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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

IDAES-PSE 2.4.0 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.. Deprecations • Convergence Analysis tool (idaes/core/util/convergence): deprecated in favor of new Parameter Sweep tools. To be removed in v3.0.0. New Beta Capabilities • Parameter Sweep Tool (idaes.core.util.parameter_sweep) o A new API for defining and performing parameter sweep studies on IDAES models has been developed • Diagnostics Tools (idaes.core.util.model_diagnostics) o New methods for identifying duplicate variables and constraints have been added to the diagnostics toolbox o New tools for detecting ill conditioning in Jacobians have been developed and are available in the model_diagnostics module. These provide alternatives to the existing DegeneracyHunter toolbox, and will eventually be merged with this capability, but initial working versions have been provided as beta capabilities for interested users o IpoptConvergenceAnalysis (replaces deprecated Convergence Analysis tool):  A new tool for performing convergence analysis studies that leverages the new Parameter Sweep tools has been developed. This tool allows users to define the input parameters to their model and sampling methods for these (leveraging Pysmo's sampling tools) and to then solve their model across the sampled domains and return a summary of the solver performance (IPOPT only) Improved Models • Thickener model (idaes.models.unit_models.solid_liquid.thickener) o Improved model to include predictive correlations for unit sizing based on settling velocity measurements (steady-state only) • Modular Property Packages o Added general support for calculating critical properties of mixtures using defined Equation of State modules. New API defined for Equation of State modules in order to define the necessary constraints for calculating critical properties (most EoS modules DO NOT support calculation of critical properties (yet)) o Added new methods to Cubic Equation of State module to support calculation of critical properties

DiagnosticsToolbox↗
DOE OSTI2024

DE-FE0029488 - North Dakota Integrated Carbon Capture and Storage Complex Feasibility Study Public Data

Data from award DE-FE0029488 - North Dakota Integrated Carbon Capture and Storage Complex Feasibility Study performed by the Energy & Environmental Research Center including the following: - 2D Seismic {Input data, sgy files, maps, logs, and descriptors} - Core Petrophysics {Core analysis of plugs from the two stratigraphic test wells (Flemmer-1 [API 33-057-00039] and BNI-1 [API 33-065-00018])} - North Dakota Oil and Gas File No 37380 Files - North Dakota Oil and Gas File No 37672 Files - Well Testing Data {Summary of well testing methods and results from the stratigraphic test wells (Flemmer-1 and BNI-1)} Additional References: https://www.netl.doe.gov/sites/default/files/2017-12/Wesley-Peck-_Mastering-the-Subsurface_CarbonSAFE-Phase-II_August-2017-final.pdf Peck, W.D., Ayash, S.C., Klapperich, R.J., Gorecki, C.D. (2019) The North Dakota integrated carbon storage complex feasibility study, International Journal of Greenhouse Gas Control, Volume 84, 2019, Pages 47-53, https://doi.org/10.1016/j.ijggc.2019.03.001

Carbon Storage↗
DOE OSTI2024

IDAES-PSE 2.5.0 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 2.5.0 Release Highlights Upcoming Changes IDAES will be switching to the new Pyomo solver interface in the next release. Whilst this will hopefully be a smooth transition for most users, there are a few important changes to be aware of. The new solver interface uses a different version of the IPOPT writer (“ipopt_v2”) and thus any custom configuration options you might have set for IPOPT will not carry over and will need to be reset. By default, the new Pyomo linear presolver will be activated with ipopt_v2. Whilst are working to identify any bugs in the presolver, it is possible that some edge cases will remain. IDAES will begin deploying a new set of scaling tools and APIs over the next few releases that make use of the new solver writers. The old scaling tools and APIs will remain for backward compatibility but will begin to be deprecated. New Models, Tools and Features New diagnostics check for near-parallel variables and constraints. New diagnostics tools for identifying causes of infeasibility in models. New example for creating a custom model of a liquid-liquid extractor unit operation. Bug Fixes Fixed bug in Gibbs reactor that caused it to appear to have additional spurious degrees of freedom. Fixed bug in the Modular Property Framework that would cause errors when trying to use phase-based material balances with phase equilibria. Fixed bug in Modular Properties Framework that caused errors when initializing models with non-vapor-liquid phase equilibria. Testing and Robustness Deployed the IDAES Diagnostics Toolbox to confirm that there are no structural or numerical issues in the core model libraries. Additional robustness tests for core model, and some associated improvements in the converge tester class. Fixed a number of issues that were causing unexpected warnings to be emitted during testing. Deprecations and Removals Removed examples for RIPE tool which has not been supported for a number of releases.

