On the exchange of momentum over the open ocean

Author Posting. © American Meteorological Society, 2013. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 43 (2013): 1589–1610, doi:10.1175/JPO-D-12-0173.1.This study investigates the exchange of momentum between the atmosphere and ocean using data collected from four oceanic field experiments. Direct covariance estimates of momentum fluxes were collected in all four experiments and wind profiles were collected during three of them. The objective of the investigation is to improve parameterizations of the surface roughness and drag coefficient used to estimate the surface stress from bulk formulas. Specifically, the Coupled Ocean–Atmosphere Response Experiment (COARE) 3.0 bulk flux algorithm is refined to create COARE 3.5. Oversea measurements of dimensionless shear are used to investigate the stability function under stable and convective conditions. The behavior of surface roughness is then investigated over a wider range of wind speeds (up to 25 m s−1) and wave conditions than have been available from previous oversea field studies. The wind speed dependence of the Charnock coefficient α in the COARE algorithm is modified to , where m = 0.017 m−1 s and b = −0.005. When combined with a parameterization for smooth flow, this formulation gives better agreement with the stress estimates from all of the field programs at all winds speeds with significant improvement for wind speeds over 13 m s−1. Wave age– and wave slope–dependent parameterizations of the surface roughness are also investigated, but the COARE 3.5 wind speed–dependent formulation matches the observations well without any wave information. The available data provide a simple reason for why wind speed–, wave age–, and wave slope–dependent formulations give similar results—the inverse wave age varies nearly linearly with wind speed in long-fetch conditions for wind speeds up to 25 m s−1.This work was funded by the National Science Foundation Grant OCE04-24536 as part of the CLIVAR Mode Water Dynamics Experiment (CLIMODE) and the Office of Naval Research Grant N00014-05-1-0139 as part of the CBLAST-LOW program.2014-02-0

Convergence of Particle Filter for Output Feedback Control

In the existing literature, convergence results for particle filters are given explicitly only for the case when the underlying dynamic model is a Markov process. When output feedback control is used, the evolution of the state process is no longer Markovian due to the dependence of inputs on the outputs. In this paper, it is shown that the random probability measures produced by the particle filter converge to the true prior and posterior measures in this nonMarkovian case. Firstly, it is proved that the recursive equations relating the prior and posterior measures continue to hold for output feedback control. These recursive equations are then used to show the required convergence of the random measures. Finally, the convergence is also illustrated using simulations on a nonlinear dynamical system

ANFIS BASED OPPURTUNISTIC POWER CONTROL FOR COGNITIVE RADIO IN SPECTRUM SHARING

Cognitive radio is a intelligent technology that helps in resolving the issue of spectrum scarcity. In a spectrum sharing network, where secondary user can communicate simultaneously along with the primary user in the same frequency band, one of the challenges in cognitive radio is to obtain balance between two conflicting goals that are to minimize the interference to the primary users and to improve the performance of the secondary user. In our thesis we have considered a primary link and a secondary link (cognitive link) in a fading channel. To improve the performance of the secondary user by maintaining the Quality of Service (Qos) to the primary user, we considered varying the transmit power of the cognitive user. Efficient utilization of power in any system helps in improving the performance of that system. For this we proposed ANFIS based opportunistic power control strategy with primary user’s SNR and primary user’s channel gain interference as inputs. By using fuzzy inference system, Qos of primary user is adhered and there is no need of complex feedback channel from primary receiver. The simulation results of the proposed strategy shows better performance than the one without power control. Initially we have considered propagation environment without path loss and then extended our concept to the propagation environment with path loss where we have considered relative distance between the links as one of the input parameters

Modeling and Control Study of Solid Oxide Fuel Cell at Low Operating Pressures

Ever-increasing energy consumption, raising public awareness for environmental protection, and higher prices of fossil fuels have motivated many to look for renewable energy sources. SOFC is one of the best alternative energy sources, but it is a highly nonlinear system. In this paper, isothermal and non-isothermal models of solid oxide fuel cell have been developed under the general condition of unchoked outlet flow. For the isothermal dynamic model of SOFC, linear and gain scheduling controllers have been designed for voltage control. Since pressure control in SOFC stack plays a significant role, a controller developed based on full-state feedback linearization and provided a comparison with a linear controller

ANFIS BASED OPPURTUNISTIC POWER CONTROL FOR COGNITIVE RADIO IN SPECTRUM SHARING

Cognitive radio is a intelligent technology that helps in resolving the issue of spectrum scarcity. In a spectrum sharing network, where secondary user can communicate simultaneously along with the primary user in the same frequency band, one of the challenges in cognitive radio is to obtain balance between two conflicting goals that are to minimize the interference to the primary users and to improve the performance of the secondary user. In our thesis we have considered a primary link and a secondary link (cognitive link) in a fading channel. To improve the performance of the secondary user by maintaining the Quality of Service (Qos) to the primary user, we considered varying the transmit power of the cognitive user. Efficient utilization of power in any system helps in improving the performance of that system. For this we proposed ANFIS based opportunistic power control strategy with primary user’s SNR and primary user’s channel gain interference as inputs. By using fuzzy inference system, Qos of primary user is adhered and there is no need of complex feedback channel from primary receiver. The simulation results of the proposed strategy shows better performance than the one without power control. Initially we have considered propagation environment without path loss and then extended our concept to the propagation environment with path loss where we have considered relative distance between the links as one of the input parameters

