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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Lessons Learned in Developing Multiple Distributed Planning Systems for the International Space Station

The planning processes for the International Space Station (ISS) Program are quite complex. Detailed mission planning for ISS on-orbit operations is a distributed function. Pieces of the on-orbit plan are developed by multiple planning organizations, located around the world, based on their respective expertise and responsibilities. The pieces are then integrated to yield the final detailed plan that will be executed onboard the ISS. Previous space programs have not distributed the planning and scheduling functions to this extent. Major ISS planning organizations are currently located in the United States (at both the NASA Johnson Space Center (JSC) and NASA Marshall Space Flight Center (MSFC)), in Russia, in Europe, and in Japan. Software systems have been developed by each of these planning organizations to support their assigned planning and scheduling functions. Although there is some cooperative development and sharing of key software components, each planning system has been tailored to meet the unique requirements and operational environment of the facility in which it operates. However, all the systems must operate in a coordinated fashion in order to effectively and efficiently produce a single integrated plan of ISS operations, in accordance with the established planning processes. This paper addresses lessons learned during the development of these multiple distributed planning systems, from the perspective of the developer of one of the software systems. The lessons focus on the coordination required to allow the multiple systems to operate together, rather than on the problems associated with the development of any particular system. Included in the paper is a discussion of typical problems faced during the development and coordination process, such as incompatible development schedules, difficulties in defining system interfaces, technical coordination and funding for shared tools, continually evolving planning concepts/requirements, programmatic and budget issues, and external influences. Techniques that mitigated some of these problems will also be addressed, along with recommendations for any future programs involving the development of multiple planning and scheduling systems. Many of these lessons learned are not unique to the area of planning and scheduling systems, so may be applied to other distributed ground systems that must operate in concert to successfully support space mission operations.

Maxwell, Theresa G.↗
NASA NTRS2019

Prototyping Operational Autonomy for Space Traffic Management

Current state of the art in Space Traffic Management (STM) relies on a handful of providers for surveillance and collision prediction, and manual coordination between operators. Neither is scalable to support the expected 10x increase in spacecraft population in less than 10 years, nor does it support automated manuever planning. We present a software prototype of an STM architecture based on open Application Programming Interfaces (APIs), drawing on previous work by NASA to develop an architecture for low-altitude Unmanned Aerial System Traffic Management. The STM architecture is designed to provide structure to the interactions between spacecraft operators, various regulatory bodies, and service suppliers, while maintaining flexibility of these interactions and the ability for new market participants to enter easily. Autonomy is an indispensable part of the proposed architecture in enabling efficient data sharing, coordination between STM participants and safe flight operations. Examples of autonomy within STM include syncing multiple non-authoritative catalogs of resident space objects, or determining which spacecraft maneuvers when preventing impending conjunctions between multiple spacecraft. The STM prototype is based on modern micro-service architecture adhering to OpenAPI standards and deployed in industry standard Docker containers, facilitating easy communication between different participants or services. The system architecture is designed to facilitate adding and replacing services with minimal disruption. We have implemented some example participant services (e.g. a space situational awareness provider/SSA, a conjunction assessment supplier/CAS, an automated maneuver advisor/AMA) within the prototype. Different services, with creative algorithms folded into then, can fulfil similar functional roles within the STM architecture by flexibly connecting to it using pre-defined APIs and data models, thereby lowering the barrier to entry of new players in the STM marketplace. We demonstrate the STM prototype on a multiple conjunction scenario with multiple maneuverable spacecraft, where an example CAS and AMA can recommend optimal maneuvers to the spacecraft operators, based on a predefined reward function. Such tools can intelligently search the space of potential collision avoidance maneuvers with varying parameters like lead time and propellant usage, optimize a customized reward function, and be implemented as a scheduling service within the STM architecture. The case study shows an example of autonomous maneuver planning is possible using the API-based framework. As satellite populations and predicted conjunctions increase, an STM architecture can facilitate seamless information exchange related to collision prediction and mitigation among various service applications on different platforms and servers. The availability of such an STM network also opens up new research topics on satellite maneuver planning, scheduling and negotiation across disjoint entities.

space traffic management↗
DOE OSTI2018

RayStation: External beam treatment planning system

RaySearch Laboratories is a world leader in the field of advanced software and creator of the RayStation treatment planning system for radiation therapy. The aim with RayStation is to deliver an unmatched user experience and leading functionality. Unique features described here include multiatlas based autosegmentation for contouring, deformable registration with 2 different algorithms, multicriteria optimization, Plan Explorer, fallback planning, ultrafast computation speed, and 4-dimensional (4D) adaptive radiation therapy. RayStation can be used to plan for electrons and photons on traditional linacs, for protons on various delivery systems, and for Accuray's helical TomoTherapy system. This paper describes some of these modalities, with reference to clinical cases and including descriptions of the impact on workflow.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2008

