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At least 163 records · Page 9

Team X Report #1401: Exoplanet Coronagraph STDT Study 2013-06

This document is intended to stimulate discussion of the topic described. All technical and cost analyses are preliminary. This document is not a commitment to work, but is a precursor to a formal proposal if it generates sufficient mutual interest. The data contained in this document may not be modified in any way. Cost estimates described or summarized in this document were generated as part of a preliminary, first-order cost class identification as part of an early trade space study, are based on JPL-internal parametric cost modeling, assume a JPL in-house build, and do not constitute a commitment on the part of JPL or Caltech. JPL and Team X add cost reserves for development and operations. Unadjusted estimate totals and cost reserve allocations would be revised as needed in future more-detailed studies as appropriate for the specific cost-risks for a given mission concept.

cost estimating↗

Space Station reference configuration description

The data generated by the Space Station Program Skunk Works over a period of 4 months which supports the definition of a Space Station reference configuration is documented. The data were generated to meet these objectives: (1) provide a focal point for the definition and assessment of program requirements; (2) establish a basis for estimating program cost; and (3) define a reference configuration in sufficient detail to allow its inclusion in the definition phase Request for Proposal (RFP).

Source record↗

Engineering and configurations of space stations and platforms

The data generated by the Space Station Program Skunk Works over a period of 4 months which supports the definition of a Space Station reference configuration is documented. The data were generated to meet these objectives: (1) provide a focal point for the definition and assessment of program requirements; (2) establish a basis for estimating program cost; and (3) define a reference configuration in sufficient detail to allow its inclusion in the definition phase Request for Proposal (RFP).

Louviere, A.↗

Radar Reflectivity in Wingtip-Generated Wake Vortices

This report documents new predictive models of radar reflectivity, with meter-scale resolution, for aircraft wakes in clear air and fog. The models result from a radar design program to locate and quantify wake vortices from commercial aircraft in support of the NASA Aircraft Vortex Spacing System (AVOSS). The radar reflectivity model for clear air assumes: 1) turbulent eddies in the wake produce small discontinuities in radar refractive index; and 2) these turbulent eddies are in the 'inertial subrange' of turbulence. From these assumptions, the maximum radar frequency for detecting a particular aircraft wake, as well as the refractive index structure constant and radar volume reflectivity in the wake can be obtained from the NASA Terminal Area Simulation System (TASS) output. For fog conditions, an empirical relationship is used to calculate radar reflectivity factor from TASS output of bulk liquid water. Currently, two models exist: 1) Atlas-based on observations of liquid water and radar reflectivity factor in clouds; and 2) de Wolf- specifically tailored to a specific measured dataset (1992 Vandenberg Air Force Base).

Marshall, Robert E.↗

Databases post-processing in Tensoral

The Center for Turbulent Research (CTR) post-processing effort aims to make turbulence simulations and data more readily and usefully available to the research and industrial communities. The Tensoral language, introduced in this document and currently existing in prototype form, is the foundation of this effort. Tensoral provides a convenient and powerful protocol to connect users who wish to analyze fluids databases with the authors who generate them. In this document we introduce Tensoral and its prototype implementation in the form of a user's guide. This guide focuses on use of Tensoral for post-processing turbulence databases. The corresponding document - the Tensoral 'author's guide' - which focuses on how authors can make databases available to users via the Tensoral system - is currently unwritten. Section 1 of this user's guide defines Tensoral's basic notions: we explain the class of problems at hand and how Tensoral abstracts them. Section 2 defines Tensoral syntax for mathematical expressions. Section 3 shows how these expressions make up Tensoral statements. Section 4 shows how Tensoral statements and expressions are embedded into other computer languages (such as C or Vectoral) to make Tensoral programs. We conclude with a complete example program.

Dresselhaus, Eliot↗

Summary of in-flight flow visualization obtained from the NASA high alpha research vehicle

A summary of the surface and off-surface flow visualization results obtained in flight on the F-18 high alpha research vehicle (HARV) is presented, highlighting the extensive 3-D vortical flow on the aircraft at angles of attack up to 50 degs. The emitted fluid technique, as well as tufts and flow cones, were used to document the surface flow. A smoke generator system injected smoke into the vortex cores generated by the forebody and leading edge extensions (LEXs). Documentation was provided by onboard still and video, by air-to-air, and by postflight photography. The surface flow visualization techniques revealed laminar separation bubbles near the forebody apex, lines of separation on the forebody and LEX, and regions of attached and separated flow on the wings and fins. The off-surface flow visualization techniques showed the path of the vortex cores on the forebody and LEX as well as the LEX vortex core breakdown location. An interaction between the forebody and LEX vortices was noted. The flow over the surfaces of the vertical tail was categorized into regions of attached, unsteady, or separated flow using flow tufts.

