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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-J-253: The Radiomics Toolbox in the Computational Environment for Radiological Research (CERR)

Purpose: To present an open source and free platform to facilitate radiomics research — The “Radiomics toolbox” in CERR. Method: There is scarcity of open source tools that support end-to-end modeling of image features to predict patient outcomes. The “Radiomics toolbox” strives to fill the need for such a software platform. The platform supports (1) import of various kinds of image modalities like CT, PET, MR, SPECT, US. (2) Contouring tools to delineate structures of interest. (3) Extraction and storage of image based features like 1st order statistics, gray-scale co-occurrence and zonesize matrix based texture features and shape features and (4) Statistical Analysis. Statistical analysis of the extracted features is supported with basic functionality that includes univariate correlations, Kaplan-Meir curves and advanced functionality that includes feature reduction and multivariate modeling. The graphical user interface and the data management are performed with Matlab for the ease of development and readability of code and features for wide audience. Open-source software developed with other programming languages is integrated to enhance various components of this toolbox. For example: Java-based DCM4CHE for import of DICOM, R for statistical analysis. Results: The Radiomics toolbox will be distributed as an open source, GNU copyrighted software. The toolbox was prototyped for modeling Oropharyngeal PET dataset at MSKCC. The analysis will be presented in a separate paper. Conclusion: The Radiomics Toolbox provides an extensible platform for extracting and modeling image features. To emphasize new uses of CERR for radiomics and image-based research, we have changed the name from the “Computational Environment for Radiotherapy Research” to the “Computational Environment for Radiological Research”.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-157: CARMEN: A MatLab-Based Research Platform for Monte Carlo Treatment Planning (MCTP) and Customized System for Planning Evaluation

Purpose: Although there exist several radiotherapy research platforms, such as: CERR, the most widely used and referenced; SlicerRT, which allows treatment plan comparison from various sources; and MMCTP, a full MCTP system; it is still needed a full MCTP toolset that provides users complete control of calculation grids, interpolation methods and filters in order to “fairly” compare results from different TPSs, supporting verification with experimental measurements. Methods: This work presents CARMEN, a MatLab-based platform including multicore and GPGPU accelerated functions for loading RT data; designing treatment plans; and evaluating dose matrices and experimental data.CARMEN supports anatomic and functional imaging in DICOM format, as well as RTSTRUCT, RTPLAN and RTDOSE. Besides, it contains numerous tools to accomplish the MCTP process, managing egs4phant and phase space files.CARMEN planning mode assist in designing IMRT, VMAT and MERT treatments via both inverse and direct optimization. The evaluation mode contains a comprehensive toolset (e.g. 2D/3D gamma evaluation, difference matrices, profiles, DVH, etc.) to compare datasets from commercial TPS, MC simulations (i.e. 3ddose) and radiochromic film in a user-controlled manner. Results: CARMEN has been validated against commercial RTPs and well-established evaluation tools, showing coherent behavior of its multiple algorithms. Furthermore, CARMEN platform has been used to generate competitive complex treatment that has been published in comparative studies. Conclusion: A new research oriented MCTP platform with a customized validation toolset has been presented. Despite of being coded with a high-level programming language, CARMEN is agile due to the use of parallel algorithms. The wide-spread use of MatLab provides straightforward access to CARMEN’s algorithms to most researchers. Similarly, our platform can benefit from the MatLab community scientific developments as filters, registration algorithms etc. Finally, CARMEN arises the importance of grid and filtering control in treatment plan comparison.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-BRB-02: An Open-Source Software Analysis Library for Linear Accelerator Quality Assurance

Purpose: Routine linac quality assurance (QA) tests have become complex enough to require automation of most test analyses. A new data analysis software library was built that allows physicists to automate routine linear accelerator quality assurance tests. The package is open source, code tested, and benchmarked. Methods: Images and data were generated on a TrueBeam linac for the following routine QA tests: VMAT, starshot, CBCT, machine logs, Winston Lutz, and picket fence. The analysis library was built using the general programming language Python. Each test was analyzed with the library algorithms and compared to manual measurements taken at the time of acquisition. Results: VMAT QA results agreed within 0.1% between the library and manual measurements. Machine logs (dynalogs & trajectory logs) were successfully parsed; mechanical axis positions were verified for accuracy and MLC fluence agreed well with EPID measurements. CBCT QA measurements were within 10 HU and 0.2mm where applicable. Winston Lutz isocenter size measurements were within 0.2mm of TrueBeam’s Machine Performance Check. Starshot analysis was within 0.2mm of the Winston Lutz results for the same conditions. Picket fence images with and without a known error showed that the library was capable of detecting MLC offsets within 0.02mm. Conclusion: A new routine QA software library has been benchmarked and is available for use by the community. The library is open-source and extensible for use in larger systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

