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Effective temperature for late type stars with or without water vapor opacity calculated for several masses, determining convective energy transport efficiency
Zero-order and first-order efficiencies of plane diffraction grazings over the 100-300-A band have been measured using the NRL beamline facility at the National Synchrotron Light Source. Measurements were taken at grating angles of 5, 10, and 15 deg, and the incident radiation was polarized primarily in the transverse magnetic mode. Four (three laminar, one blazed) of the gratings were ion etched in high quality quartz blanks, overcoated with 200 A of gold. A fifth (blazed) grating was ruled in gold. The line densities are 1000, 2000, and 3600 grooves/mm for those with laminar profiles. Both blazed gratings have a blaze angle of 8 deg and line densities of 3600 grooves/mm. Measured first-order efficiencies up to 15 percent were obtained for the laminar gratings and up to 8 percent was obtained for the ion-etched blazed grating. Much lower efficiencies (not greater than 2 percent) were measured for the ruled grating. A computer program was written to calculate grating efficiencies using a full electromagnetic model. Measured efficiencies of the ion-etched gratings agree well with predicted values, but the measured first-order efficiency of the ruled grating is much lower than predicted.
A novel Computational Fluid Dynamics (CFD) coupling framework using a conventional Reynolds-Averaged Navier-Stokes (BANS) solver to resolve the near-body flow field and a Particle-based Vorticity Transport Method (PVTM) to predict the evolution of the far field wake is developed, refined, and evaluated for fixed and rotary wing cases. For the rotary wing case, the RANS/PVTM modules are loosely coupled to a Computational Structural Dynamics (CSD) module that provides blade motion and vehicle trim information. The PVTM module is refined by the addition of vortex diffusion, stretching, and reorientation models as well as an efficient memory model. Results from the coupled framework are compared with several experimental data sets (a fixed-wing wind tunnel test and a rotary-wing hover test).
This study compares dust storm simulations using two commonly adopted methods for representing four important dust emission parameters. Compared to a dynamic dust source mask based on land use and vegetation cover, a static mask based solely on land use overestimates dust concentration and optical depth by a factor of 2, besides generating spurious emissions. The results reinforce that seasonal variations in vegetation cover can significantly affect dust emissions. For sandblasting efficiency, a clay-dependent semiempirical expression produces 10 times more dust than a physics-based expression. Simulations using model-predicted versus a fixed constant for air density differ by only 8%. However, this difference could range between 12 and 22% for annual simulations over global dust source regions. Simulations with updated versus old land use data, using the same dust source mask, differ twofold, indicating the significant impact of land use change on regional dust emission in central Arizona. The differences in the pairs of these simulations are generally larger than the uncertainty due to meteorology. The simulations align better with observation when using the dynamic dust source mask, the physics-based sandblasting efficiency, and the up-to-date land use data. Given the high sensitivity of dust to surface conditions, the results discussed have implications for improving the dust cycle in weather and climate models and for interpreting model intercomparisons.
This paper presents a preliminary study for the characterization of collection efficiency from icing tests conducted in the Icing Research Tunnel at NASA Glenn Research Center. A test method previously developed for measuring the attachment line maximum collection efficiency of a swept NACA 0012 airfoil model at zero angle of attack was applied to the leading-edge region of a 65%-scale version of the Common Research Model midspan wing section. A correlation for the stagnation line maximum collection efficiency as a function of the modified inertia parameter was obtained with LEWICE3D simulations utilizing a discrete number of drop diameters. It was then compared with the collection efficiency measurement data obtained in the IRT. For the experimental collection efficiency, two ice shape digitization procedures were utilized to extract 2-D chord-wise ice shape profiles, i.e., the Maximum Combined Cross Section or MCCS, and the Minimum Combined Cross Section or MinCCS, at selected span-wise locations from the 3-D scanned ice shapes. The preliminary result showed that the rime ice thickness method can be used to characterize the collection efficiency distribution, for conditions free of ice erosion, in the main ice shape region. However, a high-order statistical processing of the ice scan is needed for estimating the mean ice thickness in areas where feathers are prevalent. From the limited comparison of the experimental and LEWICE3D collection efficiency data in the CRM65 MS model leading edge area, it was shown that the attachment line maximum collection efficiency is reasonably estimated by the best curve-fit correlation in the range of modified inertia parameter tested. A tighter alignment control of the iced and cold clean model scans as well as the model angle of attack are needed to improve the comparison. Further evaluation of this correlation is recommended to assess its applicability for Common Research Model type swept wing icing scaling analysis.
