By Jeffery Lewins, Martin Becker
Considering the fact that its initiation in 1962, this sequence has awarded authoritative reports of crucial advancements in nuclear technological know-how and engineering, from either theoretical and utilized views. moreover, many unique contributions are integrated.
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Additional resources for Advances in Nuclear Science and Technology: Volume 26 (Advances in Nuclear Science & Technology)
This space-dependent nonlinearity-noise interplay in conjunction with multiplicative (parametric) noise is the key concept to understand the spatio-temporal patterns; the phenomenon of (a) noise rise and that of (b) noise suppression, in nuclear power reactors as complex systems. In spite of the complexity of the fluctuation phenomena in a nuclear power reactor, one conventionally adopts homogenized and/or averaged models for simplicity. Therefore, one might find in this review many challenging academic problems which must be solved, along the line of studying fluctuation phenomena in stochastic nonlinear dynamics in nuclear power reactors.
Dis. Child 12 pp 239-252. , 1988, ‘Evidence for an infective cause of childhood leukaemia: comparison of a Scottish new town with nuclear reprocessing sites in Britain’, Lancet II pp 1323-1327. , Clarke K. , 1990, ‘Evidence from population mixing in British New Towns 1946-85 of an infective basis for childhood leukaemia’, Lancet 336 II pp 577-582. J. , 1991, ‘Childhood leukaemia and poliomyelitis in relation to military encampments in England and Wales in the period ofnational military service, 1950-63’, BMJ 303 pp 1357-1362.
1976). He called the fluctuation associated with this angular momentum the irreversible circulation of fluctuation (ICF). To get an analytical expression for the ICF near the instability point is quite difficult due to complicated nonlinearity-noise interplay. Konno et al. (1994) obtained the analytic expression of ICF A for the model (124) below and above the onset point (g = 0) of Hopf bifurcation: NONLINEAR STOCHASTIC DYNAMICS 39 (125) where erf(x) is the Error function. Figure 4 shows that the ICF takes the maximum value at the bifurcation point (g = 0) and it decreases as g increases.