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At least 55 records · Page 3

Mean velocity and turbulence measurements in a 90 deg curved duct with thin inlet boundary layer

The experimental database established by this investigation of the flow in a large rectangular turning duct is of benchmark quality. The experimental Reynolds numbers, Deans numbers and boundary layer characteristics are significantly different from previous benchmark curved-duct experimental parameters. This investigation extends the experimental database to higher Reynolds number and thinner entrance boundary layers. The 5% to 10% thick boundary layers, based on duct half-width, results in a large region of near-potential flow in the duct core surrounded by developing boundary layers with large crossflows. The turbulent entrance boundary layer case at R sub ed = 328,000 provides an incompressible flowfield which approaches real turbine blade cascade characteristics. The results of this investigation provide a challenging benchmark database for computational fluid dynamics code development.

Crawford, R. A.↗

Area-Suction Boundary-Layer Control as Applied to the Trailing-Edge Flaps of a 35 Degree Swept-Wing Airplane

A wind-tunnel investigation was made to determine the effects on the aerodynamic characteristics of a 35 degree swept-wing airplane of applying area-suction boundary-layer control to the trailing-edge flaps. Flight tests of a similar airplane were then conducted to determine the effect of boundary-layer control in the handling qualities and operation of the airplane, particularly during landing. The wind-tunnel and flight tests indicated that area suction applied to the trailing-edge flaps produced significant increases in flap lift increment. Although the flap boundary-layer control reduced the stall speed only slightly, a reduction in minimum comfortable approach speed of about 12 knots was obtained.

Cook, Woodrow L↗

Moving base simulation evaluation of translational rate command systems for STOVL aircraft in hover

Using a generalized simulation model, a moving-base simulation of a lift-fan short takeoff/vertical landing fighter aircraft has been conducted on the Vertical Motion Simulator at Ames Research Center. Objectives of the experiment were to determine the influence of system bandwidth and phase delay on flying qualities for translational rate command and vertical velocity command systems. Assessments were made for precision hover control and for landings aboard an LPH type amphibious assault ship in the presence of winds and rough seas. Results obtained define the boundaries between satisfactory and adequate flying qualities for these design features for longitudinal and lateral translational rate command and for vertical velocity command.

Franklin, James A.↗

Zero-G experiments in two-phase fluids flow regimes

The two-phase flows studied were liquid and gas mixtures in a straight flow channel of circular cross-section. Boundaries between flow regimes have been defined for normogravity on coordinates of gas quality and total mass velocity; and, when combined with boundary expressions having a Froude number term, an analytical model was derived predicting boundary shifts with changes in gravity level. Experiments with air and water were performed, first in the normogravity environment of a ground laboratory and then in 'zero gravity' aboard a KC-135 aircraft flying parabolic trajectories. Data reduction confirmed regime boundary shifts in the direction predicted, although the magnitude was a little less than predicted. Pressure drop measurements showed significant increases for the low gravity condition.

Heppner, D. B.↗

Air Quality Science and Regulatory Efforts Require Geostationary Satellite Measurements

Air quality scientists and regulatory agencies would benefit from the high spatial and temporal resolution trace gas and aerosol data that could be provided by instruments on a geostationary platform. More detailed time-resolved data from a geostationary platform could be used in tracking regional transport and in evaluating mesoscale air quality model performance in terms of photochemical evolution throughout the day. The diurnal cycle of photochemical pollutants is currently missing from the data provided by the current generation of atmospheric chemistry satellites which provide only one measurement per day. Often peak surface ozone mixing ratios are reached much earlier in the day during major regional pollution episodes than during local episodes due to downward mixing of ozone that had been transported above the boundary layer overnight. The regional air quality models often do not simulate this downward mixing well enough and underestimate surface ozone in regional episodes. Having high time-resolution geostationary data will make it possible to determine the magnitude of this lower-and mid-tropospheric transport that contributes to peak eight-hour average ozone and 24-hour average PM2.5 concentrations. We will show ozone and PM(sub 2.5) episodes from the CMAQ model and suggest ways in which geostationary satellite data would improve air quality forecasting. Current regulatory modeling is typically being performed at 12 km horizontal resolution. State and regional air quality regulators in regions with complex topography and/or land-sea breezes are anxious to move to 4-km or finer resolution simulations. Geostationary data at these or finer resolutions will be useful in evaluating such models.

