lec22_11012006 - 10.34 Numerical Methods Applied to...

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10.34, Numerical Methods Applied to Chemical Engineering Professor William H. Green Lecture #22: Introduction: Models vs. Data. Models vs. Data Engineers think of practical problems and efficient solutions from the top-down. Scientists use a micro-view and can neglect the big picture in the bottom-up analysis. Models are always wrong. But experiments also never match. p(k) diffusion limit m o r e i m p o r t a n t Y model (x δ x ), θ ± δθ ) “knobs” all other we generally parameters parameters know bounds in model we can that affect on θ physically adjust results prior information (cannot control) about θ Figure 1. Normal distribution. Y data (1) (x ) Y data seldom have sampling capable of making Y data (2) (x ) t r u e d i s t r i b u t i o n c u r v e P Average Value <Y data > N e x p t s P P (<Y > Nexpts ) <Y > Nexpts (<Y data >, σ data ) σ exp.data|Nexpts ( ) ts data mean mean true N Y Y exp 2 2 2 exp σ σ σ πσ > < >
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This note was uploaded on 11/27/2011 for the course CHEMICAL E 10.302 taught by Professor Clarkcolton during the Fall '04 term at MIT.

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lec22_11012006 - 10.34 Numerical Methods Applied to...

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