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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Modeling Spacecraft Power Systems Using the SPACE Code

The NASA Glenn Research Center’s Power Architecture and Analysis Branch performs spacecraft electrical power system (EPS) performance analysis using the System Power Analysis for Capability Evaluation (SPACE) model. Accurately predicting EPS performance requires a wide range of inputs, including the spacecraft’s position, attitude, solar array temperature, load demand, and data on the EPS itself. By operating as a combined analysis tool with integrated orbit mechanics, attitude, shadowing, thermal, solar array, battery, and electrical component models, SPACE provides flexibility for the analyst to examine various scenarios without a time-consuming iterative process involving separate analysis tools and groups. SPACE also achieves short run times by tailoring the complexity of the included models, providing enough fidelity to ensure accurate EPS performance predictions without being bogged down by unnecessary detail. These advantages that SPACE has over similar power system models has contributed to its successful use on a variety of human spaceflight programs for over three decades, and makes it applicable across the development cycle from early conceptual design up through on-orbit operations.

Electrical Power System↗
DOE OSTI2022

HL-LHC Computing Review Stage 2, Common Software Projects: Data Science Tools for Analysis

This paper was prepared by the HEP Software Foundation (HSF) PyHEP Working Group as input to the second phase of the LHCC review of High-Luminosity LHC (HL-LHC) computing, which took place in November, 2021. It describes the adoption of Python and data science tools in HEP, discusses the likelihood of future scenarios, and recommendations for action by the HEP community.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRSDate not supplied

Developing A New Medical Condition List for Exploration Spaceflight

For the last decade, the National Aeronautics and Space Administration (NASA) has utilized a probabilistic risk analysis tool called the Integrated Medical Model (IMM), which contains 100 medical conditions, to conduct medical trade-space analyses and risk assessments for International Space Station (ISS) operations. Planning for future missions to the Moon and Mars will require updated tools and evidence that consider the unique features of exploration missions. To address this need, the Exploration Medical Capability (ExMC) Element of the NASA Human Research Program is developing an advanced trade-space analysis tool suite, IMPACT. As a part of this effort, ExMC has produced the IMPACT version 1 Condition List (ICL 1.0) which expands the scope and definition of medical conditions.

Amy Jean Kreykes↗
NASA NTRS1999

The Aviation System Analysis Capability Air Carrier Cost-Benefit Model

To meet its objective of assisting the U.S. aviation industry with the technological challenges of the future, NASA must identify research areas that have the greatest potential for improving the operation of the air transportation system. Therefore, NASA is developing the ability to evaluate the potential impact of various advanced technologies. By thoroughly understanding the economic impact of advanced aviation technologies and by evaluating how the new technologies will be used in the integrated aviation system, NASA aims to balance its aeronautical research program and help speed the introduction of high-leverage technologies. To meet these objectives, NASA is building the Aviation System Analysis Capability (ASAC). NASA envisions ASAC primarily as a process for understanding and evaluating the impact of advanced aviation technologies on the U.S. economy. ASAC consists of a diverse collection of models and databases used by analysts and other individuals from the public and private sectors brought together to work on issues of common interest to organizations in the aviation community. ASAC also will be a resource available to the aviation community to analyze; inform; and assist scientists, engineers, analysts, and program managers in their daily work. The ASAC differs from previous NASA modeling efforts in that the economic behavior of buyers and sellers in the air transportation and aviation industries is central to its conception. Commercial air carriers, in particular, are an important stakeholder in this community. Therefore, to fully evaluate the implications of advanced aviation technologies, ASAC requires a flexible financial analysis tool that credibly links the technology of flight with the financial performance of commercial air carriers. By linking technical and financial information, NASA ensures that its technology programs will continue to benefit the user community. In addition, the analysis tool must be capable of being incorporated into the wide-ranging suite of economic and technical models that comprise ASAC. This report describes an Air Carrier Cost-Benefit Model (CBM) that meets these requirements. The ASAC CBM is distinguished from many of the aviation cost-benefit models by its exclusive focus on commercial air carriers. The model considers such benefit categories as time and fuel savings, utilization opportunities, reliability and capacity enhancements, and safety and security improvements. The model distinguishes between benefits that are predictable and those that occur randomly. By making such a distinction, the model captures the ability of air carriers to reoptimize scheduling and crew assignments for predictable benefits. In addition, the model incorporates a life-cycle cost module for new technology, which applies the costs of nonrecurring acquisitions, recurring maintenance and operation, and training to each aircraft equipment type independently.

