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Hybrid Adaptive Ray-Moment Method (HARM2): A highly parallel method for radiation hydrodynamics on adaptive grids
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Vivid Motor Imagery as an Adaptation Method for Head Turns on a Short-Arm Centrifuge
Artificial gravity (AG) has been proposed as a potential countermeasure to the debilitating physiological effects of long duration space flight. The most economical means of implementing AG may be through the use of a short-radius (2m or less) centrifuge. For such a device to produce gravitational forces comparable to those on earth requires rotation rates in excess of 20 revolutions per minute (rpm). Head turns made out of the plane of rotation at these rates, as may be necessary if exercise is combined with AG, result in cross-coupled stimuli (CCS) that cause adverse side effects including motion sickness, illusory sensations of motion, and inappropriate eye movements. Recent studies indicate that people can adapt to CCS and reduce these side effects by making multiple head turns during centrifuge sessions conducted over consecutive days. However, about 25% of the volunteers for these studies have difficulty tolerating the CCS adaptation paradigm and often drop out due to motion sickness symptoms. The goal of this investigation was to determine whether vivid motor imagery could be used as a pseudostimulus for adapting subjects to this unique environment. Twenty four healthy human subjects (14 males, 10 females), ranging in age from 21 to 48 years (mean 33, sd 7 years) took part in this study. The experimental stimuli were produced using the NASA JSC short-arm centrifuge (SAC). Subjects were oriented supinely on this device with the nose pointed toward the ceiling and head centered on the axis of rotation. Thus, centrifuge rotation was in the body roll plane. After ramp-up the SAC rotated clockwise at a constant rate of 23 rpm, producing a centrifugal force of approximately 1 g at the feet. Semicircular canal CCS were produced by having subjects make yaw head turns from the nose up (NU) position to the right ear down (RED) position and from RED to NU. Each head turn was completed in about one second, and a 30 second recovery period separated consecutive head movements. Participants were randomly assigned to one of three groups (n=8 per group): physical adapters (PA), mental adapters (MA), or a control group (CG). Each subject participated in a one hour test session on each of three consecutive days. Each test session consisted of an initial (preadaptation) period during which the subject performed six CCS maneuvers in the dark, followed by an adaptation period with internal lighting on the centrifuge, and a final (postadaptation) period during which six more CCS maneuvers were performed in the dark. For the PA group, the adaptation period consisted of performing 30 additional CCS maneuvers in the light. For the MA and CG group the centrifuge was ramped down to 0 rpm after the pre-adaptation period and ramped back up to 23 rpm before the post-adaptation period. For the both of these groups, the adaptation period consisted of making 30 CCS maneuvers in the light with the centrifuge stationary (so no cross-coupling occurred). MA group subjects were instructed to vividly imagine the provocative sensations produced by the preadaptation CCS maneuvers in terms of magnitude, duration, and direction of illusory body tilt, as well as any accompanying levels of motion sickness. CG group subjects were asked to answer low imagery content questions (trivial pursuit) during each adaptation period head turn. During the 30 second recovery following each head turn, psychophysical data were collected including self reports of motion sickness, magnitude and direction estimates of illusory body tilt, and the overall duration of these sensations. A multilevel mixed effects linear regression analysis performed on all response variables indicated that all three groups experienced some psychophysical adaptation across the three test sessions. For illusory tilt magnitude, the PA group exhibited the most overall adaptation, followed by the MA group, and the CG group. The slopes of these adaptation trajectories by group over day were significantly diffent from one another. For the perceived duration of sensations, the CG group again exhibited the least amount of adaptation. However, the rates of adaptation of the PA and the MA groups were indistinguishable, suggesting that the imagined pseudostimulus appeared to be just as effective a means of adaptation as the actual stimulus. The MA group's rate of adaptation to motion sickness symptoms was also comparable to the PA group. The use of vivid motor imagery may be an effective method for adapting to the illusory sensations and motion sickness symptoms produced by cross-coupled stimuli. For space-based AG applications, this technique may prove quite useful in retaining astronauts considered highly susceptible to motion sickness as it reduces the number of actual CCS required to attain adaptation.