Pickering, Kenneth E.↗

Atmospheric Boundary Layer Wind Data During the Period January 1, 1998 Through January 31, 1999 at the Dallas-Fort Worth Airport: Quality Assessment - Volume 1

The quality of the Aircraft Vortex Spacing System (AVOSS) is critically dependent on representative wind profiles in the atmospheric boundary layer. These winds observed from a number of sensor systems around the Dallas-Fort Worth airport were combined into single vertical wind profiles by an algorithm developed and implemented by MIT Lincoln Laboratory. This process, called the AVOSS Winds Analysis System (AWAS), is used by AVOSS for wake corridor predictions. During times when AWAS solutions were available, the quality of the resultant wind profiles and variance was judged from a series of plots combining all sensor observations and AWAS profiles during the period 1200 to 0400 UTC daily. First, input data was evaluated for continuity and consistency from criteria established. Next, the degree of agreement among all wind sensor systems was noted and cases of disagreement identified. Finally, the resultant AWAS solution was compared to the quality-assessed input data. When profiles differed by a specified amount from valid sensor consensus winds, times and altitudes were flagged. Volume one documents the process and quality of input sensor data. Volume two documents the data processing/sorting process and provides the resultant flagged files.

Zak, J. Allen↗

Integrated Application of Active Controls (IAAC) technology to an advanced subsonic transport project-longitudinal handling qualities study of a relaxed-stability airplane

The results of a piloted simulation of longitudinal handling qualities of an airplane with relaxed static stability are described. This task was performed under the Integrated Application of Active Controls (IAAC) Technology Project within the NASA Energy Efficient Transport Program. A representative medium range transport airplane, the Boeing Model 757, was simulated. Evaluations were made of the unaugmented airplane and of the airplane with an Essential Pitch Augmented Stability (PAS) System and with a Primary PAS System at various center of gravity (cg) conditions. Level 2 pilot ratings were attained with cg locations aft to about 57% mean aerodynamic chord (MAC) or 6% aft of the neutral point for unaugmented landing approach. For Mach = 0.80, unaugmented cruise Level 2 ratings were attained to 47% MAC or 5% forward of the maneuver point. The augmented airplane model provided handling qualities close to or within the Level 1 boundary at all cg locations for both Essential and Primary PAS. Analyses of the test conditions when compared with existing handling qualities criteria based on classical unaugmented airplane characteristics agreed well with the pilot ratings. The unaugmented results are comparable to those reported by both the Douglas Aircraft Company and Lockheed California Company from simulation investigations of transport configurations with roughly similar dimensional and mass characteristics.

Source record↗

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗

Version 2 Ozone Monitoring Instrument SO2 Product (OMSO2 V2): New Anthropogenic SO2 Vertical Column Density Dataset

The Ozone Monitoring Instrument (OMI) has been providing global observations of SO2 pollution since 2004. Here we introduce the new anthropogenic SO2 vertical column density (VCD) dataset in the version 2 OMI SO2 product (OMSO2 V2). As with the previous version (OMSO2 V1.3), the new dataset is generated with an algorithm based on principal component analysis of OMI radiances, but features several updates. The most important among those is the use of expanded lookup tables and model a priori profiles to estimate SO2 Jacobians for individual OMI pixels, in order to better characterize pixel-to-pixel variations in SO2 sensitivity, including over snow and ice. Additionally, new data screening and spectral fitting schemes have been implemented to improve the quality of the spectral fit. As compared with the planetary boundary layer SO2 dataset in OMSO2 V1.3, the new dataset has substantially better data quality, especially over areas that are relatively clean or affected by the south Atlantic anomaly. The updated retrievals over snow/ice yield more realistic seasonal changes in SO2 at high latitudes and offer enhanced sensitivity to sources during wintertime. An error analysis has been conducted to assess uncertainties in SO2 VCDs from both the spectral fit and Jacobian calculations. The uncertainties from spectral fitting are reflected in SO2 slant column densities (SCDs) and largely depend on the signal-to-noise ratio of the measured radiances, as implied by the generally smaller SCD uncertainties over clouds or for smaller solar zenith angles. The SCD uncertainties for individual pixels are estimated to be~0.15-0.3 DU (Dobson Units) between ~40°S and ~40°N and to be~0.2-0.5 DU at higher latitudes. The uncertainties from the Jacobians are approximately ~50-100% over polluted areas, and primarily attributed to errors in SO2 a priori profiles and cloud pressures, as well as the lack of explicit treatment for aerosols. Finally, the daily mean and median SCDs over the presumably SO2-free equatorial East Pacific have increased by only~0.0035 DU and ~0.003 DU respectively over the entire 15-year OMI record; while the standard deviation of SCDs has grown by only~0.02 DU or ~10%. Such remarkable long-term stability makes the new dataset particularly suitable for detecting regional changes in SO2 pollution.

