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We review the conditions for the occurrence of coherence phenomena in passive coupled optical microresonators. We derive the effective steady-state response and determine conditions for induced transparency and absorption in these systems.
Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (minus 2 to plus 9 degrees Centigrade) and precipitation (minus 50 to plus 50 percent). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
Spacecraft in low altitude, high inclination (including sun-synchronous) orbits are widely used for remote sensing of the Earth s land surface and oceans, monitoring weather and climate, communications, scientific studies of the upper atmosphere and ionosphere, and a variety of other scientific, commercial, and military applications. These systems are episodically exposed to environments characterized by a high flux of energetic (approx.1 to 10 s kilovolt) electrons in regions of very low background plasma density which is similar in some ways to the space weather conditions in geostationary orbit responsible for spacecraft charging to kilovolt levels. While it is well established that charging conditions in geostationary orbit are responsible for many anomalies and even spacecraft failures, to date there have been relatively few such reports due to charging in auroral environments. This presentation first reviews the physics of the space environment and its interactions with spacecraft materials that control auroral charging rates and the anticipated maximum potentials that should be observed on spacecraft surfaces during disturbed space weather conditions. We then describe how the theoretical values compare to the observational history of extreme charging in auroral environments and discuss how space weather impacts both spacecraft design and operations for vehicles on orbital trajectories that traverse auroral charging environments.
Three groups of eight male and female subjects (aged 20-27 yr) categorized by low and high hypnotic susceptibility were taught to control their heart rates by means of an appropriate autogenic therapy/biofeedback technique. The experimental groups were trained by autogenic therapy and biofeedback, while the control group received only biofeedback. Significant differences are observed in all psychological test scores between subjects of high and low hypnotic susceptibility. The results confirm that (1) there are qualitative and quantitative differences between the performance of individuals with high and low hypnotic susceptibility; (2) interindividual-variability tests yield data relevant to individual performance in visceral learning tasks; (3) the combined autogenic therapy/biofeedback/verbal feedback technique is suitable for conditioning large stable autonomic responses in humans; and (4) this kind of conditioning is effective in eliminating or alleviating physiological reactions to some environmental stressors.
Damaged aircraft have occasionally had to rely solely on thrust to maneuver as a consequence of losing hydraulic power needed to operate flight control surfaces. The lack of successful landings in these cases inspired research into more effective methods of utilizing propulsion-only control. That research demonstrated that one of the major contributors to the difficulty in landing is the slow response of the engines as compared to using traditional flight control. To address this, research is being conducted into ways of making the engine more responsive under emergency conditions. This can be achieved by relaxing controller limits, adjusting schedules, and/or redesigning the regulators to increase bandwidth. Any of these methods can enable faster response at the potential expense of engine life and increased likelihood of stall. However, an example sensitivity analysis revealed a complex interaction of the limits and the difficulty in predicting the way to achieve the fastest response. The sensitivity analysis was performed on a realistic engine model, and demonstrated that significantly faster engine response can be achieved compared to standard Bill of Material control. However, the example indicates the need for an intelligent approach to controller limit adjustment in order for the potential to be fulfilled.
