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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Role of insulin-like growth factor-I in the regulation of skeletal muscle adaptation to increased loading

Adaptations in muscle mass stimulated by changes in muscle loading state entail alternations in the synthesis and degradation of myofiber proteins and the modulation of myonuclear number such that the ratio between the number of myonuclei and the size of the myofibers remains relatively constant. As depicted schematically in Figure 2.6, the literature regarding the role of IGF-in mediating muscle adaptation to alterations in loading state suggests the following conclusions: During periods of increased loading, myofibers upregulate the expression and secretion of IGF-I. Acting as an autocrine and/or paracrine growth factor, IGF-I stimulates myofiber anabolic processes. Acting as a paracrine growth factor, IGF-I also stimulates adjacent satellite cells to enter the cell cycle and proliferate. Continued myofiber production of IGF-I stimulates some satellite cells to differentiate and then fuse with myofibers, thus providing additional myonuclei in order to maintain or reestablish the myonucleus to myofiber size ratios of the enlarged myofibers.

Review, Academic↗
NASA NTRS1998

Sleep and vestibular adaptation: implications for function in microgravity

Optimal human performance depends upon integrated sensorimotor and cognitive functions, both of which are known to be exquisitely sensitive to loss of sleep. Under the microgravity conditions of space flight, adaptation of both sensorimotor (especially vestibular) and cognitive functions (especially orientation) must occur quickly--and be maintained--despite any concurrent disruptions of sleep that may be caused by microgravity itself, or by the uncomfortable sleeping conditions of the spacecraft. It is the three-way interaction between sleep quality, general work efficiency, and sensorimotor integration that is the subject of this paper and the focus of new work in our laboratory. To record sleep under field conditions including microgravity, we utilize a novel system called the Nightcap that we have developed and extensively tested on normal and sleep-disordered subjects. To perturb the vestibular system in ground-based studies, we utilize a variety of experimental conditions including optokinetic stimulation and both minifying and reversing goggle paradigms that have been extensively studied in relation to plasticity of the vestibulo-ocular reflex. Using these techniques we will test the hypothesis that vestibular adaptation both provokes and is enhanced by REM sleep under both ground-based and space conditions. In this paper we describe preliminary results of some of our studies.

NASA Discipline Neuroscience↗
NASA NTRS1997

A review of adaptive change in musculoskeletal impedance during space flight and associated implications for postflight head movement control

We present a review of converging sources of evidence which suggest that the differences between loading histories experienced in 1-g and weightlessness are sufficient to stimulate adaptation in mechanical impedance of the musculoskeletal system. As a consequence of this adaptive change we argue that we should observe changes in the ability to attenuate force transmission through the musculoskeletal system both during and after space flight. By focusing attention on the relation between human sensorimotor activity and support surfaces, the importance of controlling mechanical energy flow through the musculoskeletal system is demonstrated. The implications of such control are discussed in light of visual-vestibular function in the specific context of head and gaze control during postflight locomotion. Evidence from locomotory biomechanics, visual-vestibular function, ergonomic evaluations of human vibration, and specific investigations of locomotion and head and gaze control after space flight, is considered.

Review, Tutorial↗
NASA NTRS1993

Nutrition and human physiological adaptations to space flight

Space flight provides a model for the study of healthy individuals undergoing unique stresses. This review focuses on how physiological adaptations to weightlessness may affect nutrient and food requirements in space. These adaptations include reductions in body water and plasma volume, which affect the renal and cardiovascular systems and thereby fluid and electrolyte requirements. Changes in muscle mass and function may affect requirements for energy, protein and amino acids. Changes in bone mass lead to increased urinary calcium concentrations, which may increase the risk of forming renal stones. Space motion sickness may influence putative changes in gastro-intestinal-hepatic function; neurosensory alterations may affect smell and taste. Some or all of these effects may be ameliorated through the use of specially designed dietary countermeasures.

Review↗
NASA NTRS1995

An Efficient Implementation of Forward-Backward LMS Adaptive Line Enhancer

An efficient implementation of the forward-backward least mean square (FBLMS) adaptive line enhancer is presented. Without changing the characteristics of the FBLMS adaptive line enhancer, the proposed implementation technique reduces multiplications by 25 percent and additions by 12.5 percent in two successive time samples in comparison to that of the direct implementation in both prediction and weight control. It can be applied to acquisition and tracking in both stationary and nonstationary environments.

adaptive line enhancers signal acquisition trackin↗
NASA NTRS2006

Applying Parallel Adaptive Methods with GeoFEST/PYRAMID to Simulate Earth Surface Crustal Dynamics

This viewgraph presentation reviews the use Adaptive Mesh Refinement (AMR) in simulating the Crustal Dynamics of Earth's Surface. AMR simultaneously improves solution quality, time to solution, and computer memory requirements when compared to generating/running on a globally fine mesh. The use of AMR in simulating the dynamics of the Earth's Surface is spurred by future proposed NASA missions, such as InSAR for Earth surface deformation and other measurements. These missions will require support for large-scale adaptive numerical methods using AMR to model observations. AMR was chosen because it has been successful in computation fluid dynamics for predictive simulation of complex flows around complex structures.

