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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

The Effects of Long Duration Bed Rest on Brain Functional Connectivity and Sensorimotor Functioning

Long duration spaceflight has been associated with detrimental alterations in human sensorimotor functioning. Prolonged exposure to a head-down tilt (HDT) position during long duration bed rest can resemble several effects of the microgravity environment such as reduced sensory inputs, body unloading and increased cephalic fluid distribution. The question of whether microgravity affects other central nervous system functions such as brain functional connectivity and its relationship with behavior is largely unknown, but of importance to the health and performance of astronauts both during and post-flight. In the present study, we investigate the effects of prolonged exposure to HDT bed rest on resting state brain functional connectivity and its association with behavioral changes in 17 male participants. To validate that our findings were not due to confounding factors such as time or task practice, we also acquired resting state functional magnetic resonance imaging (rs-fMRI) and behavioral measurements from 14 normative control participants at four time points. Bed rest participants remained in bed with their heads tilted down six degrees below their feet for 70 consecutive days. Rs-fMRI and behavioral data were obtained at seven time points averaging around: 12 and 8 days prior to bed rest; 7, 50, and 70 days during bed rest; and 8 and 12 days after bed rest. 70 days of HDT bed rest resulted in significant increases in functional connectivity during bed rest followed by a reversal of changes in the post bed rest recovery period between motor cortical and somatosensory areas of the brain. In contrast, decreases in connectivity were observed between temporoparietal regions. Furthermore, post-hoc correlation analyses revealed a significant relationship between motor-somatosensory network connectivity and standing balance performance changes; participants that exhibited the greatest increases in connectivity strength showed the least deterioration in postural equilibrium with HDT bed rest. This suggests that neuroplastic processes may facilitate adaptation to the HDT bed rest environment. The findings from this study provide novel insights into the neurobiology and future risk assessments of long-duration spaceflight.

Cassady, K.↗
NASA NTRS2015

The Effects of Long Duration Bed Rest as a Spaceflight Analogue on Resting State Sensorimotor Network Functional Connectivity and Neurocognitive Performance

Long duration spaceflight has been associated with detrimental alterations in human sensorimotor systems and neurocognitive performance. Prolonged exposure to a head-down tilt position during long duration bed rest can resemble several effects of the microgravity environment such as reduced sensory inputs, body unloading and increased cephalic fluid distribution. The question of whether microgravity affects other central nervous system functions such as brain functional connectivity and its relationship with neurocognitive performance is largely unknown, but of potential importance to the health and performance of astronauts both during and post-flight. The aims of the present study are 1) to identify changes in sensorimotor resting state functional connectivity that occur with extended bed rest exposure, and to characterize their recovery time course; 2) to evaluate how these neural changes correlate with neurocognitive performance. Resting-state functional magnetic resonance imaging (rsfMRI) data were collected from 17 male participants. The data were acquired through the NASA bed rest facility, located at the University of Texas Medical Branch (Galveston, TX). Participants remained in bed with their heads tilted down six degrees below their feet for 70 consecutive days. RsfMRI data were obtained at seven time points: 7 and 12 days before bed rest; 7, 50, and 65 days during bed rest; and 7 and 12 days after bed rest. Functional connectivity magnetic resonance imaging (fcMRI) analysis was performed to measure the connectivity of sensorimotor networks in the brain before, during, and post-bed rest. We found a decrease in left putamen connectivity with the pre- and post-central gyri from pre bed rest to the last day in bed rest. In addition, vestibular cortex connectivity with the posterior cingulate cortex decreased from pre to post bed rest. Furthermore, connectivity between cerebellar right superior posterior fissure and other cerebellar regions decreased from pre bed rest to the last day in bed rest. In contrast, connectivity within the default mode network remained stable over the course of bed rest. We also utilized a battery of behavioral measures including spatial working memory tasks and measures of functional mobility and balance. These behavioral measurements were collected before, during, and after bed rest. We will report the preliminary findings of correlations observed between brain functional connectivity and behavioral performance changes. Our results suggest that sensorimotor brain networks exhibit decoupling with extended periods of reduced usage. The findings from this study could aid in the understanding and future design of targeted countermeasures to alleviate the detrimental health and neurocognitive effects of long-duration spaceflight.

