Collective dynamics and control of a fleet of heterogeneous marine vehicles

Cooperative control enables combinations of sensor data from multiple autonomous underwater vehicles (AUVs) so that multiple AUVs can perform smarter behaviors than a single AUV. In addition, in some situations, a human-driven underwater vehicle (HUV) and a group of AUVs need to collaborate and preform formation behaviors. However, the collective dynamics of a fleet of heterogeneous underwater vehicles are more complex than the non-trivial single vehicle dynamics, resulting in challenges in analyzing the formation behaviors of a fleet of heterogeneous underwater vehicles. The research addressed in this dissertation investigates the collective dynamics and control of a fleet of heterogeneous underwater vehicles, including multi-AUV systems and systems comprised of an HUV and a group of AUVs (human-AUV systems). This investigation requires a mathematical motion model of an underwater vehicle. This dissertation presents a review of a six-degree-of-freedom (6DOF) motion model of a single AUV and proposes a method of identifying all parameters in the model based on computational fluid dynamics (CFD) calculations. Using the method, we build a 6DOF model of the EcoMapper and validate the model by field experiments. Based upon a generic 6DOF AUV model, we study the collective dynamics of a multi-AUV system and develop a method of decomposing the collective dynamics. After the collective dynamics decomposition, we propose a method of achieving orientation control for each AUV and formation control for the multi-AUV system. We extend the results and propose a cooperative control for a human-AUV system so that an HUV and a group of AUVs will form a desired formation while moving along a desired trajectory as a team. For the post-mission stage, we present a method of analyzing AUV survey data and apply this method to AUV measurement data collected from our field experiments carried out in Grand Isle, Louisiana in 2011, where AUVs were used to survey a lagoon, acquire bathymetric data, and measure the concentration of reminiscent crude oil in the water of the lagoon after the BP Deepwater Horizon oil spill in the Gulf of Mexico in 2010.Ph.D

Evaluation of Cloud Microphysical Properties Derived from MODIS and Himawari-8 Using In-Situ Aircraft Measurements over the Southern Ocean

Cloud microphysical properties from aircraft measurements during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study are used to evaluate the cloud products from the geostationary satellite Himawari‐8 (H‐8) and the polar‐orbiting satellite the Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to the in situ aircraft observations when aircraft flew horizontally near cloud tops, the cloud droplet effective radius (r_e) and number concentration (N_d) from H‐8 (MODIS) are 33% (26%–31%) and 2% (9–13%) larger. Both the H‐8 and MODIS retrievals behave similarly for liquid‐only and mixed‐phase low‐level clouds, indicating the weak sensitivity of the satellite cloud retrieval performance to cloud phase. The r_e and N_d of the cloud profiles from aircraft measurements were also used to compare with the satellite product. It shows that H‐8 r_e and N_d agree better with aircraft measurements when considering only the in situ data acquired in the upper portions (highest 20%) of the clouds. Roughly, the r_e overestimation by H‐8 decreases from 18% to 3% when considering the upper portions of clouds compared to all cloud layer averages, except for one case with drizzles appeared. In addition, the performance of MODIS r_e and N_d is highly dependent on the wavelengths the retrieval method uses. The droplet r_e retrievals using wavelength of 1.6 μm have much larger biases than that using the other two channels. The potential effects of the cloud vertical variation and the photon penetration depth, the cloud heterogeneity, the cloud droplet size spectra, and the drizzle on satellite retrievals have also been discussed

Evaluation of Cloud Microphysical Properties Derived from MODIS and Himawari-8 Using In-Situ Aircraft Measurements over the Southern Ocean

Cloud microphysical properties from aircraft measurements during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study are used to evaluate the cloud products from the geostationary satellite Himawari‐8 (H‐8) and the polar‐orbiting satellite the Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to the in situ aircraft observations when aircraft flew horizontally near cloud tops, the cloud droplet effective radius (r_e) and number concentration (N_d) from H‐8 (MODIS) are 33% (26%–31%) and 2% (9–13%) larger. Both the H‐8 and MODIS retrievals behave similarly for liquid‐only and mixed‐phase low‐level clouds, indicating the weak sensitivity of the satellite cloud retrieval performance to cloud phase. The r_e and N_d of the cloud profiles from aircraft measurements were also used to compare with the satellite product. It shows that H‐8 r_e and N_d agree better with aircraft measurements when considering only the in situ data acquired in the upper portions (highest 20%) of the clouds. Roughly, the r_e overestimation by H‐8 decreases from 18% to 3% when considering the upper portions of clouds compared to all cloud layer averages, except for one case with drizzles appeared. In addition, the performance of MODIS r_e and N_d is highly dependent on the wavelengths the retrieval method uses. The droplet r_e retrievals using wavelength of 1.6 μm have much larger biases than that using the other two channels. The potential effects of the cloud vertical variation and the photon penetration depth, the cloud heterogeneity, the cloud droplet size spectra, and the drizzle on satellite retrievals have also been discussed

