Regional ocean models are capable of forecasting conditions for usefully long intervals of time (days) provided that initial and ongoing conditions can be measured. In resource-limited circumstances, the placement of sensors in optimal locations is essential. Here, a nonlinear optimization approach to determine optimal adaptive sampling that uses the Genetic Algorithm (GA) method is presented. The method determines sampling strategies that minimize a user-defined physics-based cost function. The method is evaluated using identical twin experiments, comparing hindcasts from an ensemble of simulations that assimilate data selected using the GA adaptive sampling and other methods. For skill metrics, we employ the reduction of the ensemble root-mean-square-error (RMSE) between the “true” data-assimilative ocean simulation and the different ensembles of data-assimilative hindcasts. A 5-glider optimal sampling study is set up for a 400 km x 400 km domain in the Middle Atlantic Bight region, along the New Jersey shelf-break. Results are compared for several ocean and atmospheric forcing conditions.

Congratulations to LT Matthew Swezey for being awarded the First Prize for his poster on “Ocean Acoustic Uncertainty – For Submarine Applications” at the ASNE Day 2016 Student Poster Competition. ASNE Day is an annual meeting of the American Society of Naval Engineers, which features keynote speakers, panel presentations, technical papers presented by subject matter experts and various student specific programs. Keynote speakers this year included Secretary Stackley the Assistant Secretary of the Navy for Research, Development and Acquisition.

Abstract: Far from boundaries, oceanic motion is primarily a mix of two modes: nearly-balanced and slowly-evolving eddies and currents, and more rapidly oscillating internal waves with near-inertial and tidal frequency. Here, we present a three-component asymptotic model which isolates the coupled evolution of near-inertial waves and quasi-geostrophic flow from the Boussinesq equations. A principal implication of our “NIW-QG” model is that near-inertial waves — which may be externally forced by winds, tides, or flow-topography interaction — can extract energy from mesoscale or submesoscale quasi-geostrophic flows. A second and separate implication of the model is that this wave-flow interaction catalyzes a loss of near-inertial energy to freely propagating near-inertial second harmonic waves with twice the inertial frequency. The newly-produced harmonic waves both propagate rapidly to depth and transfer energy back to the near-inertial wavefield at very small vertical scales. The upshot of second harmonic generation is a two-step mechanism whereby quasi-geostrophic flow catalyzes a nonlinear transfer of near-inertial energy to the small scales of wave breaking and mixing.

Biography: Greg is working with William R. Young on theories for the interaction between oceanic near-inertial waves and nearly-balanced currents. Originally from Massachusetts, he obtained his Bachelor’s and Master’s degrees in Aerospace Engineering from the University of Michigan before making his way to the Mechanical and Aerospace Engineering Department at UCSD. In addition to his current focus on geophysical fluid dynamics, topics of former research include land-based locomotion, mixing, and low Reynolds number fluid dynamics.

Host: Prof. Tom Peacock

A stochastic optimization methodology is formulated for computing energy–optimal paths from among time–optimal paths of autonomous vehicles navigating in a dynamic flow field. Based on partial differential equations, the methodology rigorously leverages the level–set equation that governs time–optimal reachability fronts for a given relative vehicle speed function. To set up the energy optimization, the relative vehicle speed is considered to be stochastic and new stochastic Dynamically Orthogonal (DO) level–set equations are derived. Their solution provides the distribution of time–optimal reachability fronts and corresponding distribution of time–optimal paths. An optimization is then performed on the vehicle’s energy–time joint distribution to select the energy–optimal paths for each arrival time, among all stochastic time–optimal paths for that arrival time. Numerical schemes to solve the reduced stochastic DO level–set equations are obtained and accuracy and efficiency considerations are discussed. These reduced equations are first shown to be efficient at solving the governing stochastic level-sets, in part by comparisons with direct Monte Carlo simulations.To validate the methodology and illustrate its overall accuracy, comparisons with `semi–analytical’ energy–optimal path solutions are then completed. In particular, we consider the energy–optimal crossing of a canonical steady front and set up its `semi–analytical’ solution using a dual energy–time nested nonlinear optimization scheme. We then showcase the inner workings and nuances of the energy–optimal path planning, considering different mission scenarios. Finally, we study and discuss results of energy-optimal missions in a strong dynamic double–gyre flow field.

Abstract

In this talk, we will discuss the recent development of a class of hybridized DG methods for implicit large eddy simulation (ILES) of compressible flows. This class of DG methods encompass the hybridizable DG (HDG) method, the embedded DG (EDG) method, as well as new hybridized DG methods resulting from the marriage of the HDG method and the EDG method. While the HDG method is more accurate and robust that the EDG method, the latter is significantly less expensive than the former. This motives us to combine HDG and EDG to obtain new hybridized DG methods that enjoy the advantages of both HDG and EDG. However, this approach presents challenging issues in terms of domain decomposition preconditioners and parallelization because the resulting linear system has complicated sparsity structure. We will discuss our domain decomposition preconditioner and strategy to address some of the issues and leave other issues for future work. In addition, we will talk about various choices of the stabilization tensor and their influence on both nonlinear and linear convergence. Finally, we present ILES results and validate them against experimental data and other simulation data.

This is the joint work with Pablo Fernandez and Jaime Peraire.

Biography

Dr. Nguyen’s current research is focused on efficient methods for simulation of multiscale and multi-physics phenomena across disciplines and on uncertainty quantification techniques for inverse/design problems in engineering. He received his BE degree with first class honors in Aeronautical Engineering from HCMC, University of Technology in 2001, and his Ph.D. degree in High Performance Computation for Engineered Systems from National University of Singapore in 2005. Dr. Nguyen is the author and co-author of more than 25 research articles. He has presented his work in several major conferences, invited talks, and workshops.