Cassady, K.↗
NASA NTRSDate not supplied

Perturbations in Brain Functional Connectivity Patterns After Waking From Slow Wave Sleep Under Different Cognitive States

Sleep inertia refers to the state of transition between sleep and wake characterized by impaired alertness, confusion, and reduced cognitive and behavioral performance. While the behavioral symptoms of sleep inertia are well described, the neurological changes that lead to this state remain elusive. Here, to understand the state of sleep inertia and the reorganization that the brain undergoes, we took a graph theoretical approach and compared the EEG derived brain connectivity patterns before sleep and after waking up while participants (n = 10) performed multiple tasks that differed in cognitive complexities. We focused on how the degree and the clustering coefficient of brain regions (EEG sensors) change immediately after participants wake up from slow wave sleep. During a psychomotor vigilance task (PVT), designed to assess vigilant attention, we find that the brain regions with strong network connectivity (degree) before sleep show a reduction in connectivity after waking. In contrast, those with low connectivity before sleep have greater connectivity after waking. The regions that undergo these changes are specific to each participant and these findings are unique to the beta frequency range, which plays a key role in sensorimotor functioning and preserving the current state of the brain. Moreover, in tasks that required inhibitory control and arithmetic reasoning, we found that only regions with weak connectivity before sleep exhibited more connections after waking, but regions with high connectivity prior to sleeping remained unchanged, highlighting task specific effects. Furthermore, we find that during the PVT, the clustering coefficient within low frequency oscillations of the brain is reduced upon waking while it remains unchanged during other tasks. These results suggest that the connections between regions that are lost after abrupt awakening can be reallocated to other regions in order to renormalize the brain. However, this response may only be evident during specific cognitive states and may be more nuanced during complex task performance.

sleep inertia↗
NASA NTRS1980

Analytic and approximate boundary-fitted coordinate systems for fluid flow simulation

A method for constructing a direct functional relation between the computational and physical domains such that boundaries in the computational domain map into boundaries in the physical domain is presented. This method which is called the 'two-boundary technique' is based on generating functions that represent two disconnected boundaries in the physical domain and an independent function which connects one boundary with another to generate grids. The suggested connecting functions are simple linear and cubic parametric polynomials with parameters based on position and derivatives at the disconnected boundaries. The boundaries can be defined by analytical, approximate, and tension spline functions; the grid density in specified regions is controlled by embedding contraction functions in either the boundary representation or the connecting function.

Smith, R. E.↗
NASA NTRS2016

Exercise Effects on the Brain and Sensorimotor Function in Bed Rest

Long duration spaceflight microgravity results in cephalad fluid shifts and deficits in posture control and locomotion. Effects of microgravity on sensorimotor function have been investigated on Earth using head down tilt bed rest (HDBR). HDBR serves as a spaceflight analogue because it mimics microgravity in body unloading and bodily fluid shifts. Preliminary results from our prior 70 days HDBR studies showed that HDBR is associated with focal gray matter (GM) changes and gait and balance deficits, as well as changes in brain functional connectivity. In consideration of the health and performance of crewmembers we investigated whether exercise reduces the effects of HDBR on GM, functional connectivity, and motor performance. Numerous studies have shown beneficial effects of exercise on brain health. We therefore hypothesized that an exercise intervention during HDBR could potentially mitigate the effects of HDBR on the central nervous system. Eighteen subjects were assessed before (12 and 7 days), during (7, 30, and ~70 days) and after (8 and 12 days) 70 days of 6-degrees HDBR at the NASA HDBR facility in UTMB, Galveston, TX, US. Each subject was randomly assigned to a control group or one of two exercise groups. Exercise consisted of daily supine exercise which started 20 days before the start of HDBR. The exercise subjects participated either in regular aerobic and resistance exercise (e.g. squat, heel raise, leg press, cycling and treadmill running), or aerobic and resistance exercise using a flywheel apparatus (rowing). Aerobic and resistance exercise intensity in both groups was similar, which is why we collapsed the two exercise groups for the current experiment. During each time point T1-weighted MRI scans and resting state functional connectivity scans were obtained using a 3T Siemens scanner. Focal changes over time in GM density were assessed using voxel based morphometry (VBM8) under SPM. Changes in resting state functional connectivity was assessed using both a region of interest (ROI, or seed-to-voxel) approach as well as a whole brain intrinsic connectivity (i.e., voxel-to-voxel) analysis. For the ROI analysis we selected 11 ROIs of brain regions that are involved in sensorimotor function (i.e., L. Insular C., L. Putamen, R. Premotor C., L.+R. Primary Motor C., R. Vestibular C., L. Posterior Cingulate G., R. Cerebellum Lobule V + VIIIb + Crus I, and the R. Superior Parietal G.) and correlated their time course of brain activation during rest with all other voxels in the brain. The whole brain connectivity analysis tests changes in the strength of the global connectivity pattern between each voxel and the rest of the brain. Functional mobility was assessed using an obstacle course. Vestibular contribution to balance was measured using Neurocom Sensory Organization Test 5. Behavioral measures were assessed pre-HDBR, and 0, 8 and 12 days post-HDBR. Linear mixed models were used to test for effects of time, group, and group-by-time interactions. Family-wise error corrected VBM revealed significantly larger increases in GM volume in the right primary motor cortex in bed rest control subjects than in bed rest exercise subjects. No other significant group by time interactions in gray matter changes with bed rest were observed. Functional connectivity MRI revealed that the increase in connectivity during bed rest of the left putamen with the bilateral midsagittal precunes and the right cingulate gyrus was larger in bed rest control subjects than in bed rest exercise subjects. Furthermore, the increase in functional connectivity with bed rest of the right premotor cortex with the right inferior frontal gyrus and the right primary motor cortex with the bilateral premotor cortex was smaller in bed rest control subjects than in bed rest exercise subjects. Functional mobility performance was less affected by HDBR in exercise subjects than in control subjects and post HDBR exercise subjects recovered faster than control subjects. The group performance differences and GM changes were not related. Exercise in HDBR partially mitigates the adverse effect of HDBR on functional mobility, particularly during the post-bed rest recovery phase. In addition, exercise appears to result in differential brain structural and functional changes in motor regions such as the primary motor cortex, the premotor cortex and the putamen. Whether these central nervous system changes are related to motor behavioral changes including gait and balance warrants further research.

