scholarly journals Computer simulation of the steam--graphite reaction under isothermal and steady-state conditions

1975 ◽  
Author(s):  
D. S. Joy ◽  
S. C. Stem
Keyword(s):  
Computer Simulation ◽  
Steady State

Dynamic Stability of an Impact System Connected With Rock Drilling

1969 ◽  
Vol 36 (4) ◽  
pp. 743-749 ◽  
Author(s):  
C. C. Fu
Keyword(s):  
Computer Simulation ◽  
Exact Solution ◽  
Steady State ◽  
Asymptotic Stability ◽  
Piecewise Linear ◽  
Steady State Solution ◽  
External Excitation ◽  
Rock Drilling ◽  
Impact System ◽  
Number Of Cycles

This paper deals with asymptotic stability of an analytically derived, synchronous as well as nonsynchronous, steady-state solution of an impact system which exhibits piecewise linear characteristics connected with rock drilling. The exact solution, which assumes one impact for a given number of cycles of the external excitation, is derived, its asymptotic stability is examined, and ranges of parameters are determined for which asymptotic stability is assured. The theoretically predicted stability or instability is verified by a digital computer simulation.

Pancreatic islet discrimination of hexose anomers. I. Steady-state computer simulation

1988 ◽  
Vol 255 (2) ◽  
pp. E189-E200
Author(s):  
M. J. Achs ◽  
D. Garfinkel
Keyword(s):  
Computer Simulation ◽  
Insulin Secretion ◽  
Steady State ◽  
Pancreatic Islets ◽  
Glucose Level ◽  
Pancreatic Islet ◽  
Specific Model ◽  
The Other ◽  
Glycolytic Rate ◽  
Hexose Phosphates

Pancreatic islets detect glucose level by phosphorylating it and converting the glycolytic rate to a signal to secrete insulin. Insulin secretion is greater from the alpha- than from the beta-anomer when the D-glucose level is below 22 mM. D-mannose behaves similarly but at nearly twofold higher concentrations. Two explanations have been proposed: 1) glucokinase, which has the same anomeric preference, is the principal hexose phosphorylating enzyme and limits glycolytic rate. 2) Phosphofructokinase limits glycolysis and hexokinase is the principal enzyme phosphorylating hexose; hexosediphosphate activators of phosphofructokinase are more readily synthesized from alpha-anomers of hexose phosphates. We have simulated both alternatives with a detailed anomerically specific model of the hexose-metabolizing glycolytic enzymes. The pathway preference for alpha-anomer of both hexoses was adequately reproduced with anomerically active limiting glucokinase. The other mechanism did not reproduce the observed pathway preference.

Optimizing the Feedback Gains of the Robust Linear Regulator

1999 ◽  
Vol 121 (3) ◽  
pp. 346-350
Author(s):  
Jie Huang
Keyword(s):  
Computer Simulation ◽  
Steady State ◽  
Transient Response ◽  
Closed Loop ◽  
Feedback Gain ◽  
Frobenius Norm ◽  
Closed Loop System ◽  
Optimal Feedback ◽  
Linear Regulator ◽  
Fixed Set

This paper aims to improve the transient response of a linear regulator system by optimizing the feedback gains associated with a fixed set of desirable eigenvalues of the closed-loop system. The optimal feedback gain is such that the Frobenius norm of the steady state of the compensator is minimized. Computer simulation shows that this scheme is effective in improving the transient response of the regulator system.

COMPUTER SIMULATION FOR THE CROSSING TIME IN A DIPLOID, ASYMMETRIC, SHARPLY-PEAKED LANDSCAPE IN THE INFINITE POPULATION LIMIT

2013 ◽  
Vol 24 (01) ◽  
pp. 1250091 ◽  
Author(s):  
WONPYONG GILL
Keyword(s):  
Computer Simulation ◽  
Steady State ◽  
Selective Advantage ◽  
Sequence Length ◽  
Initial State ◽  
Infinite Population ◽  
Crossing Time ◽  
Increasing Function ◽  
Fitness Parameter ◽  
Extension Parameter

This study calculated the crossing time in the diploid mutation–selection model in an infinite population limit for various dominance parameters, h, and selective advantages, by switching on a diploid, asymmetric, sharply-peaked landscape, from an initial state which is the steady state in a diploid, sharply-peaked landscape. The crossing time for h < 1 was found to diverge at the critical fitness parameter, which increased with increasing selective advantage and decreased with increasing sequence length. When the sequence length was increased with a fixed extension parameter, there was no crossing time for h < 1 when the sequence length was longer than the critical sequence length, which increased with increasing selective advantage. The crossing time for h ≤ 1 was found to be an exponentially increasing function of the sequence length, and the crossing time for h > 1 became saturated at a long sequence length. The crossing time decreased with increasing selective advantage, mainly because the larger selective advantage caused the increase in relative density of the reversal allele to grow exponentially at an earlier time.

A computer simulation using spreadsheets for learning concept of steady-state equilibrium

2016 ◽  
Vol 51 (2) ◽  
pp. 025007
Author(s):  
Vandana Sharda ◽  
O S K S Sastri ◽  
Jyoti Bhardwaj ◽  
Arbind K Jha
Keyword(s):  
Computer Simulation ◽  
Steady State ◽  
State Equilibrium ◽  
Learning Concept
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