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System Advisor Model, SAM 2014.1.14: General Description

This document describes the capabilities of the U.S. Department of Energy and National Renewable Energy Laboratory's System Advisor Model (SAM), Version 2013.9.20, released on September 9, 2013. SAM is a computer model that calculates performance and financial metrics of renewable energy systems. Project developers, policy makers, equipment manufacturers, and researchers use graphs and tables of SAM results in the process of evaluating financial, technology, and incentive options for renewable energy projects. SAM simulates the performance of photovoltaic, concentrating solar power, solar water heating, wind, geothermal, biomass, and conventional power systems. The financial model can represent financial structures for projects that either buy and sell electricity at retail rates (residential and commercial) or sell electricity at a price determined in a power purchase agreement (utility). SAM's advanced simulation options facilitate parametric and sensitivity analyses, and statistical analysis capabilities are available for Monte Carlo simulation and weather variability (P50/P90) studies. SAM can also read input variables from Microsoft Excel worksheets. For software developers, the SAM software development kit (SDK) makes it possible to use SAM simulation modules in their applications written in C/C++, C#, Java, Python, and MATLAB. NREL provides both SAM and the SDK as free downloads at http://sam.nrel.gov. Technical support and more information about the software are available on the website.

14 SOLAR ENERGY↗

A temperature-dependent coarse-grained model for the thermoresponsive polymer poly(N-isopropylacrylamide)

A coarse-grained (CG) model is developed for the thermoresponsive polymer poly(N-isopropylacrylamide) (PNIPAM), using a hybrid top-down and bottom-up approach. Nonbonded parameters are fit to experimental thermodynamic data following the procedures of the SDK (Shinoda, DeVane, and Klein) CG force field, with minor adjustments to provide better agreement with radial distribution functions from atomistic simulations. Bonded parameters are fit to probability distributions from atomistic simulations using multi-centered Gaussian-based potentials. The temperature-dependent potentials derived for the PNIPAM CG model in this work properly capture the coil–globule transition of PNIPAM single chains and yield a chain-length dependence consistent with atomistic simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

SU-E-T-99: Design and Development of Isocenter Parameter System for CT Simulation Laser Based On DICOM RT

Purpose: In order to receive DICOM files from treatment planning system and generate patient isocenter positioning parameter file for CT laser system automatically, this paper presents a method for communication with treatment planning system and calculation of isocenter parameter for each radiation field. Methods: Coordinate transformation and laser positioning file formats were analyzed, isocenter parameter was calculated via data from DICOM CT Data and DICOM RTPLAN file. An in-house software-DicomGenie was developed based on the object-oriented program platform-Qt with DCMTK SDK (Germany OFFIS company DICOM SDK) . DicomGenie was tested for accuracy using Philips CT simulation plan system (Tumor LOC, Philips) and A2J CT positioning laser system (Thorigny Sur Marne, France). Results: DicomGenie successfully established DICOM communication between treatment planning system, DICOM files were received by DicomGenie and patient laser isocenter information was generated accurately. Patient laser parameter data files can be used for for CT laser system directly. Conclusion: In-house software DicomGenie received and extracted DICOM data, isocenter laser positioning data files were created by DicomGenie and can be use for A2J laser positioning system.

60 APPLIED LIFE SCIENCES↗

spot_bt_ros v.0.1.0

SAND2024-08478O The spot_bt_ros tool allows users to easily construct complex autonomous behaviors for Boston Dynamic's Spot robot without having to overly rely on the Spot software development kit (SDK). This package specifically allows a user to run the Robot Operating System 2 alongside the SDK. Using a form-of-state machine called a "behavior tree,” the software develops different "leaves" or actions that the robot undergoes. Attaching multiple actions in sequence creates a "branch" and several of these make up the "tree." 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.

