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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Space Launch System: Core Stage Thrust Vector Control Systems Engineering Challenges in Reusing Heritage Hardware

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. The Space Shuttle heritage hardware directly reused on SLS includes the Orbiter TVC hydraulic servo-actuators (with two slight design modifications), the Orbiter hydraulic circulation pumps, the Orbiter gimbal block/bearing, and the Solid Rocket Booster hydraulic pumps. The Core Auxiliary Power Unit (CAPU) is derived from the Orbiter Auxiliary Power Unit (APU). The Orbiter and Solid Rocket Booster APU turbines are powered by hot gas produced by catalyzed hydrazine decomposition. On the SLS Core Stage, the CAPU turbine is spun using cold gas tapped-off from the RS-25 to CS liquid hydrogen autogenous pressurization line. While direct reuse or slight modification of existing hardware may seem to be a triple-win for a program in cost, schedule, and technical risk mitigation, those benefits can only be realized when its degree of application in a new system is carefully and thoughtfully managed. The heritage hardware reuse should be prescribed within the heritage design capability and reuse environments must lie within the envelope of heritage qualification testing. Despite the significant test and flight experience of the Shuttle heritage hardware components, successful integration with the newly designed CS TVC components and incorporation into the stage design proved to be a challenge which required re-qualification of the heritage hardware as well as thorough integrated testing to support flight certification. Examples of the challenges that were overcome include: re-qualifying heritage hardware to survive new shock and vibration environments, certifying performance of extensively modified heritage hardware, regenerating design insight due to lack of available heritage vendor data, showing compliance to modern structural design standards, translation of heritage requirements for analog avionics to modern digital avionics, and interfacing heritage mechanical hardware with newly designed avionics. This paper is the second installment in a seven-paper series surveying the design, engineering, test validation, and flight performance of the Core Stage Thrust Vector Control system. This paper will discuss several engineering challenges encountered during the development process for SLS CS TVC and how they were successfully overcome to reach flight readiness.

Thrust Vector Control↗
NASA NTRS1981

The MSFC vector magnetograph

The NASA/Marshall Space Flight Center's solar vector magnetograph system allows measurements of all components of the Sun's photospheric magnetic field over a 5 x 5 or 2.5 x 2.5 arc min square field of view with an optimum time resolution of approximately 100 sec and an optimum signal-to-noise of approximately 1000. The basic system components are described, including the optics, detector, digital system, and associated electronics. Automatic sequencing and control functions are outlined as well as manual selections of system parameters which afford unique system flexibility. Results of system calibration and performance are presented, including linearity, dynamic range, uniformity, spatial and spectral resolutions, signal-to-noise, electro-optical retardation and polarization calibration.

Hagyard, M. J.↗
NASA NTRSDate not supplied

EARLY INFORMATION PARAMETER-SET ANALYSIS FOR SATELLITE CLOSE APPROACHES USING MACHINE LEARNING

In spaceflight navigation applications, understanding and accurately applying orbital mechanics by leveraging force models for trajectory predictions will always remain an important aspect in space mission design and operations. In the process of capturing the dynamics and perturbations in the space environment, the force models are not all encompassing in that these models are subject to errors, commonly referred to as process noise. Therefore, in predicting state vectors of space objects such as spacecraft or debris over long periods of time, these errors in the process noise tend to grow over time.

machine learning↗
NASA NTRS1995

Estimation of attitude sensor timetag biases

This paper presents an extended Kalman filter for estimating attitude sensor timing errors. Spacecraft attitude is determined by finding the mean rotation from a set of reference vectors in inertial space to the corresponding observed vectors in the body frame. Any timing errors in the observations can lead to attitude errors if either the spacecraft is rotating or the reference vectors themselves vary with time. The state vector here consists of the attitude quaternion, timetag biases, and, optionally, gyro drift rate biases. The filter models the timetags as random walk processes: their expectation values propagate as constants and white noise contributes to their covariance. Thus, this filter is applicable to cases where the true timing errors are constant or slowly varying. The observability of the state vector is studied first through an examination of the algebraic observability condition and then through several examples with simulated star tracker timing errors. The examples use both simulated and actual flight data from the Extreme Ultraviolet Explorer (EUVE). The flight data come from times when EUVE had a constant rotation rate, while the simulated data feature large angle attitude maneuvers. The tests include cases with timetag errors on one or two sensors, both constant and time-varying, and with and without gyro bias errors. Due to EUVE's sensor geometry, the observability of the state vector is severely limited when the spacecraft rotation rate is constant. In the absence of attitude maneuvers, the state elements are highly correlated, and the state estimate is unreliable. The estimates are particularly sensitive to filter mistuning in this case. The EUVE geometry, though, is a degenerate case having coplanar sensors and rotation vector. Observability is much improved and the filter performs well when the rate is either varying or noncoplanar with the sensors, as during a slew. Even with bad geometry and constant rates, if gyro biases are independently known, the timetag error for a single sensor can be accurately estimated as long as its boresight is not too close to the spacecraft rotation axis.