OMI, SO2, Remote Sensing↗

Downscaling Reanalysis over Continental Africa with a Regional Model: NCEP Versus ERA Interim Forcing

Five annual climate cycles (1998-2002) are simulated for continental Africa and adjacent oceans by a regional atmospheric model (RM3). RM3 horizontal grid spacing is 0.44deg at 28 vertical levels. Each of 2 simulation ensembles is driven by lateral boundary conditions from each of 2 alternative reanalysis data sets. One simulation downs cales National Center for Environmental Prediction reanalysis 2 (NCPR2) and the other the European Centre for Medium Range Weather Forecasts Interim reanalysis (ERA-I). NCPR2 data are archived at 2.5deg grid spacing, while a recent version of ERA-I provides data at 0.75deg spacing. ERA-I-forced simulations are recomrp. ended by the Coordinated Regional Downscaling Experiment (CORDEX). Comparisons of the 2 sets of simulations with each other and with observational evidence assess the relative performance of each downscaling system. A third simulation also uses ERA-I forcing, but degraded to the same horizontal resolution as NCPR2. RM3-simulated pentad and monthly mean precipitation data are compared to Tropical Rainfall Measuring Mission (TRMM) data, gridded at 0.5deg, and RM3-simulated circulation is compared to both reanalyses. Results suggest that each downscaling system provides advantages and disadvantages relative to the other. The RM3/NCPR2 achieves a more realistic northward advance of summer monsoon rains over West Africa, but RM3/ERA-I creates the more realistic monsoon circulation. Both systems recreate some features of JulySeptember 1999 minus 2002 precipitation differences. Degrading the resolution of ERA-I driving data unrealistically slows the monsoon circulation and considerably diminishes summer rainfall rates over West Africa. The high resolution of ERA-I data, therefore, contributes to the quality of the downscaling, but NCPR2laterai boundary conditions nevertheless produce better simulations of some features.

Reanalysis↗

Development of a Perfectly Matched Layer Technique for a Discontinuous-Galerkin Spectral-Element Method

The numerical simulation of many aerodynamic non-periodic flows of practical interest involves discretized computational domains that often must be artificially truncated. Appropriate boundary conditions are required at these truncated domain boundaries, and ideally, these boundary conditions should be perfectly "absorbing" or "nonreflecting" so that they do not contaminate the flow field in the interior of the domain. The proper specification of these boundaries is critical to the stability, accuracy, convergence, and quality of the numerical solution, and has been the topic of considerable research. The need for accurate boundary specification has been underscored in recent years with efforts to apply higher-fidelity methods (DNS, LES) in conjunction with high-order low-dissipation numerical schemes to realistic flow configurations. One of the most popular choices for specifying these boundaries is the characteristics-based boundary condition where the linearized flow field at the boundaries are decomposed into characteristic waves using either one-dimensional Riemann or other multi-dimensional Riemann approximations. The values of incoming characteristics are then suitably modified. The incoming characteristics are specified at the in flow boundaries, and at the out flow boundaries the variation of the incoming characteristic is zeroed out to ensure no reflection. This, however, makes the problem ill-posed requiring the use of an ad-hoc parameter to allow small reflections that make the solution stable. Generally speaking, such boundary conditions work reasonably well when the characteristic flow direction is normal to the boundary, but reflects spurious energy otherwise. An alternative to the characteristic-based boundary condition is to add additional "buffer" regions to the main computational domain near the artificial boundaries, and solve a different set of equations in the buffer region in order to minimize acoustic reflections. One approach that has been used involves modeling the pressure fluctuations as acoustic waves propagating in the far-field relative to a single noise-source inside the buffer region. This approach treats vorticity-induced pressure fluctuations the same as acoustic waves. Another popular approach, often referred to as the "sponge layer," attempts to dampen the flow perturbations by introducing artificial dissipation in the buffer region. Although the artificial dissipation removes all perturbations inside the sponge layer, incoming waves are still reflected from the interface boundary between the computational domain and the sponge layer. The effect of these refkections can be somewhat mitigated by appropriately selecting the artificial dissipation strength and the extent of the sponge layer. One of the most promising variants on the buffer region approach is the Perfectly Matched Layer (PML) technique. The PML technique mitigates spurious reflections from boundaries and interfaces by dampening the perturbation modes inside the buffer region such that their eigenfunctions remain unchanged. The technique was first developed by Berenger for application to problems involving electromagnetic wave propagation. It was later extended to the linearized Euler, Euler and Navier-Stokes equations by Hu and his coauthors. The PML technique ensures the no-reflection property for all waves, irrespective of incidence angle, wavelength, and propagation direction. Although the technique requires the solution of a set of auxiliary equations, the computational overhead is easily justified since it allows smaller domain sizes and can provide better accuracy, stability, and convergence of the numerical solution. In this paper, the PML technique is developed in the context of a high-order spectral-element Discontinuous Galerkin (DG) method. The technique is compared to other approaches to treating the in flow and out flow boundary, such as those based on using characteristic boundary conditions and sponge layers. The superiority of the current PML technique over other approaches is demonstrated for a range of test cases, viz., acoustic pulse propagation, convective vortex, shear layer flow, and low-pressure turbine cascade flow. The paper is structured as follows. We first derive the PML equations from the non{linear Euler equations. A short description of the higher-order DG method used is then described. Preliminary results for the four test cases considered are then presented and discussed. Details regarding current work that will be included in the final paper are also provided.

