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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Cognitive Modeling of Social Behaviors

The driving theme of cognitive modeling for many decades has been that knowledge affects how and which goals are accomplished by an intelligent being (Newell 1991). But when one examines groups of people living and working together, one is forced to recognize that whose knowledge is called into play, at a particular time and location, directly affects what the group accomplishes. Indeed, constraints on participation, including roles, procedures, and norms, affect whether an individual is able to act at all (Lave & Wenger 1991; Jordan 1992; Scribner & Sachs 1991). To understand both individual cognition and collective activity, perhaps the greatest opportunity today is to integrate the cognitive modeling approach (which stresses how beliefs are formed and drive behavior) with social studies (which stress how relationships and informal practices drive behavior). The crucial insight is that norms are conceptualized in the individual &nd as ways of carrying out activities (Clancey 1997a, 2002b). This requires for the psychologist a shift from only modeling goals and tasks - why people do what they do - to modeling behavioral patterns-what people do-as they are engaged in purposeful activities. Instead of a model that exclusively deduces actions from goals, behaviors are also, if not primarily, driven by broader patterns of chronological and located activities (akin to scripts). This analysis is particular inspired by activity theory (Leont ev 1979). While acknowledging that knowledge (relating goals and operations) is fundamental for intelligent behavior, activity theory claims that a broader driver is the person s motives and conceptualization of activities. Such understanding of human interaction is normative (i.e., viewed with respect to social standards), affecting how knowledge is called into play and applied in practice. Put another way, how problems are discovered and framed, what methods are chosen, and indeed who even cares or has the authority to act, are all constrained by norms, which are conceived and enacted by individuals.

Clancey, William J.↗
NASA NTRS1992

Technology test results from an intelligent, free-flying robot for crew and equipment retrieval in space

The ground-based demonstrations of Extra Vehicular Activity (EVA) Retriever, a voice-supervised, intelligent, free-flying robot, are designed to evaluate the capability to retrieve objects (astronauts, equipment, and tools) which have accidentally separated from the Space Station. The EVA Retriever software is required to autonomously plan and execute a target rendezvous, grapple, and return to base while avoiding stationary and moving obstacles with subsequent object handover. The software architecture incorporates a heirarchical decomposition of the control system that is horizontally partitioned into five major functional subsystems: sensing, perception, world model, reasoning, and acting. The design provides for supervised autonomy as the primary mode of operation. It is intended to be an evolutionary system improving in capability over time and as it earns crew trust through reliable and safe operation. This paper gives an overview of the hardware, a focus on software, and a summary of results achieved recently from both computer simulations and air bearing floor demonstrations. Limitations of the technology used are evaluated. Plans for the next phase, during which moving targets and obstacles drive realtime behavior requirements, are discussed.

Erickson, J.↗
NASA NTRS2022

Brief Communication: Preliminary ICESat-2 (Ice, Cloud and Land Elevation Satellite-2) Measurements of Outlet Glaciers Reveal Heterogeneous Patterns of Seasonal Dynamic Thickness Change

Dynamic changes of marine-terminating outlet glaciers are projected to be responsible for about half of future ice loss from the Greenland Ice Sheet. However, we lack a unified, process-based understanding that can explain the observed dynamic changes of all outlet glaciers. Many glaciers undergo seasonal dynamic thickness changes, and classifying the patterns of seasonal thickness change can improve our understanding of the processes that drive glacier behavior. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) provides space-based, seasonally repeating altimetry measurements of the ice sheets, allowing us to quantify near-termini seasonal dynamic thickness patterns of 37 outlet glaciers around the Greenland Ice Sheet. We classify the glaciers into seven common patterns of seasonal thickness change over a 2-year period from 2019 to 2020. We find small groupings of neighboring glaciers with similar patterns of seasonal thickness change, but, within larger sectors of the ice sheet, patterns of seasonal thickness change are mostly heterogeneous. Future studies can build upon our results by extending these time series, comparing seasonal dynamic thickness changes with external forcings, such as ocean temperature and meltwater runoff, and with other dynamic variables such as seasonal glacier velocity and terminus position changes.

Christian J Taubenberger↗
NASA NTRSDate not supplied

Advanced Modeling of Control-Structure Interaction in Thrust Vector Control Systems