Gaier, Eric M.↗
NASA NTRS1998

Safety Analysis of FMS/CTAS Interactions During Aircraft Arrivals

This grant funded research on human-computer interaction design and analysis techniques, using future ATC environments as a testbed. The basic approach was to model the nominal behavior of both the automated and human procedures and then to apply safety analysis techniques to these models. Our previous modeling language, RSML, had been used to specify the system requirements for TCAS II for the FAA. Using the lessons learned from this experience, we designed a new modeling language that (among other things) incorporates features to assist in designing less error-prone human-computer interactions and interfaces and in detecting potential HCI problems, such as mode confusion. The new language, SpecTRM-RL, uses "intent" abstractions, based on Rasmussen's abstraction hierarchy, and includes both informal (English and graphical) specifications and formal, executable models for specifying various aspects of the system. One of the goals for our language was to highlight the system modes and mode changes to assist in identifying the potential for mode confusion. Three published papers resulted from this research. The first builds on the work of Degani on mode confusion to identify aspects of the system design that could lead to potential hazards. We defined and modeled modes differently than Degani and also defined design criteria for SpecTRM-RL models. Our design criteria include the Degani criteria but extend them to include more potential problems. In a second paper, Leveson and Palmer showed how the criteria for indirect mode transitions could be applied to a mode confusion problem found in several ASRS reports for the MD-88. In addition, we defined a visual task modeling language that can be used by system designers to model human-computer interaction. The visual models can be translated into SpecTRM-RL models, and then the SpecTRM-RL suite of analysis tools can be used to perform formal and informal safety analyses on the task model in isolation or integrated with the rest of the modeled system. We had hoped to be able to apply these modeling languages and analysis tools to a TAP air/ground trajectory negotiation scenario, but the development of the tools took more time than we anticipated.

Nancy G. Leveson↗
NASA NTRSDate not supplied

Form Factors, Grey Bodies and Radiation Conductances (Radks)

With today's analysis tools, large, complex thermal radiation problems are easily solved; But, as with any analytical tool, lack of an understanding of the fundamental equations and technique limitations may leave you with the wrong answer; Whether you are a new engineer or a seasoned veteran, an understanding of the techniques employed by these powerful analysis tools is crucial.

Thermal↗
DOE OSTI2025

Nuclear Materials Packaging, Transportation, and Systems Analysis Group Software Quality Assurance Plan: ANSYS Mechanical Finite Element Analysis Software Version 2023R1

ANSYS Inc. develops and markets engineering simulation software and services used in the aerospace, automotive, manufacturing, electronics, biomedical, energy, defense, and many other industries. ANSYS is dedicated to engineering simulation and is the world’s leading software provider. ANSYS was founded in 1970 and is headquartered in Canonsburg, Pennsylvania. ANSYS provides an engineering analysis tool combining structural, thermal, computational fluid dynamics, acoustic, and electromagnetic simulation capabilities. ANSYS has two main programs, which use the same solvers: (1) Mechanical APDL (ANSYS Design Parametric Language), a Fortran-based coding platform, and (2) ANSYS Workbench, which uses a graphical user interface to aid in finite element analysis implementation. This plan covers both APDL and Workbench. The ANSYS computer program is a large-scale, multipurpose finite element program that can be used to solve several classes of engineering analyses. The analysis capabilities of ANSYS include the ability to solve static and dynamic structural analyses, steady-state and transient heat transfer problems, mode-frequency and buckling eigenvalue problems, static or time-varying magnetic analyses, and various types of field and coupled-field applications. The program contains many special features that allow nonlinearities or secondary effects such as plasticity, large strain, hyperelasticity, creep, swelling, large deflections, contact, stress stiffening, temperature dependency, material anisotropy, and radiation to be included in the solution. As ANSYS has been developed, other special capabilities such as substructuring, submodeling, random vibration, kinetostatics, kinetodynamics, free convection fluid analysis, acoustics, magnetics, piezoelectrics, coupled-field analysis, and design optimization have been added to the program. These capabilities contribute further to making ANSYS a multipurpose analysis tool for varied engineering disciplines. The ANSYS program has been in commercial use for over 50 years and has been used extensively in the aerospace, automotive, construction, electronic, energy services, manufacturing, nuclear, plastics, oil, and steel industries. Additionally, many consulting firms and hundreds of universities have used ANSYS for analysis, research, and educational purposes. ANSYS is recognized worldwide as one of the most widely used and capable programs of its type. Ansys design analysis software is the first created within a quality system with ISO 9001 certification, the internationally accepted quality standard. Product development, testing, maintenance and support processes also meet the United States Nuclear Regulatory Commission's quality requirements, as they have for nearly four decades. The Quality Assurance Service Agreement is suitable for the customers working in the nuclear industry who need to meet specific federal regulations including 10CRF50 Appendix B and provisions of 10CFR21. ANSYS has retained its original International Organization for Standardization (ISO) 9001 accreditation certificate since1995-05-04, It’s current certificate is valid until 2027-05-29.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