AS↗
DOE OSTI2025

APOLLO: a facility-scale differentiable virtual accelerator at Fermilab FAST/IOTA

As the design complexity of modern accelerators grows, there is more interest in using advanced simulations that have fast execution time or yield additional insights like gradients. The FAST/IOTA facility has been working on implementing and experimentally validating an end-to-end digital twin that is both fast and gradient-aware, allowing for rapid prototyping of new software and experiments with minimal beam time costs. Our framework integrates physics and ML codes for linac and ring simulation through a set of generic interfaces between surrogate and physics-based sections. To reproduce device inputs and outputs, system state is exposed as a deterministic event loop in a specialized discrete event simulator architecture. Because Fermilab is undergoing control system transition, several APIs were implemented as final user interfaces - a fully asynchronous EPICS soft IOC, a gRPC-based Data Pool Manager (DPM), and legacy ACNET protocols. We discuss implementation details as well as challenges handling live data assimilation and future plans to extend modelling to main complex proton accelerators like PIPII and Booster.

Kuklev, Nikita [Fermilab]↗
DOE OSTI2020

An Approach to DevOps and Microservices

This report presents work performed in 2017 and is a reissue of the end-of-year report SAND2018-0240, A Proof-of-Concept Approach to DevOps and Microservices, January 2018, modified for unlimited release. This report describes the approach, investigation, and prototyping efforts to create an API management solution and automated deployment pipeline for REST-based microservices. Additionally, it reviews: the microservices created for testing purposes; our experiences and results in using an open-source API management tool; and future plans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Integrating System to Edge-of-Network Architecture and Management for SHINES (SEAMS) Technologies of High Penetration Grids

Consistent with the U.S. DOE’s EERE SHINES FOA objectives, the goal of this project was to enable integration from EMS to variable DER in a way that delivered visibility and opportunities for managing and controlling distributed resources using SEAMS technologies. This project implemented several SHINES technologies, which included: (1) three Stem battery systems; (2) six ConnectDER PV collars; (3) two E-Gear PV/Battery storage interfaces; and (4) eight Kitu smart inverter emulators. A Siemens Energy IP DEMS was deployed in a development environment and integrated with the SHINES technologies via OpenADR and IEEE 2030.5 protocols with documented architecture. In the case of IEEE 2030.5, a suite of technologies including a gateway, an API, software and a server from Kitu Systems (the Citadel, Convoy, Kitu Crosslink API, and Kitu Spark) were used to demonstrate that the utility could achieve visibility of distributed energy resources (DER) and control of single and multiple DER systems with a single command. The project also included a LCOE analysis of SHINES technologies compared to traditional system and circuit mitigation measures.

14 SOLAR ENERGY↗
DOE OSTI2021

Accessing Microsoft Access Databases Using ODBC and RODBC

ODBC (Open Database Connectivity) is an industry-standard API (Application Program Interface) that provides a standard interface, based on SQL (Structured Query Language) between applications and databases. This insulates applications from specific details of different database management systems (DBMS). Microsoft Windows TM provides an implementation of ODBC, which, along with drivers for various databases, supports the API. Windows also provides a driver for Access databases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Enhancements supporting IC usage of PEM libraries on next-gen platforms