The NASA Occupant Protection Group is charged with ensuring crewmembers are protected during all dynamic phases of spaceflight. Previous work with outside experts has led to the development of a definition of acceptable risk (DAR) for space capsule vehicles. The DAR defines allowable probability rates for various categories of injuries. An important question is how to validate these probabilities for a given vehicle. One approach is to impact test human volunteers under projected nominal landing loads. The main drawback is the large number of subject tests required to attain a reasonable level of confidence that the injury probability rates would meet those outlined in the DAR. An alternative is to mine existing databases containing human responses to impact. Testing an anthropomorphic test device (ATD) at the same human‐exposure levels could yield a range of ATD responses that would meet DAR. As one aspect of future vehicle validation, the ATD could be tested in the vehicle's seat and suit configuration at nominal landing loads and compared with the ATD responses supported by the human data set. This approach could reduce the number of human‐volunteer tests NASA would need to conduct to validate that a vehicle meets occupant protection standards. METHODS: The U.S. Air Force has recorded hundreds of human responses to frontal, lateral, and spinal impacts at many acceleration levels and pulse durations. All of this data are stored on the Collaborative Biomechanics Data Network (CBDN), which is maintained by the Wright Patterson Air Force Base (WPAFB). The test device for human occupant restraint (THOR) ATD was impact tested on WPAFB's horizontal impulse accelerator (HIA) matching human‐volunteer exposures on the HIA to 5 frontal and 3 spinal loading conditions. No human injuries occurred as a result of these impact conditions. Peak THOR response variables for neck axial tension and compression, and thoracic‐spine axial compression were collected. Maximal chest deflection was determined from motion capture video of the impact test. HIC‐ 15 and BRIC were calculated from head acceleration responses. Given the number of human subjects for each test condition a confidence interval of injury probability will be obtained. RESULTS: Results will be discussed in terms of injury‐risk probability estimates based on the human data set evaluated. Also, gaps in the data set will be identified. These gaps could be one of two types. One is areas where additional THOR testing would increase the comparable human data set, thereby improving confidence in the injury probability rate. The other is where additional human testing would assist in obtaining information on other acceleration levels or directions. DISCUSSION: The historical human data showed validity of the THOR ATD for supplemental testing. The historical human data are limited in scope, however. Further data are needed to characterize the effects of sex, age, anthropometry, and deconditioning due to spaceflight on risk of injury
The frequency of blade temperature to exhaust gas temperature is presented for two locations in the blade and at several operating conditions. The frequency response was determined by Fourier analysis of transient data. Two analytical methods are presented, and results are compared with experimental data. Dynamic response of turbine-blade temperature to exhaust-gas temperature exhibited the form of an approximate first-order lag. The results are used to predict blade temperature for a typical controlled and uncontrolled gas-turbine engine.
A research program is underway to develop strain rate dependent deformation and failure models for the analysis of polymer matrix composites subject to high strain rate impact loads. Under these types of loading conditions, the material response can be highly strain rate dependent and nonlinear. State variable constitutive equations based on a viscoplasticity approach have been developed to model the deformation of the polymer matrix. The constitutive equations are then combined with a mechanics of materials based micromechanics model which utilizes fiber substructuring to predict the effective mechanical and thermal response of the composite. To verify the analytical model, tensile stress-strain curves are predicted for a representative composite over strain rates ranging from around 1 x 10(exp -5)/sec to approximately 400/sec. The analytical predictions compare favorably to experimentally obtained values both qualitatively and quantitatively. Effective elastic and thermal constants are predicted for another composite, and compared to finite element results.
This paper investigates flight circuit application bias and irradiation dose rate dependencies (“test as you fly” conditions) in the total ionizing dose (TID) response of various electronic components considered for use in a space radiation environment.
The Mars Science Laboratory (MSL) was protected during its atmospheric entry by an instrumented heatshield that used NASA's Phenolic Impregnated Carbon Ablator (PICA) material [1]. PICA is a lightweight carbon fiber/polymeric resin material that offers outstanding performance for protecting probes during planetary entry. Data from the Mars Entry Descent and Landing Instrument (MEDLI) suite on MSL offers unique in-flight validation data for models of material response and atmospheric entry. MEDLI recorded, among other things, time-resolved in-depth temperature data of PICA using thermocouple sensors assembled in the MEDLI Integrated Sensor Plugs (MISP) [2]. A space-grade silicone-based coating commercially known as NuSil CV-1144-0 [3] was applied to the entire MSL heatshield, including the MEDLI plugs, to mitigate the spread of dust from PICA. Modeling the thermal response of PICA-NuSil (PICA-N) system is still an open challenge. Ground testing of PICA-N models exhibited surface temperature jumps of the order of 150 K due to oxide scale formation and sub-sequent NuSil burn-off. It is therefore critical to include a validated model for the material response of the coating in engineering codes. A test campaign has been conducted at the NASA’s Langley HyMETS [4] facility to screen the response of PICA-N and gather detailed data on its behavior [5]. A first model of PICA-N thermal response has been developed using the Hy-METS experiments [6]. The objective of this work is to analyze the material response of the latest PICA-N model compared to the engineering model used to simulate the entry of MSL. The environment and material response around the MSL aeroshell during Mars atmospheric entry is simulated using a collection of tools. The Direct Simulation Monte Carlo SPARTA code [7] is used in the rarefied regime, the Data Parallel Line Relaxation (DPLR) code [8] is used in the continuum regime and radiative heating conditions are provided by the Nonequilibrium air radiation (NEQAIR) code [9] to estimate the environmental conditions. The thermal response inside the material is computed using the Porous material Analysis Toolbox based on Open-FOAM (PATO) [10,11,12]. Thermodynamic and chemistry properties are estimated using the Mutation++ library [13]. The approach implemented in PATO as a first cut PICA-N thermal response model is outlined in Figure 1. While the recession is less than the coating thickness, the Surface mass and energy balance Boundary Condition (SBC) uses the NuSil B’ tables. Once the recession removes the coating, the usual PICA B’ tables are used for the SBC. The B’ tables are computed using an equilibrium solver implemented in Mutation++, given the temperature, pressure, blowing rate, composition of the pyrolysis and environment gases, and the condensed species at the surface. Preliminary results of the 3D material response of the MSL heat-shield at the peak heating (80 sec after Entry Interface) are shown in Figure 2. Current NASA’s mission to Mars, Mars 2020, used the spare heatshield of MSL for thermal protection during entry, descent, and landing. In preparation for Mars 2020 post-flight analysis, the PATO high-fidelity material response capability was benchmarked against flight data from MEDLI. This effort represents an important milestone toward the development of validated predictive capabilities for designing thermal protection systems for planetary probes. This bench-marking is awaiting the final release of the MEDLI-2 data.