A Novel Non-Parametric Density Estimation Approach to Measuring Muon Ionization Cooling and Reverse Emittance Exchange in the MICE Experiment

The Muon Ionization Cooling Experiment (MICE) located at Rutherford Ap- pleton Laboratory in the U.K. has demonstrated muon ionization beam cooling for the rst time. A beam of muons in MICE is produced from high-energy proton beam collision with a xed target, generating pions which in turn decay into muons. Pion-decay muons, thus, are tertiary particles and, as a result, occupy a large vol- ume in position{momentum phase space. To t the muon beam into smaller and more cost-eective accelerating devices, muon beam phase{space volume needs to be reduced (beam cooling). Ionization beam cooling, which before MICE has never been demonstrated experimentally for muons, is the only technique fast enough to be used for muons within their short lifetime. Ionization cooling occurs when muons traverse an absorbing material and lose momentum through ionization energy loss. The cooling eect in MICE is measured using two scintillating-ber tracking detec- tors. These trackers, one upstream and one downstream of the absorber, reconstruct and measure the position and momentum coordinates of each muon. Given the pre- cision MICE needed to demonstrate beam cooling, it is necessary to develop analysis tools that can account for any eects that may lead to inaccurate measurement of cooling, such as non{linear eects in beam optics. Non{parametric density estima- tion techniques, such as kernel density estimation (KDE), provide a basis for creating analysis tools that are robust against these eects, directly calculating the muon beam phase-space density and volume for demonstrating beam cooling. This thesis focuses on the application of KDE to the measurement of beam cooling in MICE. The KDE technique is validated using known distributions and is applied to sim- ulated and experimental MICE data corresponding to the various magnet, optics, and absorber congurations. Using the KDE technique, muon beam cooling in the four{dimensional transverse phase space, as well as reverse emittance exchange using MICE data have been demonstrated.

43 PARTICLE ACCELERATORS↗
NASA NTRS1996

Improving Conceptual Design for Launch Vehicles

This report summarizes activities performed during the second year of a three year cooperative agreement between NASA - Langley Research Center and Georgia Tech. Year 1 of the project resulted in the creation of a new Cost and Business Assessment Model (CABAM) for estimating the economic performance of advanced reusable launch vehicles including non-recurring costs, recurring costs, and revenue. The current year (second year) activities were focused on the evaluation of automated, collaborative design frameworks (computation architectures or computational frameworks) for automating the design process in advanced space vehicle design. Consistent with NASA's new thrust area in developing and understanding Intelligent Synthesis Environments (ISE), the goals of this year's research efforts were to develop and apply computer integration techniques and near-term computational frameworks for conducting advanced space vehicle design. NASA - Langley (VAB) has taken a lead role in developing a web-based computing architectures within which the designer can interact with disciplinary analysis tools through a flexible web interface. The advantages of this approach are, 1) flexible access to the designer interface through a simple web browser (e.g. Netscape Navigator), 2) ability to include existing 'legacy' codes, and 3) ability to include distributed analysis tools running on remote computers. To date, VAB's internal emphasis has been on developing this test system for the planetary entry mission under the joint Integrated Design System (IDS) program with NASA - Ames and JPL. Georgia Tech's complementary goals this year were to: 1) Examine an alternate 'custom' computational architecture for the three-discipline IDS planetary entry problem to assess the advantages and disadvantages relative to the web-based approach.and 2) Develop and examine a web-based interface and framework for a typical launch vehicle design problem.

