Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

SCI Publications

2003


 M. Ikits, J.D. Brederson, C.D. Hansen, C.R. Johnson. “A Constraint-Based Technique for Haptic Volume Exploration,” In Proceedings IEEE Visualization 2003, Seattle, WA, pp. 263--269. October, 2003.


J. Jeon, A.E. Lefohn, G. A. Voth. “An Improved Polarflex Water Model,” In The Journal of Chemical Physics, Vol. 118, No. 16, pp. 7504--7518. 2003.


C.R. Johnson, A.R. Sanderson. “A Next Step: Visualizing Errors and Uncertainty,” In IEEE Computer Graphics and Applications, Vol. 23, No. 5, pp. 6--10. September/October, 2003.


S. Joshi, P. Lorenzen, G. Gerig, E. Bullitt. “Structural and Radiometric Asymmetry in Brain Images,” In Med Image Anal, Vol. 7, No. 2, pp. 155--170. June, 2003.


G. Kindlmann, R.T. Whitaker, T. Tasdizen, T. Möller. “Curvature-Based Transfer Functions for Direct Volume Rendering: Methods and Applications,” In Proceedings Visualization 2003, pp. 67. October, 2003.


R.M. Kirby, G.E. Karniadakis. “De-Aliasing on Non-Uniform Grids: Algorithms and Applications,” In J. Comp. Phys., Vol. 191, No. 1, pp. 267--282. October, 2003.


G.E. Karniadakis, R.M. Kirby. “Parallel Scientific Computing in C++ and MPI: A Seamless Approach to Parallel Algorithms and their Implementation,” Cambridge University Press, pp. 628 pages. June, 2003.
ISBN: 0521520800


R.M. Kirby, G.E. Karniadakis. “De-Aliasing on Non-Uniform Grids: Algorithms and Applications,” In Journal of Computational Physics, Vol. 191, pp. 249--264. 2003.


J.M. Kniss, S. Premoze, C.D. Hansen, P. Shirley, A. McPherson. “A Model for Volume Lighting and Modeling,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 9, No. 2, April - June, 2003.


J.M. Kniss, S. Premoze, M. Ikits, A.E. Lefohn, C.D. Hansen, E. Praun. “Gaussian Transfer Functions for Multi-Field Volume Visualization,” In IEEE Visualization 2003, Seattle, Wa., pp. 497--504. October, 2003.


J.M. Kniss, S. Premoze, M. Ikits, A.E. Lefohn, C.D. Hansen. “Closed Form Solution to the Volume Rendering Integral with Gaussian Transfer Functions,” Technical Report, No. UUCS-03-013, University of Utah School of Computing, 2003.


G. Krishnamoorthy, J.M. Veranth. “Computational Modeling of CO-CO2 Ratio Inside Single Char Particles during Pulverized Coal Combustion,” In Energy and Fuels, Vol. 17, No. 5, pp. 1367--1371. August, 2003.
DOI: 10.1021/ef030006k

ABSTRACT

A recently developed model was used to study the CO/CO2 ratio inside a burning pulverized coal particle, to better understand the effect of bulk gas composition on the equilibrium partial pressure of reduced metal species at the surface of ash inclusions. The motivation was to improve the ability to model submicrometer particle formation by ash vaporization, as a function of furnace conditions. Assumptions for the CO/CO2 ratio that have been made in previous studies are compared to predictions from a psuedo-steady-state model for a single porous particle that considers homogeneous and heterogeneous reaction kinetics and mass transfer both in particle pores and in the boundary layer. This is the first publication of model predictions for the CO/CO2 ratio as a function of radius for a coal char particle in a furnace with a bulk gas CO2 concentration in the range of 0%−79%. A method is proposed for summarizing the effects on the CO/CO2 ratio that are due to changes in the bulk furnace gas O2 and CO2 concentration, furnace temperature, and particle size, using an empirical equation that is suitable for incorporation as a submodel into comprehensive computational fluid dynamics-based codes for combustion simulation. Trends from the model simulations show general agreement with experimental data; however, the accuracy of the predictions is limited by the lack of fuel-specific input data.


 E. Lamar, V. Pascucci. “A Multi-Layered Image Cache For Scientific Visualization,” In Proceedings of IEEE Symposium on Parallel and Large-Data Visualization and Graphics, Note: UCRL-JC-152963, pp. 61--68. October, 2003.


M. Langbein, G. Scheuermann, X. Tricoche. “An Efficient Point Location Method for Visualization in Large Unstructured Grids,” In Proceedings of Vision, Modeling, and Visualization 2003, IOS Press, pp. 27--36. 2003.


M. Lazar, D.M. Weinstein, J.S. Tsuruda, K.M. Hasan, K. Arfanakis, E. Meyer, B. Badie, H.A. Rowley, V. Haughton, A. Field, A.L. Alexander. “White Matter Tractography Using Diffusion Tensor Deflection,” In Human Brain Mapping, Vol. 18, pp. 306--321. 2003.


A.E. Lefohn, J.M. Kniss, C.D. Hansen, R.T. Whitaker. “Interactive Deformation and Visualization of Level Set Surfaces Using Graphics Hardware,” In IEEE Visualization 2003, Seattle, Wa., pp. 75--82. October, 2003.


A.E. Lefohn, J. Cates, R.T. Whitaker. “Interactive, GPU-Based Level Sets for 3D Brain Tumor Segmentation,” Technical Report, No. UUCS-03-004, University of Utah School of Computing, 2003.


A.E. Lefohn, R.T. Caruso, E. Reinhard, B. Budge, P. Shirley. “An Ocularist's Approach to Human Iris Synthesis,” In IEEE Computer Graphics and Applications, Vol. 23, No. 6, pp. 70--75. 2003.


A.E. Lefohn, J.M. Kniss, C.D. Hansen, R.T. Whitaker. “Interactive Deformation and Visualization of Level Set Surfaces Using Graphics Hardware,” Technical Report, No. UUCS-03-005, University of Utah School of Computing, 2003.


A.E. Lefohn, J. Cates, R.T. Whitaker. “Interactive, GPU-Based Level Sets for 3D Segmentation,” In Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 564--572. 2003.