Optimal Planning and Problem-Solving

CTAEMS MDP Optimal Planner is a problem-solving software designed to command a single spacecraft/rover, or a team of spacecraft/rovers, to perform the best action possible at all times according to an abstract model of the spacecraft/rover and its environment. It also may be useful in solving logistical problems encountered in commercial applications such as shipping and manufacturing. The planner reasons around uncertainty according to specified probabilities of outcomes using a plan hierarchy to avoid exploring certain kinds of suboptimal actions. Also, planned actions are calculated as the state-action space is expanded, rather than afterward, to reduce by an order of magnitude the processing time and memory used. The software solves planning problems with actions that can execute concurrently, that have uncertain duration and quality, and that have functional dependencies on others that affect quality. These problems are modeled in a hierarchical planning language called C_TAEMS, a derivative of the TAEMS language for specifying domains for the DARPA Coordinators program. In realistic environments, actions often have uncertain outcomes and can have complex relationships with other tasks. The planner approaches problems by considering all possible actions that may be taken from any state reachable from a given, initial state, and from within the constraints of a given task hierarchy that specifies what tasks may be performed by which team member.

Clemet, Bradley↗
NASA NTRS1991

NASA software documentation standard software engineering program

The NASA Software Documentation Standard (hereinafter referred to as Standard) can be applied to the documentation of all NASA software. This Standard is limited to documentation format and content requirements. It does not mandate specific management, engineering, or assurance standards or techniques. This Standard defines the format and content of documentation for software acquisition, development, and sustaining engineering. Format requirements address where information shall be recorded and content requirements address what information shall be recorded. This Standard provides a framework to allow consistency of documentation across NASA and visibility into the completeness of project documentation. This basic framework consists of four major sections (or volumes). The Management Plan contains all planning and business aspects of a software project, including engineering and assurance planning. The Product Specification contains all technical engineering information, including software requirements and design. The Assurance and Test Procedures contains all technical assurance information, including Test, Quality Assurance (QA), and Verification and Validation (V&V). The Management, Engineering, and Assurance Reports is the library and/or listing of all project reports.

Source record↗
NASA NTRS1991

NASA Software Documentation Standard

The NASA Software Documentation Standard (hereinafter referred to as "Standard") is designed to support the documentation of all software developed for NASA; its goal is to provide a framework and model for recording the essential information needed throughout the development life cycle and maintenance of a software system. The NASA Software Documentation Standard can be applied to the documentation of all NASA software. The Standard is limited to documentation format and content requirements. It does not mandate specific management, engineering, or assurance standards or techniques. This Standard defines the format and content of documentation for software acquisition, development, and sustaining engineering. Format requirements address where information shall be recorded and content requirements address what information shall be recorded. This Standard provides a framework to allow consistency of documentation across NASA and visibility into the completeness of project documentation. The basic framework consists of four major sections (or volumes). The Management Plan contains all planning and business aspects of a software project, including engineering and assurance planning. The Product Specification contains all technical engineering information, including software requirements and design. The Assurance and Test Procedures contains all technical assurance information, including Test, Quality Assurance (QA), and Verification and Validation (V&V). The Management, Engineering, and Assurance Reports is the library and/or listing of all project reports.

Source record↗
NASA NTRS1972

Vehicle management and mission planning systems with shuttle applications

A preliminary definition of a concept for an automated system is presented that will support the effective management and planning of space shuttle operations. It is called the Vehicle Management and Mission Planning System (VMMPS). In addition to defining the system and its functions, some of the software requirements of the system are identified and a phased and evolutionary method is recommended for software design, development, and implementation. The concept is composed of eight software subsystems supervised by an executive system. These subsystems are mission design and analysis, flight scheduler, launch operations, vehicle operations, payload support operations, crew support, information management, and flight operations support. In addition to presenting the proposed system, a discussion of the evolutionary software development philosophy that the Mission Planning and Analysis Division (MPAD) would propose to use in developing the required supporting software is included. A preliminary software development schedule is also included.

