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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 217 records · Page 12

FORCE Integration with DRAFT and IDAES

Integrated energy systems (IES) combine, in mutually beneficial ways, power from variable renewable energy sources and nuclear power plants (NPP) to produce multiple commodities and improve economic viability under uncertain market conditions. Technical and economic analysis of IES requires modeling of complex processes with software models having enough fidelity to capture real-world dynamics while still being capable of running using reasonable computing resources. The open-source Framework for Optimization of Resources and Economics (FORCE) tool suite, developed at Idaho National Laboratory (INL), has enabled comprehensive modeling and simulation of IES. The capabilities within FORCE include grid portfolio optimization through the Holistic Energy Resource Optimization Network (HERON) and the transient process model analysis library HYBRID, among others. Recent developments for the FORCE toolset have centered on strengthening the flexibility and modularity to link with external models and other available software to enhance IES simulations. Adding versatility to the FORCE toolset improves capability resulting in better techno-economic simulation of nuclear and IES components (both in potential higher fidelity and accuracy to expected performance after deployment). It also helps leverage existing work in the IES field and improve efficiency in national code development. This report focuses on two such endeavors: integration of the Dynamic Reliability Analysis Framework Tool (DRAFT) and the Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) software packages into FORCE.

97 MATHEMATICS AND COMPUTING↗

Science of the Van Allen Probes Science Operations Centers

The Van Allen Probes mission operations materialized through a distributed model in which operational responsibility was divided between the Mission Operations Center (MOC) and separate instrument specific SOCs. The sole MOC handled all aspects of telemetering and receiving tasks as well as certain scientifically relevant ancillary tasks. Each instrument science team developed individual instrument specific SOCs proficient in unique capabilities in support of science data acquisition, data processing, instrument performance, and tools for the instrument team scientists. In parallel activities, project scientists took on the task of providing a significant modeling tool base usable by the instrument science teams and the larger scientific community. With a mission as complex as Van Allen Probes, scientific inquiry occurred due to constant and significant collaboration between the SOCs and in concert with the project science team. Planned cross-instrument coordinated observations resulted in critical discoveries during the seven-year mission. Instrument cross-calibration activities elucidated a more seamless set of data products. Specific topics include post-launch changes and enhancements to the SOCs, discussion of coordination activities between the SOCs, SOC specific analysis software, modeling software provided by the Van Allen Probes project, and a section on lessons learned. One of the most significant lessons learned was the importance of the original decision to implement individual team SOCs providing timely and well-documented instrument data for the NASA Van Allen Probes Mission scientists and the larger magnetospheric and radiation belt scientific community.

47 OTHER INSTRUMENTATION↗

PIPES (Pipeline for Integrated Projects in Energy Systems) [SWR-24-89]

The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. https://github.com/nrel-pipes/pipes-api https://github.com/nrel-pipes/pipes-web https://github.com/nrel-pipes/nrel-pipes

Gu, Jianli↗

Pipeline for Integrated Projects in Energy Systems (PIPES): A Tool for Integrated System Planning [Slides]

The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. This presentation introduces PIPES a multi-model tool for integrated system planning; it describes the underlying architecture, deep dives into common user workflows, and outlines the upcoming development roadmap beyond its current alpha state.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Stochastic models for software project management

A method for determining the number and characteristics of milestones to be achieved during a development project is presented in order that effective monitors of progress can be provided. Projections of progress data lead to estimates of the completion with determinable accuracy, but accuracy imposes a requirement that the number of milestones be inversely proportional to the estimate-error variance, and that the milestones themselves be defined in such a way that each represents approximately the same level of effort to complete.

Tausworthe, R. C.↗

Statistical modeling of software reliability

This working paper discusses the statistical simulation part of a controlled software development experiment being conducted under the direction of the System Validation Methods Branch, Information Systems Division, NASA Langley Research Center. The experiment uses guidance and control software (GCS) aboard a fictitious planetary landing spacecraft: real-time control software operating on a transient mission. Software execution is simulated to study the statistical aspects of reliability and other failure characteristics of the software during development, testing, and random usage. Quantification of software reliability is a major goal. Various reliability concepts are discussed. Experiments are described for performing simulations and collecting appropriate simulated software performance and failure data. This data is then used to make statistical inferences about the quality of the software development and verification processes as well as inferences about the reliability of software versions and reliability growth under random testing and debugging.

