Publications

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Monolithic photonic integrated circuits (PICs) have a long history reaching back more than 40 years. During that time, and particularly in the past 15 years, the technology has matured and the application space grown to span sophisticated tunable diode lasers, 40 Gb/s electrical-to-optical signal converters with complex data formats, wavelength multiplexors and routers, as well as chemical/biological sensors. Most of this activity has centered in recent years on optical circuits built on either Silicon or InP substrates. This talk will review the three classes of PIC and highlight the unique strengths, and weaknesses, of PICs based on Silicon and InP substrates. Examples will be provided from recent R&D activity.

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It is known that, in general, the correlation structure in the joint distribution of model parameters is critical to the uncertainty analysis of that model. Very often, however, studies in the literature only report nominal values for parameters inferred from data, along with confidence intervals for these parameters, but no details on the correlation or full joint distribution of these parameters. When neither posterior nor data are available, but only summary statistics such as nominal values and confidence intervals, a joint PDF must be chosen. Given the summary statistics it may not be reasonable nor necessary to assume the parameters are independent random variables. We demonstrate, using a Bayesian inference procedure, how to construct a posterior density for the parameters exhibiting self consistent correlations, in the absence of data, given (1) the fit-model, (2) nominal parameter values, (3) bounds on the parameters, and (4) a postulated statistical model, around the fit-model, for the missing data. Our approach ensures external Bayesian updating while marginalizing over possible data realizations. We then address the matching of given parameter bounds through the choice of hyperparameters, which are introduced in postulating the statistical model, but are not given nominal values. We discuss some possible approaches, including (1) inferring them in a separate Bayesian inference loop and (2) optimization. We also perform an empirical evaluation of the algorithm showing the posterior obtained with this data free inference compares well with the true posterior obtained from inference against the full data set.

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One of the authors previously conjectured that the wrinkling of propagating fronts by weak random advection increases the bulk propagation rate (turbulent burning velocity) in proportion to the 4/3 power of the advection strength. An exact derivation of this scaling is reported. The analysis shows that the coefficient of this scaling is equal to the energy density of a lower-dimensional Burgers fluid with a white-in-time forcing whose spatial structure is expressed in terms of the spatial autocorrelation of the flow that advects the front. The replica method of field theory has been used to derive an upper bound on the coefficient as a function of the spatial autocorrelation. High precision numerics show that the bound is usefully sharp. Implications for strongly advected fronts (e.g., turbulent flames) are noted.

Recent work suggests that cloud effects remain one of the largest sources of uncertainty in model-based estimates of climate sensitivity. In particular, the entrainment rate in stratocumulus-topped mixed layers needs better models. More than thirty years ago a clever laboratory experiment was conducted by McEwan and Paltridge to examine an analog of the entrainment process at the top of stratiform clouds. Sayler and Breidenthal extended this pioneering work and determined the effect of the Richardson number on the dimensionless entrainment rate. The experiments gave hints that the interaction between molecular effects and the one-sided turbulence seems to be crucial for understanding entrainment. From the numerical point of view large-eddy simulation (LES) does not allow explicitly resolving all the fine scale processes at the entrainment interface. Direct numerical simulation (DNS) is limited due to the Reynolds number and is not the tool of choice for parameter studies. Therefore it is useful to investigate new modeling strategies, such as stochastic turbulence models which allow sufficient resolution at least in one dimension while having acceptable run times. We will present results of the One-Dimensional Turbulence stochastic simulation model applied to the experimental setup of Sayler and Breidenthal. The results on radiatively induced entrainment follow quite well the scaling of the entrainment rate with the Richardson number that was experimentally found for a set of trials. Moreover, we investigate the influence of molecular effects, the fluids optical properties, and the artifact of parasitic turbulence experimentally observed in the laminar layer. In the simulations the parameters are varied systematically for even larger ranges than in the experiment. Based on the obtained results a more complex parameterization of the entrainment rate than currently discussed in the literature seems to be necessary.

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We report air filamentation by a 1550 nm subpicosecond pulse. During filamentation, the continuum generated was less than expected. A large amount of third harmonic was also generated.

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In this paper, we report the progress made in our project recently funded by the US Department of Energy (DOE) toward developing a computational capability, which includes a two-phase, three-dimensional PEM (polymer electrolyte membrane) fuel cell model and its coupling with DAKOTA (a design and optimization toolkit developed and being enhanced by Sandia National Laboratories). We first present a brief literature survey in which the prominent/notable PEM fuel cell models developed by various researchers or groups are reviewed. Next, we describe the two-phase, three-dimensional PEM fuel cell model being developed, tested, and later validated by experimental data. Results from case studies are presented to illustrate the utility of our comprehensive, integrated cell model. The coupling between the PEM fuel cell model and DAKOTA is briefly discussed. Our efforts in this DOE-funded project are focused on developing a validated computational capability that can be employed for PEM fuel cell design and optimization.

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