Source record↗
DOE OSTI2022

A solar energy desalination analysis tool, sedat, with data and models for selecting technologies and regions

Abstract There is interest for desalination technologies powered by solar energy as arid areas are typically bestowed with good solar potential. In response to a US DOE call for solar desalination analysis tools, we developed an open-source solar energy desalination analysis tool, sedat , for techno-economical evaluation of desalination technologies and selection of regions with the highest potential for using solar energy to power desalination plants. It is expected that this software will simplify the planning, design, and valuation of solar desalination systems in the U.S. and worldwide. Sedat uses Dash for integrating various layers of large volumes of GIS data with Python-based models of solar energy generation and desalination technologies. It derives time-series of energy generation and water production, with details of plant performance and suggestions for improving the solar-desalination coupling. This paper summarizes the various phases of the tool’s development, presents example results showing the potential, under multiple objectives, of solar desalination in parts of the U.S. southwest, and discusses method details that would be useful for future model development.

14 SOLAR ENERGY↗
DOE OSTI2014

SU-E-T-248: Near Real-Time Analysis of Radiation Delivery and Imaging, Accuracy to Ensure Patient Safety

Purpose: To develop and optimize an effective software method for comparing planned to delivered control point machine parameters for all VARIAN TrueBeam treatments so as to permit (1) assessment of a large patient pool throughout their treatment course to quantify treatment technique specific systematic and random uncertainty of observables, (2) quantify the site specific daily imaging shifts required for target alignment, and (3) define tolerance levels for mechanical parameters and imaging parameters based on statistical analysis data gathered, and the dosimetric impact of variations. Methods: Treatment and imaging log files were directly compared to plan parameters for Eclipse and Pinnacle planned treatments via 3D, IMRT, control point, RapidArc, and electrons. Each control point from all beams/arcs (7984) for all fractions (1940) of all patients treated over six months were analyzed. At each control point gantry angle, collimator angle, couch angle, jaw positions, MLC positions, MU were compared. Additionally per-treatment isocenter shifts were calculated. Results were analyzed as a whole in treatment type subsets: IMRT, 3D, RapidArc; and in treatment site subsets: brain, chest/mediastinum, esophagus, H and N, lung, pelvis, prostate. Results: Daily imaging isocenter shifts from initial external tattoo alignment were dependent on the treatment site with < 0.5 cm translational shifts for H and N, Brain, and lung SBRT, while pelvis, esophagus shifts were ∼1 cm. Mechanical delivery parameters were within tolerance levels for all sub-beams. The largest variations were for RapidArc plans: gantry angle 0.11±0.12,collimator angle 0.00±0.00, jaw positions 0.48±0.26, MLC leaf positions 0.66±0.08, MU 0.14±0.34. Conclusion: Per-control point validation reveals deviations between planned and delivered parameters. If used in a near real-time error checking system, patient safety can be improved by equipping the treatment delivery system with additional forcing functions which by-pass human error avenues.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-C-12A-12: Veritas: An Open Source Tool to Facilitate User Interaction with TrueBeam Developer Mode

Purpose: To address the challenges of creating delivery trajectories and imaging sequences with TrueBeam Developer Mode, a new open-source graphical XML builder, Veritas, has been developed, tested and made freely available. Veritas eliminates most of the need to understand the underlying schema and write XML scripts, by providing a graphical menu for each control point specifying the state of 30 mechanical/dose axes. All capabilities of Developer Mode are accessible in Veritas. Methods: Veritas was designed using QT Designer, a ‘what-you-is-what-you-get’ (WYSIWIG) tool for building graphical user interfaces (GUI). Different components of the GUI are integrated using QT's signals and slots mechanism. Functionalities are added using PySide, an open source, cross platform Python binding for the QT framework. The XML code generated is immediately visible, making it an interactive learning tool. A user starts from an anonymized DICOM file or XML example and introduces delivery modifications, or begins their experiment from scratch, then uses the GUI to modify control points as desired. The software automatically generates XML plans following the appropriate schema. Results: Veritas was tested by generating and delivering two XML plans at Brigham and Women's Hospital. The first example was created to irradiate the letter ‘B’ with a narrow MV beam using dynamic couch movements. The second was created to acquire 4D CBCT projections for four minutes. The delivery of the letter ‘B’ was observed using a 2D array of ionization chambers. Both deliveries were generated quickly in Veritas by non-expert Developer Mode users. Conclusion: We introduced a new open source tool Veritas for generating XML plans (delivery trajectories and imaging sequences). Veritas makes Developer Mode more accessible by reducing the learning curve for quick translation of research ideas into XML plans. Veritas is an open source initiative, creating the possibility for future developments and collaboration with other researchers. I am an employee of Varian Medical Systems.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

SU-E-T-580: On the Significance of Model Based Dosimetry for Breast and Head and Neck 192Ir HDR Brachytherapy