Thermal Adaptation of Enzyme‐Mediated Processes Reduces Simulated Soil CO2 Fluxes Upon Soil Warming
Abstract Understanding factors influencing carbon effluxes from soils to the atmosphere is important in a world experiencing climatic change. Two important uncertainties related to soil organic carbon (SOC) stock responses to a changing climate are (a) whether soil microbial communities acclimate or adapt to changes in soil temperature and (b) how to represent this process in SOC models. To further explore these issues, we included thermal adaptation of enzyme‐mediated processes in a mechanistic SOC model (ReSOM) using the macromolecular rate theory. Thermal adaptation is defined here to encompass all potential responses of soil microbes and microbial communities following a change in temperature. To assess the effects of thermal adaptation of enzyme‐mediated processes on simulated SOC losses, ReSOM was applied to data collected from a 13‐year soil warming experiment. Results show that a model omitting thermal adaptation of enzyme‐mediated processes substantially overestimates observed CO 2 effluxes during the initial years of soil warming. The bias against observed CO 2 effluxes was lower for models including thermal adaptation of enzyme‐mediated processes. In addition, for a simulated linear 3°C soil warming over 100 years, models including thermal adaptation of enzyme‐mediated processes simulated SOC losses of a factor of three smaller than models omitting this process. As thermal adaptation of microbial community characteristics is generally not included in models simulating feedback between the soil, biosphere and atmosphere, we encourage future studies to assess the potential impact that microbial adaptation has on soil carbon – climate feedback representations in models. Plain Language Summary A major uncertainty in projecting how much soil organic carbon (SOC) will be converted to CO 2 as a consequence of climate change is related to how soil microbes may adapt to increasing soil temperatures. While this “microbial thermal adaptation” has been shown to occur in short‐term lab incubation experiments, its effect on SOC cycling on a decadal timescale is not clear. To address this knowledge gap, a mechanistic SOC model was used to simulate data collected from a 13‐year soil warming experiment, to assess how microbial thermal adaptation affects predicted SOC losses upon soil warming. The model results show that incorporating microbial thermal adaptation into the model led to reduced CO 2 effluxes from the soil to the atmosphere compared to the common approach of omitting this mechanism. Our results imply that projected SOC losses for the decades to come may be reduced when this mechanism is incorporated in land models. We therefore advocate for more research on the mechanisms controlling microbial thermal adaptation, and how to implement this mechanism in SOC models. Key Points A crucial aspect of soil organic carbon (SOC) models is the representation of soil microbes Predicted soil CO 2 fluxes upon soil warming are reduced when accounting for microbial thermal adaptation On a centennial time scale, this thermal adaptation results in up to a factor of three lower predicted SOC loss
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.
Visually induced adaptation in three-dimensional organization of primate vestibuloocular reflex
The adaptive plasticity of the spatial organization of the vestibuloocular reflex (VOR) has been investigated in intact and canal-plugged primates using 2-h exposure to conflicting visual (optokinetic, OKN) and vestibular rotational stimuli about mutually orthogonal axes (generating torsional VOR + vertical OKN, torsional VOR + horizontal OKN, vertical VOR + horizontal OKN, and horizontal VOR + vertical OKN). Adaptation protocols with 0.5-Hz (+/-18 degrees ) head movements about either an earth-vertical or an earth-horizontal axis induced orthogonal response components as high as 40-70% of those required for ideal adaptation. Orthogonal response gains were highest at the adapting frequency with phase leads present at lower and phase lags present at higher frequencies. Furthermore, the time course of adaptation, as well as orthogonal response dynamics were similar and relatively independent of the particular visual/vestibular stimulus combination. Low-frequency (0. 05 Hz, vestibular stimulus: +/-60 degrees ; optokinetic stimulus: +/-180 degrees ) adaptation protocols with head movements about an earth-vertical axis induced smaller orthogonal response components that did not exceed 20-40% of the head velocity stimulus (i.e., approximately 10% of that required for ideal adaptation). At the same frequency, adaptation with head movements about an earth-horizontal axis generated large orthogonal responses that reached values as high as 100-120% of head velocity after 2 h of adaptation (i.e., approximately 40% of ideal adaptation gains). The particular spatial and temporal response characteristics after low-frequency, earth-horizontal axis adaptation in both intact and canal-plugged animals strongly suggests that the orienting (and perhaps translational) but not inertial (velocity storage) components of the primate otolith-ocular system exhibit spatial adaptability. Due to the particular nested arrangement of the visual and vestibular stimuli, the optic flow pattern exhibited a significant component about the third spatial axis (i.e., orthogonal to the axes of rotation of the head and visual surround) at twice the oscillation frequency. Accordingly, the adapted VOR was characterized consistently by a third response component (orthogonal to both the axes of head and optokinetic drum rotation) at twice the oscillation frequency after earth-horizontal but not after earth-vertical axis 0.05-Hz adaptation. This suggests that the otolith-ocular (but not the semicircular canal-ocular) system can adaptively change its spatial organization at frequencies different from those of the head movement.