Fisher, David F.↗

Using a Large Language Model as a Building Block to Generate Usable Validation and Verification Suite for OpenMP

In the HPC area, both hardware and software move quickly. Often new hardware is developed and deployed, the corresponding software stack, including compilers and other tools, are under active development while leading edge software developers are working to port and tune their applications, all at the same time. While the software ecosystem is in flux, one of the key challenges for users is obtaining insight into the state of implementation of key features in the programming languages and models their applications are using – whether they have been implemented, and whether the implementation conforms to the specification, especially for newly implemented features (less tested by widespread use). OpenMP is one of the most prominent shared memory programming models used for on-node programming in HPC. With the shift towards accelerators (such as GPUs and FPGAs) and heterogeneous programming OpenMP features are getting more complex. It is natural to ask whether generative AI approaches, and large language models (LLMs) in particular, can help in producing validation and verification test suites to allow users better and faster insights into the availability and correctness of OpenMP features of interest. In this work, we explore the use of ChatGPT-4 to generate a suite of tests for OpenMP features. We have chosen a set of directives and clauses, a total of 78 combinations, which first appeared in OpenMP 3.0 (released in May 2008) but are also relevant for accelerators. We prompted ChatGPT to generate tests in the C and Fortran languages, for both host (CPU) and device (accelerator). On the Summit super-computer using the GNU implementation, we found that, of the 78 generated tests 67 C tests and 43 Fortran tests compiled successfully and fewer than those executed to completion. On further analysis we show that not all generated tests are valid. We document the process, results, and provide detailed analysis regarding the quality of tests generated. With the aim of providing input to a production quality validation and verification suite, we manually implement the corrections required to make the tests valid according to the current OpenMP specification. We quantify this effort as small, medium, or large, and record the lines of code changed to correct the invalid tests. With the corrected tests we validate recent implementations from HPE, AMD, and GNU on the Frontier supercomputer. Our experiment and subsequent analysis show that although LLMs are capable of producing HPC specific codes, they are limited by their understanding of the deeper semantics and restrictions of programming models such as OpenMP. Unsurprisingly more commonly used features have better support, while some OpenMP 3.0 directives such as sections and tasking are not universally supported on accelerators. We demonstrate that successful compilation and execution to completion are inadequate metrics for evaluating generated code and that, at this time, commodity LLMs require expert intervention for code verification. This points to gaps in the training data that is currently available for HPC. We demonstrate that with "small" effort 37% of generated invalid C tests and 63% of generated invalid Fortran tests could be corrected. This improves productivity of test generation as we circumvent writing from scratch and the common programming errors associated with it.

Pophale, Swaroop [ORNL] (ORCID:0000000185446367)↗

Program For Systematic Documentation Of Tasks

Task Analysis Report Generation Tool (TARGET) computer program is graphical-interface software tool used to capture procedural knowledge and translate knowledge into hierarchical report. Designed to help programmer document expert's task, enabling programmer to build expert-system computer program that helps others perform task. Flexibility key element of design and of knowledge-acquisition session. Easy to use and tailored to accommodate users ranging from novice to experienced builder of expert systems. Written in C language.

Ortiz, Christopher John↗

Space Environments and Spacecraft Effects Concept: Transitioning Research to Operations and Applications