John R. Olds↗
DOE OSTI2020

Use of the Tool to Support Renewable Energy Auctions Processes (RE Data Explorer)

Renewable energy auctions are now a common competitive approach to procure low-cost renewable power around the world. Ensuring a successful auction process increasingly depends on the capabilities of auction designers and participants to identify actionable and defensible insights from large data sets (on renewable energy resources and complementary data) to both attract potential investors and address stakeholder concerns. The Renewable Energy (RE) Data Explorer is a user-friendly geospatial analysis tool for analyzing renewable energy potential and informing decisions. Developed by the National Renewable Energy Laboratory (NREL) and supported by the U.S. Agency for International Development (USAID), RE Data Explorer performs visualization and analysis of renewable energy potential that can be customized for different scenarios. RE Data Explorer can support prospecting, integrated planning, policymaking, and other decision-making activities to accelerate renewable energy deployment. The broader RE Explorer website provides guidance and information to link the RE Data Explorer geospatial analysis tool to key decision areas. This document provides information on how the RE Data Explorer can be used to support renewable energy auction processes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2005

On-Line Loss of Control Detection Using Wavelets

Wavelet transforms are used for on-line detection of aircraft loss of control. Wavelet transforms are compared with Fourier transform methods and shown to more rapidly detect changes in the vehicle dynamics. This faster response is due to a time window that decreases in length as the frequency increases. New wavelets are defined that further decrease the detection time by skewing the shape of the envelope. The wavelets are used for power spectrum and transfer function estimation. Smoothing is used to tradeoff the variance of the estimate with detection time. Wavelets are also used as front-end to the eigensystem reconstruction algorithm. Stability metrics are estimated from the frequency response and models, and it is these metrics that are used for loss of control detection. A Matlab toolbox was developed for post-processing simulation and flight data using the wavelet analysis methods. A subset of these methods was implemented in real time and named the Loss of Control Analysis Tool Set or LOCATS. A manual control experiment was conducted using a hardware-in-the-loop simulator for a large transport aircraft, in which the real time performance of LOCATS was demonstrated. The next step is to use these wavelet analysis tools for flight test support.

Brenner, Martin J.↗
NASA NTRS2014

Trajectory Design for the Transiting Exoplanet Survey Satellite

The Transiting Exoplanet Survey Satellite (TESS) is a National Aeronautics and Space Administration (NASA) mission, scheduled to be launched in 2017. TESS will travel in a highly eccentric orbit around Earth, with initial perigee radius near 17 Earth radii (Re) and apogee radius near 59 Re. The orbit period is near 2:1 resonance with the Moon, with apogee nearly 90 degrees out-of-phase with the Moon, in a configuration that has been shown to be operationally stable. TESS will execute phasing loops followed by a lunar flyby, with a final maneuver to achieve 2:1 resonance with the Moon. The goals of a resonant orbit with long-term stability, short eclipses and limited oscillations of perigee present significant challenges to the trajectory design. To rapidly assess launch opportunities, we adapted the Schematics Window Methodology (SWM76) launch window analysis tool to assess the TESS mission constraints. To understand the long-term dynamics of such a resonant orbit in the Earth-Moon system we employed Dynamical Systems Theory in the Circular Restricted 3-Body Problem (CR3BP). For precise trajectory analysis we use a high-fidelity model and multiple shooting in the General Mission Analysis Tool (GMAT) to optimize the maneuver delta-V and meet mission constraints. Finally we describe how the techniques we have developed can be applied to missions with similar requirements. Keywords: resonant orbit, stability, lunar flyby, phasing loops, trajectory optimization

Stationkeepting↗
DOE OSTI2020

Advances in Uncertainty and Sensitivity Analysis Methods and Applications in GDSA Framework.

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST ''Geologic Disposal Safety Assessment'' (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, ''GDSA Framework'', for evaluating disposal system performance for nuclear waste in geologic media. ''GDSA Framework'' is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign. This report fulfills the GDSA Uncertainty and Sensitivity Analysis Methods work package (SF-20SN01030403) level 3 milestone — ''Advances in Uncertainty and Sensitivity Analysis Methods and Applications in GDSA Framework'' (M3SF-20SN010304032). It presents high level objectives and strategy for development of uncertainty and sensitivity analysis tools, demonstrates uncertainty quantification (UQ) and sensitivity analysis (SA) tools in GDSA Framework in FY20, and describes additional UQ/SA tools whose future implementation would enhance the UQ/SA capability of ''GDSA Framework''. This work was closely coordinated with the other Sandia National Laboratory GDSA work packages: the GDSA Framework Development work package (SF- 2051\101030404), the GDSA Repository Systems Analysis work package (SF-2051\101030405), and the GDSA PFLOTRAN Development work package (SF-20SN01030406). This report builds on developments reported in previous ''GDSA Framework'' milestones, particularly M2SF- 19SNO1030403.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Uncertainty and Sensitivity Analysis Methods and Applications in the GDSA Framework (FY2021)

