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This viewgraph presentation covers the advantages of mesh adaptation, unstructured grids, and dynamic load balancing. It illustrates parallel adaptive communications, and explains PLUM (Parallel dynamic load balancing for adaptive unstructured meshes), and PSAW (Proper Self Avoiding Walks).
This paper presents the development of a mesh adaptation module for a multilevel Cartesian solver. While the module allows mesh refinement to be driven by a variety of different refinement parameters, a central feature in its design is the incorporation of a multilevel error estimator based upon direct estimates of the local truncation error using tau-extrapolation. This error indicator exploits the fact that in regions of uniform Cartesian mesh, the spatial operator is exactly the same on the fine and coarse grids, and local truncation error estimates can be constructed by evaluating the residual on the coarse grid of the restricted solution from the fine grid. A new strategy for adaptive h-refinement is also developed to prevent errors in smooth regions of the flow from being masked by shocks and other discontinuous features. For certain classes of error histograms, this strategy is optimal for achieving equidistribution of the refinement parameters on hierarchical meshes, and therefore ensures grid converged solutions will be achieved for appropriately chosen refinement parameters. The robustness and accuracy of the adaptation module is demonstrated using both simple model problems and complex three dimensional examples using meshes with from 10(exp 6), to 10(exp 7) cells.
The recently developed essentially fourth-order or higher low dissipative shock-capturing scheme of Yee, Sandham and Djomehri (1999) aimed at minimizing nu- merical dissipations for high speed compressible viscous flows containing shocks, shears and turbulence. To detect non smooth behavior and control the amount of numerical dissipation to be added, Yee et al. employed an artificial compression method (ACM) of Harten (1978) but utilize it in an entirely different context than Harten originally intended. The ACM sensor consists of two tuning parameters and is highly physical problem dependent. To minimize the tuning of parameters and physical problem dependence, new sensors with improved detection properties are proposed. The new sensors are derived from utilizing appropriate non-orthogonal wavelet basis functions and they can be used to completely switch to the extra numerical dissipation outside shock layers. The non-dissipative spatial base scheme of arbitrarily high order of accuracy can be maintained without compromising its stability at all parts of the domain where the solution is smooth. Two types of redundant non-orthogonal wavelet basis functions are considered. One is the B-spline wavelet (Mallat & Zhong 1992) used by Gerritsen and Olsson (1996) in an adaptive mesh refinement method, to determine regions where re nement should be done. The other is the modification of the multiresolution method of Harten (1995) by converting it to a new, redundant, non-orthogonal wavelet. The wavelet sensor is then obtained by computing the estimated Lipschitz exponent of a chosen physical quantity (or vector) to be sensed on a chosen wavelet basis function. Both wavelet sensors can be viewed as dual purpose adaptive methods leading to dynamic numerical dissipation control and improved grid adaptation indicators. Consequently, they are useful not only for shock-turbulence computations but also for computational aeroacoustics and numerical combustion. In addition, these sensors are scheme independent and can be stand alone options for numerical algorithm other than the Yee et al. scheme.
This paper examines the feasibility, potential benefits and implementation issues associated with retrofitting a neural-adaptive flight control system (NFCS) to existing transport aircraft, including both cable/hydraulic and fly-by-wire configurations. NFCS uses a neural network based direct adaptive control approach for applying alternate sources of control authority in the presence of damage or failures in order to achieve desired flight control performance. Neural networks are used to provide consistent handling qualities across flight conditions, adapt to changes in aircraft dynamics and to make the controller easy to apply when implemented on different aircraft. Full-motion piloted simulation studies were performed on two different transport models: the Boeing 747-400 and the Boeing C-17. Subjects included NASA, Air Force and commercial airline pilots. Results demonstrate the potential for improving handing qualities and significantly increased survivability rates under various simulated failure conditions.
An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.
An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.
This paper presents an ad hoc adaptive, multivariable controller tuning rule that compensates for a thrust response variation in an engine whose performance has been degraded though use and wear. The upset appears when a large throttle transient is performed such that the engine controller switches from low-speed to high-speed mode. A relationship was observed between the level of engine degradation and the overshoot in engine temperature ratio, which was determined to cause the thrust response variation. This relationship was used to adapt the controller. The method is shown to work very well up to the operability limits of the engine. Additionally, since the level of degradation can be estimated from sensor data, it would be feasible to implement the adaptive control algorithm on-line.
Growing experimental and theoretical evidence suggests a functional synergy in the processing of otolith and semicircular canal signals for the generation of the vestibulo-ocular reflexes (VORs). In this study we have further tested this functional interaction by quantifying the adaptive changes in the otolith-ocular system during both rotational and translational movements after surgical inactivation of the semicircular canals. For 0.1- 0.5 Hz (stimuli for which there is no recovery of responses from the plugged canals), pitch and roll VOR gains recovered during earth- horizontal (but not earth-vertical) axis rotations. Corresponding changes were also observed in eye movements elicited by translational motion (0.1 - 5 Hz). Specifically, torsional eye movements increased during lateral motion, whereas vertical eye movements increased during fore-aft motion. The findings indicate that otolith signals can be adapted according to compromised strategy that leads to improved gaze stabilization during motion. Because canal-plugged animals permanently lose the ability to discriminate gravitoinertial accelerations, adapted animals can use the presence of gravity through otolith-driven tilt responses to assist gaze stabilization during earth-horizontal axis rotations.