Horizontal vestibuloocular reflex evoked by high-acceleration rotations in the squirrel monkey. IV. Responses after spectacle-induced adaptation
The horizontal angular vestibuloocular reflex (VOR) evoked by sinusoidal rotations from 0.5 to 15 Hz and acceleration steps up to 3,000 degrees /s(2) to 150 degrees /s was studied in six squirrel monkeys following adaptation with x2.2 magnifying and x0.45 minimizing spectacles. For sinusoidal rotations with peak velocities of 20 degrees /s, there were significant changes in gain at all frequencies; however, the greatest gain changes occurred at the lower frequencies. The frequency- and velocity-dependent gain enhancement seen in normal monkeys was accentuated following adaptation to magnifying spectacles and diminished with adaptation to minimizing spectacles. A differential increase in gain for the steps of acceleration was noted after adaptation to the magnifying spectacles. The gain during the acceleration portion, G(A), of a step of acceleration (3,000 degrees /s(2) to 150 degrees /s) increased from preadaptation values of 1.05 +/- 0.08 to 1.96 +/- 0.16, while the gain during the velocity plateau, G(V), only increased from 0.93 +/- 0.04 to 1.36 +/- 0.08. Polynomial fits to the trajectory of the response during the acceleration step revealed a greater increase in the cubic than the linear term following adaptation with the magnifying lenses. Following adaptation to the minimizing lenses, the value of G(A) decreased to 0.61 +/- 0.08, and the value of G(V) decreased to 0.59 +/- 0.09 for the 3,000 degrees /s(2) steps of acceleration. Polynomial fits to the trajectory of the response during the acceleration step revealed that there was a significantly greater reduction in the cubic term than in the linear term following adaptation with the minimizing lenses. These findings indicate that there is greater modification of the nonlinear as compared with the linear component of the VOR with spectacle-induced adaptation. In addition, the latency to the onset of the adapted response varied with the dynamics of the stimulus. The findings were modeled with a bilateral model of the VOR containing linear and nonlinear pathways that describe the normal behavior and adaptive processes. Adaptation for the linear pathway is described by a transfer function that shows the dependence of adaptation on the frequency of the head movement. The adaptive process for the nonlinear pathway is a gain enhancement element that provides for the accentuated gain with rising head velocity and the increased cubic component of the responses to steps of acceleration. While this model is substantially different from earlier models of VOR adaptation, it accounts for the data in the present experiments and also predicts the findings observed in the earlier studies.
Cockpit Adaptive Automation and Pilot Performance
The introduction of high-level automated systems in the aircraft cockpit has provided several benefits, e.g., new capabilities, enhanced operational efficiency, and reduced crew workload. At the same time, conventional 'static' automation has sometimes degraded human operator monitoring performance, increased workload, and reduced situation awareness. Adaptive automation represents an alternative to static automation. In this approach, task allocation between human operators and computer systems is flexible and context-dependent rather than static. Adaptive automation, or adaptive task allocation, is thought to provide for regulation of operator workload and performance, while preserving the benefits of static automation. In previous research we have reported beneficial effects of adaptive automation on the performance of both pilots and non-pilots of flight-related tasks. For adaptive systems to be viable, however, such benefits need to be examined jointly in the context of a single set of tasks. The studies carried out under this project evaluated a systematic method for combining different forms of adaptive automation. A model for effective combination of different forms of adaptive automation, based on matching adaptation to operator workload was proposed and tested. The model was evaluated in studies using IFR-rated pilots flying a general-aviation simulator. Performance, subjective, and physiological (heart rate variability, eye scan-paths) measures of workload were recorded. The studies compared workload-based adaptation to to non-adaptive control conditions and found evidence for systematic benefits of adaptive automation. The research provides an empirical basis for evaluating the effectiveness of adaptive automation in the cockpit. The results contribute to the development of design principles and guidelines for the implementation of adaptive automation in the cockpit, particularly in general aviation, and in other human-machine systems. Project goals were met or exceeded. The results of the research extended knowledge of automation-related performance decrements in pilots and demonstrated the positive effects of adaptive task allocation. In addition, several practical implications for cockpit automation design were drawn from the research conducted. A total of 12 articles deriving from the project were published.
