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David J. Lovell

Associate Professor

1173 Glenn L. Martin Hall, College Park, MD, 20742 Phone: (301) 405-7995 Fax: (301) 405-2585 email: lovell@umd.edu

 

Department of Civil and Environmental Engineering Institute for Systems Research Applied Mathematics & Statistics, and Scientific Computation Program Engineers Without Borders

 

 

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TEACHING

Spring 2010

ENCE 402 - Simulation and Design of Experiments. Review of statistics and hypothesis testing, sample design and design of experiments, generation of discrete and continuous distributions and their applications. Introduction of simulation languages and simulation of discrete and continuous engineering systems. Output analysis, model validation and sensitivity and reliability analysis.

ENCE 289i - Engineering in the Developing World. Survey of engineering methods to provide basic human needs - food, water, shelter, clothing, energy, and transportation - in the developing world. Lessons learned from the developed world. Close interaction with the student chapter of Engineers Without Borders.

Summer 2010

ENES 100 (Young Scholars Program) - Introduction to Engineering Design. Students work in teams to design and build predator and prey robot platforms.

 

Fall 2010

ENES 100 - Introduction to Engineering Design. Students work in teams to design and build hovercraft.

 

ENCE 289j Planes, Trains and Automobiles: Transportation Innovation. A historical perspective on innovations largely associated with transportation technologies. From manufacturing improvements ushered in by the boom of automobile sales to control, communications, and propulsion innovations in rail systems to advances in noise control and engine emissions coupled with the aviation industry. Also offers insight into how the public need for mobility has driven various fields of science to innovative solutions to difficult problems.

Spring 2011

ENCE 302 - Probability and Statistics for Civil and Environmental Engineers. Statistics is the science of data. Civil Engineers must often make decisions based on incomplete, variable or uncertain information. In addition, modern methods of design and analysis need to account for variability in natural, engineered and human systems. After successful completion of this class, a student should have facility and familiarity with established basic techniques for managing data, modeling variability and uncertainty, communicating about data and decisions, and supporting or defending a decision or judgment based on uncertain or incomplete data.