The Space Launch System (SLS) Core Stage (CS) Thrust Vector Control (TVC) system is comprised of 8 mechanical feedback Shuttle heritage Type III TVC actuators and four RS-25 engines, each attached to a Shuttle heritage gimbal block/bearing. Two actuators are used to move each engine in two planes perpendicular to one another (i.e., pitch and yaw). The TVC system design leverages hardware from the Space Shuttle program as well as new hardware designed specifically for the Core Stage. During the development of the SLS TVC system, a family of advanced dynamics models were developed to extend and compliment the simplified quasi-linear “simplex” model historically used for flight control design and stability analysis. The importance of these advanced models became increasingly evident after ambient and hot fire testing of the Core Stage, which revealed a number of findings associated with the dynamic response of the TVC integrated system. Test responses suggested that the TVC did not meet its performance specifications and its step and frequency responses exhibited unexpected departures from prior lab tests and modeled behavior. One driving factor for these results was a higher-than-expected degree of coupling between the TVC system, the engine dynamics, and the Core Stage structure. This paper is the third installment in a seven-paper series surveying the design, engineering, test validation, and flight performance of the Core Stage Thrust Vector Control system. In this paper, a new method of modeling rocket vehicle thrust vectoring servoelastic dynamics is presented. In this approach, the load dynamics are replaced by a detailed finite element model containing both the rigid body and elastic modes. A partitioning technique is used to compute the effective compliance from the modal data and obtain accurate simulation results using a reduced number of generalized coordinates. Coupled backup structure and nozzle attach compliance effects on multiple engines are captured in higher fidelity than with a spring approximation, eliciting novel effects due to the complex load paths involved in the Core Stage structure. Validation of the model is demonstrated using a variety of structural/modal, laboratory, and full-scale hot fire test data.

Launch Vehicles↗
NASA NTRSDate not supplied

Advanced Modeling of Control-Structure Interaction in Thrust Vector Control Systems

The Space Launch System (SLS) Core Stage (CS) Thrust Vector Control (TVC) system is comprised of 8 mechanical feedback Shuttle heritage Type III TVC actuators and four RS-25 engines, each attached to a Shuttle heritage gimbal block/bearing. Two actuators are used to move each engine in two planes perpendicular to one another (i.e., pitch and yaw). The TVC system design leverages hardware from the Space Shuttle program as well as new hardware designed specifically for the Core Stage. During the development of the SLS TVC system, a family of advanced dynamics models were developed to extend and compliment the simplified quasi-linear “simplex” model historically used for flight control design and stability analysis. The importance of these advanced models became increasingly evident after ambient and hot fire testing of the Core Stage, which revealed a number of findings associated with the dynamic response of the TVC integrated system. Test responses suggested that the TVC did not meet its performance specifications and its step and frequency responses exhibited unexpected departures from prior lab tests and modeled behavior. One driving factor for these results was a higher-than-expected degree of coupling between the TVC system, the engine dynamics, and the Core Stage structure. This paper is the third installment in a seven-paper series surveying the design, engineering, test validation, and flight performance of the Core Stage Thrust Vector Control system. In this paper, a new method of modeling rocket vehicle thrust vectoring servoelastic dynamics is presented. In this approach, the load dynamics are replaced by a detailed finite element model containing both the rigid body and elastic modes. A partitioning technique is used to compute the effective compliance from the modal data and obtain accurate simulation results using a reduced number of generalized coordinates. Coupled backup structure and nozzle attach compliance effects on multiple engines are captured in higher fidelity than with a spring approximation, eliciting novel effects due to the complex load paths involved in the Core Stage structure. Validation of the model is demonstrated using a variety of structural/modal, laboratory, and full-scale hot fire test data.

Launch Vehicles↗
NASA NTRS1983

Traction calculations and design data for two traction fluids

The rheological properties of the fluid under these certain conditions which precludes the use of most of the conventional instruments for steady state measurement were studied. The only suitable type of instrument is a disk machine where most of the conditions are the same of similar to those in traction drives. From the resulting traction tests, certain models are inferred. To the designer of traction drives, the traction behavior of the fluid under the severe conditions is of utmost importance because of the direct influence that it has on the efficiency, size, and life of a given drive.

Tevaarwerk, J. L.↗
NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗
NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗
NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗
NASA NTRS1989

The unusual near-threshold FCG behavior of a single crystal superalloy and the resolved shear stress as the crack driving force

An investigation of the fatigue growth (FCG) behavior of PWA 1480 single crystal nickel base superalloy was conducted. Typical Paris region behavior was observed above a Delta-K of 8 MPa sq rt m. However, below that stress intensity range, the alloy exhibted highly unusual behavior. The behavior consisted of a region where the crack growth rate became essentially independent of the applied stress intensity. The transition in the FCG behavior was related to a change in the observed crack growth mechanisms.

Telesman, Jack↗
NASA NTRS1981

The UV resonance lines of three hot Ap stars: HD 133029, HD 175362 and HD 219749

High resolution spectra of the Ap stars HD 133929, HD 175329 and HD 219749 were obtained during two 16 hour shifts with the IUE satellite at Goddard Space Flight Center. Stellar wind, extended atmosphere in rigid corotation with the stellar surface, and the influence of the strong magnetic field on the upper part of the atmosphere would explain the shape and the strength of the resonance lines. The resonance lines of HD 175362 in particular show very peculiar behavior. The necessary driving forces for the expanding envelope are not compatible with the diffusion theory of the Ap atmospheres.

Rakos, K. D.↗
NASA NTRS1979

Traction drive performance prediction for the Johnson and Tevaarwerk traction model

The fluid rheology model is used to investigate the traction behavior for typical traction drive contacts. The aspect ratio of the contact and the invariably present spin are investigated. Contacts with a low aspect ratio predict a superior performance in that they show less slip for the same degree of traction. Spin always has a diminishing effect on the traction at the same slip. At sufficiently high spin the model may be simplified to a limiting shear stress model. The conventional rigid plastic analysis applies here equally well.