Adaptive Controller Effects on Pilot Behavior
Adaptive control provides robustness and resilience for highly uncertain, and potentially unpredictable, flight dynamics characteristic. Some of the recent flight experiences of pilot-in-the-loop with an adaptive controller have exhibited unpredicted interactions. In retrospect, this is not surprising once it is realized that there are now two adaptive controllers interacting, the software adaptive control system and the pilot. An experiment was conducted to categorize these interactions on the pilot with an adaptive controller during control surface failures. One of the objectives of this experiment was to determine how the adaptation time of the controller affects pilots. The pitch and roll errors, and stick input increased for increasing adaptation time and during the segment when the adaptive controller was adapting. Not surprisingly, altitude, cross track and angle deviations, and vertical velocity also increase during the failure and then slowly return to pre-failure levels. Subjects may change their behavior even as an adaptive controller is adapting with additional stick inputs. Therefore, the adaptive controller should adapt as fast as possible to minimize flight track errors. This will minimize undesirable interactions between the pilot and the adaptive controller and maintain maneuvering precision.
Automated Unstructured Grid Adaptation on a Strut Fuel Injector at Hypervelocity Flow Conditions
Computational fluid dynamics (CFD) analysis is presented with the use of an automated unstructured grid adaptation tool on a strut fuel injector at hypervelocity flow conditions. The analysis was carried out with the VULCAN-CFD solver using Reynolds-averaged simulations (RAS). The hypervelocity flow conditions match the high Mach number flow of the experiments conducted as part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center (LaRC). The current work uses an automated grid adaptation tool recently implemented in VULCAN-CFD, and explores this tool’s ability to solve highspeed mixing problems. Simulation results obtained using the unstructured adaptive grid approach are compared to those on a user-generated structured grid. These results are evaluated by analyzing how efficiently comparable fidelity results are obtained from both adapted and structured simulations. In addition, two adaptation strategies were used to explore the impact on the final solution. In the current work, the unstructured grid adaptation tool automatically generates unstructured grids and performs adaptation of the grid based on a Hessian error estimate of a specified flowfield parameter. Multiple adaptations were executed using each run strategy with the one-dimensional values of the mixing efficiency used to determine grid convergence and for comparison with the structured grid simulation results. It was found that the unstructured adaptive grid simulations were able to produce results that matched closely with those on structured grids using far fewer grid cells, and thus, requiring far less computational time to reach the solution. It was also discovered that the adaptation run strategy influenced the total number of grid cells and the efficiency with which a final grid-adapted solution was reached. Furthermore, motivated by the grid convergence index (GCI) used for structured grid simulations, a grid convergence estimate (GCE) was developed and demonstrated for the grid adaptation. Overall, the investigation demonstrated that the automated unstructured grid adaptation tool implemented in VULCAN-CFD is capable of accurately and efficiently solving complex high-speed mixing problems with only a fraction of the grid cells required to obtain comparable results on a user-generated structured grid.
Automated Unstructured Grid Adaptation on a Strut Fuel Injector at Hypervelocity Flow Conditions
Computational fluid dynamics (CFD) analysis is presented with the use of an automated unstructured grid adaptation tool on a strut fuel injector at hypervelocity flow conditions. The analysis was carried out with the VULCAN-CFD solver using Reynolds-averaged simulations (RAS). The hypervelocity flow conditions match the high Mach number flow of the experiments conducted as part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center (LaRC). The current work uses an automated grid adaptation tool recently implemented in VULCAN-CFD, and explores this tool’s ability to solve highspeed mixing problems. Simulation results obtained using the unstructured adaptive grid approach are compared to those on a user-generated structured grid. These results are evaluated by analyzing how efficiently comparable fidelity results are obtained from both adapted and structured simulations. In addition, two adaptation strategies were used to explore the impact on the final solution. In the current work, the unstructured grid adaptation tool automatically generates unstructured grids and performs adaptation of the grid based on a Hessian error estimate of a specified flowfield parameter. Multiple adaptations were executed using each run strategy with the one-dimensional values of the mixing efficiency used to determine grid convergence and for comparison with the structured grid simulation results. It was found that the unstructured adaptive grid simulations were able to produce results that matched closely with those on structured grids using far fewer grid cells, and thus, requiring far less computational time to reach the solution. It was also discovered that the adaptation run strategy influenced the total number of grid cells and the efficiency with which a final grid-adapted solution was reached. Furthermore, motivated by the grid convergence index (GCI) used for structured grid simulations, a grid convergence estimate (GCE) was developed and demonstrated for the grid adaptation. Overall, the investigation demonstrated that the automated unstructured grid adaptation tool implemented in VULCAN-CFD is capable of accurately and efficiently solving complex high-speed mixing problems with only a fraction of the grid cells required to obtain comparable results on a user-generated structured grid.