Kakish, Zahi↗

Datum: A Scientific Metadata Catalog

The data catalog market is currently flooded with a myriad of different products, but none serve the scientific community well. There are cloud-native tools like Databricks, Snowflake,to on-premise solutions like Collibra and Datahub. The common failing of all these tools however, is their inability to serve the scientific data community directly. Most catalogs are targeted towards financial, health, or user data - not sensor or scientific domain data. They also prioritize integrations that often don’t exist or are just starting to be used in the scientific realm - all while ignoring common scientific tools and file types. Datum is a catalog which targets the scientific data directly, including the tools and networks in which those tools are used. We work with the producers and consumers of the data where they are, targeting cloud and on-premise with a focus on classified networks. Datum is an Erlang/Elixir application. Technical Features Note: The features listed below are still under development and may change, slightly, upon final delivery of the product. File Formats - Datum has the ability to read additional metadata and provides processing pipelines for the following file formats: Plain Text, PDF, LaTeX, HTML, Open Document Format (.odt), XML, CSV/TSV (and other standard delimiters), OpenDocument Database and Spreadsheets, Geo-Referenced TIFF, Common Data Format, HDF/HDF5, LabView TDMS, Excel, DeltaTables, Parquet, Apache Iceberg, Apache Hudi and many others. Metadata Collection - Scanners for the local and networked file systems and cloud storage providers. Network integration with common databases such as MSSQL and MySQL. User Plugin System - Users are able to provide either file processing, metadata extraction, or sampling plugins in the programming language of their choice. Authentication/Authorization -: OIDC integration, SCIM provisioning and EntraID integration out of the box. Full user and group management system with a “least privilege” operating mode. Governance - Customizable data governance platform; dictate and enforce required metadata, enforce data embargos, and enforce user agreements and NDAs before data access. Ability to create health checks on data, rejecting abandoned or poorly curated data and automatically removing it from the search index. Ability for users to submit corrections. Search - Semantic search is a first class citizen. No licenses to expensive, external software required. Integrated use of vectors and vector-based search allows for AI agent integration at all levels of operation. Metadata Model - Display and control data’s lineage and connections to other data and data directories. Data is modeled after a filesystem - an organization instantly recognizable and navigable by most any user. CLI and SDK - Ships with a Command Line Interface (CLI) tool and with a fully-featured Python SDK. This allows for rapid and programmatic use of Datum by every level of user. Minimal Infrastructure - Datum ships as a single executable file and can be run on any operating system and most CPU architectures. Datum has no reliance on external databases, search indexing tools, or other outside services - and it runs equally well on edge computing devices, cloud services, or in a clustered HPC environment.

darrington, john↗

System Advisor Model (SAM) General Description (Version 2017.9.5)

This document describes the capabilities of the System Advisor Model (SAM) developed and distributed by the U.S. Department of Energy's National Renewable Energy Laboratory. The document is for potential users and others wanting to learn about the model's capabilities. SAM is a techno-economic computer model that calculates performance and financial metrics of renewable energy projects. Project developers, policy makers, equipment manufacturers, and researchers use graphs and tables of SAM results in the process of evaluating financial, technology, and incentive options for renewable energy projects. SAM simulates the performance of photovoltaic, concentrating solar power, solar water heating, wind, geothermal, biomass, and conventional power systems. The financial models are for projects that either buy and sell electricity at retail rates (residential and commercial) or sell electricity at a price determined in a power purchase agreement (PPA). SAM's simulation tools facilitate parametric and sensitivity analyses, Monte Carlo simulation and weather variability (P50/P90) studies. SAM can also read input variables from Microsoft Excel worksheets. For software developers, the SAM software development kit (SDK) makes it possible to use SAM simulation modules in their applications written in C/C plus plus, C sharp, Java, Python, MATLAB, and other languages. NREL provides both SAM and the SDK as free downloads at https://sam.nrel.gov. SAM is an open source project, so its source code is available to the public. Researchers can study the code to understand the model algorithms, and software programmers can contribute their own models and enhancements to the project. Technical support and more information about the software are available on the website.

47 OTHER INSTRUMENTATION↗

GridPACK: Library Architecture and Usability Assessment

The LLNL team has carried out extensive analysis of GridPACK code, including two major code reviews (each being a 2-day event) and a vendor emulation exercise, where the team integrated GridPACK library within a third-party application. The code analysis identified a number of areas for improvement of GridPACK code architecture, but it did not find fundamental flaws in the GridPACK design. The team found the GridPACK strategy of developing a software development kit (SDK) for power systems application development to be sound and well aligned with Office of Electricity long term objectives. The SDK aims to provide power systems software vendors an easy access to high performance computing (HPC) libraries.