Sedlak, J.↗
NASA NTRS2001

Noise Mitigation Controller Tools

During the past decade, the National Aeronautics and Space Administration (NASA) has been developing and evaluating a suite of decision support tools (DSTs) to aid the air traffic controller in the management of traffic. These tools are known collectively as the Center/TRACON Automation System (CTAS). The primary focus of CTAS is increased capacity. As part of a new NASA program called Quiet Aircraft Technology (QAT), the following question is being addressed: Can CTAS technology also support the noise mitigation requirements imposed by the community? Controllers currently support a variety of low noise procedures in low traffic densities but, as traffic increases, these must be abandoned due to excessive spacing requirements for vectoring or inter-arrival spacing requirements needed to handle a spectrum of low noise procedures. NASA is currently investigating how to provide controllers with noise-mitigation-based advisories which address these issues without negatively impacting capacity. These issues are of global concern which must be addressed as the demand for air travel continues to increase.

Tobias, Leonard↗
NASA NTRS1980

Short-interval SMS wind vector determinations for a severe local storms area

Short-interval SMS-2 visible digital image data are used to derive wind vectors from cloud tracking on time-lapsed sequences of geosynchronous satellite images. The cloud tracking areas are located in the Central Plains, where on May 6, 1975 hail-producing thunderstorms occurred ahead of a well defined dry line. Cloud tracking is performed on the Goddard Space Flight Center Atmospheric and Oceanographic Information Processing System. Lower tropospheric cumulus tracers are selected with the assistance of a cloud-top height algorithm. Divergence is derived from the cloud motions using a modified Cressman (1959) objective analysis technique which is designed to organize irregularly spaced wind vectors into uniformly gridded wind fields. The results demonstrate the feasibility of using satellite-derived wind vectors and their associated divergence fields in describing the conditions preceding severe local storm development. For this case, an area of convergence appeared ahead of the dry line and coincided with the developing area of severe weather. The magnitude of the maximum convergence varied between -10 to the -5th and -10 to the -14th per sec. The number of satellite-derived wind vectors which were required to describe conditions of the low-level atmosphere was adequate before numerous cumulonimbus cells formed. This technique is limited in areas of advanced convection.

Peslen, C. A.↗
NASA NTRSDate not supplied

Gimbal Bearing Friction in the SLS Core Stage Thrust Vector Control System

The Space Launch System (SLS) Core Stage Thrust Vector Control (TVC) system is comprised of eight mechanical feedback Shuttle heritage Type III TVC actuators that vector the four Shuttle heritage RS-25 engines about a Shuttle heritage gimbal block/bearing. The MSFC Controls community has long regarded gimbal friction to be a negligible effect on the overall control of gimbaled RS-25 engines. This is corroborated by Space Shuttle test and flight data that does not appear to show degraded effects, nor limit cycling at the end of the shuttle flight. For this reason, friction was not expected to be a driving factor of performance and control of the reused RS-25 engines aboard the SLS. However, after test data showed a large shift in frequency behavior and a highly damped step-response in the time series, there was further investigation into what could have caused this behavior. Heritage friction models used in previous gimbal and ball bearings were evaluated such as Coulomb, Dahl and LuGre, but the single degree of freedom friction models alone were not enough to explain the behavior and shifts seen in the test data. This paper presents the additional findings and modeling efforts regarding friction on the RS-25 engines. Using the Two Actuator Operational Simulation (TAOS), the difference from modeling separate friction degrees of freedom to coupled degrees of freedom was investigated to deduce the effects of one axis’s movement on the other. Next, due to the vibration environment, a modified LuGre model has been proposed that adds an additional term to decrease the friction coefficient at low engine velocity amplitudes. Lastly, the addition of the stiffness in each half of the gimbal bearing has increased modeling fidelity by also adding the effect on the gimbal bearing bending in compliance to both the friction torque on the surface of the gimbal bearing and the actuator force that is forcing the engine in a specified direction. Through these effects, the time and frequency domain behavior seen in test can be characterized accurately.