Alternating prism exposure causes dual adaptation and generalization to a novel displacement
In two experiments, we examined the hypothesis that repeatedly adapting and readapting to two mutually conflicting sensory environments fosters the development of a separate adaptation to each situation (dual adaptation) as well as an increased ability to adapt to a novel displacement (adaptive generalization). In the preliminary study, subjects alternated between adapting their visuomotor coordination to 30-diopter prismatic displacement and readapting to normal vision. Dual adaptation was observed by the end of 10 alternation cycles. However, an unconfounded test of adaptive generalization was prevented by an unexpected prism-adaptive shift in preexposure baselines for the dual-adapted subjects. In the primary experiment, the subjects adapted and readapted to opposite 15-diopter displacements for a total of 12 cycles. Both dual adaptation and adaptive generalization to a 30-diopter displacement were obtained. These findings may be understood in terms of serial reversal learning and 'learning to learn'.
Adaptive Flight Control Design with Optimal Control Modification on an F-18 Aircraft Model
In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to as the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly; however, a large adaptive gain can lead to high-frequency oscillations which can adversely affect the robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient robustness. A damping term (v) is added in the modification to increase damping as needed. Simulations were conducted on a damaged F-18 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) with both the standard baseline dynamic inversion controller and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model.
Verification and Validation Challenges for Adaptive Flight Control of Complex Autonomous Systems
Autonomy of aerospace systems requires the ability for flight control systems to be able to adapt to complex uncertain dynamic environment. In spite of the five decades of research in adaptive control, the fact still remains that currently no adaptive control system has ever been deployed on any safety-critical or human-rated production systems such as passenger transport aircraft. The problem lies in the difficulty with the certification of adaptive control systems since existing certification methods cannot readily be used for nonlinear adaptive control systems. Research to address the notion of metrics for adaptive control began to appear in the recent years. These metrics, if accepted, could pave a path towards certification that would potentially lead to the adoption of adaptive control as a future control technology for safety-critical and human-rated production systems. Development of certifiable adaptive control systems represents a major challenge to overcome. Adaptive control systems with learning algorithms will never become part of the future unless it can be proven that they are highly safe and reliable. Rigorous methods for adaptive control software verification and validation must therefore be developed to ensure that adaptive control system software failures will not occur, to verify that the adaptive control system functions as required, to eliminate unintended functionality, and to demonstrate that certification requirements imposed by regulatory bodies such as the Federal Aviation Administration (FAA) can be satisfied. This presentation will discuss some of the technical issues with adaptive flight control and related V&V challenges.
Automated Unstructured Grid Adaptation on a Strut Fuel Injector at Hypervelocity Flow Conditions
Computational fluid dynamics (CFD) analysis is presented with the use of an automated unstructured grid adaptation tool on a strut fuel injector at hypervelocity flow conditions. The analysis was carried out with the VULCAN-CFD solver using Reynolds-averaged simulations (RAS). The hypervelocity flow conditions match the high Mach number flow of the experiments conducted as part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center (LaRC). The current work utilizes an automated grid adaptation tool recently implemented into VULCAN-CFD, and explores this tool’s ability to solve high-speed mixing problems. Simulation results obtained using the unstructured adaptive grid approach are compared to those on a user generated structured grid. These results are evaluated by analyzing how efficiently comparable fidelity results are obtained from both adapted and structured simulations. In addition, two adaptation strategies were used to explore the impact on the final solution. In the current work, the unstructured grid adaptation tool automatically generates unstructured grids and performs adaptation of the grid based on a Hessian error estimate of a specified flow field parameter. Multiple adaptations were executed using each run strategy with the one-dimensional values of the mixing efficiency used to determine grid convergence and for comparison with the structured grid simulation results. It was found that the unstructured adaptive grid simulations were able to produce results that matched closely with those on structured grids using far fewer grid cells, and thus, requiring far less computational time to reach the solution. It was also discovered that the adaptation run strategy influenced the total number of grid cells and the efficiency with which a final grid-adapted solution was reached. Overall, the investigation demonstrated that the automated unstructured grid adaptation tool implemented in VULCAN-CFD is capable of accurately and efficiently solving complex highspeed mixing problems using only a fraction of the grid cells required to obtain comparable results using a user-generated structured grid.