MLIR loop optimizations for High-Level Synthesis: a case study

High-Level Synthesis (HLS) tools simplify the design of hardware accelerators by automatically generating Verilog/VHDL code starting from a general purpose software programming language. They include a wide range of optimization techniques in the process, most of them performed on a low-level intermediate representation (IR) of the code. Introducing optimizations on a higher level of abstraction could significantly contribute to the automated design process results; for example, polyhedral techniques for the manipulation of loops could have a significant impact on the generated accelerators when applied on a specialized IR. We use loop pipelining as a case study to explore the introduction of compiler-based transformations on top of an existing HLS process. We leverage the Multi-Level Intermediate Representation (MLIR) framework and an external scheduler to implement the required transformations, and couple them with existing HLS tools to evaluate the improvements that loop pipelining brings to the performance of generated accelerators. The proposed approach can be integrated with other high-level transformations on the MLIR representation, combining different techniques to obtain pre-optimized inputs for HLS that do not have to rely on a specific backend tool.

Curzel, Serena↗
DOE OSTI2015

Emulating Multiple Inheritance in Fortran 2003/2008

Although the high-performance computing (HPC) community increasingly embraces object-oriented programming (OOP), most HPC OOP projects employ the C++ programming language. Until recently, Fortran programmers interested in mining the benefits of OOP had to emulate OOP in Fortran 90/95. The advent of widespread compiler support for Fortran 2003 now facilitates explicitly constructing object-oriented class hierarchies via inheritance and leveraging related class behaviors such as dynamic polymorphism. Although C++ allows a class to inherit from multiple parent classes, Fortran and several other OOP languages restrict or prohibit explicit multiple inheritance relationships in order to circumvent several pitfalls associated with them. Nonetheless, what appears as an intrinsic feature in one language can be modeled as a user-constructed design pattern in another language. The present paper demonstrates how to apply the facade structural design pattern to support a multiple inheritance class relationship in Fortran 2003. The design unleashes the power of the associated class relationships for modeling complicated data structures yet avoids the ambiguities that plague some multiple inheritance scenarios.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Scheduling Multilevel Deadline-Constrained Scientific Workflows on Clouds Based on Cost Optimization

This paper presents a cost optimization model for scheduling scientific workflows on IaaS clouds such as Amazon EC2 or RackSpace. We assume multiple IaaS clouds with heterogeneous virtual machine instances, with limited number of instances per cloud and hourly billing. Input and output data are stored on a cloud object store such as Amazon S3. Applications are scientific workflows modeled as DAGs as in the Pegasus Workflow Management System. We assume that tasks in the workflows are grouped into levels of identical tasks. Our model is specified using mathematical programming languages (AMPL and CMPL) and allows us to minimize the cost of workflow execution under deadline constraints. We present results obtained using our model and the benchmark workflows representing real scientific applications in a variety of domains. The data used for evaluation come from the synthetic workflows and from general purpose cloud benchmarks, as well as from the data measured in our own experiments with Montage, an astronomical application, executed on Amazon EC2 cloud. We indicate how this model can be used for scenarios that require resource planning for scientific workflows and their ensembles.

Malawski, Maciej↗
DOE OSTI2014

toyplot

Toyplot integrates high quality interactive, animated plotting capabilities into web browsers using the Python programming language.

Shead, TimothyM.↗
DOE OSTI2015

Stonix, Version 0.x

STONIX is a program for configuring UNIX and Linux computer operating systems. It applies configurations based on the guidance from publicly accessible resources such as: NSA Guides, DISA STIGs, the Center for Internet Security (CIS), USGCB and vendor security documentation. STONIX is written in the Python programming language using the QT4 and PyQT4 libraries to provide a GUI. The code is designed to be easily extensible and customizable.