Purpose: To assess the dosimetric and radiobiological differences between TG43-based and model-based dosimetry in the treatment planning of {sup 192}Ir HDR brachytherapy for breast and head and neck cancer. Methods: Two cohorts of 57 Accelerated Partial Breast Irradiation (APBI) and 22 head and neck (H&N) patients with oral cavity carcinoma were studied. Dosimetry for the treatment plans was performed using the TG43 algorithm of the Oncentra Brachy v4.4 treatment planning system (TPS). Corresponding Monte Carlo (MC) simulations were performed using MCNP6 with input files automatically prepared by the BrachyGuide software tool from DICOM RT plan data. TG43 and MC data were compared in terms of % dose differences, Dose Volume Histograms (DVHs) and related indices of clinical interest for the Planning Target Volume (PTV) and the Organs-At-Risk (OARs). A radiobiological analysis was also performed using the Equivalent Uniform Dose (EUD), mean survival fraction (S) and Tumor Control Probability (TCP) for the PTV, and the Normal Tissue Control Probability (N TCP) and the generalized EUD (gEUD) for the OARs. Significance testing of the observed differences performed using the Wilcoxon paired sample test. Results: Differences between TG43 and MC DVH indices, associated with the increased corresponding local % dose differences observed, were statistically significant. This is mainly attributed to their consistency however, since TG43 agrees closely with MC for the majority of DVH and radiobiological parameters in both patient cohorts. Differences varied considerably among patients only for the ipsilateral lung and ribs in the APBI cohort, with a strong correlation to target location. Conclusion: While the consistency and magnitude of differences in the majority of clinically relevant DVH indices imply that no change is needed in the treatment planning practice, individualized dosimetry improves accuracy and addresses instances of inter-patient variability observed. Research co-financed by the ESF and Greek funds through the Operational Program Education and Lifelong Learning Investing in Knowledge Society of the NSRF. Research Funding Program Aristeia. Nucletron, an Elekta company (Veenendaal, The Netherlands) is gratefully acknowledged for providing Oncentra Brachy v4.4 for research purposes.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

Optimization of an on-board imaging system for extremely rapid radiation therapy

Purpose: Next-generation extremely rapid radiation therapy systems could mitigate the need for motion management, improve patient comfort during the treatment, and increase patient throughput for cost effectiveness. Such systems require an on-board imaging system that is competitively priced, fast, and of sufficiently high quality to allow good registration between the image taken on the day of treatment and the image taken the day of treatment planning. In this study, three different detectors for a custom on-board CT system were investigated to select the best design for integration with an extremely rapid radiation therapy system. Methods: Three different CT detectors are proposed: low-resolution (all 4 × 4 mm pixels), medium-resolution (a combination of 4 × 4 mm pixels and 2 × 2 mm pixels), and high-resolution (all 1 × 1 mm pixels). An in-house program was used to generate projection images of a numerical anthropomorphic phantom and to reconstruct the projections into CT datasets, henceforth called “realistic” images. Scatter was calculated using a separate Monte Carlo simulation, and the model included an antiscatter grid and bowtie filter. Diagnostic-quality images of the phantom were generated to represent the patient scan at the time of treatment planning. Commercial deformable registration software was used to register the diagnostic-quality scan to images produced by the various on-board detector configurations. The deformation fields were compared against a “gold standard” deformation field generated by registering initial and deformed images of the numerical phantoms that were used to make the diagnostic and treatment-day images. Registrations of on-board imaging system data were judged by the amount their deformation fields differed from the corresponding gold standard deformation fields—the smaller the difference, the better the system. To evaluate the registrations, the pointwise distance between gold standard and realistic registration deformation fields was computed. Results: By most global metrics (e.g., mean, median, and maximum pointwise distance), the high-resolution detector had the best performance but the medium-resolution detector was comparable. For all medium- and high-resolution detector registrations, mean error between the realistic and gold standard deformation fields was less than 4 mm. By pointwise metrics (e.g., tracking a small lesion), the high- and medium-resolution detectors performed similarly. For these detectors, the smallest error between the realistic and gold standard registrations was 0.6 mm and the largest error was 3.6 mm. Conclusions: The medium-resolution CT detector was selected as the best for an extremely rapid radiation therapy system. In essentially all test cases, data from this detector produced a significantly better registration than data from the low-resolution detector and a comparable registration to data from the high-resolution detector. The medium-resolution detector provides an appropriate compromise between registration accuracy and system cost.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Dispatch Optimization Variable Engine

The Dispatch Optimization Variable Engine (DOVE) is software tool written in python, developed at Idaho National Laboratory (INL) that provides an easily accessible application-programming-interface (API) to performing resource dispatch optimization analysis for integrated energy system (IES) configurations. DOVE is an integral part of the Framework for Optimization of Resources and Economics (FORCE) software suite and is leveraged by codes such as the Holistic Energy Resource Optimization Network (HERON) and the Optimization of Real-Time Capacity Allocation (ORCA). The philosophy behind DOVE is to provide a modular software solution to IES planning and operation by utilizing state-of-the-art algorithms and machine learning. The goal is to accurately capture the dispatching behavior of a complex energy system given varying time-dependent signals for demand and commodity pricing.