Stochastic state-feedback control using homotopy optimization and particle filtering

In this paper, a method of designing control inputs for stochastic nonlinear processes under state-feedback is proposed. The objective is to determine a control input that minimizes the expected value of the integral of error between the set-point and the states. Since the states may not be measured, they are estimated using a particle filtering algorithm. The optimal control design is then reformulated as a parameter estimation problem using control vector parameterization where the inputs are considered as a nonlinear function of the error between the state estimates and the set-point. The parameters are then computed through a homotopy based optimization method. The control performance resulting from proposed homotopy based optimization method is compared with that of direct optimization and an existing nonlinear control method on a Solid Oxide Fuel Cell (SOFC) stack model. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature

Error Bounds for Identification of a Class of Continuous LTI Systems

The main problem in identification of continuous LTI systems is the lack of derivative information of the outputs. If all the derivatives are known exactly, a least squares approach is sufficient to obtain the parameter estimates. In this paper, we propose a method which can provide theoretical bounds on the error in the parameter estimates assuming only a few derivatives are known accurately. The error bounds are given for the finite data case as opposed to the asymptotic regimes considered in existing identification approaches. The method is based on transforming the differential equation into the Laplace domain to obtain a linear-in-parameter form for the ODE parameters. As the system is not well conditioned, the method of Tikhonov Regularization is applied to find an approximate solution. Since, exact derivative information is seldom known in practice, B-spline approximation is incorporated in the simulation study where the accuracy of method is demonstrated

A note on modeling mixing in the upper layers of the Bay of Bengal: importance of water type, water column structure and precipitation

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kantha, L., Weller, R. A., Farrar, J. T., Rahaman, H., & Jampana, V. A note on modeling mixing in the upper layers of the Bay of Bengal: importance of water type, water column structure and precipitation. Deep-Sea Research Part II-Topical Studies in Oceanography, 168, (2019): 104643. doi: 10.1016/j.dsr2.2019.104643.Turbulent mixing in the upper layers of the northern Bay of Bengal is affected by a shallow layer overlying the saline waters of the Bay, which results from the huge influx of freshwater from major rivers draining the Indian subcontinent and from rainfall over the Bay during the summer monsoon. The resulting halocline inhibits wind-driven mixing in the upper layers. The brackish layer also alters the optical properties of the water column. Air-sea interaction in the Bay is expected to play a significant role in the intraseasonal variability of summer monsoons over the Indian subcontinent, and as such the sea surface temperature (SST) changes during the summer monsoon are of considerable scientific and societal importance. In this study, data from the heavily instrumented Woods Hole Oceanographic Institution (WHOI) mooring, deployed at 18oN, 89.5oE in the northern Bay from December 2014 to January 2016, are used to drive a one-dimensional mixing model, based on second moment closure model of turbulence, to explore the intra-annual variability in the upper layers. The model results highlight the importance of the optical properties of the upper layers (and hence the penetration of solar insolation in the water column), as well as the temperature and salinity in the upper layers prescribed at the start of the model simulation, in determining the SST in the Bay during the summer monsoon. The heavy rainfall during the summer monsoon also plays an important role. The interseasonal and intraseasonal variability in the upper layers of the Bay are contrasted with those in the Arabian Sea, by the use of the same model but driven by data from an earlier deployment of a WHOI mooring in the Arabian Sea at 15.5 oN, 61.5 oE from December 1994 to December 1995.LK was supported by U.S. Office of Naval Research (ONR) MISO/BoB DRI under grant number N00014-17-1-2716. RW and JTF were supported by ONR Grants N00014-13-1-0453 and N00014-17-1-2880, and the WHOI mooring was funded by Grant N00014-13-1-0453. RW was supported by ONR for the 1994–1995 deployment of the surface mooring in the Arabian Sea. HR and VJ wish to thank Dr. SSC Shenoi, the Director of INCOIS and Dr. M Ravichandran, Director, NCPOR for the encouragement and support to carry out this study. This work was supported by the Ministry of Earth Sciences (MoES), Govt. of India. This is also INCOIS Contribution number 349

On the non-parametric changepoint detection of flow regimes in cyclone Amphan

The Bay of Bengal was witness to a severe cyclone named Amphan during the summer of the year 2020. The National Institute of Ocean Technology (NIOT), INDIA moorings BD08 and BD09 happened to be in the vicinity of the cyclone. The highly instrumented mooring recorded near-surface meteorological parameters like wind speed, sea surface temperature, and near-surface pressure. This article explores the possibility of using a non-parametric algorithm to identify different flow regimes using a one-month long time-series data of the near-surface parameters. The changes in the structure of the time series signal were statistically segmented using an unconstrained non-parametric algorithm. The non-parametric changepoint method was applied to time series of near-surface winds, sea surface temperature, sea level pressure, air temperature and salinity and the segmentations are consistent with visual observations. Identifying different data segments and their simple parameterization is a crucial component and relating them to different flow regimes is useful for the development of parametrization schemes in weather and climate models. The segmentations can considerably simplify the parametrization schemes when expressed as linear functions. Moreover, the usefulness of non-parametric automatic detection of data segments of similar statistical properties shall be more apparent when dealing with relatively long time series data

Corrigendum : On the exchange of momentum over the open ocean

Author Posting. © American Meteorological Society, 2014. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 44 (2014): 2590, doi:10.1175/JPO-D-14-0140.1