Source record↗
DOE OSTI2016

Python GST Implementation v. 0.9 beta

PyGSTi is an implementation of Gate Set Tomography in the python programming language. Gate Set Tomography (GST) is a theory and protocol for simultaneously estimating the state preparation, gate operations, and measurement effects of a physical system of one or many quantum bits (qubits). These estimates are based entirely on the statistics of experimental measurements, and their interpretation and analysis can provide a detailed understanding of the types of errors/imperfections in the physical system. In this way, GST provides not only a means of certifying the "goodness" of qubits but also a means of debugging (i.e. improving) them.

Nielsen, Erik↗
DOE OSTI2016

Mads.jl

Mads.jl (Model analysis and decision support in Julia) is a code that streamlines the process of using data and models for analysis and decision support. It is based on another open-source code developed at LANL and written in C/C++ (MADS; http://mads.lanl.gov; LA-CC-11- 035). Mads.jl can work with external models of arbitrary complexity as well as built-in models of flow and transport in porous media. It enables a number of data- and model-based analyses including model calibration, sensitivity analysis, uncertainty quantification, and decision analysis. The code also can use a series of alternative adaptive computational techniques for Bayesian sampling, Monte Carlo, and Bayesian Information-Gap Decision Theory. The code is implemented in the Julia programming language, and has high-performance (parallel) and memory management capabilities. The code uses a series of third party modules developed by others. The code development will also include contributions to the existing third party modules written in Julia; this contributions will be important for the efficient implementation of the algorithm used by Mads.jl. The code also uses a series of LANL developed modules that are developed by Dan O'Malley; these modules will be also a part of the Mads.jl release. Mads.jl will be released under GPL V3 license. The code will be distributed as a Git repo at gitlab.com and github.com. Mads.jl manual and documentation will be posted at madsjulia.lanl.gov.

Vesselinov, Velimir↗
DOE OSTI2016

PcapDB: Search Optimized Packet Capture, Version 0.1.0.0

PcapDB is a packet capture system designed to optimize the captured data for fast search in the typical (network incident response) use case. The technology involved in this software has been submitted via the IDEAS system and has been filed as a provisional patent. It includes the following primary components: capture: The capture component utilizes existing capture libraries to retrieve packets from network interfaces. Once retrieved the packets are passed to additional threads for sorting into flows and indexing. The sorted flows and indexes are passed to other threads so that they can be written to disk. These components are written in the C programming language. search: The search components provide a means to find relevant flows and the associated packets. A search query is parsed and represented as a search tree. Various search commands, written in C, are then used resolve this tree into a set of search results. The tree generation and search execution management components are written in python. interface: The PcapDB web interface is written in Python on the Django framework. It provides a series of pages, API's, and asynchronous tasks that allow the user to manage the capture system, perform searches, and retrieve results. Web page components are written in HTML,CSS and Javascript.

Ferrell, Paul↗
DOE OSTI2017

A Latency Tolerant Runtime System for Mass Market Computer Systems

Grappa is a latency tolerant runtime system for mass market computer systems comprised of multiple nodes having x86 multicore processors and infiniband interconnect. Grappa is designed to enable in-memory irregular applications written in the C++ programming language to scale up on multiprocessor systems. Grappa addresses the load balance, communication, and synchronization challenges posed by running applications that do not partition evenly across large systems. Two mechanisms are essential to its success: fast task switching and message aggregation. Fast task switching enables processors to tolerate communication and synchronization delays; aggregation sustains high bandwidth on low injection rate networks.

Source record↗
DOE OSTI2017

TRANSFORM - TRANsient Simulation Framework of Reconfigurable Models

A Modelica based library for modeling thermal hydraulic energy systems and other multi-physics systems. Existing development tools for early stage design and scoping of energy systems are often time consuming to use, proprietary, and do not contain the necessary function to model complete systems (i.e., controls, primary, and secondary systems) in a common platform. The Modelica programming language based TRANSFORM tool (1) provides a standardized, common simulation environment for early design of energy systems (i.e., power plants), (2) provides a library of baseline component modules to be assembled into full plant models using available geometry, design, and thermal-hydraulic data, (3) defines modeling conventions for interconnecting component models, and (4) establishes user interfaces and support tools to facilitate simulation development (i.e., configuration and parameterization), execution, and results display and capture.

