tools (CDAT) library (Dean N. Williams et al., 2009) (D. N. Williams, 2014) (Doutriaux et al., 2019) and leverage powerful packages in the Xarray ecosystem including xESMF (Zhuang et al., 2023), xgcm (Abernathey et al., 2022), and CF xarray (Cherian et al., 2023). To ensure general compatibility across various climate models, xCDAT operates on datasets that are compliant with the Climate and Forecast (CF) metadata conventions (Hassell et al., 2017).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

hashin_shtrikman_mp: a package for the optimal design and discovery of multi-phase composite materials

hashin_shtrikman_mp is a tool for composites designers who have desired composite properties in mind, but who do not yet have an underlying formulation. The library utilizes the tightest theoretical bounds on the effective properties of composite materials with unspecified microstructure – the Hashin-Shtrikman bounds – to identify candidate theoretical materials, find real materials that are close to the candidates, and determine the optimal volume fractions for each of the constituents in the resulting composite. Its features include (i) leveraging of materials in the Materials Project database, (ii) integration with the Materials Project API, (iii) use of genetic machine-learning, (iv) agnosticism to underlying microstructure, and (v) ultimate engineering application, make it a tool with much broader applications than its predecessors.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

JAXtronomy: A JAX port of lenstronomy

Gravitational lensing is a phenomenon where light bends around massive objects, resulting in distorted images seen by an observer. Studying gravitationally lensed systems provides insights into cosmology and astrophysics, including constraints of the expansion rate of the Universe and the distribution of dark matter. Thus, we introduce JAXtronomy, a re-implementation of the gravitational lensing software package lenstronomy (Birrer, 2021; Birrer & Amara, 2018) using JAX (Bradbury et al., 2018). JAX is a Python library that uses an accelerated linear algebra (XLA) compiler to improve the performance of computing software. Our core design principle of JAXtronomy is to maintain an identical API to that of lenstronomy. The main JAX features utilized in JAXtronomy are just-in-time compilation, which can lead to significant reductions in execution time, and automatic differentiation, which allows for the implementation of gradient-based algorithms that were previously impossible. Additionally, JAX allows code to be run on GPUs or parallelized across CPU cores, further boosting the performance of JAXtronomy.

astronomy↗

DOE OSTI2017

Decaf: Decoupled Dataflows for In Situ High-Performance Workflows

Decaf is a dataflow system for the parallel communication of coupled tasks in an HPC workflow. The dataflow can perform arbitrary data transformations ranging from simply forwarding data to complex data redistribution. Decaf does this by allowing the user to allocate resources and execute custom code in the dataflow. All communication through the dataflow is efficient parallel message passing over MPI. The runtime for calling tasks is entirely message-driven; Decaf executes a task when all messages for the task have been received. Such a messagedriven runtime allows cyclic task dependencies in the workflow graph, for example, to enact computational steering based on the result of downstream tasks. Decaf includes a simple Python API for describing the workflow graph. This allows Decaf to stand alone as a complete workflow system, but Decaf can also be used as the dataflow layer by one or more other workflow systems to form a heterogeneous task-based computing environment. In one experiment, we couple a molecular dynamics code with a visualization tool using the FlowVR and Damaris workflow systems and Decaf for the dataflow. In another experiment, we test the coupling of a cosmology code with Voronoi tessellation and density estimation codes using MPI for the simulation, the DIY programming model for the two analysis codes, and Decaf for the dataflow. Such workflows consisting of heterogeneous software infrastructures exist because components are developed separately with different programming models and runtimes, and this is the first time that such heterogeneous coupling of diverse components was demonstrated in situ on HPC systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

FY17 CSSE L2 Milestone Report: Analyzing Power Usage Characteristics of Workloads Running on Trinity.

This report summarizes the work performed as part of a FY17 CSSE L2 milestone to in- vestigate the power usage behavior of ASC workloads running on the ATS-1 Trinity plat- form. Techniques were developed to instrument application code regions of interest using the Power API together with the Kokkos profiling interface and Caliper annotation library. Experiments were performed to understand the power usage behavior of mini-applications and the SNL/ATDM SPARC application running on ATS-1 Trinity Haswell and Knights Landing compute nodes. A taxonomy of power measurement approaches was identified and presented, providing a guide for application developers to follow. Controlled scaling study experiments were performed on up to 2048 nodes of Trinity along with smaller scale ex- periments on Trinity testbed systems. Additionally, power and energy system monitoring information from Trinity was collected and archived for post analysis of "in-the-wild" work- loads. Results were analyzed to assess the sensitivity of the workloads to ATS-1 compute node type (Haswell vs. Knights Landing), CPU frequency control, node-level power capping control, OpenMP configuration, Knights Landing on-package memory configuration, and algorithm/solver configuration. Overall, this milestone lays groundwork for addressing the long-term goal of determining how to best use and operate future ASC platforms to achieve the greatest benefit subject to a constrained power budget.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Sensor Placement Optimization using Chama

Continuous or regularly scheduled monitoring has the potential to quickly identify changes in the environment. However, even with low - cost sensors, only a limited number of sensors can be deployed. The physical placement of these sensors, along with the sensor technology and operating conditions, can have a large impact on the performance of a monitoring strategy. Chama is an open source Python package which includes mixed - integer, stochastic programming formulations to determine sensor locations and technology that maximize monitoring effectiveness. The methods in Chama are general and can be applied to a wide range of applications. Chama is currently being used to design sensor networks to monitor airborne pollutants and to monitor water quality in water distribution systems. The following documentation includes installation instructions and examples, description of software features, and software license. The software is intended to be used by regulatory agencies, industry, and the research community. It is assumed that the reader is familiar with the Python Programming Language. References are included for addit ional background on software components. Online documentation, hosted at http://chama.readthedocs.io/, will be updated as new features are added. The online version includes API documentation .

47 OTHER INSTRUMENTATION↗

DOE OSTI2017

ATDM Rover Milestone Report STDA02-1 (FY2017 Q4)

We have successfully completed the MS-4/Y1 Milestone STDA02-1 for the Rover Project. This document describes the milestone and provides an overview of the technical details and artifacts of the milestone. This milestone is focused on building a GPU accelerated ray tracing package capable of doing multi-group radiography, both back-lit and with self-emission as well as serving as a volume rendering plot in VisIt and other VTK-based visualization tools. The long term goal is a package with in-situ capability, but for this first version integration into VisIt is the primary goal. Milestone Execution Plan: Create API for GPU Raytracer that supports multi-group transport (up to hundreds of groups); Implement components into one or more of: VTK-m, VisIt, and a new library/package implementation to be hosted on LLNL Bitbucket (initially), before releasing to the wider community.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Simulation Data Management - Requirements and Design Specification

Simulation Data Management (SDM), the ability to securely organize, archive, and share analysis models and the artifacts used to create them, is a fundamental requirement for modern engineering analysis based on computational simulation. We have worked separately to provide secure, network SDM services to engineers and scientists at our respective laboratories for over a decade. We propose to leverage our experience and lessons learned to help develop and deploy a next-generation SDM service as part of a multi-laboratory team. This service will be portable across multiple sites and platforms, and will be accessible via a range of command-line tools and well-documented APIs. In this document, we’ll review our high-level and low-level requirements for such a system, review one existing system, and briefly discuss our proposed implementation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