24 POWER TRANSMISSION AND DISTRIBUTION↗

The Pandora multi-algorithm approach to automated pattern recognition in LAr TPC detectors

Pattern recognition is the identification of structures and regularities in data. In high energy physics, it is a vital stage in the reconstruction of events recorded by fine-granularity detectors. The development and operation of Liquid Argon Time Projection Chambers (LAr TPCs) for neutrino physics has created a need for new approaches to pattern recognition, in order to fully exploit the superb imaging capabilities offered by this technology. Whereas the human brain excels at identifying features in the recorded events, it is a significant challenge to develop an automated solution. The Pandora Software Development Kit (SDK) provides functionality to aid the process of designing, implementing and running pattern recognition algorithms. In particular, it promotes the use of a multi-algorithm approach to pattern recognition: individual algorithms each aim to address a specific task in a particular topology; a series of many tens of algorithms then carefully build-up a picture of the event and, together, provide a robust automated pattern recognition solution. Building on successful use of the Pandora SDK for pattern recognition at collider experiments, a sophisticated chain of algorithms has been created to perform pattern recognition for neutrino experiments utilising LAr TPCs like MicroBooNE. The input to the Pandora pattern recognition is a list of 2D Hits. The output from the chain of over 70 algorithms is a hierarchy of reconstructed 3D Particles, each with an identified particle type, vertex and direction. In this document, we present details of the Pandora pattern recognition algorithms used to reconstruct cosmic-ray and neutrino events in LAr TPCs. We also present metrics that assess the current reconstruction performance using simulated data from MicroBooNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Proof of Concept Study of Trade Space Configuration Tool for Spacecraft Design

Spacecraft design is a very difficult and time consuming process because requirements and criteria are often changed or modified as the design is refined. Accounting for these adjustments in the design constraints plays a significant role in furthering the overall progress. There are numerous aspects and variables that hold significant influence on various characteristics of the design. This can be especially frustrating when attempting to conduct rapid trade space analysis on system configurations. Currently, the data and designs considered for trade space evaluations can only be displayed by using the traditional interfaces of Excel spreadsheets or CAD (Computer Aided Design) models. While helpful, these methods of analyzing the data from a systems engineering approach can be rather complicated and overwhelming. As a result, a proof of concept was conducted on a dynamic data visualization software called Thinkmap SDK (Software Developer Kit) to allow for better organization and understanding of the relationships between the various aspects that make up an entire design. The Orion Crew Module Aft Bay Subsystem was used as the test case for this study because the design and layout of many of the subsystem components will be significant in ensuring the overall center of gravity of the capsule is correct. A simplified model of this subsystem was created and programmed using Thinkmap SDK to create a preliminary prototype application of a Trade Space Configuration Tool. The completed application ensures that the core requirements for the Tool can be met. Further development is strongly suggested to produce a full prototype application to allow final evaluations and recommendations of the software capabilities.

Glidden, Geoffrey L.↗

SNLSimMagic v 2.0

This software is an iOS (Apple) Augmented Reality (AR) application that runs on the iPhone and iPad. It is designed to scan in a photograph or graphic and "play" an associated video. This release, SNLSimMagic, was built using Wikitude Augmented Reality (AR) software development kit (SDK) integrated into Apple iOS SDK application and the Cordova libraries. These codes enable the generation of runtime targets using cloud recognition and developer-defined target features which are then accessed by means of a custom application.

Source record↗

Towards a Software Development Framework for Interconnected Science Ecosystems

The innovative science of the future must be multi-domain and interconnected to usher in the next generation of “self-driving” laboratories enabling consequential discoveries and transformative inventions. Such a disparate and interconnected ecosystem of scientific instruments will need to evolve using a system-of-systems (SoS) approach. The key to enabling application integration with such an SoS will be the use of Software Development Kits (SDKs). Currently, SDKs facilitate scientific research breakthroughs via algorithmic automation, databases and storage, optimization and structure, pervasive environmental monitoring, among others. However, existing SDKs lack instrument-interoperability and reusability capabilities, do not effectively work in an open federated architectural environment, and are largely isolated within silos of the respective scientific disciplines. Inspired by the scalable SoS framework, this work proposes the development of INTERSECT-SDK to provide a coherent environment for multi-domain scientific applications to benefit from the open federated architecture in an interconnected ecosystem of instruments. This approach will decompose functionality into loosely coupled software services for interoperability among several solutions that do not scale beyond a single domain and/or application. Furthermore, the proposed environment will allow operational and managerial inter-dependence while providing opportunities for the researchers to reuse software components from other domains and build universal solution libraries. We demonstrate this research for microscopy use-case, where we show how INTERSECT-SDK is developing the tools necessary to enable advanced scanning methods and accelerate scientific discovery.