GeoFEST↗
NASA NTRS2012

A Phase-Shifting Zernike Wavefront Sensor for the Palomar P3K Adaptive Optics System

A phase-shifting Zernike wavefront sensor has distinct advantages over other types of wavefront sensors. Chief among them are: 1) improved sensitivity to low-order aberrations and 2) efficient use of photons (hence reduced sensitivity to photon noise). We are in the process of deploying a phase-shifting Zernike wavefront sensor to be used with the realtime adaptive optics system for Palomar. Here we present the current state of the Zernike wavefront sensor to be integrated into the high-order adaptive optics system at Mount Palomar's Hale Telescope.

adaptive optics↗
NASA NTRS2013

Evolving Systems: Nonlinear Adaptive Key Component Control with Persistent Disturbance Rejection

This paper presents an introduction to Evolving Systems, which are autonomously controlled subsystems that self-assemble into a new Evolved System with a higher purpose. Evolving Systems of aerospace structures often require additional control when assembling to maintain stability during the entire evolution process. This is the concept of Adaptive Key Component Control which operates through one specific component to maintain stability during the evolution. In addition this control must overcome persistent disturbances that occur while the evolution is in progress. We present theoretical results for the successful operation of Nonlinear Adaptive Key Component control in the presence of such disturbances and an illustrative example.

adaptive control↗
NASA NTRS2013

Adaptive Sensing of Time Series with Application to Remote Exploration

We address the problem of adaptive informationoptimal data collection in time series. Here a remote sensor or explorer agent throttles its sampling rate in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility -- all collected datapoints lie in the past, but its resource allocation decisions require predicting far into the future. Our solution is to continually fit a Gaussian process model to the latest data and optimize the sampling plan on line to maximize information gain. We compare the performance characteristics of stationary and nonstationary Gaussian process models. We also describe an application based on geologic analysis during planetary rover exploration. Here adaptive sampling can improve coverage of localized anomalies and potentially benefit mission science yield of long autonomous traverses.

artificial intelligence↗
NASA NTRS2016

Climate Change Adaptation Activities at the NASA John F. Kennedy Space Center, FL., USA

In 2010, the Office of Strategic Infrastructure and Earth Sciences established the Climate Adaptation Science Investigators (CASI) program to integrate climate change forecasts and knowledge into sustainable management of infrastructure and operations needed for the NASA mission. NASA operates 10 field centers valued at $32 billion dollars, occupies 191,000 acres and employs 58,000 people. CASI climate change and sea-level rise forecasts focus on the 2050 and 2080 time periods. At the 140,000 acre Kennedy Space Center (KSC) data are used to simulate impacts on infrastructure, operations, and unique natural resources. KSC launch and processing facilities represent a valued national asset located in an area with high biodiversity including 33 species of special management concern. Numerical and advanced Bayesian and Monte Carlo statistical modeling is being conducted using LiDAR digital elevation models coupled with relevant GIS layers to assess potential future conditions. Results are provided to the Environmental Management Branch, Master Planning, Construction of Facilities, Engineering Construction Innovation Committee and our regional partners to support Spaceport development, management, and adaptation planning and design. Potential impacts to natural resources include conversion of 50% of the Center to open water, elevation of the surficial aquifer, alterations of rainfall and evapotranspiration patterns, conversion of salt marsh to mangrove forest, reductions in distribution and extent of upland habitats, overwash of the barrier island dune system, increases in heat stress days, and releases of chemicals from legacy contamination sites. CASI has proven successful in bringing climate change planning to KSC including recognition of the need to increase resiliency and development of a green managed shoreline retreat approach to maintain coastal ecosystem services while maximizing life expectancy of Center launch and payload processing resources.

plant communities↗
NASA NTRSDate not supplied

A Framework for Mesh-Geometry Associativity during Mesh Adaptation

A framework has been developed for describing how a computational mesh is associated to the geometry model it discretizes. The target application of this framework is surface mesh adaptation in a CFD flow solver. The framework, called MeshLink, consists of two components. First, a schema has been defined for describing the one-to-many associativity of a surface mesh to the geometry model entities to which it is attached. Second, a high-level library provides a kernel-agnostic wrapper for providing the necessary geometry queries to an application (e.g., mesher, flow solver). Both the schema and library are provided freely and openly. MeshLink’s ability to support solution mesh adaptation on linear and curved meshes for a high-order flow solution is demonstrated on several test cases relevant to the aerospace and automotive industries.