Greenwood, MichaelS. [Oak Ridge National Lab. (ORN↗
DOE OSTI2019

MinCostFlows.jl

MinCostFlows.jl is a utility package in the Julia programming language for efficiently solving min-cost network flow problems through an implementation of the relaxation dual ascent method (Bertsekas, 1985, https://link.springer.com/article/10.1007/BF01586087). The use of a dual ascent method allows for warm-starting the solver with a solution from a previously-solved problem that need not be feasible for the new problem, providing computational performance benefits when repeatedly solving similar network flow problems. This approach is particularly useful for Monte Carlo power system resource adequacy models.

Gordon, Stephen↗
DOE OSTI2020

GEOPHIRES v2.0

GEOPHIRES v2.0 is a geothermal techno-economic simulation tool upgraded from GEOPHIRES v1.2. The name stands for "GEOthermal energy for Production of Heat and electricity (“IR”) Economically Simulated". GEOPHIRES combines reservoir, wellbore, and surface plant technical models with cost correlations and levelized cost models to estimate the capital and operation and maintenance costs, instantaneous and lifetime energy production, and overall levelized cost of energy of a geothermal plant. In addition to electricity generation, direct-use heat applications and combined heat and power or cogeneration can be modeled. Various upgrades have been implemented to develop GEOPHIRES v2.0, including: updating the built-in cost correlations, coupling to the external reservoir simulator TOUGH2, enhancing the built-in wellbore simulator, converting the programming language to Python, and making the code open-source. Users are encouraged to build upon the tool, e.g. by implementing their own correlations or coupling to other simulators.

Beckers, Koenraad↗
DOE OSTI2020

Semi-analytic model of magnetized liner inertial fusion

The code that was developed is called SAMM (Semi-Analytic MagLIF Model). In 2015, McBride and Slutz published all of the equations that are solved by the code in the original SAMM paper: R. D. McBride and S. A. Slutz, ?A semi-analytic model of magnetized liner inertial fusion?, Phys. Plasmas 22, 052708 (2015); http://doi.org/10.1063/1.4918953. The SAMM code is now implemented in both the MATLAB and Python programming languages. Students from multiple universities have requested copies of the code so that they can become more familiar with the MagLIF concept. We would like to seek an open-source solution. There is no market value to this code, as there are plenty of more sophisticated simulation codes already available; SAMM is merely a simplified model that is purely for educational purposes. In fact, at least one graduate student (from the University of California, San Diego) has already implemented and published his own modified version of the model: J. Narkis, H. U. Rahman, J. C. Valenzuela, F. Conti, R. D. McBride, D. Venosa, and F. N. Beg, ?A semi-analytic model of gas-puff liner-on-target magneto-inertial fusion?, Phys. Plasmas 26, 032708 (2019); https://doi.org/10.1063/1.5086056. SAND2020-12244 M 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.

Moore, Thomas↗
DOE OSTI2020

Computation of graph hitting time moments; Chapel code implementation.

The project that developed this is unclassified, with the mandate to produce open source code. This code computes the hitting time moments of a graph using a linear algebra configuration. The goal of this work is to explore the performance capabilities of the Chapel programming language. Toward that end, we generate random adjacency matrices which represent a random graph. The code can also read in an adjacency matrix from a file. The main computation is the Conjugate Gradient method.SAND2020-12651 M. 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.

Barrett, Richard↗
DOE OSTI2019

ASCENDS: Advanced Data SCiENce Toolkit for Non-Data Scientists

Data science capabilities are becoming important more and more. Even though there exist many data science and machine learning tools, they are not intuitive for many traditional scientists who are not familiar with programming. ASCENDS provides easy and intuitive interfaces (command-line interface, graphical user interface) to users so they can use data science capabilities without having to understand programming language. ASCENDS performs data visualization, machine learning model training, storing and prediction (regression problem), automatic hyperparameter tuning, and supports various machine learning models including random forest, kernel ridge, nearest neighbor, neural network, etc. Users can use standard CSV (comma separated values) format data set and investigate the feature columns that are highly correlated to a target column; further, they can learn ML models, then store and use them for predictions. The tool is supposed to be generically applicable so that it can be used for many different applications.

Sangkeun, MattLee↗