Miller, Douglas R.↗

Transitioning from Software Requirements Models to Design Models

Summary: 1. Proof-of-concept of state machine synthesis from scenarios - CTAS case study. 2. CTAS team wants to use the syntheses algorithm to validate trajectory generation. 3. Extending synthesis algorithm towards requirements validation: (a) scenario relationships' (b) methodology for generalizing/refining scenarios, and (c) interaction patterns to control synthesis. 4. Initial ideas tested on conflict detection scenarios.

Lowry, Michael↗

Transitioning from Software Requirements Models to Design Models

The Scenario Creation and Simulation Process (SCASP) includes the following steps: 1) Write Requirements; 2) Write Use Cases; 3) Prioritize Use Cases; 4) Write Nominal Scenarios; 5) Identify Relationships; 6) Refine/Generalize Scenarios; 7) Transform to State Machines. SCASP provides thorough simulation of use cases before design/implementation, resulting in: 1) Reduced cost; 2) Fewer misunderstandings; 3) Reuse of executable form of use cases. SCASP gives systematic guidelines on how to 1) Separate concerns in use case descriptions; 2) Elicit non-nominal scenarios (alternatives, exceptions, concurrent scenarios, etc.); 3) Transform those scenarios automatically into a set of concurrent state machines; 4) Execute those state machines, i.e., scenario simulation.

Whittle, Jon↗

Theoretical Tools and Software for Modeling, Simulation and Control Design of Rocket Test Facilities

A rocket test stand and associated subsystems are complex devices whose operation requires that certain preparatory calculations be carried out before a test. In addition, real-time control calculations must be performed during the test, and further calculations are carried out after a test is completed. The latter may be required in order to evaluate if a particular test conformed to specifications. These calculations are used to set valve positions, pressure setpoints, control gains and other operating parameters so that a desired system behavior is obtained and the test can be successfully carried out. Currently, calculations are made in an ad-hoc fashion and involve trial-and-error procedures that may involve activating the system with the sole purpose of finding the correct parameter settings. The goals of this project are to develop mathematical models, control methodologies and associated simulation environments to provide a systematic and comprehensive prediction and real-time control capability. The models and controller designs are expected to be useful in two respects: 1) As a design tool, a model is the only way to determine the effects of design choices without building a prototype, which is, in the context of rocket test stands, impracticable; 2) As a prediction and tuning tool, a good model allows to set system parameters off-line, so that the expected system response conforms to specifications. This includes the setting of physical parameters, such as valve positions, and the configuration and tuning of any feedback controllers in the loop.

Richter, Hanz↗

A Software Risk Model

Topics on the slide include:quality, test coverage, functional criticality, exposure, criticality, and risk index.

Lee, Alice↗

NASA Tech Briefs, May 2004

Topics include: Embedded Heaters for Joining or Separating Plastic Parts; Curing Composite Materials Using Lower-Energy Electron Beams; Aluminum-Alloy-Matrix/Alumina-Reinforcement Composites; Fibrous-Ceramic/Aerogel Composite Insulating Tiles; Urethane/Silicone Adhesives for Bonding Flexing Metal Parts; Scalable Architecture for Multihop Wireless ad Hoc Networks; Improved Thermoplastic/Iron-Particle Transformer Cores; Cooperative Lander-Surface/Aerial Microflyer Missions for Mars Exploration Dual-Frequency Airborne Scanning Rain Radar Antenna System Eight-Channel Continuous Timer Reduction of Phase Ambiguity in an Offset-QPSK Receiver Ambient-Light-Canceling Camera Using Subtraction of Frames Lightweight, Flexible, Thin, Integrated Solar-Power Packs Windows(Registered Trademark)-Based Software Models Cyclic Oxidation Behavior Software for Analyzing Sequences of Flow-Related Images Improved Ball-and-Socket Docking Mechanism Two-Stage Solenoid Ordered Nanostructures Made Using Chaperonin Polypeptides Low-Temperature Plasma Functionalization of Carbon Nanotubes Improved Cryostat for Cooling a Wide Panel Current Pulses Momentarily Enhance Thermoelectric Cooling Hand-Held Color Meters Based on Interference Filters Calculating Mass Diffusion in High-Pressure Binary Fluids Fresnel Lenses for Wide-Aperture Optical Receivers Increasing Accuracy in Computed Inviscid Boundary Conditions Higher-Order Finite Elements for Computing Thermal Radiation Radar for Monitoring Hurricanes from Geostationary Orbit Time-Transfer System for Two Orbiting Spacecraft

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