This milestone reports on the culmination of several years of effort by multiple PEM support software development teams to provide capabilities for use in LLNL-developed integrated codes on next-gen ASC platforms, including GPU support. We will provide a survey of relevant Application Program Interfaces (API) that are required to support LLNL IC code capability on relevant architectures, with a focus on Sierra and El Capitan. We will identify and summarize all dependencies between PEM supported libraries and IC supported physics codes. We will provide an assessment of algorithmic improvements that have been deployed, as well as future developments that are required to complete the GPU porting efforts. This assessment will include a description of programming models adopted by each of the PEM projects, distinct algorithmic challenges for each of the capabilities, and information about sharing GPU memory between the APIs and host codes. We will develop targeted test problems to assess computational performance. Finally, this milestone will result in identification of gaps in our effort to assist the LLNL ASC program in prioritization of effort for porting software to El Capitan.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Exploring OpenSNAPI Use Cases and Evolving Requirements [Slides]

Emerging system architectures are rapidly transforming in order to meet shifting requirements. Motivated by expanding data volumes, energy efficiency concerns, and the omnipresent need to improve performance, architectures are increasingly adopting a data-centric approach. At the core of this concept is the goal of minimizing data motion and instead processing data in-situ to the greatest degree possible. Therefore, data-centric designs, in contrast to conventional CPU-centric models, typically distribute compute capabilities throughout the architecture. As part of this paradigm shift, a novel class of devices known as data processing units (DPUs), alongside CPUs and GPUs, are quickly forming a third pillar of data-centric systems. These devices, which include smart network adapters and switches, seek to offload computation on data at the network edge as well as in-flight within the network fabric. The Open Smart Network API (OpenSNAPI) project seeks to develop a unified API for DPU devices. In our previous talks, we introduced the OpenSNAPI project and detailed our investigations regarding the viability of offloading compute intensive kernels to BlueField DPUs. In contrast, in this talk we detail our efforts to offload application-level file I/O to the DPU. We also discuss plans and early efforts to explore in-network compute capabilities. Finally, we describe our observations with respect to the evolving design of OpenSNAPI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

VERAView Programmer’s Manual

VERAView’s component-based design facilitates its extension and integration into other applications and systems. In addition to describing VERAView’s architecture and components, this document provides details on the component application programming interfaces (APIs), illustrates how to use the APIs independently of VERAView itself, and explains how to extend VERAView with custom visualizations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Crosslink V.0.11.x User Manual

CrossLink is a novel two-dimensional and three-dimensional geometry and mesh generation software package developed by the Simulation Tools team at Los Alamos National Laboratory. This software represents the third generation of topology-based mesh generation technology developed by the Department of Defense and the Department of Energy with a special focus on complex multi-material hydrodynamic applications, mesh scalability, and high-order element mesh generation. The topology-based meshing approach offered by CrossLink enables users to quickly and easily mesh complex geometries in a repeatable and robust manner. CrossLink’s topology-based meshing approach is well-suited for parametric design studies, parametric design optimization, damage scenario assessment, and iterative design modification (i.e. feature addition and/or removal). CrossLink’s python API allows workflow scripting of the geometry creation and mesh generation process for traceability, repeatability, data provenance, and version control. CrossLink consists of three main components: a graphical user interface (GUI), a geometry creation and mesh generation engine, and a python API that provides a workflow scripting interface to the geometry and meshing functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

System Response Characterization for a d–t Neutron Radiography System

We report the system response of a pixelated associated particle imaging (API) neutron radiography system. The detector readout currently consists of a 2x2 array of organic glass scintillator detectors, each with an 8x8 array of optically isolated pixels that match the size and pitch of the ARRAYJ-60035-64P-PCB Silicon Photomultiplier (SiPM) array from SensL/onsemi with 6x6 mm 2 SiPMs. The alpha screen of the API deuterium-tritium neutron generator is read out with the S13361-3050AE-08 from Hamamatsu, which is an 8x8 array of 3x3 mm 2 SiPMs. Data from the 320 channel system is acquired with the TOFPET2-based readout system. We present the predicted imaging capability of an eventual 5x5 detector array, the waveform-based energy and pulse shape characterization of the individual detectors, and the timing and energy response from the TOFPET2 system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Tools for Assessing Performance: FY23-Q1 Report [Slides]