Friction↗
NASA NTRSDate not supplied

Gimbal Bearing Friction in the SLS Core Stage Thrust Vector Control System

The Space Launch System (SLS) Core Stage Thrust Vector Control (TVC) system is comprised of eight mechanical feedback Shuttle heritage Type III TVC actuators that vector the four Shuttle heritage RS-25 engines about a Shuttle heritage gimbal block/bearing. The MSFC Controls community has long regarded gimbal friction to be a negligible effect on the overall control of gimbaled RS-25 engines. This is corroborated by Space Shuttle test and flight data that does not appear to show degraded effects, nor limit cycling at the end of the shuttle flight. For this reason, friction was not expected to be a driving factor of performance and control of the reused RS-25 engines aboard the SLS. However, after test data showed a large shift in frequency behavior and a highly damped step-response in the time series, there was further investigation into what could have caused this behavior. Heritage friction models used in previous gimbal and ball bearings were evaluated such as Coulomb, Dahl and LuGre, but the single degree of freedom friction models alone were not enough to explain the behavior and shifts seen in the test data. This paper presents the additional findings and modeling efforts regarding friction on the RS-25 engines. Using the Two Actuator Operational Simulation (TAOS), the difference from modeling separate friction degrees of freedom to coupled degrees of freedom was investigated to deduce the effects of one axis’s movement on the other. Next, due to the vibration environment, a modified LuGre model has been proposed that adds an additional term to decrease the friction coefficient at low engine velocity amplitudes. Lastly, the addition of the stiffness in each half of the gimbal bearing has increased modeling fidelity by also adding the effect on the gimbal bearing bending in compliance to both the friction torque on the surface of the gimbal bearing and the actuator force that is forcing the engine in a specified direction. Through these effects, the time and frequency domain behavior seen in test can be characterized accurately.

Friction↗
NASA NTRS2001

Spatial scale of motion segmentation from speed cues

For the accurate perception of multiple, potentially overlapping, surfaces or objects, the visual system must distinguish different local motion vectors and selectively integrate similar motion vectors over space to segment the retinal image properly. We recently showed that large differences in speed are required to yield a percept of motion transparency. In the present study, to investigate the spatial scale of motion segmentation from speed cues alone, we measured the speed-segmentation threshold (the minimum speed difference required for 75% performance accuracy) for 'corrugated' random-dot patterns, i.e. patterns in which dots with two different speeds were alternately placed in adjacent bars of variable width. In a first experiment, we found that, at large bar widths, a smaller speed difference was required to segment and perceive the corrugated pattern of moving dots, while at small bar-widths, a larger speed difference was required to segment the two speeds and perceive two transparent surfaces of moving dots. Both the perceptual and segmentation performance transitions occurred at a bar width of around 0.4 degrees. In a second experiment, speed-segmentation thresholds were found to increase sharply when dots with different speeds were paired within a local pooling area. The critical pairing distance was about 0.2 degrees in the fovea and increased linearly with stimulus eccentricity. However, across the range of eccentricities tested (up to 15 degrees ), the critical pairing distance did not change much and remained close to the receptive field size of neurons within the primate primary visual cortex. In a third experiment, increasing dot density changed the relationship between speed-segmentation thresholds and bar width. Thresholds decreased for large bar widths, but increased for small bar widths. All of these results are well fit by a simple stochastic model, which estimates the probabilities of having identical or different motion vectors within a local pooling area whose size is the same as that of primate V1 neurons. Altogether, these results demonstrate that speed-based segmentation can function well, even at small spatial scales (i.e. high-spatial frequencies of spatial corrugation) and thereby emphasizes the critical role of a local pooling process early in the cortical motion-processing pathway.

NASA Discipline Neuroscience↗
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↗
DOE OSTI2025

Accelerating high-order continuum kinetic plasma simulations using multiple GPUs

Kinetic plasma simulations solve the Vlasov-Poisson or Vlasov-Maxwell equations to evolve scalar-variable distribution functions in position-velocity phase space and vector-variable electromagnetic fields in configuration space. The immense computational cost of evolving high-dimensional variables, and their large number of degrees of freedom, often limits the utility of continuum kinetic simulations and presents a challenge when it comes to accurately simulating real-world physical phenomena. To address this challenge, we present techniques that accelerate and minimize the computational work required for a scalable Vlasov-Poisson solver. We show theoretical hardware compute and communication bounds for solving a fourth-order finite-volume Vlasov-Poisson system. These bounds are then used to inform and evaluate the design of performance portable algorithms for a multiple graphics processing unit (GPU) accelerated version of the Vlasov-Poisson solver VCK-CPU [1]. We demonstrate that the multi-GPU Vlasov solver implementation, VCK-GPU, simultaneously minimizes required inter-process data transfer while also being bounded by the machine network performance limits. This results in an overall strong scaling speedup per timestep of up to 40x in three-dimensional phase space (one position, two velocity coordinates) and 54x in four dimensional phase space (two position, two velocity coordinates) and a 341x increase in simulation throughput of the GPU accelerated code over the existing CPU code. The GPU code is also able to weak scale up to 256 compute nodes and 1024 GPUs. In conclusion, we demonstrate that the improved compute performance enables exploring configurations which were previously computationally infeasible, including resolving fine-scale distribution function filamentation and multi-species dynamics with realistic electron-proton mass ratios.

Continuum kinetics↗