Koppelmans, V.↗
NASA NTRS2017

Neural Predictors of Visuomotor Adaptation Rate and Multi-Day Savings

Recent studies of sensorimotor adaptation have found that individual differences in task-based functional brain activation are associated with the rate of adaptation and savings at subsequent sessions. However, few studies to date have investigated offline neural predictors of adaptation and multi-day savings. In the present study, we explore whether individual differences in the rate of visuomotor adaptation and multi-day savings are associated with differences in resting state functional connectivity and gray matter volume. Thirty-four participants performed a manual adaptation task during two separate test sessions, on average 9 days apart. We found that resting state functional connectivity strength between sensorimotor, anterior cingulate, and temporoparietal areas of the brain was a significant predictor of adaptation rate during the early, cognitive phase of practice. In contrast, default mode network functional connectivity strength was found to predict late adaptation rate and savings on day two, which suggests that these behaviors may rely on overlapping processes. We also found that gray matter volume in temporoparietal and occipital regions was a significant predictor of early learning, whereas gray matter volume in superior posterior regions of the cerebellum was a significant predictor of late adaptation. The results from this study suggest that offline neural predictors of early adaptation facilitate the cognitive mechanisms of sensorimotor adaptation, with support from by the involvement of temporoparietal and cingulate networks. In contrast, the neural predictors of late adaptation and savings, including the default mode network and the cerebellum, likely support the storage and modification of newly acquired sensorimotor representations. These findings provide novel insights into the neural processes associated with individual differences in sensorimotor adaptation.

Cassady, Kaitlin↗
NASA NTRS1993

GDCF - An interactive approach to domain connectivity among systems of overset grids

The graphical domain connectivity function (GDCF) graphical interface, which was designed to address the need to simplify hole cutter shape selection and positioning in order to exploit the computational savings obtainable through the domain connectivity function (DCF) is described. GDCF was used to prepare data for the V22 tilt-rotor geometry, and data input time was decreased by an order of magnitude. The combined use of GDCF and DCF to satisfy domain connectivity requirements for systems of overset grids is found to minimize both human and computational resources.