McDowell, DylanJ. [Idaho National Laboratory (INL)↗
DOE OSTI2017

Carbon Capture Multidisciplinary Simulation Center Trilab Support Team (TST) Fall Meeting 2016 Report

The theme of this year’s meeting was “Predictivity: Now and in the Future”. After welcoming remarks, Erik Draeger gave a talk on the NNSA Labs’ history of predictive simulation and the new challenges faced by upcoming architecture changes. He described an example where the volume of analysis data produced by a set of inertial confinement fusion (ICF) simulations on the Trinity machine was too large to store or transfer, and the steps needed to reduce it to a manageable size. He also described the software re-engineering plan for LLNL’s suite of multiphysics codes and physics packages with a new push toward common components, making collaboration with teams like the CCMSC who already have experience trying to architect complex multiphysics code infrastructure on next-generation architectures all the more important. Phil Smith then gave an overview outlining the goals of the project, namely to accelerate development of new technology in the form of high efficiency carbon capture pulverized coal power generation as well as further optimize existing state of the art designs. He then presented a summary of the Center’s top-down uncertainty quantification approach, in which ultimate target predictivity informs uncertainty targets for lower-level components, and gave data on how close all the different components currently are to their targets. Most components still need an approximately two-fold reduction in uncertainty to hit the ultimate predictivity target, but the current accuracy is already rather impressive.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2017

The CPAT 2.0.2 Domain Model - How CPAT 2.0.2 "Thinks" From an Analyst Perspective.

To help effectively plan the management and modernization of their large and diverse fleets of vehicles, the Program Executive Office Ground Combat Systems (PEO GCS) and the Program Executive Office Combat Support and Combat Service Support (PEO CS &CSS) commissioned the development of a large - scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This report contains a description of the organizational fleet structure and a thorough explanation of the business rules that the CPAT formulation follows involving performance, scheduling, production, and budgets. This report, which is an update to the original CPAT domain model published in 2015 (SAND2015 - 4009), covers important new CPAT features. This page intentionally left blank

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

The Capability Portfolio Analysis Tool (CPAT): A Mixed Integer Linear Programming Formulation for Fleet Modernization Analysis (Version 2.0.2).

In order to effectively plan the management and modernization of their large and diverse fleets of vehicles, Program Executive Office Ground Combat Systems (PEO GCS) and Program Executive Office Combat Support and Combat Service Support (PEO CS&CSS) commis- sioned the development of a large-scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This paper contains a thor- ough documentation of the terminology, parameters, variables, and constraints that comprise the fleet management mixed integer linear programming (MILP) mathematical formulation. This paper, which is an update to the original CPAT formulation document published in 2015 (SAND2015-3487), covers the formulation of important new CPAT features.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

The LSST Dark Energy Science Collaboration (DESC) Science Requirements Document

The Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC) will use five cosmological probes: galaxy clusters, large scale structure, supernovae, strong lensing, and weak lensing. This Science Requirements Document (SRD) quantifies the expected dark energy constraining power of these probes individually and together, with conservative assumptions about analysis methodology and follow-up observational resources based on our current understanding and the expected evolution within the field in the coming years. We then define requirements on analysis pipelines that will enable us to achieve our goal of carrying out a dark energy analysis consistent with the Dark Energy Task Force definition of a Stage IV dark energy experiment. This is achieved through a forecasting process that incorporates the flowdown to detailed requirements on multiple sources of systematic uncertainty. Future versions of this document will include evolution in our software capabilities and analysis plans along with updates to the LSST survey strategy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

The Capability Portfolio Analysis Tool (CPAT): A Mixed Integer Linear Programming Formulation for Fleet Modernization Analysis.

In order to effectively plan the management and modernization of its large and diverse fleet of vehicles, the Program Executive Office Ground Combat Systems (PEO GCS) commissioned the development of a large-scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This paper contains a thorough documentation of the terminology, parameters, variables, and constraints that comprise the fleet management mixed integer linear programming (MILP) mathematical formulation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