LANL was tasked with working with NREL to implement QUIC within the TAP API by the end of Q1, FY2023. If QUICURB cannot be implemented in a way that it can be run outside of the QUIC platform, there will be no further R&D funding for QUIC development. QUIC includes a graphical user interface that imports and preprocesses the various input data streams (3D building databases, ambient wind, vegetation, etc.) and writes them in a format that the QUICURB Fortran executable requires. In order to interface with the TAP API, a Python script was developed that would replace the functionality previously only available within QUICGUI. LANL worked with NREL to test the Python script to ensure that it was operation and fulfilled the requirements.

58 GEOSCIENCES↗
DOE OSTI2024

Failure Analysis for Molten Salt Thermal Energy Storage Tanks for In-Service CSP Plants

Thermal Energy Storage (TES) is a fundamental component in concentrating solar power (CSP) plants to increase the plant's dispatchability, capacity factor, while reducing the levelized cost of electricity. In central receivers CSP plants, nitrate molten salts have been used for several years for operation temperatures of up to 565 degrees C. Despite many efforts to advance nitrate salt to higher operation temperatures (even considering a replacement with molten chloride salts) to achieve higher energy conversion efficiencies, the 565 degrees C temperature is currently considered the state-of-the art. Although molten salt tanks have been broadly deployed in commercial CSP plants worldwide, several failures have been reported in these tanks after a few months or years of operation, causing significant economic loss and mistrust in CSP technologies. Most of these failures are associated with the infancy of the technology and multiple issues related to tank design, fabrication, commissioning, and aggressive operation. A technical standard dedicated to the design and fabrication of molten nitrate TES tanks does not exist today. Current in-service molten salt tanks have been generally designed based on the American Petroleum Institute's (API) 650 and ASME Section II standards. The API 650 code provides guidelines for dimensions and fabrication for oil storage tanks up to 260 degrees C. The ASME standard provides allowable stress values for various materials at a range of temperatures and conditions. Both standards seem to be limited for molten salt TES tanks where high temperatures, thermal cycling, and transient conditions are expected. In 2020, NREL released the Concentrating Solar Power Best Practices Study (NREL/TP-5500-75763) that summarized multiple issues in CSP plants, along with potential alternatives and recommendations to address those issues based on information collected from participants representing about 80% of operating CSP plants in the world. One of the recommendations from this study was the development of accurate and validated models to evaluate the plant's transient operation, capable of capturing the effect of short-term clouds and operator response, while being flexible in being adapted to various spatial and temporal resource data. The "Failure Analysis for Molten Salt Thermal Energy Tanks for In-Service CSP Plants" project was inspired on this recommendation and was focused on (1) the development and validation of a physics-based model for a representative, commercial-scale molten salt tank, (2) performing simulations to evaluate the behavior of the tank as a function of typical plant operation conditions, (3) understanding tank failures mechanisms, (4) determining the residual stress and distortion in the tank floor after welding fabrication and evaluating their impact in the stresses developed in the tank during operation, (5) assessing the impact of key operation parameters on the temperature and stress distribution, (6) conduct a preliminary evaluation of design features to reduce stress and improve tank's reliability, and (7) estimate the tank's service life based on the stress developed under diverse operation scenarios. From the analysis conducted in the project and presented in this report, it was found that maximum stresses surpassing the yield strength point of the stainless steel (SS) 347H are developed on the tank floor near the perimeter. These large stresses are strongly influenced by the initial residual stresses and distortion of the tank floor after welding fabrication. During operation, large stresses are developed in the tank floor at high operation temperatures with large salt inventory levels during transient operation. High stresses are also related to elevated temperature gradients in the tank floor that could be attributed to insufficient mixing within the salt inflow and the salt inventory. Based on the analysis, creep is the predominant failure mechanism. However, the large stress levels could favor the plastic deformation into buckles, and crack formation due to stress relaxation cracking during cycle operation. A lifetime below 3 years was estimated for the typical plant operation conditions and a specific initial residual stress and deformation distribution of the tank floor. The estimated life agrees with the service time to failure reported in several commercial molten salt tanks. Desing and operation guidelines can be extracted from the analysis presented in this report, which could be adopted by tank manufacturers and CSP operators to advance toward an ultimate solution for tank failures by reducing residual and operational stresses to achieve a tank service life of more than 30 years. Addressing failures in molten salt TES tanks is fundamental for the CSP industry's survivability, but it is also important for other industrial and power generation applications using this technology, including nuclear and concentrating solar thermal.