The National Aeronautics and Space Administration (NASA) is embarking on a course to expand human presence beyond Low Earth Orbit (LEO) while expanding its mission to explore the solar system. Destinations such as Near Earth Asteroids (NEA), Mars and its moons, and the outer planets are but a few of the mission targets. NASA has established numerous organizations specializing in specific space environments disciplines that will serve to enable these missions. To complement these existing discipline organizations, a concept is presented focusing on the development of a space environment and spacecraft effects organization. This includes space climate, space weather, natural and induced space environments, and effects on spacecraft materials and systems. This space environment and spacecraft effects organization would be comprised of Technical Working Groups (TWG) focusing on, for example: a) Charged Particles (CP), b) Space Environmental Effects (SEE), and c) Interplanetary and Extraterrestrial Environments (IEE). These technical working groups will generate products and provide knowledge supporting four functional areas: design environments, environment effects, operational support, and programmatic support. The four functional areas align with phases in the program mission lifecycle and are briefly described below. Design environments are used primarily in the mission concept and design phases of a program. Environment effects focuses on the material, component, sub-system and system-level selection and the testing to verify design and operational performance. Operational support provides products based on real time or near real time space weather observations to mission operators to aid in real time and near-term decision-making. The programmatic support function maintains an interface with the numerous programs within NASA and other federal agencies to ensure that communications are well established and the needs of the programs are being met. The programmatic support function also includes working in coordination with the program in anomaly resolution and generation of lesson learned documentation. The goal of this space environment and spacecraft effects organization is to develop decision-making tools and engineering products to support the mission phases of mission concept through operations by focusing on transitioning research to application. Products generated by this space environments and spacecraft effects organization are suitable for use in anomaly investigations. This paper will describe the organizational structure for this space environments and spacecraft effects organization, and outline the scope of conceptual TWG's and their relationship to the functional areas.

Edwards, D. L.↗

Space Environments and Spacecraft Effects Organization Concept

The National Aeronautics and Space Administration (NASA) is embarking on a course to expand human presence beyond Low Earth Orbit (LEO) while also expanding its mission to explore the solar system. Destinations such as Near Earth Asteroids (NEA), Mars and its moons, and the outer planets are but a few of the mission targets. Each new destination presents an opportunity to increase our knowledge of the solar system and the unique environments for each mission target. NASA has multiple technical and science discipline areas specializing in specific space environments disciplines that will help serve to enable these missions. To complement these existing discipline areas, a concept is presented focusing on the development of a space environments and spacecraft effects (SENSE) organization. This SENSE organization includes disciplines such as space climate, space weather, natural and induced space environments, effects on spacecraft materials and systems and the transition of research information into application. This space environment and spacecraft effects organization will be composed of Technical Working Groups (TWG). These technical working groups will survey customers and users, generate products, and provide knowledge supporting four functional areas: design environments, engineering effects, operational support, and programmatic support. The four functional areas align with phases in the program mission lifecycle and are briefly described below. Design environments are used primarily in the mission concept and design phases of a program. Engineering effects focuses on the material, component, sub-system and system-level selection and the testing to verify design and operational performance. Operational support provides products based on real time or near real time space weather to mission operators to aid in real time and near-term decision-making. The programmatic support function maintains an interface with the numerous programs within NASA, other federal government agencies, and the commercial sector to ensure that communications are well established and the needs of the programs are being met. The programmatic support function also includes working in coordination with the program in anomaly resolution and generation of lessons learned documentation. The goal of this space environment and spacecraft effects organization is to develop decision-making tools and engineering products to support all mission phases from mission concept through operations by focusing on transitioning research to application. Products generated by this space environments and effects application are suitable for use in anomaly investigations. This paper will describe the scope of the TWGs and their relationship to the functional areas, and discuss an organizational structure for this space environments and spacecraft effects organization.

Edwards, David L.↗

An Overview of the Space Environments and Spacecraft Effects Organization Concept

The National Aeronautics and Space Administration (NASA) is embarking on a course to expand human presence beyond Low Earth Orbit (LEO) while also expanding its mission to explore our Earth, and the solar system. Destinations such as Near Earth Asteroids (NEA), Mars and its moons, and the outer planets are but a few of the mission targets. Each new destination presents an opportunity to increase our knowledge on the solar system and the unique environments for each mission target. NASA has multiple technical and science discipline areas specializing in specific space environments fields that will serve to enable these missions. To complement these existing discipline areas, a concept is presented focusing on the development of a space environment and spacecraft effects (SESE) organization. This SESE organization includes disciplines such as space climate, space weather, natural and induced space environments, effects on spacecraft materials and systems, and the transition of research information into application. This space environment and spacecraft effects organization will be composed of Technical Working Groups (TWG). These technical working groups will survey customers and users, generate products, and provide knowledge supporting four functional areas: design environments, engineering effects, operational support, and programmatic support. The four functional areas align with phases in the program mission lifecycle and are briefly described below. Design environments are used primarily in the mission concept and design phases of a program. Environment effects focuses on the material, component, sub-system, and system-level response to the space environment and include the selection and testing to verify design and operational performance. Operational support provides products based on real time or near real time space weather to mission operators to aid in real time and near-term decision-making. The programmatic support function maintains an interface with the numerous programs within NASA, other federal government agencies, and the commercial sector to ensure that communications are well established and the needs of the programs are being met. The programmatic support function also includes working in coordination with the program in anomaly resolution and generation of lessons learned documentation. The goal of this space environment and spacecraft effects organization is to develop decision-making tools and engineering products to support all mission phases from mission concept through operations by focusing on transitioning research to application. Products generated by this space environments and effects application are suitable for use in anomaly investigations. This paper will describe the scope and purpose of the space environments and spacecraft effects organization and describe the TWG's and their relationship to the functional areas.