Mccann, Karen M.↗
NASA NTRS2012

Best Practices for the Application of Functional Near Infrared Spectroscopy to Operator State Sensing

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neuronal measurement technique with many advantages for application in operational and training contexts. Instrumentation and protocol improvements, however, are required to obtain useful signals and produce expeditiously self-applicable, comfortable and unobtrusive headgear. Approaches for improving the validity and reliability of fNIRS data for the purpose of sensing the mental state of commercial aircraft operators are identified, and an exemplary system design for attentional state monitoring is outlined. Intelligent flight decks of the future can be responsive to state changes to optimally support human performance. Thus, the identification of cognitive performance decrement, such as lapses in operator attention, may be used to predict and avoid error-prone states. We propose that attentional performance may be monitored with fNIRS through the quantification of hemodynamic activations in cortical regions which are part of functionally-connected attention and resting state networks. Activations in these regions have been shown to correlate with behavioral performance and task engagement. These regions lie beneath superficial tissue in head regions beyond the forehead. Headgear development is key to reliably and robustly accessing locations beyond the hair line to measure functionally-connected networks across the whole head. Human subject trials using both fNIRS and functional Magnetic Resonance Imaging (fMRI) will be used to test this system. Data processing employs Support Vector Machines for state classification based on the fNIRS signals. If accurate state classification is achieved based on sensed activation patterns, fNIRS will be shown to be useful for monitoring attentional performance.

Harrivel, Angela R.↗
NASA NTRS2014

Customizing Countermeasure Prescriptions using Predictive Measures of Sensorimotor Adaptability

Astronauts experience sensorimotor disturbances during the initial exposure to microgravity and during the readapation phase following a return to a gravitational environment. These alterations may lead to disruption in the ability to perform mission critical functional tasks during and after these gravitational transitions. Astronauts show significant inter-subject variation in adaptive capability following gravitational transitions. The ability to predict the manner and degree to which each individual astronaut will be affected would improve the effectiveness of a countermeasure comprised of a training program designed to enhance sensorimotor adaptability. Due to this inherent individual variability we need to develop predictive measures of sensorimotor adaptability that will allow us to predict, before actual space flight, which crewmember will experience challenges in adaptive capacity. Thus, obtaining this information will allow us to design and implement better sensorimotor adaptability training countermeasures that will be customized for each crewmember's unique adaptive capabilities. Therefore the goals of this project are to: 1) develop a set of predictive measures capable of identifying individual differences in sensorimotor adaptability, and 2) use this information to design sensorimotor adaptability training countermeasures that are customized for each crewmember's individual sensorimotor adaptive characteristics. To achieve these goals we are currently pursuing the following specific aims: Aim 1: Determine whether behavioral metrics of individual sensory bias predict sensorimotor adaptability. For this aim, subjects perform tests that delineate individual sensory biases in tests of visual, vestibular, and proprioceptive function. Aim 2: Determine if individual capability for strategic and plastic-adaptive responses predicts sensorimotor adaptability. For this aim, each subject's strategic and plastic-adaptive motor learning abilities are assessed using a test of locomotor function designed specifically to delineate both mechanisms. Aim 3: Develop predictors of sensorimotor adaptability using brain structural and functional metrics. We will measure individual differences in regional brain volumes (structural MRI), white matter integrity (diffusion tensor imaging, or DTI), functional network integrity (resting state functional connectivity MRI), and sensorimotor adaptation task-related functional brain activation (functional MRI). We decided to complete the data collection for Specific Aims 1, 2 and 3 simultaneously on the same subjects to increase data capture. By having the same subjects perform all three specific aims we can enhance our ability to detect how a wider range of factors can predict adaptability in a specific individual. This provides a much richer database and potentially a better understanding of the predictive power of the selected factors. In this presentation I will discuss preliminary data obtained to date.

Bloomberg, J. J.↗
NASA NTRS2014

Physiological Observations and Omics to Develop Personalized Sensormotor Adaptability Countermeasures Using Bed Rest and Space Flight Data