This paper presents a preliminary study for the characterization of collection efficiency from icing tests conducted in the Icing Research Tunnel at NASA Glenn Research Center. A test method previously developed for measuring the attachment line maximum collection efficiency of a swept NACA 0012 airfoil model at zero angle of attack was applied to the leading-edge region of a 65%-scale version of the Common Research Model midspan wing section. A correlation for the stagnation line maximum collection efficiency as a function of the modified inertia parameter was obtained with LEWICE3D simulations utilizing a discrete number of drop diameters. It was then compared with the collection efficiency measurement data obtained in the IRT. For the experimental collection efficiency, two ice shape digitization procedures were utilized to extract 2-D chord-wise ice shape profiles, i.e., the Maximum Combined Cross Section or MCCS, and the Minimum Combined Cross Section or Min CCS, at selected span-wise locations from the 3-D scanned ice shapes. The preliminary result showed that the rime ice thickness method can be used to characterize the collection efficiency distribution, for conditions free of ice erosion, in the main ice shape region. However, a high-order statistical processing of the ice scan is needed for estimating the mean ice thickness in areas where feathers are prevalent. From the limited comparison of the experimental and LEWICE3D collection efficiency data in the CRM65 MS model leading edge area, it was shown that the attachment line maximum collection efficiency is reasonably estimated by the best curve-fit correlation in the range of modified inertia parameter tested. A tighter alignment control of the iced and cold clean model scans is needed to improve the comparison. Further evaluation of this correlation is recommended to assess its applicability for Common Research Model type swept wing icing scaling analysis.
Transport equations which describe the flow of holes and electrons in the heavily doped regions of a solar cell are presented in a form that is suitable for device modeling. Two experimentally determinable parameters, the effective bandgap shrinkage and the effective asymmetry factor are required to completely model the cell in these regions. Nevertheless, a knowledge of only the effective bandgap shrinkage is sufficient to model the terminal characteristics of the cell. The results of computer simulations of the effects of heavy doping are presented. The insensitivity of the terminal characteristics to the choice of effective asymmetry factor is shown along with the sensitivity of the electric field and quasielectric fields to this parameter. The dependence of the terminal characteristics on the effective bandgap shrinkage is also presented.
A prototype of a versatile, observer-based magnetic bearing controller for aerospace flywheels was successfully developed and demonstrated on a magnetic bearing test rig (see the photograph) and an actual flywheel module. The objective of this development included a fast, yet low risk, control development process, and a robust, high-performance controller for a large variety of flywheels. This required a good system model, an efficient development procedure, and a model-based controller that addressed the key problems associated with flywheel and bearing imbalance, sensor error, and vibration. The model used in this control development and tuning procedure included the flexible rotor dynamics and motor-induced vibrations. Such a model was essential for low-risk scheduling of speed-dependent control parameters and for reliable evaluation of novel control strategies. The successfully tested control prototype utilized an extended Kalman filter to estimate the true rotor principal-axis motion from the raw sensor position feedback. For control refinement, the extended Kalman filter also estimated and eliminated the combined effects of mass-imbalance and sensor runouts from the input data. A key advantage of the design based on the extended Kalman filter is its ability to accurately estimate both the rotor's principal-axis position and gyroscopic rates with the least amount of phase lag. This is important for control parameter scheduling to dampen the gyroscopic motions. Because of large uncertainties in the magnetic bearing and imbalance characteristics, this state-estimation scheme alone is insufficient for containing the rotor motion within the desired 1-mil excursion radius. A nonlinear gain adjustment based on an estimation of the principal-axis orbit size was needed to provide a coarse (nonoptimal), but robust, control of the orbit growth. Control current minimization was achieved with a (steepest gradient) search of synchronous errors in the principal-axis position input data. Actual flywheel tests of this observer-based controller (developed entirely in-house) at the NASA Glenn Research Center showed that the model correctly predicted the rotor orbit growth as a function of rotational speed, and it demonstrated the capability of gain adjustments to arrest this growth. Data from these tests on an actual flywheel module spun to 26,000 rpm proved that the controller was able to contain the shaft motion to within much less than 0.5 mils of radial excursion with axis currents less than 300 mA in root-mean-square estimate. The test speed range was limited because of thermal expansion concerns for this particular flywheel unit, not because of any deficiency in the controller. Simulations for this unit indicated that the controller should be robust up to its top operating speed of 60,000 rpm. Aside from these important achievements, and most significantly, it took less than 1 week to adapt this controller from the simple test rig to the actual flywheel and to demonstrate full five-axis levitation and control. This demonstration showed that both the controller and the model-based development and tuning framework are easily adaptable to a wide range of rotors and bearing configurations and, hence, are capable of reducing design risks and costs for many future flywheel technology developments.