Edwards, David L.↗

DOCU-TEXT: A tool before the data dictionary

DOCU-TEXT, a proprietary software package that aids in the production of documentation for a data processing organization and can be installed and operated only on IBM computers is discussed. In organizing information that ultimately will reside in a data dictionary, DOCU-TEXT proved to be a useful documentation tool in extracting information from existing production jobs, procedure libraries, system catalogs, control data sets and related files. DOCU-TEXT reads these files to derive data that is useful at the system level. The output of DOCU-TEXT is a series of user selectable reports. These reports can reflect the interactions within a single job stream, a complete system, or all the systems in an installation. Any single report, or group of reports, can be generated in an independent documentation pass.

Carter, B.↗

Validation Ice Crystal Icing Engine Test in the Propulsion Systems Laboratory at NASA Glenn Research Center

The Propulsion Systems Laboratory (PSL) is an existing altitude simulation jet engine test facility located at NASA Glenn Research Center in Clevleand, OH. It was modified in 2012 with the integration of an ice crystal cloud generation system. This paper documents the inaugural ice crystal cloud test in PSLthe first ever full scale, high altitude ice crystal cloud turbofan engine test to be conducted in a ground based facility. The test article was a Lycoming ALF502-R5 high bypass turbofan engine, serial number LF01. The objectives of the test were to validate the PSL ice crystal cloud calibration and engine testing methodologies by demonstrating the capability to calibrate and duplicate known flight test events that occurred on the same LF01 engine and to generate engine data to support fundamental and computational research to investigate and better understand the physics of ice crystal icing in a turbofan engine environment while duplicating known revenue service events and conducting test points while varying facility and engine parameters. During PSL calibration testing it was discovered than heated probes installed through tunnel sidewalls experienced ice buildup aft of their location due to ice crystals impinging upon them, melting and running back. Filtered city water was used in the cloud generation nozzle system to provide ice crystal nucleation sites. This resulted in mineralization forming on flow path hardware that led to a chronic degradation of performance during the month long test. Lacking internal flow path cameras, the response of thermocouples along the flow path was interpreted as ice building up. Using this interpretation, a strong correlation between total water content (TWC) and a weaker correlation between median volumetric diameter (MVD) of the ice crystal cloud and the rate of ice buildup along the instrumented flow path was identified. For this test article the engine anti-ice system was required to be turned on before ice crystal icing would occur. The ice crystal icing event, an uncommanded reduction in thrust, was able to be turned on and off by manipulating cloud TWC. A flight test point where no ice crystal icing event occurred was also duplicated in PSL. Physics based computational tools were successfully used to predict tunnel settings to induce ice buildup along the low pressure compression system flow path for several test points at incrementally lower altitudes, demonstrating that development of ice crystal icing scaling laws is potentially feasible. Analysis of PSL test data showed that uncommanded reduction in thrust occurs during ice crystal cloud on operation prior to fan speed reduction. This supports previous findings that the reduction of thrust for this test article is due to ice buildup leading to a restricted airflow from either physical or aerodynamic blockage in the engine core flow path.

