Design of a nonhydrostatic atmospheric model based on a generalized vertical coordinate

Department Head: Dick Johnson.2008 Summer.Includes bibliographical references.The isentropic system of equations has particular advantages in the numerical modeling of weather and climate. These include the elimination of the vertical velocity in adiabatic flow, which simplifies the motion to a two-dimensional problem and greatly reduces the numerical errors associated with vertical advection. Vertical resolution is enhanced in regions of high static stability which leads to better resolving of features such as the tropopause boundary. Also, sharp horizontal gradients of atmospheric properties found along frontal boundaries in traditional Eulerian coordinate systems are nonexistent in the isentropic coordinate framework. The extreme isentropic overturning that can occur in fine-scale atmospheric motion presents a challenge to nonhydrostatic modeling with the isentropic vertical coordinate. This dissertation presents a new nonhydrostatic atmospheric model based on a generalized vertical coordinate. The coordinate is specified in a similar manner as Konor and Arakawa, but elements of arbitrary Eulerian-Lagrangian methods are added to provide the flexibility to maintain coordinate monotonicity in regions of negative static stability and return the coordinate levels to their isentropic targets in statically stable regions. The model is mass-conserving and implements a vertical differencing scheme that satisfies two additional integral constraints for the limiting case of z-coordinates. The hybrid vertical coordinate model is tested with mountain wave experiments which include a downslope windstorm with breaking gravity waves. The results show that the advantages of the isentropic coordinate are realized in the model with regards to vertical tracer and momentum transport. Also, the isentropic overturning associated with the wave breaking is successfully handled by the coordinate formulation

Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input- Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi- domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Accepted versio

Development of a flash flood confidence index from disaster reports and geophysical susceptibility

The analysis of historical disaster events is a critical step towards understanding current risk levels and changes in disaster risk over time. Disaster databases are potentially useful tools for exploring trends, however, criteria for inclusion of events and for associated descriptive characteristics is not standardized. For example, some databases include only primary disaster types, such as ‘flood’, while others include subtypes, such as ‘coastal flood’ and ‘flash flood’. Here we outline a method to identify candidate events for assignment of a specific disaster subtype—namely, ‘flash floods’—from the corresponding primary disaster type—namely, ‘flood’. Geophysical data, including variables derived from remote sensing, are integrated to develop an enhanced flash flood confidence index, consisting of both a flash flood confidence index based on text mining of disaster reports and a flash flood susceptibility index from remote sensing derived geophysical data. This method was applied to a historical flood event dataset covering Ecuador. Results indicate the potential value of disaggregating events labeled as a primary disaster type into events of a particular subtype. The outputs are potentially useful for disaster risk reduction and vulnerability assessment if appropriately evaluated for fitness of use.Campus Lima Centr

Controls of Multimodal Wave Conditions in a Complex Coastal Setting

Coastal hazards emerge from the combined effect of wave conditions and sea level anomalies associated with storms or low-frequency atmosphere-ocean oscillations. Rigorous characterization of wave climate is limited by the availability of spectral wave observations, the computational cost of dynamical simulations, and the ability to link wave-generating atmospheric patterns with coastal conditions. We present a hybrid statistical-dynamical approach to simulating nearshore wave climate in complex coastal settings, demonstrated in the Southern California Bight, where waves arriving from distant, disparate locations are refracted over complex bathymetry and shadowed by offshore islands. Contributions of wave families and large-scale atmospheric drivers to nearshore wave energy flux are analyzed. Results highlight the variability of influences controlling wave conditions along neighboring coastlines. The universal method demonstrated here can be applied to complex coastal settings worldwide, facilitating analysis of the effects of climate change on nearshore wave climate.This work was funded by the U.S. Geological Survey (USGS) Coastal and Marine Geology Program. The authors thank Jorge Perez, IH Cantabria, for providing the GOW wave hindcast and for assistance with wave spectra, and Sean Vitousek, University of Chicago, for a helpful review. This material is based upon work supported by the U.S. Geological Survey under grant/cooperative agreement GI5AC00426. A. R., J. A. A. A., and F. J. M. acknowledge the support of the Spanish “Ministerio de Economía y Competitividad” under grant BIA2014-59643-R. J. A. A. A. was funded by the Spanish “Ministerio de Educación, Cultura y Deporte” FPU (Formación del Profesorado Universitario) studentship BOE-A-2013-12235. Reanalyses of ocean data are available for research purposes through IH Cantabria (contact [email protected]). Southern California Bight look-up table data are available at https://doi.org/10.1594/PANGAEA.880314. Related Southern California nearshore wave data can be found at http://dx.doi.org/10.5066/F7N29V2V