Malviya, Addi Thakur↗

Leveraging OpenStudio's Application Programming Interfaces: Preprint

OpenStudio development efforts have been focused on providing Application Programming Interfaces (APIs) where users are able to extend OpenStudio without the need to compile the open source libraries. This paper will discuss the basic purposes and functionalities of the core libraries that have been wrapped with APIs including the Building Model, Results Processing, Advanced Analysis, UncertaintyQuantification, and Data Interoperability through Translators. Several building energy modeling applications have been produced using OpenStudio's API and Software Development Kits (SDK) including the United States Department of Energy's Asset ScoreCalculator, a mobile-based audit tool, an energy design assistance reporting protocol, and a portfolio scale incentive optimization analysismethodology. Each of these software applications will be discussed briefly and will describe how the APIs were leveraged for various uses including high-level modeling, data transformations from detailed building audits, error checking/quality assurance of models, and use of high-performance computing for mass simulations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

OpenACC Unified Programming Environment for Multi-hybrid Acceleration with GPU and FPGA

Accelerated computing in HPC such as with GPU, plays a central role in HPC nowadays. However, in some complicated applications with partially different performance behavior is hard to solve with a single type of accelerator where GPU is not the perfect solution in these cases. We are developing a framework and transpiler allowing the users to program the codes with a single notation of OpenACC to be compiled for multi-hybrid accelerators, named MHOAT (Multi-Hybrid OpenACC Translator) for HPC applications. MHOAT parses the original code with directives to identify the target accelerating devices, currently supporting NVIDIA GPU and Intel FPGA, dispatching these specific partial codes to background compilers such as NVIDIA HPC SDK for GPU and OpenARC research compiler for FPGA, then assembles binaries for the final object with FPGA bitstream file. In this paper, we present the concept, design, implementation, and performance evaluation of a practical astrophysics simulation code where we successfully enhanced the performance up to 10 times faster than the GPU-only solution.

Boku, Taisuke↗

Learning Coarse-Grained Potentials for Binary Fluids

Even though atomistic and CG models have been used to simulate liquid nanodroplets in vapor, very few rigorous studies of the liquid-liquid interface structure are available, and most of them are limited to planar interfaces. In this work, we evaluate several existing force fields (FF)s, including two atomistic and three CG FFs, with respect to modeling the interface structure and thermodynamic properties of the water-hexane interface. Both atomistic FFs are able to quantitatively reproduce the interfacial tension and the coexisting densities of the experimentally-observed planar interface. We use the atomistic FFs to model water droplets in hexane and use these simulations to test the CG FFs. We found that the tested CG FFs cannot reproduce the interfacial tensions of planar and/or curved interfaces. Finally, we propose a new approach for learning coarse-grained (CG) potentials within the CG SDK (Shinoda-DeVane- Klein) FF framework from atomistic simulation data. We demonstrate that the new potential significantly improves the prediction of both the interfacial tension and structure of water-hexane planar and curved interfaces.

Gao, Peiyuan↗

Metagenome-assembled genome extraction and analysis from microbiomes using KBase

Uncultivated Bacteria and Archaea account for the vast majority of species on Earth, but obtaining their genomes directly from the environment, using shotgun sequencing, has only become possible recently. In order to realize the hope of capturing Earth’s microbial genetic complement and to facilitate the investigation of the functional roles of specific lineages in a given ecosystem, technologies that accelerate the recovery of high-quality genomes are necessary. We present a series of analysis steps and data products for the extraction of high-quality metagenome-assembled genomes (MAGs) from microbiomes using the U.S. Department of Energy Systems Biology Knowledgebase (KBase) platform (http://www.kbase.us/). Overall, these steps take about a day to obtain extracted genomes when starting from smaller environmental shotgun read libraries, or up to about a week from larger libraries. In KBase, the process is end-to-end, allowing a user to go from the initial sequencing reads all the way through to MAGs, which can then be analyzed with other KBase capabilities such as phylogenetic placement, functional assignment, metabolic modeling, pangenome functional profiling, RNA-Seq and others. While portions of such capabilities are available individually from other resources, the combination of the intuitive usability, data interoperability and integration of tools in a freely available computational resource makes KBase a powerful platform for obtaining MAGs from microbiomes. While this workflow offers tools for each of the key steps in the genome extraction process, it also provides a scaffold that can be easily extended with additional MAG recovery and analysis tools, via the KBase software development kit (SDK).