A computational fluid dynamics (CFD) method has been employed to compute vortical flows around slender wing/body configurations. The emphasis of the paper is on the effectiveness of an adaptive grid procedure in "capturing" concentrated vortices generated at sharp edges or flow separation lines of lifting surfaces flying at high angles of attack. The method is based on a tetrahedral unstructured grid technology developed at the NASA Langley Research Center. Two steady-state, subsonic, inviscid and Navier-Stokes flow test cases are presented to demonstrate the applicability of the method for solving practical vortical flow problems. The first test case concerns vortex flow over a simple 65 delta wing with different values of leading-edge radius. Although the geometry is quite simple, it poses a challenging problem for computing vortices originating from blunt leading edges. The second case is that of a more complex fighter configuration. The superiority of the adapted solutions in capturing the vortex flow structure over the conventional unadapted results is demonstrated by comparisons with the wind-tunnel experimental data. The study shows that numerical prediction of vortical flows is highly sensitive to the local grid resolution and that the implementation of grid adaptation is essential when applying CFD methods to such complicated flow problems.
Although all tissues in the body can adapt to varying physiological/pathological conditions, muscle is the most adaptable. To understand the significance of cellular events and their role in controlling metabolic adaptations in complex physiological systems, it is necessary to link cellular and system levels by means of mechanistic computational models. The main objective of this work is to improve understanding of the regulation of energy metabolism during skeletal/cardiac muscle ischemia by combining in vivo experiments and quantitative models of metabolism. Our main focus is to investigate factors affecting lactate metabolism (e.g., NADH/NAD) and the inter-regulation between carbohydrate and fatty acid metabolism during a reduction in regional blood flow. A mechanistic mathematical model of energy metabolism has been developed to link cellular metabolic processes and their control mechanisms to tissue (skeletal muscle) and organ (heart) physiological responses. We applied this model to simulate the relationship between tissue oxygenation, redox state, and lactate metabolism in skeletal muscle. The model was validated using human data from published occlusion studies. Currently, we are investigating the difference in the responses to sudden vs. gradual onset ischemia in swine by combining in vivo experimental studies with computational models of myocardial energy metabolism during normal and ischemic conditions.
While the neurovestibular system is capable of adapting to altered environments such as microgravity, the adaptive state achieved in space in inadequate for 1G. This leads to giant and postural instabilities when returning to a gravity environment and may create serious problems in future mission to Mars. New methods are needed to improve the understanding of the adaptive capabilities of the human neurovestibular system and to develop more effective countermeasures. The concept behind the current study is that by challenging the neurovestibular system while walking or running a treadmill can help to read just the relationship between the visual, vestibular and proprioceptive signals that are altered in a microgravity environment. As a countermeasure, this device could also benefit the musculoskeletal and cardiovascular systems and at the same time decrease the overall time spent exercising. The overall goal of this research is to design, develop, build and test a dual track treadmill, which utilizes virtual reality, VR, displays.
Abstract This paper presents the development and application of one approach to the control of aircraft with large numbers of control effectors. This approach, referred to as real-time adaptive control allocation, combines a nonlinear method for control allocation with actuator failure detection and isolation. The control allocator maps moment (or angular acceleration) commands into physical control effector commands as functions of individual control effectiveness and availability. The actuator failure detection and isolation algorithm is a model-based approach that uses models of the actuators to predict actuator behavior and an adaptive decision threshold to achieve acceptable false alarm/missed detection rates. This integrated approach provides control reconfiguration when an aircraft is subjected to actuator failure, thereby improving maneuverability and survivability of the degraded aircraft. This method is demonstrated on a next generation military aircraft Lockheed-Martin Innovative Control Effector) simulation that has been modified to include a novel nonlinear fluid flow control control effector based on passive porosity. Desktop and real-time piloted simulation results demonstrate the performance of this integrated adaptive control allocation approach.
The SUBSTOR crop growth model was adapted for controlled-environment hydroponic production of potato (Solanum tuberosum L. cv. Norland) under elevated atmospheric carbon dioxide concentration. Adaptations included adjustment of input files to account for cultural differences between the field and controlled environments, calibration of genetic coefficients, and adjustment of crop parameters including radiation use efficiency. Source code modifications were also performed to account for the absorption of light reflected from the surface below the crop canopy, an increased leaf senescence rate, a carbon (mass) balance to the model, and to modify the response of crop growth rate to elevated atmospheric carbon dioxide concentration. Adaptations were primarily based on growth and phenological data obtained from growth chamber experiments at Rutgers University (New Brunswick, N.J.) and from the modeling literature. Modified-SUBSTOR predictions were compared with data from Kennedy Space Center's Biomass Production Chamber for verification. Results show that, with further development, modified-SUBSTOR will be a useful tool for analysis and optimization of potato growth in controlled environments.