Assessment of Postflight Locomotor Performance Utilizing a Test of Functional Mobility: Strategic and Adaptive Responses
Space flight induces adaptive modification in sensorimotor function, allowing crewmembers to operate in the unique microgravity environment. This adaptive state, however, is inappropriate for a terrestrial environment. During a re-adaptation period upon their return to Earth, crewmembers experience alterations in sensorimotor function, causing various disturbances in perception, spatial orientation, posture, gait, and eye-head coordination. Following long duration space flight, sensorimotor dysfunction would prevent or extend the time required to make an emergency egress from the vehicle; compromising crew safety and mission objectives. We are investigating two types of motor learning that may interact with each other and influence a crewmember's ability to re-adapt to Earth's gravity environment. In strategic learning, crewmembers make rapid modifications in their motor control strategy emphasizing error reduction. This type of learning may be critical during the first minutes and hours after landing. In adaptive learning, long-term plastic transformations occur, involving morphological changes and synaptic modification. In recent literature these two behavioral components have been associated with separate brain structures that control the execution of motor strategies: the strategic component was linked to the posterior parietal cortex and the adaptive component was linked to the cerebellum (Pisella, et al. 2004). The goal of this paper was to demonstrate the relative contributions of the strategic and adaptive components to the re-adaptation process in locomotor control after long duration space flight missions on the International Space Station (ISS). The Functional Mobility Test (FMT) was developed to assess crewmember s ability to ambulate postflight from an operational and functional perspective. Sixteen crewmembers were tested preflight (3 sessions) and postflight (days 1, 2, 4, 7, 25) following a long duration space flight (approx 6 months) on the ISS. We have further analyzed the FMT data to characterize strategic and adaptive components during the postflight readaptation period. Crewmembers walked at a preferred pace through an obstacle course set up on a base of 10 cm thick medium density foam (Sunmate Foam, Dynamic Systems, Inc., Leicester, NC). The 6.0m X 4.0m course consisted of several pylons made of foam; a Styrofoam barrier 46.0cm high that crewmembers stepped over; and a portal constructed of two Styrofoam blocks, each 31cm high, with a horizontal bar covered by foam and suspended from the ceiling which was adjusted to the height of the crewmember s shoulder. The portal required crewmembers to bend at the waist and step over a barrier simultaneously. All obstacles were lightweight, soft and easily knocked over. Crewmembers were instructed to walk through the course as quickly and as safely as possible without touching any of the objects on the course. This task was performed three times in the clockwise direction and three times in the counterclockwise direction that was randomly chosen. The dependent measures for each trial were: time to complete the course (seconds) and the number of obstacles touched or knocked down. For each crewmember, the time to complete each FMT trial from postflight days 1, 2, 4, 7 and 25 were further analyzed. A single logarithmic curve using a least squares calculation was fit through these data to produce a single comprehensive curve (macro). This macro curve composed of data spanning 25 days, illustrates the re-adaptive learning function over the longer time scale term. Additionally, logarithmic curves were fit to the 6 data trials within each individual post flight test day to produce 5 separate daily curves. These micro curves, produced from data obtained over the course of minutes, illustrates the strategic learning function exhibited over a relative shorter time scale. The macro curve for all subjects exhibited adaptive motor learning patterns over the 25 day period. Howev, 9/16 crewmembers exhibited significant strategic motor learning patterns in their micro curves, as defined by m > 1 in the equation of the line y=m*LN(x) +b. These data indicate that postflight recovery in locomotor function involves both strategic and adaptive mechanisms. Future countermeasures will be designed to enhance both recovery processes.
Adaptive Control of Flexible Structures Using Residual Mode Filters
Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter. We have proposed a modified adaptive controller with a residual mode filter. The RMF is used to accommodate troublesome modes in the system that might otherwise inhibit the adaptive controller, in particular the ASPR condition. This new theory accounts for leakage of the disturbance term into the Q modes. A simple three-mode example shows that the RMF can restore stability to an otherwise unstable adaptively controlled system. This is done without modifying the adaptive controller design.
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.