Sue Ellen Haupt

Current Research Topics

Atmospheric Data Assimilation and Sensor Data Fusion for Atmospheric Dispersion - Dr. Haupt is collaborating with Dr. George S. Young and colleagues at the University of Buffalo to design methods to assimilate monitored data into dispersion models under funding from the Defense Threat Reduction Agency.  The Penn State team is concentrating on several tasks: 1) developing a mathematical paradigm that encompasses the assimilation/sensor data fusion process, 2) assessing various methods of meteorological data assimilation in the context of atmospheric dispersion, 3) testing the assimilation methods in the context of source and meteorological data characterization for dispersion, and d) rectifying the methods of sensor data fusion with meterological data assimlation.  They are working with Kerrie Long (ARL Professional) and several graduate students on this project:  Anke Beyer, Andrew Annuzio, and Luna Rodriguez.

Radar Reflectors for Submarines - Under Tech Solutions funding from the Office of Naval Research, Drs. S.E. Haupt and R.L. Haupt are assessing the radar cross sections of submarines and of several commercial radar reflectors to determine the best way to prevent collisions with submarines near port. As part of this project, they are doing field work to measure the range of sight of various reflectors, modeling these reflectors, and working to design new reflectors.

Artificial Intelligence and Genetic Algorithms - Dr. S.E. Haupt has been investigating the use of artificial intelligence (AI) techniques, primarily genetic algorithms (GAs), to solve problem in fluid mechanics.  These applications range from using GAs to solve traditional optimization problems, through rather novel applications such as solving strongly nonlinear partial differential equations, nonlinear empirical modeling, and matching eigenvalues.  She has also used genetic algorithms in educational situations, developing a GA to produce artistic images as a student illustration.  Some of this work has been done in collaboration with Randy Haupt (PSU ARL & EE). Together they wrote Practical Genetic Algorithms, 1st and 2nd editions (Wiley 1998, 2004).  Recent work with George Young (Meteorology) and Chris Allen (graduate student) has used GAs to characterize sources and sites of air contaminant for the purposes of Homeland Security.

Atmospheric Dispersion Uncertainty - Dr. Haupt has been working with Drs. L. Joel Peltier (ARL & ME), Wyngaard, Stauffer, and Deng (all Meteorology) to develop methods for analyzing uncertainty in air dispersion models under contract to the Defense Threat Reduction Agency (DTRA).  This work uses both theory and numerical investigations to develop models for dispersion uncertainty and its relationship to variations in meteorology.  This work uses ensemble mesoscale modeling analyses.  Student Jared Lee is demonstrating these technqiues using data from the CAPTEX field test.  Dr. Haupt has also specialized in statistical techniques to best use the information from ensemble model data to predict the most likely solution as well as to characterize its uncertainty.  In addition, Dr. Haupt is working with Dr. David Stauffer and Walter Kolczynski to design new methods to calibrate the actual variance and covariance given the ensemble variance and covariance to assess dispersion uncertainty.  These studies will impact application of SCIPUFF, DTRA's dispersion model.

Computation Fluid Mechanics Applications - CFD has proven quite useful at describing the details of flow about structures.  Dr. Haupt has used these techniques, both in the context of ARL Navy contracts and in modeling atmospheric flows, to study boundary layer, thermal effects, and impact of an immersed body on the flow.  Recently, she has worked with Drs. L. Joel Peltier and Robert F. Kunz (ARL), and Mr. Robert Wilson (undergraduate honors student) to compute details of flow about a cubical structure at high Reynolds number characteristic of atmospheric flow regimes.  These computations match full scale field measurements better than those of prior investigators and capture the details of turbulent separation a shedding by using Detached Eddy Simulation (DES) methods, which combine Reynolds Averaged Navier Stokes (RANS) and Large Eddy Simulation (LES) techniques. They have recently done studies of how relaxing modeling assumptions and grid fidelity impacts the flow characteristics plus producing CFD comparisons to Water Tunnel testing.  Another recent project combined high fidelity CFD techniques with state-of-the art radiation/conduction/convection models to analyze flow inside an enviromentally friendly building.  The results of this modeling effort impacted building design plus provided information on the importance of including heating computations on both the flow characteristics and contaminant dispersion about and within the building.  Dr. Haupt is now working with Prof. Philip Morris (Aerospace Engineering) and student Jonathon Stergiou to use immersed boundary conditions in atmospheric dispersion calculations.

Empirical Modeling of Fluid Flows - Dr. Haupt has done several projects using inverse empirical modeling techniques which take measured or modeled data as the beginning point to build a stochastic empirical model.  The first such project was accomplished with Grant Branstator of the National Center for Atmospheric Research.  They used these empirical techniques to build an inverse climate model which was able to produce responses to forcing better than the traditional time integration model.  More recently, she developed techniques for nonlinear inverse modeling that involve finding the solutions with genetic algorithms.  She expects to apply these techniques to atmospheric problems in the near future.

Ensemble Weather Forecasting with Artifial Intelligence Techniques - Dr. Haupt worked with honors student, Steven Greybush, to design and test new methods of calibrating ensemble forecasts using techniques from statistics and artificial intelligence. That work is being continued by honors student, Tyler McCandless.