We present the Efficient CH4-CO-OH Module (ECCOH) that allows for the simulation of the methane, carbon monoxide and hydroxyl radical (CH4-CO-OH cycle, within a chemistry climate model, carbon cycle model, or earth system model. The computational efficiency of the module allows many multi-decadal, sensitivity simulations of the CH4-CO-OH cycle, which primarily determines the global tropospheric oxidizing capacity. This capability is important for capturing the nonlinear feedbacks of the CH4-CO-OH system and understanding the perturbations to relatively long-lived methane and the concomitant impacts on climate. We implemented the ECCOH module into the NASA GEOS-5 Atmospheric Global Circulation Model (AGCM), performed multiple sensitivity simulations of the CH4-CO-OH system over two decades, and evaluated the model output with surface and satellite datasets of methane and CO. The favorable comparison of output from the ECCOH module (as configured in the GEOS-5 AGCM) with observations demonstrates the fidelity of the module for use in scientific research.
The complexity of the geopotential model can heavily impact the navigation error in satellites and spacecraft. Geopotential models of the accuracy needed for spaceflight are too complicated for flight computers to run at the rate needed by the navigation system. There are methods to make the geopotential model more efficient while maintaining the needed accuracy, which include: using an efficient method for the full model, propagating to avoid singularities, and running the full model at a low rate and propagating to the needed rate. These methods can decrease the computational requirement enough to be run by the flight computer at the rate required of the navigation system.
In engineering and aerospace applications, it is vital to operational success to have insight into the expected performance and health of physical systems. The field of prognostics and health management provides quantitative methods for monitoring, predicting, and managing system health. Prognostics algorithms can be employed to assess the current state of a system, propagate the system throughout time, and predict potential anomalies or failures that may occur. While they can provide accurate prediction results, effective prognostics algorithms can be challenging to use in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for future remaining useful life predictions. In this work, we implement new algorithmic approaches for prediction, quantitatively compare them via a battery degradation use-case, and provide recommendations of potential improvements to a prognostics framework. One approach to prediction is through sampling, whereby the current state of a physical system is sampled many times and each sample is propagated forward until failure is reached, resulting in a distribution of failure values. To improve the efficiency of this process, we implemented five new algorithmic approaches to prediction, including three distinct sampling methods (standard Monte Carlo, Quasi-Monte Carlo, and Latin Hypercube Sampling), a variable time step algorithm, and a variable sample size algorithm. To compare the algorithms, we employ a variety of metrics designed specifically to analyze both computational efficiency and model accuracy. Our metrics include accuracy to compare the average predicted value to ground truth, mean absolute deviation to illustrate dispersion, specific percentile error to describe accuracy within a user-defined risk tolerance, and code run-time. To quantitatively analyze our results, we employ a use-case of degradation of a Lithium-ion battery. We use an electrochemistry-based model to describe the current health state of the battery, and implement our prediction algorithms to propagate forward in time until end-of-discharge (EOD) is reached. Notably, through this work it was found that none of our sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. We find that while the sampling methods are unique, the distributions they generate are similar, ultimately producing final predictions that are nearly identical. In exploring the effect of the time step within the prediction algorithm, we found that prediction accuracy was highly dependent on the time step used, and that implementing a variable time step within a particular prediction may provide an increase in computational efficiency while also maintaining prediction accuracy. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment can result in improved computation speed and maintained prediction accuracy. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency.
