Calibration of Computational Models with Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA

It has become commonplace to use complex computer models to predict outcomes in regions where data does not exist. Typically these models need to be calibrated and validated using some experimental data, which often consists of multiple correlated outcomes. In addition, some of the model parameters may be categorical in nature, such as a pointer variable to alternate models (or submodels) for some of the physics of the system. Here we present a general approach for calibration in such situations where an emulator of the computationally demanding models and a discrepancy term from the model to reality are represented within a Bayesian Smoothing Spline (BSS) ANOVA framework. The BSS-ANOVA framework has several advantages over the traditional Gaussian Process, including ease of handling categorical inputs and correlated outputs, and improved computational efficiency. Finally this framework is then applied to the problem that motivated its design; a calibration of a computational fluid dynamics model of a bubbling fluidized which is used as an absorber in a CO2 capture system

Studies on the physics and chemistry of estuarine waters In Chesapeake Bay

Estuarine waters are understood to be those water masses which by virtue of their position are directly subject to the combined action of river and tidal currents. They may be considered to reflect certain dominant forces as well as progressive trends that determine their individualistic though changing hydrographic as well as biologic properties. The factors that lend individuality to these bodies may exhibit profound differences in different latitudes and yet, in certain fundamental respects, the waters possess important characteristics in common...

Information capacity of genetic regulatory elements

Changes in a cell's external or internal conditions are usually reflected in the concentrations of the relevant transcription factors. These proteins in turn modulate the expression levels of the genes under their control and sometimes need to perform non-trivial computations that integrate several inputs and affect multiple genes. At the same time, the activities of the regulated genes would fluctuate even if the inputs were held fixed, as a consequence of the intrinsic noise in the system, and such noise must fundamentally limit the reliability of any genetic computation. Here we use information theory to formalize the notion of information transmission in simple genetic regulatory elements in the presence of physically realistic noise sources. The dependence of this "channel capacity" on noise parameters, cooperativity and cost of making signaling molecules is explored systematically. We find that, at least in principle, capacities higher than one bit should be achievable and that consequently genetic regulation is not limited the use of binary, or "on-off", components.Comment: 17 pages, 9 figure

An optical-IR jet in 3C133

We report the discovery of a new optical-IR synchrotron jet in the radio galaxy 3C133 from our HST/NICMOS snapshot survey. The jet and eastern hotspot are well resolved, and visible at both optical and IR wavelengths. The IR jet follows the morphology of the inner part of the radio jet, with three distinct knots identified with features in the radio. The radio-IR SED's of the knots are examined, along with those of two more distant hotspots at the eastern extreme of the radio feature. The detected emission appears to be synchrotron, with peaks in the NIR for all except one case, which exhibits a power-law spectrum throughout.Comment: ApJ accepted. 14 pages, 6 figure

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.

Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost

Gas turbine combustor

A gas turbine engine has a combustor module including an annular combustor having a liner assembly that defines an annular combustion chamber having a length, L. The liner assembly includes a radially inner liner, a radially outer liner that circumscribes the inner liner, and a bulkhead, having a height, H1, which extends between the respective forward ends of the inner liner and the outer liner. The combustor has an exit height, H3, at the respective aft ends of the inner liner and the outer liner interior. The annular combustor has a ratio H1/H3 having a value less than or equal to 1.7. The annular combustor may also have a ration L/H3 having a value less than or equal to 6.0