Astronauts experience sensorimotor disturbances during the initial exposure to microgravity and during the re-adapation phase following a return to an earth-gravitational environment. These alterations may disrupt the ability to perform mission critical functional tasks requiring ambulation, manual control and gaze stability. Interestingly, astronauts who return from space flight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts would be affected would improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. For such an approach to succeed, we must develop predictive measures of sensorimotor adaptability that will allow us to foresee, before actual space flight, which crewmembers are likely to experience the greatest challenges to their adaptive capacities. The goals of this project are to identify and characterize this set of predictive measures that include: 1) behavioral tests to assess sensory bias and adaptability quantified using both strategic and plastic-adaptive responses; 2) imaging to determine individual brain morphological and functional features using structural magnetic resonance imaging (MRI), diffusion tensor imaging, resting state functional connectivity MRI, and sensorimotor adaptation task-related functional brain activation; 3) genotype markers for genetic polymorphisms in Catechol-O-Methyl Transferase, Dopamine Receptor D2, Brain-derived neurotrophic factor and genetic polymorphism of alpha2-adrenergic receptor that play a role in the neural pathways underlying sensorimotor adaptation. We anticipate these predictive measures will be significantly correlated with individual differences in sensorimotor adaptability after long-duration space flight and an analog bed rest environment. We will be conducting a retrospective study leveraging data already collected from relevant ongoing/completed bed rest and space flight studies. These data will be combined with predictor metrics that will be collected prospectively - behavioral, brain imaging and genomic measures; from these returning subjects to build models for predicting post-mission (bed rest - non-astronauts or space flight - astronauts) adaptive capability as manifested in their outcome measures. Comparisons of model performance will allow us to better design and implement sensorimotor adaptability training countermeasures that are customized for each crewmember's sensory biases, adaptive capacity, brain structure and functional capacities, and genetic predispositions against decrements in post-mission adaptive capability. This ability will allow more efficient use of crew time during training and will optimize training prescriptions for astronauts to ensure expected outcomes.

Mulavara, A. P.↗
NASA NTRS2016

Behavioral, Brain Imaging and Genomic Measures to Predict Functional Outcomes Post - Bed Rest and Spaceflight

Astronauts experience sensorimotor disturbances during their initial exposure to microgravity and during the re-adaptation phase following a return to an Earth-gravitational environment. These alterations may disrupt crewmembers' ability to perform mission critical functional tasks requiring ambulation, manual control and gaze stability. Interestingly, astronauts who return from spaceflight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts would be affected would improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. For such an approach to succeed, we must develop predictive measures of sensorimotor adaptability that will allow us to foresee, before actual spaceflight, which crewmembers are likely to experience the greatest challenges to their adaptive capacities. The goals of this project are to identify and characterize this set of predictive measures. Our approach includes: 1) behavioral tests to assess sensory bias and adaptability quantified using both strategic and plastic-adaptive responses; 2) imaging to determine individual brain morphological and functional features, using structural magnetic resonance imaging (MRI), diffusion tensor imaging, resting state functional connectivity MRI, and sensorimotor adaptation task-related functional brain activation; and 3) assessment of genotypic markers of genetic polymorphisms in the catechol-O-methyl transferase, dopamine receptor D2, and brain-derived neurotrophic factor genes and genetic polymorphisms of alpha2-adrenergic receptors that play a role in the neural pathways underlying sensorimotor adaptation. We anticipate that these predictive measures will be significantly correlated with individual differences in sensorimotor adaptability after long-duration spaceflight and exposure to an analog bed rest environment. We will be conducting a retrospective study, leveraging data already collected from relevant ongoing or completed bed rest and spaceflight studies. These data will be combined with predictor metrics that will be collected prospectively (as described for behavioral, brain imaging and genomic measures) from these returning subjects to build models for predicting post-mission (bed rest - non-astronauts or space flight - astronauts) adaptive capability as manifested in their outcome measures. To date we have completed a study on 15 normal subjects with all of the above measures. In this presentation we will discuss the optimized set of tests for predictive metrics to be used for evaluating post mission adaptive capability as manifested in their outcome measures. Comparisons of model performance will allow us to better design and implement sensorimotor adaptability training countermeasures against decrements in post-mission adaptive capability that are customized for each crewmember's sensory biases, adaptive capacity, brain structure and functional capacities, and genetic predispositions. The ability to customize adaptability training will allow more efficient use of crew time during training and will optimize training prescriptions for astronauts to ensure expected outcomes.

Mulavara, A. P.↗
NASA NTRS2017

Behavioral, Brain Imaging and Genomic Measures to Predict Functional Outcomes Post-Bed Rest and Space Flight