Discussed is the numerical implementation of turbulence models used in viscous compressible flow simulations and their performance described with respect to numerical accuracy, efficiency and stability. The basic approach utilizes the Reynolds-averaged compressible Navier-Stokes equations in which the Reynolds stresses and heat fluxes are mathematically modeled by suitable turbulence models. The turbulence models investigated include zero-, one-, and two-equation eddy viscosity models. The flow fields investigated include theoretical and supersonic flows about two-dimensional and axisymmetric bodies. Discussions concerning the numerical implementation of models include differencing procedures and boundary conditions used to assume numerical stability and accuracy. Numerical performance is also evaluated by comparing computations with experimental results.
This paper presents a new approach to AutoRegressive Moving Average (ARMA or ARX) modeling which automatically seeks the best model order to represent investigated linear, time invariant systems using their input/output data. The algorithm seeks the ARMA parameterization which accounts for variability in the output of the system due to input activity and contains the fewest number of parameters required to do so. The unique characteristics of the proposed system identification algorithm are its simplicity and efficiency in handling systems with delays and multiple inputs. We present results of applying the algorithm to simulated data and experimental biological data In addition, a technique for assessing the error associated with the impulse responses calculated from estimated ARMA parameterizations is presented. The mapping from ARMA coefficients to impulse response estimates is nonlinear, which complicates any effort to construct confidence bounds for the obtained impulse responses. Here a method for obtaining a linearization of this mapping is derived, which leads to a simple procedure to approximate the confidence bounds.
Economical batch process includes resolidification step to improve conversion efficiency. Experimental models made by process vary in conversion efficiency from 1.4 to more than 6 percent.
Terminal Arrival Efficiency Rate (TAER) is a measure of TRACON performance and the impact of Traffic Management Initiatives (TMI) within 100 miles of the airport. This measures not just the approach control performance, but also ARTCC performance to deliver traffic evenly. Identifying conditions that lead to low TAER can allow development of method to improve it. In this study, we developed linear and decision tree models of TAER.
Most nonlinear circuit analysis programs that exist today were designed primarily for transient analysis. By incorporating more accurate models in simulation programs, accurate predictions of GaAs field effect transistors (FET) behavior can be accomplished. However, should the designer need to simulate GaAs FETs that operate at high DC-to-RF conversion efficiencies, a more sophisticated model is needed. A relevant and appropriate method is called harmonic-balance, as described by Quere et al.
Using a fairly comprehensive model, a parametric variation study was performed of the InP shallow homojunction solar cell with a view to determining the maximum realistically achievable efficiency and an optimum design that would yield this efficiency. Calculations show that with good-quality epitaxial material, a beginning-of-life efficiency of about 20.3 precent at 1AM0, 25 C may be possible. The design parameters of the near-optimum cell are given. Also presented are the expected effect on the performance parameters of radiation damage by 1-MeV electrons and a possible explanation of the high radiation tolerance of InP solar cells.
We present the Efficient CH4-CO-OH (ECCOH) chemistry module that allows for the simulation of the methane, carbon monoxide, and hydroxyl radical (CH4-CO- OH) system, within a chemistry climate model, carbon cycle model, or Earth system model. The computational efficiency of the module allows many multi-decadal sensitivity simulations of the CH4-CO-OH system, which primarily determines the global atmospheric oxidizing capacity. This capability is important for capturing the nonlinear feedbacks of the CH4-CO-OH system and understanding the perturbations to methane, CO, and OH, and the concomitant impacts on climate. We implemented the ECCOH chemistry module in the NASA GEOS-5 atmospheric global circulation model (AGCM), performed multiple sensitivity simulations of the CH4-CO-OH system over 2 decades, and evaluated the model output with surface and satellite data sets of methane and CO. The favorable comparison of output from the ECCOH chemistry module (as configured in the GEOS- 5 AGCM) with observations demonstrates the fidelity of the module for use in scientific research.