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, GDSA Framework, for evaluating disposal system performance for nuclear waste in geologic media. GDSA Framework is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign. This report fulfills the GDSA Uncertainty and Sensitivity Analysis Methods work package (SF-21SN01030404) level 3 milestone, Uncertainty and Sensitivity Analysis Methods and Applications in GDSA Framework (FY2021) (M3SF-21SN010304042). It presents high level objectives and strategy for development of uncertainty and sensitivity analysis tools, demonstrates uncertainty quantification (UQ) and sensitivity analysis (SA) tools in GDSA Framework in FY21, and describes additional UQ/SA tools whose future implementation would enhance the UQ/SA capability of GDSA Framework. This work was closely coordinated with the other Sandia National Laboratory GDSA work packages: the GDSA Framework Development work package (SF-21SN01030405), the GDSA Repository Systems Analysis work package (SF-21SN01030406), and the GDSA PFLOTRAN Development work package (SF-21SN01030407). This report builds on developments reported in previous GDSA Framework milestones, particularly M3SF 20SN010304032.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2001

Simultaneous Aerodynamic and Structural Design Optimization (SASDO) for a 3-D Wing

The formulation and implementation of an optimization method called Simultaneous Aerodynamic and Structural Design Optimization (SASDO) is shown as an extension of the Simultaneous Aerodynamic Analysis and Design Optimization (SAADO) method. It is extended by the inclusion of structure element sizing parameters as design variables and Finite Element Method (FEM) analysis responses as constraints. The method aims to reduce the computational expense. incurred in performing shape and sizing optimization using state-of-the-art Computational Fluid Dynamics (CFD) flow analysis, FEM structural analysis and sensitivity analysis tools. SASDO is applied to a simple. isolated, 3-D wing in inviscid flow. Results show that the method finds the saine local optimum as a conventional optimization method with some reduction in the computational cost and without significant modifications; to the analysis tools.

Gumbert, Clyde R.↗
NASA NTRS2012

Elastic Plastic Fracture Analysis of an Aluminum COPV Liner

Onboard any space-launch vehicle, composite over-wrapped pressure vessels (COPVs) may be utilized by propulsion or environmental control systems. The failure of a COPV has the potential to be catastrophic, resulting in the loss of vehicle, crew or mission. The latest COPV designs have reduced the wall-thickness of the metallic liner to the point where the material strains plastically during operation. At this time, the only method to determine the damage tolerance lifetime (safe-life) of a plastically responding metallic liner is through full-scale COPV testing. Conducting tests costs substantially more and can be far more time consuming than performing an analysis. As a result of this cost, there is a need to establish a qualifying process through the use of a crack growth analysis tool. This paper will discuss fracture analyses of plastically responding metallic liners in COPVs. Uni-axial strain tests have been completed on laboratory specimens to collect elastic-plastic crack growth data. This data has been modeled with the crack growth analysis tool, NASGRO 6.20 to predict the response of laboratory specimens and subsequently the complexity of a COPV.

Forth, Scott↗
NASA NTRS1989

Three approaches to reliability analysis

It is noted that current reliability analysis tools differ not only in their solution techniques, but also in their approach to model abstraction. The analyst must be satisfied with the constraints that are intrinsic to any combination of solution technique and model abstraction. To get a better idea of the nature of these constraints, three reliability analysis tools (HARP, ASSIST/SURE, and CAME) were used to model portions of the Integrated Airframe/Propulsion Control System architecture. When presented with the example problem, all three tools failed to produce correct results. In all cases, either the tool or the model had to be modified. It is suggested that most of the difficulty is rooted in the large model size and long computational times which are characteristic of Markov model solutions.

Mesh networks↗
NASA NTRS1975

Dispersion analysis techniques within the space vehicle dynamics simulation program

The Space Vehicle Dynamics Simulation (SVDS) program was evaluated as a dispersion analysis tool. The Linear Error Analysis (LEA) post processor was examined in detail and simulation techniques relative to conducting a dispersion analysis using the SVDS were considered. The LEA processor is a tool for correlating trajectory dispersion data developed by simulating 3 sigma uncertainties as single error source cases. The processor combines trajectory and performance deviations by a root-sum-square (RSS process) and develops a covariance matrix for the deviations. Results are used in dispersion analyses for the baseline reference and orbiter flight test missions. As a part of this study, LEA results were verified as follows: (A) Hand calculating the RSS data and the elements of the covariance matrix for comparison with the LEA processor computed data. (B) Comparing results with previous error analyses. The LEA comparisons and verification are made at main engine cutoff (MECO).

Snow, L. S.↗