Astronauts experience sensorimotor disturbances during their initial exposure to microgravity and during the re-adaptation phase following a return to an Earth-gravitational environment. These alterations may disrupt crewmembers' ability to perform mission critical functional tasks requiring ambulation, manual control and gaze stability. Interestingly, astronauts who return from spaceflight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts are affected will improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. For such an approach to succeed, we must develop predictive measures of sensorimotor adaptability that will allow us to foresee, before actual spaceflight, which crewmembers are likely to experience greater challenges to their adaptive capacities. The goals of this project are to identify and characterize this set of predictive measures. Our approach includes: 1) behavioral tests to assess sensory bias and adaptability quantified using both strategic and plastic-adaptive responses; 2) imaging to determine individual brain morphological and functional features, using structural magnetic resonance imaging (MRI), diffusion tensor imaging, resting state functional connectivity MRI, and sensorimotor adaptation task-related functional brain activation; and 3) assessment of genetic polymorphisms in the catechol-O-methyl transferase, dopamine receptor D2, and brain-derived neurotrophic factor genes and genetic polymorphisms of alpha2-adrenergic receptors that play a role in the neural pathways underlying sensorimotor adaptation. We anticipate that these predictive measures will be significantly correlated with individual differences in sensorimotor adaptability after long-duration spaceflight and exposure to an analog bed rest environment. We will be conducting a retrospective study, leveraging data already collected from relevant ongoing or completed bed rest and spaceflight studies. This data will be combined with predictor metrics that will be collected prospectively (as described for behavioral, brain imaging and genomic measures) from these returning subjects to build models for predicting post spaceflight and bed rest adaptive capability. In this presentation we will discuss the optimized set of tests for predictive metrics to be used for evaluating post mission adaptive capability as manifested in their outcome measures. Comparisons of model performance will allow us to better design and implement sensorimotor adaptability training countermeasures against decrements in post-mission adaptive capability that are customized for each crewmember's sensory biases, adaptive ability, brain structure, brain function, and genetic predispositions. The ability to customize adaptability training will allow more efficient use of crew time during training and will optimize training prescriptions for astronauts to mitigate the deleterious effects of spaceflight.

Mulavara, A. P.↗
NASA NTRS1951

The structure of Airy's stress function in multiply connected regions

In solving two-dimensional problems using Airy's stress function for multiply connected regions, the form of the function depends on the dislocations and boundary forces present. The structure of Airy's function is shown to consist of a part expressible in terms of boundary forces and a part expressible in the manner of Poincare. Meanings of the constants occurring in Poincare's expression are discussed.

FRAMES, GRIDWORKS AND TRUSSES↗
NASA NTRS1986

A minimum entropy principle in the gas dynamics equations

Let u(x bar,t) be a weak solution of the Euler equations, governing the inviscid polytropic gas dynamics; in addition, u(x bar, t) is assumed to respect the usual entropy conditions connected with the conservative Euler equations. We show that such entropy solutions of the gas dynamics equations satisfy a minimum entropy principle, namely, that the spatial minimum of their specific entropy, (Ess inf s(u(x,t)))/x, is an increasing function of time. This principle equally applies to discrete approximations of the Euler equations such as the Godunov-type and Lax-Friedrichs schemes. Our derivation of this minimum principle makes use of the fact that there is a family of generalized entrophy functions connected with the conservative Euler equations.

Tadmor, E.↗
NASA NTRS1972

Resonance-inert stabilization for space stations

An approach to stabilizing control systems is presented which structures controllers like passive mechanical systems. The controller is visualized as a structural part with a passive behavior similar to springs, dashpots, and masses. If such a controller is connected by a proper feedback arrangement, then a passive mechanical plant cannot upset stability, regardless of masses, resonances, and three-dimensional coupling. The concept of resonance-inert stabilization is explained by structuring the controller of a simple feedback loop. Reactive functions, connections, and matrices are defined and used in the stabilization concept. The realization of a possible Skylab control system is discussed and compared with the present design. This example demonstrates the applicability to three-dimensional problems with lagging controllers.

Vonpragenau, G. L.↗
NASA NTRS1991

Rule-based simulation models

Procedural modeling systems, rule based modeling systems, and a method for converting a procedural model to a rule based model are described. Simulation models are used to represent real time engineering systems. A real time system can be represented by a set of equations or functions connected so that they perform in the same manner as the actual system. Most modeling system languages are based on FORTRAN or some other procedural language. Therefore, they must be enhanced with a reaction capability. Rule based systems are reactive by definition. Once the engineering system has been decomposed into a set of calculations using only basic algebraic unary operations, a knowledge network of calculations and functions can be constructed. The knowledge network required by a rule based system can be generated by a knowledge acquisition tool or a source level compiler. The compiler would take an existing model source file, a syntax template, and a symbol table and generate the knowledge network. Thus, existing procedural models can be translated and executed by a rule based system. Neural models can be provide the high capacity data manipulation required by the most complex real time models.

Nieten, Joseph L.↗