59 BASIC BIOLOGICAL SCIENCES↗

C2QA - Bosonic Qiskit

The practical benefits of hybrid quantum information processing hardware that contains continuous-variable objects (bosonic modes such as mechanical or electromagnetic oscillators) in addition to traditional (discrete-variable) qubits have recently been demonstrated by experiments with bosonic codes that reach the break-even point for quantum error correction [1]–[5] and by efficient Gaussian boson sampling simulation of the Franck-Condon spectra of triatomic molecules [6] that is well beyond the capabilities of current qubit-only hardware. The goal of this Co-design Center for Quantum Advantage (C2QA) project is to develop an instruction set architecture (ISA) for hybrid qubit/bosonic mode systems that contains an inventory of the fundamental operations and measurements that are possible in such hardware. The corresponding abstract machine model (AMM) would also contain a description of the appropriate error models associated with the gates, measurements and time evolution of the hardware. This information has been implemented as an extension of IBM Qiskit. IBM Qiskit is an open-source software development toolkit (SDK) for simulating the quantum state of a quantum circuit and for running the same circuits on prototype hardware within the IBM Quantum Experience. We introduce the Bosonic Qiskit software to enable the simulation of hybrid qubit/bosonic systems using the existing Qiskit software development kit [7]. This implementation can be used for simulating new hybrid systems, verifying proposed physical systems, and modeling systems larger than can currently be constructed. We also cover tutorials and example use cases included within the software to study JaynesCummings models, bosonic Hubbard models, plotting Wigner functions and animations, and calculating maximum likelihood estimations using Wigner functions.

quantum, boson, Qiskit↗

Mission planning for photogrammetry-based autonomous 3D Mapping of Dams using a commercial UAV

The application of autonomous unmanned aerial vehicles (UAVs) for conducting inspections of dams represents an innovative approach aimed at enhancing safety, efficiency, and cost-effectiveness. In this context, this paper presents algorithms for UAV mission design in autonomous dam inspections that include the creation of a 3D map based on photogrammetry. The algorithms were systematically developed to incorporate a comprehensive set of parameters that account for the geometric characteristics of the dam and adhere to photogrammetry specifications. To validate the proposed methodology, we utilized a commercial programmable quadrotor, specifically the Parrot Anafi USA Gov drone, which is equipped with high-quality cameras and can be programmed with the help of a software development kit (SDK) provided by the manufacturer. Our results demonstrate the efficacy of our method, highlighting how the generated maps can be used for hazard detection in the downstream slope of dams.

42 ENGINEERING↗

Evaluating and Optimizing OpenCL Base64 Data Unpacking Kernel with FPGA

Development of applications using OpenCL targeting FPGAs is an emerging approach on heterogeneous computing systems. This paper uses the data unpacking algorithm in Base64 encoding as a case study to present programming and optimization techniques, and experimental results of the OpenCL-based implementations on an FPGA. We explain the algorithm and evaluate the performance of the kernel implementations with Intel's FPGA OpenCL SDK. The experimental results show kernel vectorization and duplication are two optimization techniques that can improve the kernel performance. The performance of kernel duplication is also closely related to the local work size. Our experiment shows 16-lane vectorization increases the bandwidth by a factor of 2 to 10 for large input data sizes. Moreover, the performance of kernel duplication using 16 compute units is 40% to 1.5% less than that of kernel vectorization depending on the input size. Tuning the local work size can improve the kernel performance by a factor of 3 to 23. For this kernel, using local memory is not an effective technique to improve the kernel performance because input data is not reused. A combination of vectorization and duplication achieves the highest performance of 12.3 GiB/s. Compared to an Intel Xeon E5 CPU and an Nvidia Tesla K80 GPU, the performance of the kernel on the Arria 10 FPGA is 6.7X faster than the CPU and 3X slower than the GPU. The performance per watt on the FPGA is 20.5X higher than the CPU and 1.19X lower than the GPU.

Base64 Encoding↗