Spectral↗

Blowing-Type Boundary-Layer Control as Applied to the Trailing-Edge Flaps of a 35 Degree Swept-Wing Airplane

A wind-tunnel investigation was made to determine the effects on the aerodynamic characteristics of a 35 degree swept-wing airplane of applying blowing-type boundary-layer control to the trailing-edge flaps. Flight tests of a similar airplane were then conducted to determine the effects of boundary-layer control on the handling qualities and operation of the airplane, particularly during landing and take-off. The wind-tunnel and flight tests indicated that blowing over the flaps produced large increases in flap lift increment, and significant increases in maximum lift. The use of blowing permitted reductions in the landing approach speeds of as much as 12 knots.

Kelly, Mark W↗

Supersonic and hypersonic shock/boundary-layer interaction database

An assessment is given of existing shock wave/tubulent boundary-layer interaction experiments having sufficient quality to guide turbulence modeling and code validation efforts. Although the focus of this work is hypersonic, experiments at Mach numbers as low as 3 were considered. The principal means of identifying candidate studies was a computerized search of the AIAA Aerospace Database. Several hundred candidate studies were examined and over 100 of these were subjected to a rigorous set of acceptance criteria for inclusion in the data-base. Nineteen experiments were found to meet these criteria, of which only seven were in the hypersonic regime (M is greater than 5).

Settles, Gary S.↗

Atmospheric Chemistry Modeling Using a Regression Forest Model

Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM. We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for improving this approach.

Keller, Christoph A.↗

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry is a high-dimensionality, large-data problem and thus may be suited to machine-learning algorithms. We show here the potential of a random forest regression algorithm to replace the gas-phase chemistry solver in the GEOS-Chem chemistry model. In this proof-of-concept study, we used one month of model output to train random forest regression models to predict the concentrations of each long-lived chemical species after integration based upon the physical and chemical conditions before the chemical integration. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for very long-lived species and the absolute concentration for shorter lived species. The skill of the machine learning algorithm is further improved by using a family approach for NO and NO2 rather than treating them independently.By replacing the numerical integrator with the random forest algorithm and running this model for one month, we find that the model is able to reproduce many of the features of the reference chemistry simulation. Replacing the integration methodology with a machine learning algorithm has the potential to be substantially faster. There are a wide range of applications for such an approach, e.g. to generate boundary conditions, for use in air quality forecasts or chemical data assimilation systems, etc.

Keller, Christoph A.↗

Atmospheric Chemistry Modeling Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗

The Ozone Water-Land Environmental Transition Study: An Innovative Strategy for Understanding Chesapeake Bay Pollution Events

Coastal regions have historically represented a significant challenge for air quality investigations because of water-land boundary transition characteristics and a paucity of measurements available over water. Prior studies have identified the formation of high levels of ozone over water bodies, such as the Chesapeake Bay, that can potentially recirculate back over land to significantly impact populated areas. Earth-observing satellites and forecast models face challenges in capturing the coastal transition zone where small-scale meteorological dynamics are complex and large changes in pollutants can occur on very short spatial and temporal scales. An observation strategy is presented to synchronously measure pollutants “over land” and “over water” to provide a more complete picture of chemical gradients across coastal boundaries for both the needs of state and local environmental management and new remote sensing platforms. Intensive vertical profile information from ozone lidar systems and ozonesondes, obtained at two main sites, one over land and the other over water, are complemented by remote sensing and in situ observations of air quality from ground-based, airborne (both personned and unpersonned), and shipborne platforms. These observations, coupled with reliable chemical transport simulations, such as the National Oceanic and Atmospheric Administration (NOAA) National Air Quality Forecast Capability (NAQFC), are expected to lead to a more fully characterized and complete land–water interaction observing system that can be used to assess future geostationary air quality instruments, such as the National Aeronautics and Space Administration (NASA) Tropospheric Emissions: Monitoring of Pollution (TEMPO), and current low-Earth-orbiting satellites, such as the European Space Agency’s Sentinel-5 Precursor (S5-P) with its Tropospheric Monitoring Instrument (TROPOMI).

Forecast Models↗