Single-trial event-related responses collected during the course of an experiment are typically averaged before analysis resulting in a rather crude picture of event-related brain dynamics. It has been quite clear for some time that these responses exhibit trial-to-trial variability: however, the computational techniques necessary to deal with such responses in noisy conditions have not been available. To this end we have developed the multiple-component, event-related potential model (mcERP), which assumes that the each event-related response consists of a sum of multiple evoked components each described by a stereotypical waveshape. These waveshapes are allowed to vary in amplitude and onset latency from trial to trial, which allows us to capture, to first-order, the trial-dependent variations in event-related brain dynamics. We have constructed many sets of synthetic data designed to simulate intracortical recordings from a 15 channel, linear-array multielectrode implanted acutely in V1 of an awake-behaving macaque undergoing visual stimulation with a red light flash. This synthetic data was used to characterize the performance of the mcERP algorithm. First we quantified the degree to which such trial-to-trial variability aids in the identification of multiple components, and we demonstrate that amplitude variability is a more important factor in component separation than latency variability. Second, we quantified the behavior of the algorithm under two distinct signal-to-noise ratio (SNR) conditions: Gaussian noise independently present in each channel, and highly correlated (1/f distributed), far-field noise presented identically in each channel of the array. The mcERP algorithm was found to be robust to noise accurately identifying all component waveshapes and their associated single-trial characteristics down to SNR levels of -20dB for Gaussian noise and -7dB for 1/f far-field noise. Comparisons of the performance of this algorithm with factor analysis (FA) and independent component analysis (ICA) will be described by Knuth et al. (SFN abstracts, 2002). In addition, the advantages of application of mcERP to real data will be described by Shah et al, (these abstracts, 2002: SFN abstracts, 2002).
A constant current loop measuring system measures a property including the temperature of a sensor responsive to an external condition being measured. The measuring system includes thermocouple conductors connected to the sensor, sensing first and second induced voltages responsive to the external condition. In addition, the measuring system includes a current generator and reverser generating a constant current, and supplying the constant current to the thermocouple conductors in forward and reverse directions generating first and second measured voltages, and a determining unit receiving the first and second measured voltages from the current generator and reverser, and determining the temperature of the sensor responsive to the first and second measured voltages.
Turbine generator (T-G) units with low tuned concrete foundations were investigated experimentally during both startup and normal operating conditions. The dynamic response of the turbine generators and their foundation systems were characterized by resonance curves obtained during the startup transient and by vibration mode shapes at the normal operating condition. No severe resonance at the fundamental frequencies of the system was observed. The deflection or displacement of the foundation pedestal does not lead to unacceptable T-G vibration. The experimental results are compared and discussed with some previous theoretical studies.
We investigate whether bias conditions arid electrical and thermal stresses can affect the Total Ionizing Dose response ofDDR2 SDRAMs
Effective analysis tools were developed for predicting the nonlinear rotordynamic behavior of the Space Shuttle Main Engine (SSME) turbopumps under steady and transient operating conditions. Using these methods, preliminary parametric studies were conducted on both generic and actual HPOTP (high pressure oxygen turbopump) models. In particular, a novel modified harmonic balance/alternating Fourier transform (HB/AFT) method was developed and used to conduct a preliminary study of the effects of fluid, bearing and seal forces on the unbalanced response of a multi-disk rotor in the presence of bearing clearances. The method makes it possible to determine periodic, sub-, super-synchronous and chaotic responses of a rotor system. The method also yields information about the stability of the obtained response, thus allowing bifurcation analyses. This provides a more effective capability for predicting the response under transient conditions by searching in proximity of resonance peaks. Preliminary results were also obtained for the nonlinear transient response of an actual HPOTP model using an efficient, newly developed numerical method based on convolution integration. Currently, the HB/AFT is being extended for determining the aperiodic response of nonlinear systems. Initial results show the method to be promising.
Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.
Multiple stressors in the spaceflight environment, including microgravity and radiation, have the potential to alter microbial pathogenesis and virulence characteristics. Understanding how these and other factors may exacerbate the risk of infectious disease for astronauts is important to ensure the success of the mission. One understudied question in our assessment of infectious disease risk is the contribution carbon dioxide (CO2) at atmospheric concentrations higher than would be observed in terrestrial settings. Previous terrestrial studies have shown altered gene expression, selective bacterial inhibition, increased growth and diversity, and increased antibiotic resistance of bacterial communities individually and in biofilms when exposed to increased levels of CO2. While many of these findings are based on levels of CO2 higher than would be found during spaceflight exploration missions, the potential for subtle changes in CO2 levels to exacerbate infectious disease risks warrants an evaluation of pathogen responses to spaceflight CO2 conditions. To address this question, we are evaluating the effects of elevated atmospheric CO2 on microbial concentration, diversity, and pathogenesis-related characteristics using medically significant microorganisms cultured at CO2 levels representing terrestrial atmospheric concentration (≈0.04 %), ISS elevated concentration (0.4 %), and an unexpectedly elevated concentration (1 %) to represent a worst-case scenario. To determine if microbial responses to these CO2 concentrations may be exacerbated in response to the spaceflight environment, microorganisms are being cultured under static conditions and both spaceflight analogue and control conditions using the Rotating Wall Vessel (RWV) bioreactor (Synthecon, Houston, TX). For these studies, we will evaluate the response of Staphylococcus aureus, Streptococcus pneumoniae, and Enterobacter aerogenes, which are opportunistic bacterial pathogens that are of medical significance and have been or are likely to be found aboard human-inhabited spacecraft. Bacteria will be evaluated individually and together as a consortium. The following Specific Aims are be investigated: Aim 1. Characterize the growth, diversity, and pathogenesis-related stress responses of medically significant microorganisms grown under multiple CO2 concentrations under static growth in a liquid medium. Stress responses include acid stress, oxidative stress, and thermal stress. Aim 2. Characterize the growth, diversity, and pathogenesis-related stress responses of medically significant microorganisms grown under multiple CO2 concentrations in both spaceflight analogue and control conditions. Stress responses include acid stress, oxidative stress, and thermal stress.
The avoidance of foods which are associated with uncomfortable or aversive internal states has long been recognized. Many people are aware, either directly or via anecdotal reports, of individuals who avoid foods which were eaten just before the onset of sickness. Awareness of this phenomenon can be traced to the writings of John Locke. The disruption of diet during cancer therapy is sometimes ascribed to the attribution of an unpleasant quality to foods eaten preceding the sickness induced by therapy itself. In addition, it has long been recognized by the manufacturers of rodent poisons that animals avoid the injection of food treated with nonlethal doses of poison. An important part of the laboratory study of this phenomenon was directed toward studying the role learning plays in this type of avoidance behavior. Following the lead of Garcia and his associates, this avoidance has come to be interpreted as arising from a form of classical conditioning. In typical laboratory studies of this behavior, a novel food is ingested just prior to exposure to some stimulus, commonly poisoning or irradiation, which produces illness. Following the terminology of classical conditioning, it is common to describe this procedure as one of 'pairing' a conditioned stimulus (CS), the novel food, with an unconditioned stimulus (US), the illness induced by toxicosis or irradiation. Avoidance of the food in succeeding feeding opportunities is viewed as a learned response or a conditioned taste aversion (CTA). Garcia et al. asserted that motion sickness could produce 'gustatory' aversions, but passive motion was first reported as an US to establish CTA by Green and Rachlin. The purpose is to review the manner in which CTA has been used to study motion sickness. Numerous reviews concentrating on other aspects of CTA are available in the existing literature. Readers are encouraged to consult the various papers and edited books for extensive information on other aspects of this literature.