Accurate motor control requires adaptive processes that correct for gradual and rapid perturbations in the properties of the controlled object. The ability to quickly switch between different movement synergies using sensory cues, referred to as context-dependent adaptation, is a subject of considerable interest at present. The potential function of the cerebellum in context-dependent adaptation remains uncertain, but the data reviewed below suggest that it may play a fundamental role in this process.
A new adaptive antenna array architecture for low-earth-orbiting satellite ground stations is being investigated. These ground stations are intended to have no moving parts and could potentially be operated in populated areas, where terrestrial interference is likely. The architecture includes multiple, moderately directive phased arrays. The phased arrays, each steered in the approximate direction of the satellite, are adaptively combined to enhance the Signal-to-Noise and Interference-Ratio (SNIR) of the desired satellite. The size of each phased array is to be traded-off with the number of phased arrays, to optimize cost, while meeting a bit-error-rate threshold. Also, two phased array architectures are being prototyped: a spacefed lens array and a reflect-array. If two co-channel satellites are in the field of view of the phased arrays, then multi-user detection techniques may enable simultaneous demodulation of the satellite signals, also known as Space Division Multiple Access (SDMA). We report on Phase I of the project, in which fixed directional elements are adaptively combined in a prototype to demodulate the S-band downlink of the EO-1 satellite, which is part of the New Millennium Program at NASA.
BACKGROUND: Chronic N(G)-nitro-L-arginine methyl ester (L-NAME), which inhibits nitric oxide synthesis, causes hypertension and would therefore be expected to induce robust cardiac hypertrophy. However, L-NAME has negative metabolic effects on protein synthesis that suppress the increase in left ventricular (LV) mass in response to sustained pressure overload. In the present study, we used L-NAME-induced hypertension to test the hypothesis that adaptation to pressure overload occurs even when hypertrophy is suppressed. METHODS AND RESULTS: Male rats received L-NAME (50 mg. kg(-1). d(-1)) or no drug for 6 weeks. Rats with L-NAME-induced hypertension had levels of systolic wall stress similar to those of rats with aortic stenosis (85+/-19 versus 92+/-16 kdyne/cm). Rats with aortic stenosis developed a nearly 2-fold increase in LV mass compared with controls. In contrast, in the L-NAME rats, no increase in LV mass (1. 00+/-0.03 versus 1.04+/-0.04 g) or hypertrophy of isolated myocytes occurred (3586+/-129 versus 3756+/-135 microm(2)) compared with controls. Nevertheless, chronic pressure overload was not accompanied by the development of heart failure. LV systolic performance was maintained by mechanisms of concentric remodeling (decrease of in vivo LV chamber dimension relative to wall thickness) and augmented myocardial calcium-dependent contractile reserve associated with preserved expression of alpha- and beta-myosin heavy chain isoforms and sarcoplasmic reticulum Ca(2+) ATPase (SERCA-2). CONCLUSIONS: When the expected compensatory hypertrophic response is suppressed during L-NAME-induced hypertension, severe chronic pressure overload is associated with a successful adaptation to maintain systolic performance; this adaptation depends on both LV remodeling and enhanced contractility in response to calcium.
While the neurovestibular system is capable of adapting to altered environments such as microgravity, the adaptive state achieved in space in inadequate for 1G. This leads to gait and postural instabilities when returning to a gravity environment and may create serious problems in future missions to Mars. New methods are needed to improve the understanding of the adaptive capabilities of the human neurovestibular system and to develop more effective countermeasures. The concept behind the current study is that by challenging the neurovestibular system while walking or running, a treadmill can help to readjust the relationship between the visual, vestibular and proprioceptive signals that are altered in a microgravity environment. As a countermeasure, this device could also benefit the musculoskeletal and cardiovascular systems and at the same time decrease the overall time spent exercising. The overall goal of this research is to design, develop, build and test a dual track treadmill, which utilizes virtual reality,
The goal of this research is to develop and demonstrate innovative adaptive seal technologies that can lead to dramatic improvements in engine performance, life, range, and emissions, and enhance operability for next generation gas turbine engines. This work is concentrated on the development of self-adaptive clearance control systems for gas turbine engines. Researchers have targeted the high-pressure turbine (HPT) blade tip seal location for following reasons: Current active clearance control (ACC) systems (e.g., thermal case-cooling schemes) cannot respond to blade tip clearance changes due to mechanical, thermal, and aerodynamic loads. As such they are prone to wear due to the required tight running clearances during operation. Blade tip seal wear (increased clearances) reduces engine efficiency, performance, and service life. Adaptive sealing technology research has inherent impact on all envisioned 21st century propulsion systems (e.g. distributed vectored, hybrid and electric drive propulsion concepts).