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ASE has successfully demonstrated that a spacecraft can be driven by science analysis and autonomously controlled. ASE is available for flight on other missions. Mission hardware design should consider ASE requirements for available onboard data storage, onboard memory size and processor speed.
The Autonomous Sciencececraft Experiment (ASE), currently flying onboard the Earth Observing-1 (EO-1) spacecraft, integrates several autnomoy software technologies enabling autnomous science analysis and mission planning. The experiment demonstrates the potential for future space missions to use onboard decision-making to respond autonomously to capture short-lived science phenomena. The AAAI software demonstration will consist of two sections: a real-time display of an ASE-commanded ground contact from the EO-1 spacecraft, and a simulation of the full ASE autonomous science-response scenario.
The Autonomous Sciencecraft Experiment (ASE), currently flying onboard the Earth Observing-1 (EO-I) spacecraft, integrates several autonomy software technologies enabling autonomous science analysis and mission planning. The experiment demonstrates the potential for future space missions to use onboard decision-making to respond autonomously to capture short-lived science phenomena. The AAAI software demonstration will consist of two sections: a real-time display of an ASE-commanded ground contact from the EO-1 spacecraft, and a simulation of the full ASE autonomous science-response scenario.
The Autonomous Sciencecraft Experiment (ASE), part of the New Millennium Space Technology 6 Project, is flying onboard the Earth Orbiter 1 (EO-1) mission. The ASE software enables EO-1 to autonomously detect and respond to science events such as: volcanic activity, flooding, and water freeze/thaw. ASE uses classification algorithms to analyze imagery onboard to detect chang-e and science events. Detection of these events is then used to trigger follow-up imagery. Onboard mission planning software then develops a response plan that accounts for target visibility and operations constraints. This plan is then executed using a task execution system that can deal with run-time anomalies. In this paper we describe the autonomy flight software and how it enables a new paradigm of autonomous science and mission operations. We will also describe the current experiment status and future plans.
Adoption of advanced automated SE (ASE) tools would be more favored if a business case could be made that these tools are more valuable than alternate methods. In theory, software prediction models can be used to make that case. In practice, this is complicated by the 'local tuning' problem. Normally. predictors for software effort and defects and threat use local data to tune their predictions. Such local tuning data is often unavailable. This paper shows that assessing the relative merits of different SE methods need not require precise local tunings. STAR 1 is a simulated annealer plus a Bayesian post-processor that explores the space of possible local tunings within software prediction models. STAR 1 ranks project decisions by their effects on effort and defects and threats. In experiments with NASA systems. STARI found one project where ASE were essential for minimizing effort/ defect/ threats; and another project were ASE tools were merely optional.
The National Aeronautics and Space Administration (NASA) is launching a bold and ambitious new space initiative. A significant part of this new initiative includes exploration of new worlds, the development of more innovative technologies, and expansion our presence in the solar system. A common theme to this initiative is the exploration of space beyond Low Earth Orbit (LEO). As currently organized, NASA does not have an Agency-level office that provides coordination of space environment research and development. This has contributed to the formation of a gap between spaceflight environments knowledge and the application of this knowledge for multi-program use and for use outside NASA. This paper outlines a concept to establish a NASA-level Applied Spaceflight Environments (ASE) office that will provide coordination and funding for sustained multi-program support in three technical areas that have demonstrated these needs through customer requests. These technical areas are natural environments characterization and modeling, materials and systems analysis and test, and operational space environments modeling. Additionally the ASE office will serve as an entry point of contact for external users who wish to take advantage of data and assets associated with space environments, including space weather. This paper will establish the need for the ASE, discuss a concept for organizational structure and outline the scope in the three technical areas.
A summary of computational and experimental aeroelastic (AE) and aeroservoelastic (ASE) results for the Semi-Span Super-Sonic Transport (S4T) wind-tunnel model is presented. A broad range of analyses and multiple AE and ASE wind-tunnel tests of the S4T wind-tunnel model have been performed in support of the ASE element in the Supersonics Program, part of the NASA Fundamental Aeronautics Program. This paper is intended to be an overview of multiple papers that comprise a special S4T technical session. Along those lines, a brief description of the design and hardware of the S4T wind-tunnel model will be presented. Computational results presented include linear and nonlinear aeroelastic analyses, and rapid aeroelastic analyses using CFD-based reduced-order models (ROMs). A brief survey of some of the experimental results from two open-loop and two closed-loop wind-tunnel tests performed at the NASA Langley Transonic Dynamics Tunnel (TDT) will be presented as well.
The purpose of this study was to ascertain the colleges and universities from which engineering interns were chosen for the Langley Aerospace Research Student Scholars (LARSS) internship program over a six year period. While the initial question was where the interns were from, information was also obtained on these colleges and universities' rankings with respect to female faculty in tenure/tenure-track positions as well as research expenditures according to the American Society for Engineering Education's (ASEE) 2012 Annual Report. The majority of interns were selected from 14 institutions. Eight of the institutions for female interns and 7 for male interns were also cited as having the largest percentages/numbers of female faculty according to ASEE data. In 10 of the 14 institutions for male interns and 9 of 14 for female interns, the institutions were cited as having some of the highest research expenditures. Last, data from ASEE's report on bachelor degrees awarded in 2011 was assessed with respect to the 21 institutions listed as having higher numbers of women in tenure/tenure-track positions. The majority of these institutions represented larger engineering programs with a sizeable number of engineering faculty. These 21institutions accounted for 24.4% of the total engineering bachelor degrees awarded in 2011, with 27.4% of all female and 23.8% of all male graduates in the U.S.