Ice Crystal Icing↗

Validation Ice Crystal Icing Engine Test in the Propulsion Systems Laboratory at NASA Glenn Research Center

The Propulsion Systems Laboratory (PSL) is an existing altitude simulation jet engine test facility located at NASA Glenn Research Center in Cleveland, OH. It was modified in 2012 with the integration of an ice crystal cloud generation system. This paper documents the inaugural ice crystal cloud test in PSL--the first ever full scale, high altitude ice crystal cloud turbofan engine test to be conducted in a ground based facility. The test article was a Lycoming ALF502-R5 high bypass turbofan engine, serial number LF01. The objectives of the test were to validate the PSL ice crystal cloud calibration and engine testing methodologies by demonstrating the capability to calibrate and duplicate known flight test events that occurred on the same LF01 engine and to generate engine data to support fundamental and computational research to investigate and better understand the physics of ice crystal icing in a turbofan engine environment while duplicating known revenue service events and conducting test points while varying facility and engine parameters. During PSL calibration testing it was discovered than heated probes installed through tunnel sidewalls experienced ice buildup aft of their location due to ice crystals impinging upon them, melting and running back. Filtered city water was used in the cloud generation nozzle system to provide ice crystal nucleation sites. This resulted in mineralization forming on flow path hardware that led to a chronic degradation of performance during the month long test. Lacking internal flow path cameras, the response of thermocouples along the flow path was interpreted as ice building up. Using this interpretation, a strong correlation between total water content (TWC) and a weaker correlation between median volumetric diameter (MVD) of the ice crystal cloud and the rate of ice buildup along the instrumented flow path was identified. For this test article the engine anti-ice system was required to be turned on before ice crystal icing would occur. The ice crystal icing event, an uncommanded reduction in thrust, was able to be turned on and off by manipulating cloud TWC. A flight test point where no ice crystal icing event occurred was also duplicated in PSL. Physics based computational tools were successfully used to predict tunnel settings to induce ice buildup along the low pressure compression system flow path for several test points at incrementally lower altitudes, demonstrating that development of ice crystal icing scaling laws is potentially feasible. Analysis of PSL test data showed that uncommanded reduction in thrust occurs during ice crystal cloud on operation prior to fan speed reduction. This supports previous findings that the reduction of thrust for this test article is due to ice buildup leading to a restricted airflow from either physical or aerodynamic blockage in the engine core flow path.

Turbofan Engine↗

Generative large language models for predictive maintenance planning

Maintenance planning and the generation of necessary components for tasks can prove time-consuming and complex. Automating the creation of recurring or similar tasks by leveraging previous planning packages and data, while uncovering insights to automate planning package generation, presents an opportunity to conserve valuable time and resources. This work aims to harness the textual and probabilistic capabilities of large language models (LLMs) to automate the generation of planning packages. Utilizing diverse data sources ranging from raw data to handwritten text, both singular and collaborative LLMs are trained and tested. Results demonstrate their capability to generate essential planning package components, effectively replicating the statistical patterns in the data. This demonstrates the use of these tools inside a digital asset for automated planning. This work outlines a methodology for constructing datasets, a training suite, and evaluation methods for LLM-based textual and conversational planning tools utilized in an asset digital twin. Results indicate that the fine-tuned models generate estimated planning information within the statistical ranges observed in real maintenance data. The models achieve high accuracy (>90%) in document question-answering and instruction generation tasks. Furthermore, the conversational retrieval-augmented generation (RAG) assistant system achieves 100% document retrieval accuracy, while conversational information capture exceeds 98% across the majority of work-package assistant modules.

97 MATHEMATICS AND COMPUTING↗

Automatic Certification of Kalman Filters for Reliable Code Generation

AUTOFILTER is a tool for automatically deriving Kalman filter code from high-level declarative specifications of state estimation problems. It can generate code with a range of algorithmic characteristics and for several target platforms. The tool has been designed with reliability of the generated code in mind and is able to automatically certify that the code it generates is free from various error classes. Since documentation is an important part of software assurance, AUTOFILTER can also automatically generate various human-readable documents, containing both design and safety related information. We discuss how these features address software assurance standards such as DO-178B.

Denney, Ewen↗

Experiment Document Information System (EDIS) evolution

The EDIS is the second generation of a system designed to produce and control a document containing large amounts of text in combination with tables and graphs of mathematical/scientific data. The first generation system proved the concept, but the slow, unfriendly user interface resulted in an effort to find an off-the-shelf product to improve the interface capability while maintaining the system requirements. The basic design of that first system was combined with the hypertext concepts inherent in HyperCard to generate the most usable EDIS. Currently in the latter stages of design, the EDIS promises to be the first step in the automation of the process required for defining complete packages of Life Sciences experiments for the Shuttle missions.

Moorhead, Jane↗