mesh adaptation↗
NASA NTRS1996

Necessary and Sufficient Conditions for LTI Representations of Adaptive Systems with Sinusoidal Regressors

This paper establishes necessary and sufficient coneditions for an adaptive system with a sinusoidal regressor (ie., a regressor comprised of sinusoidal signals) to admit an exact linear time-invariant (LTI) representation. These conditions are important because a large number of adaptive systems used in practice have sinusoidal regressors, and the stability, convergence and robustness properties of systems having LTI representations can be completely analyzed by well-known methods.

adaptive↗
NASA NTRS1995

Exponential Convergence of the Tracking Error in Adaptive Systems Without Persistent Excitation

Persistent excitation conditions which ensure parameter convergence in adaptive algorithms have been studied by many researchers. Here, conditions are investigated for exponential convergence of the tracking error in feedforward adaptive systems without persistent excitation. Particular attention is paid to the continuous-time LMS algorithm in the overparametrized case. Results are presented.

control↗
NASA NTRS1993

Overview of NASA's Adaptive Structures Program

The paper reviews NASA's research program, flight experiments, and flight application related to Adaptive Structures. Emphasis is placed on results from both experimental and flight hardware test programs that helps validate its theoretical performance. The paper includes the role of Adaptive Structures to meet NASA's current and future technology and programmatic needs.

adaptive↗
NASA NTRS1993

An Unipolar Terminal-Attractor Based Neural Associative Memory with Adaptive Threshold and Perfect Convergence

For the first time, a unipolar terminal-attractor based neural associative memory (TABAM)system with adaptive threshold and perfect convergence is presented. By adaptively setting thethreshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors and inner-product approach, we demonstrate via computer simulation the achievement ofperfect convergence and correct retrieval. The simulation is completed with a small number of storedstates (M) and a small number of neurons (N) but a large M/N ratio. An experiment with exclusive-or logic operation using LCTV SLMs is used to show feasibility of the optoelectronic implementationof the models.

Associative↗
NASA NTRSDate not supplied

Adaptive Learning for Reliability Analysis using Support Vector Machines

A novel algorithm is presented for adaptive learning of an unknown function that separates two regions of a domain.In the context of reliability analysis these two regions represent the failure domain, where a set of constraints or requirements are violated, and a safe domain where they are satisfied. The Limit State Function (LSF) separates these two regions. Evaluating the constraints for a given parameter point requires the evaluation of a computational model that may well be expensive. For this reason we wish to construct a meta-model that can estimate the LSFas accurately as possible, using only a limited amount of training data. This work presents an adaptive strategy employing a Support Vector Machine (SVM) as a meta-model to provide a semi-algebraic approximation of the LSF.We describe an optimization process that is used to select informative parameter points to add to training data at each iteration to improve the accuracy of this approximation. A formulation is introduced for bounding the predictions of the meta-model; in this way we seek to incorporate this aspect of Gaussian Process Models (GPMs) within anSVM meta-model. Finally, we apply our algorithm to two benchmark test cases, demonstrating performance that is comparable with, if not superior, to a standard technique for reliability analysis that employs GPMs

Adaptive learning↗
NASA NTRS2020

Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning

Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many applications such as autonomous driving, failures cannot be completely eliminated due to the complex stochastic environment in which the system operates.As a result, safety validation is not only concerned about whether a failure can occur, but also discovering which failures are most likely to occur. This article presents adaptive stress testing (AST), a framework for finding the most likely path to a failure event in simulation. We consider a general black box setting for partially observable and continuous-valued systems operating in an environment with stochastic disturbances. We formulate the problem as a Markov decision process and use reinforcement learning to optimize it. The approach is simulation-based and does not require internal knowledge of the system, making it suitable for black-box testing of large systems. We present different formulations depending on whether the state is fully observable or partially observable. In the latter case, we present a modified Monte Carlo tree search algorithm that only requires access to the pseudorandom number generator of the simulator to overcome partial observability. We also present an extension of the framework, called differential adaptive stress testing (DAST), that can find failures that occur in one system but not in another. This type of differential analysis is useful in applications such as regression testing, where we are concerned with finding areas of relative weakness compared to a baseline. We demonstrate the effectiveness of the approach on an aircraft collision avoidance application, where a prototype aircraft collision avoidance system is stress tested to find the most likely scenarios of near mid-air collision.

Verification and Validation↗