Spatiotemporal Variations of Precipitation in China Using Surface Gauge Observations from 1961 to 2016

Long-term precipitation trend is a good indicator of climate and hydrological change. The data from 635 ground stations are used to quantify the temporal trends of precipitation with different intensity in China from 1961 to 2016. These sites are roughly uniformly distributed in the east or west regions of China, while fewer sites exist in the western region. The result shows that precipitation with a rate of 70%. With a 95% confidence level, there is no significant temporal change of annually averaged precipitation in the whole of China. Seasonally, there are no significant temporal changes except for a robust decreasing trend in autumn. Spatially, significant differences in the temporal trends of precipitation are found among various regions. The increasing trend is the largest in Northwest China, and the decreasing trend is the largest in North China. The annually averaged number of precipitation days shows a decreasing trend in all regions except for Northwest China. Regarding precipitation type, the number of light precipitation days shows a robust decreasing trend for almost all regions, while other types show no significant change. Considering the high frequency, the temporal trends of light precipitation could highly explain the temporal variation of the total precipitation amount in China

Spatiotemporal Variations of Precipitation in China Using Surface Gauge Observations from 1961 to 2016

Long-term precipitation trend is a good indicator of climate and hydrological change. The data from 635 ground stations are used to quantify the temporal trends of precipitation with different intensity in China from 1961 to 2016. These sites are roughly uniformly distributed in the east or west regions of China, while fewer sites exist in the western region. The result shows that precipitation with a rate of 70%. With a 95% confidence level, there is no significant temporal change of annually averaged precipitation in the whole of China. Seasonally, there are no significant temporal changes except for a robust decreasing trend in autumn. Spatially, significant differences in the temporal trends of precipitation are found among various regions. The increasing trend is the largest in Northwest China, and the decreasing trend is the largest in North China. The annually averaged number of precipitation days shows a decreasing trend in all regions except for Northwest China. Regarding precipitation type, the number of light precipitation days shows a robust decreasing trend for almost all regions, while other types show no significant change. Considering the high frequency, the temporal trends of light precipitation could highly explain the temporal variation of the total precipitation amount in China

Robust Geometric Formation Control of Multiple Autonomous

©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the American Control Conference (ACC 2013), 17-19 June 2013, Washington, D.C.This paper develops a robust controller for autonomous underwater vehicles with bounded time delays, so that the AUVs form and keep a desired formation shape and track a desired trajectory. We use a six-degree-of-freedom dynamic model for each AUV to describe its motions in the three-dimensional space. We design an orientation controller based on feedback linearization, so that the orientation of each AUV converges to its desired value. We derive formation dynamics of AUVs and decouple the dynamics into a formation shape and a formation center, using the Jacobi transform. We treat couplings in the formation dynamics as perturbations and design a robust formation-keeping controller to tolerate both the perturbations and the time delays. We demonstrate the effectiveness of our controller in simulations

Enlarging rainfall area of tropical cyclones by atmospheric aerosols

The size of a tropical cyclone (TC), measured by the area of either rainfall or wind, is an important indicator for the potential damage by TC. Modeling studies suggested that aerosols tend to enhance rainfall in the outer rainbands, which enlarges the eyewall radius and expands the extent of rainfall area. However, no observational evidence has yet been reported. Using TC rainfall area and aerosol optical depth (AOD) data, we find that aerosols have a distinguishable footprint in the TC size. Other dynamical factors for TC size, such as relative SST and Coriolis parameter, are also quantified and discussed. We show that, on average, TC rainfall size increases 9–20 km for each 0.1 increase of AOD in the western North Pacific. This finding implies that anthropogenic aerosol pollution can increase not only TC rainfall rate, but also TC rainfall area, resulting in potentially more destructive flooding affecting larger areas

Spatial Representativeness of PM_(2.5) Concentrations Obtained Using Reduced Number of Network Stations

Haze has been a focused air pollution phenomenon in China, and its characterization is highly desired. Aerosol properties obtained from a single station are frequently used to represent the haze condition over a large domain, such as tens of kilometers, which could result in high uncertainties due to their spatial variation. Using a high resolution network observation over an urban city in North China from November 2015 to February 2016, this study examines the spatial representativeness of ground station observations of particulate matter with diameters less than 2.5 μm (PM_(2.5)). We developed a new method to determine the representative area of PM_(2.5) measurements from limited stations. The key idea is to determine the PM_(2.5) spatial representative area using its spatial variability and temporal correlation. We also determine stations with large representative area using two grid networks with different resolutions. Based on the high spatial resolution measurements, the representative area of PM_(2.5) at one station can be determined from the grids with high correlations and small differences of PM_(2.5). The representative area for a single station in the study period ranges from 0.25 to 16.25 km^2, but is less than 3 km^2 for more than half of the stations. The representative area varies with locations, and observation at 10 optimal stations would have a good representativeness of those obtained from 169 stations for the four-month time scale studied. Both evaluations with an empirical orthogonal function (EOF) analysis and with independent dataset corroborate the validity of the results found in this study