14 SOLAR ENERGY↗
DOE OSTI2023

Domain Aware Deep-learning Algorithms Integrated with Scientific-computing Technologies (DADAIST)

This technical report summarized the contribution of the DADAIST project funded by the Data Model Convergence Initiative via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL). Specifically, we report the development of the NeuroMANCER (Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations), a new open-source Scientific Machine Learning library for formulating and solving parametric constrained optimization problems, physics-informed system identification, and parametric optimal control problems. NeuroMANCER is using differentiable programming to combine modern data-driven models and optimization modeling language into a coherent algorithmic and software framework. NeuroMANCER is a Pytorch-based framework and adopts much of its philosophy focused on research and development, rapid prototyping, and streamlined deployment. Strong emphasis is given to extensibility, interoperability with the PyTorch ecosystem, and quick adaptability to custom domain problems. Neuromancer repository contains a comprehensive library of differentiable modules, including custom activation functions, matrix factorizations, deep learning architectures, neural differential equations, differential equation solvers, implicit layers such as iterative solvers, high-level API for symbolic expressions, API for modeling and control of dynamical systems, and extensive set of tutorial code examples in the form of python scripts and jupyter notebooks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Agilent CRADA (Abstract)

The CRADA between Agilent Technologies Inc. and Battelle will focus on five software components as listed below: Prototype 4D Feature Finding functionality with a particular focus on recovering low level features and extending the bottom end dynamic range of IM-MS technology. Compare and contrast developments to current 4D Feature Finding capabilities. Highlight important algorithmic aspects employed. Implement the PNNL saturation correction algorithm. Agilent will give PNNL the needed data file access API and assistance in understanding it implementation and any needed instrumental aspects. Supported high resolution products to include Agilent’s TOF, QTOF and IM-QTOF mass spectrometers. PNNL will then work with Agilent to benchmark performance. Implementation of the PNNL Hadamard de-multiplexing algorithm. Agilent will give provide PNNL the needed date file access API access and as needed assistance in understanding the current Agilent multiplexed IM offering. PNNL will then work with Agilent on benchmark performance. Add ion mobility collision cross sections to existing and new metabolomic libraries for data analysis with Agilent’s informatics program MPP/ID Browser. PNNL will work with Agilent to create a software pipeline that takes data from chemical and metabolic standards and properly formats it for inclusion in MPP accessible libraries, using the collision cross section as a new separation dimension. Improvements of MPP multidimensional matching to identify metabolomic features using multiple characteristics beyond retention time and accurate mass. Most significantly matching will include analyte collision cross section with proposed support for sample fraction or RapidFire cartridge and fragmentation spectra. PNNL will work with Agilent to modify and improve the current MPP analysis pipeline to allow for creating, aligning, and identifying MS features defined by accurate mass, collision cross section and chromatographic retention time. As additional criteria such as fraction or RapidFire cartridge type are supported in the identification process, then they also will become part of the automation workflow. This includes the automation of said system to work with command line program (i.e. not a GUI) sufficient for programmatic execution in a pipeline.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Nion Swift FY2021 phase I Final Technical Report DE-SC0021546

Phase I of the project is an on-going success. The enhanced and extended new application programming interface (API) developed in Phase I is in use at customer sites around the world: Berlin, UK, US, China, France, Austria. The modules developed to demonstrate the new API features are in daily use at these sites for acquiring, processing, and analyzing microscopy data from 4D- and 5D-STEM to single-point spectroscopy at highest resolution.

Lovejoy, Tracy↗