This paper presents a holistic model order reduction (MOR) methodology and framework that integrates key technological elements of sequential model reduction, consistent model representation, and model interpolation for constructing high-quality linear parameter-varying (LPV) aeroservoelastic (ASE) reduced order models (ROMs) of flexible aircraft. The sequential MOR encapsulates a suite of reduction techniques, such as truncation and residualization, modal reduction, and balanced realization and truncation to achieve optimal ROMs at grid points across the flight envelope. The consistence in state representation among local ROMs is obtained by the novel method of common subspace reprojection. Model interpolation is then exploited to stitch ROMs at grid points to build a global LPV ASE ROM feasible to arbitrary flight condition. The MOR method is applied to the X-56A MUTT vehicle with flexible wing being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies demonstrated that relative to the fullorder model, our X-56A ROM can accurately and reliably capture vehicles dynamics at various flight conditions in the target frequency regime while the number of states in ROM can be reduced by 10X (from 180 to 19), and hence, holds great promise for robust ASE controller synthesis and novel vehicle design.
The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.
The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattem recognition to radically increase science retum by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.
The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecraft science agent. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.
The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecruft science agent. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.
Ice-mass change induces regionally varying patterns of sea-level change due to gravitational, rotational, and deformational (GRD) effects, which in turn influence marine-based ice stability in Antarctica. For improved projection of the Antarctic Ice Sheet (AIS), there is a need for including GRD effects in modeling and improving understanding of basin-by-basin sensitivity of ice evolution to GRD effects under a range of climate scenarios. We couple a high-resolution, higher-order ice-sheet model with a 1D global sea-level model that fully captures GRD effects, and simulate ice evolution in Antarctica under the Ice Sheet Model Intercomparison Project for CMIP6 experiments. We perform two sets of coupled simulations incorporating 1D Maxwell solid Earth structure suitable for West and East Antarctica and show that the Amundsen Sea Embayment (ASE) in West Antarctica has the highest sensitivity to GRD effects—in high-emission scenarios, grounding-line retreat accelerates by hundreds of kilometers by 2300 without GRD effects, but GRD effects delay this retreat on a timescale of decades. However, we find that delay times do not show a clear relationship to the strength of climate forcing alone. Furthermore, GRD effects can influence ice-sheet dynamics more than the choice of climate model for a given emissions scenario. In contrast, East Antarctica exhibits minimal sensitivity to GRD effects throughout the study period. These findings underscore the critical role of GRD effects in shaping future West AIS evolution, highlighting the importance of constraining the regional 3D Earth structure and bed topography in West Antarctica, particularly the ASE.
Low energy conceptual stage designs and adaptations to existing/planned shuttle upper stages were developed and their performance established. Selected propulsion modes and subsystems were used as a basis to develop airborne support equipment (ASE) design concepts. Orbiter installation and integration (both physical and electrical interfaces) were defined. Low energy stages were adapted to the orbiter and ASE interfaces. Selected low energy stages were then used to define and describe typical ground and flight operations.
Risk assessment, safety analysis of the automotive stirling engine (ASE) mod I, design criteria and materials properties for the ASE mod I and reference engines, combustion are flower development, and the mod I engine starter motor are discussed. The stirling engine system, external heat system, hot engine system, cold engine system, and engine drive system are also discussed.
In the initial stages of developing the automotive Stirling engine (ASE), data has been accumulated under the program's Quality Assurance Report (QAR) program to identify problem areas encountered during engine operation. This data has been used as the basis for developing design modifications to existing hardware, identifying diagnostic techniques and instrumentation, and providing guidance towards component and system development requirements for future engine designs, such as the Mod II engine currently in the initial design stage. The QAR has proven itself to be an essential part of the ASE Program, and has successfully guided the development of the automotive application of this emerging engine technology.
The Inertial Upper Stage (IUS) is a two-stage booster used by NASA and the Defense Department to insert payloads into geosynchronous orbit from low-Earth orbit. The hydraulic mechanism discussed here was designed to perform a specific dynamic and static interface function within the Space Transportation System's Orbiter. Requirements, configuration, and application of the hydraulic mechanism with emphasis on performance and methods of achieving zero external hydraulic leakage are discussed. The hydraulic load-leveler mechanism meets the established design requirements for operation in a low-Earth orbit. Considerable testing was conducted to demonstrate system performance and verification that external leakage had been reduced to zero. Following each flight use of an ASE, all hydraulic mechanism components are carefully inspected for leakage. The ASE, including the hydraulic mechanism, has performed without any anomalies during all IUS flights.