Tevaarwerk, J. L.↗
NASA NTRS1995

Dynamics and control of instrumented harmonic drives

Since torque in harmonic drives is transmitted by a pure couple, harmonic drives do not generate radial forces and therefore can be instrumented with torque sensors without interference from radial forces. The installation of torque sensors on the stationary component of harmonic drives (the Flexipline cup in this research work) produce backdrivability needed for robotic and telerobotic compliant maneuvers. Backdrivability of a harmonic drive, when used as torque increaser, means that the output shaft can be rotated via finite amount of torque. A high ratio harmonic drive is non-backdrivable because its output shaft cannot be turned by applying a torque on it. This article first develops the dynamic behavior of a harmonic drive, in particular the non-backdrivability, in terms of a sensitivity transfer function. The instrumentation of the harmonic drive with torque sensor is then described. This leads to a description of the control architecture which allows modulation of the sensitivity transfer function within the limits established by the closed-loop stability. A set of experiments on an active hand controller, powered by a DC motor coupled to an instrumented harmonic drive, is given to exhibit this method's limitations.

NASA Discipline Space Human Factors↗
NASA NTRS1992

Driven and unloading aspects of magnetospheric substorms

Issues concerning the 'driven' versus 'unloading' nature of substorms are presented. The original concepts attendant to this debate are presented and substorms are concluded to inextricably combine aspects that are driven with aspects that represent a loading-unloading system. For isolated substorms, the magnetosphere-ionosphere system is shown to exhibit a bimodal response to solar wind changes. A 20 min response characteristic is associated with the driven aspect of substorms, while a 1 hr response time is associated with unloading. It is found that for strong solar wind input conditions, the magnetospheric response becomes more nearly unimodal. This is interpreted in terms of a nonlinear dynamical evolution of the system. Simple analog models are described which capture the essence of the nonlinear magnetospheric behavior. These models exhibit chaotic transitions for strong driving conditions: this may explain the observed behavior of the magnetosphere during strong geomagnetic activity.

Baker, D. N.↗
NASA NTRS1994

Controllability of Free-piston Stirling Engine/linear Alternator Driving a Dynamic Load

This paper presents the dynamic behavior of a Free-Piston Stirling Engine/linear alternator (FPSE/LA) driving a single-phase fractional horse-power induction motor. The controllability and dynamic stability of the system are discussed by means of sensitivity effects of variations in system parameters, engine controller, operating conditions, and mechanical loading on the induction motor. The approach used expands on a combined mechanical and thermodynamic formulation employed in a previous paper. The application of state-space technique and frequency domain analysis enhances understanding of the dynamic interactions. Engine-alternator parametric sensitivity studies, similar to those of the previous paper, are summarized. Detailed discussions are provided for parametric variations which relate to the engine controller and system operating conditions. The results suggest that the controllability of a FPSE-based power system is enhanced by proper operating conditions and built-in controls.

Kankam, M. David↗
NASA NTRS1992

The neurochemical basis of photic entrainment of the circadian pacemaker

Circadian rhythmicity in mammals is controlled by the action of a light-entrainable hypothalamus, in association with two cell clusters known as the supra chiasmatic nuclei (SCN). In the absence of temporal environmental clues, this pacemaker continues to measure time by an endogenous mechanism (clock), driving biochemical, physiological, and behavioral rhythms that reflect the natural period of the pacemaker oscillation. This endogenous period usually differs slightly from 24 hours (i.e., circadian). When mammals are maintained under a 24 hour light-dark (LD) cycle, the pacemaker becomes entrained such that the period of the pacemaker oscillation matches that of the LD cycle. Potentially entraining photic information is conveyed to the SCN via a direct retinal projection, the retinohypothalamic tract (RHT). RHT neurotransmission is thought to be mediated by the release of excitatory amino acids (EAA) in the SCN. In support of this hypothesis, recent experiments using nocturnal rodents have shown that EAA antagonists block the effects of light on pacemaker-driven behavioral rhythms, and attenuate light induced gene expression in SCN cells. An understanding of the neurochemical basis of the photic entrainment process would facilitate the development of pharmacological strategies for maintaining synchrony among shift workers in environments, such as the Space Station, which provide unreliable or conflicting temporal photic clues.

Rea, Michael A.↗
NASA NTRS2000

Parametric Study of Reactive Melt Infiltration

Reactive melt infiltration is viewed as a promising means of achieving near-net shape manufacturing with quick processing time and at low cost. Since the reactants and products are, in general, of varying density, overall conservation of mass dictates that there is a force related to chemical conversion which can directly influence infiltration behavior. In effect, the driving pressure forces may compete with the forces from chemical conversion, affecting the advancement of the front. We have developed a two-dimensional numerical code to examine these effects, using reaction-formed silicon carbide as a model system for this process. We have examined a range of initial porosities, pore radii, and reaction rates in order to investigate their effects on infiltration dynamics.

Nelson, Emily S.↗