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Laurens E. Howle
Associate Professor of Mechanical Engineering and Materials Science
Professor Howle's research interests span the disciplines of thermal science, fluid dynamics, and nonlinear dynamics. His present research projects - visualization of convective fluid patterns, stabilization of the no-motion state in free convection and bifurcation in imperfect or distributed parameter systems - are split evenly between experimental and computational methods.
A key problem facing researchers studying convection in fluid-saturated porous media is the lack of a general, non-invasive method for pattern visualization and wave number measurement. Professor Howle designed innovative porous media which allow optical techniques to be used for the first time as a pattern visualization tool in the study of porous media convection.
Computational spectral methods are efficient methods of simulation of small aspect ratio convection systems. For large problems, these methods can become too expensive to be practical. Professor Howle developed a reduced Galerkin method which decreases the execution time by orders of magnitude for large problems. This extends the range of problems for which certain spectral methods may be used. He is currently studying porous free convection in systems with distributed properties and binary convection using the reduced Galerkin method.
Appointments and Affiliations
- Associate Professor of Mechanical Engineering and Materials Science
- Associate Professor in the Division of Marine Science and Conservation
- Associate Professor of Radiology
- Faculty Network Member of The Energy Initiative
- Office Location: 239 Hudson Eng Bldg, Durham, NC 27708-0300
- Office Phone: (919) 660-5331
- Email Address: firstname.lastname@example.org
- Ph.D. Duke University, 1993
Hydroelastic modeling of deformable structures, transport in thermal and chemical systems, experimental and computational fluid dynamics, nonlinear and complex systems, heat and mass transport in biological systems, stability of fluid motions, machine learning, data mining, econophysics, reduced order modeling, modeling of decompression sickness, pharmacokinetics and pharmacodynamics, mechanical design, manufacturing engineering, wind power.
Awards, Honors, and Distinctions
- Fellows. American Society of Mechanical Engineers. 2012
- ME 321L: Mechanical Engineering Analysis for Design
- ME 491: Special Projects in Mechanical Engineering
- ME 492: Special Projects in Mechanical Engineering
- ME 555: Advanced Topics in Mechanical Engineering
- ME 639: Computational Fluid Mechanics and Heat Transfer
In the News
- Predicting How Bad the Bends Will Be (Mar 20, 2017 | Pratt School of Engineering )
- Murphy, FG; Hada, EA; Doolette, DJ; Howle, LE, Probabilistic pharmacokinetic models of decompression sickness in humans: Part 2, coupled perfusion-diffusion models., Computers in Biology and Medicine, vol 92 (2018), pp. 90-97 [10.1016/j.compbiomed.2017.11.011] [abs].
- Murphy, FG; Swingler, AJ; Gerth, WA; Howle, LE, Iso-risk air no decompression limits after scoring marginal decompression sickness cases as non-events., Computers in Biology and Medicine, vol 92 (2018), pp. 110-117 [10.1016/j.compbiomed.2017.11.012] [abs].
- King, AE; Murphy, FG; Howle, LE, Bimodal decompression sickness onset times are not related to dive type or event severity., Computers in Biology and Medicine, vol 91 (2017), pp. 59-68 [10.1016/j.compbiomed.2017.10.010] [abs].
- Murphy, FG; Hada, EA; Doolette, DJ; Howle, LE, Probabilistic pharmacokinetic models of decompression sickness in humans, part 1: Coupled perfusion-limited compartments., Computers in Biology and Medicine, vol 86 (2017), pp. 55-64 [10.1016/j.compbiomed.2017.04.014] [abs].
- Howle, LE; Weber, PW; Nichols, JM, Bayesian approach to decompression sickness model parameter estimation., Computers in Biology and Medicine, vol 82 (2017), pp. 3-11 [10.1016/j.compbiomed.2017.01.006] [abs].