Monte Carlo Particle Transport Methods: Neutron and Photon - gnssn

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312 Monte Carlo Particle Transport Methods: Neutron and Photon CalculationsThenL (1)=jdP'€,(P,P',P")and clearly€,(P,P',P") = R,(P,P',P")^(P,P',P')}" 2Hence, according to Holder's inequalitydP"L m(P,P") ^rfdP"L (0)(P,P'')fdP''L (2)(P,P'')It follows from Equations (6.5) and (6.17) that the RHS of the inequality is less than unityand therefore the same is true with the supremum of the integrals, i.e.,sup dP"L (1)(P,P") < 1p'which calls forth the boundedness of the correlation term M{s,s 2}. Thus, we have provedthe following theorem.Theorem 6.1 — The variance of a correlated game which estimates the difference ofreaction rates in different systems if bounded inequality (6.17) holds for the integral kernelin Equation (6.16).In order to make condition (6.17) more specific, let us use the explicit forms of thekernels.* From Equations (5.32) and (5.33)T(P,P')dP' = a(P')exp[-T(P,P')]dDandC(P',F) dP' = c s(P') C 5(P',F) dE'where D - |r'-rj, P = (r,w,E), P' = (r',w,E) » (r + Dw,
313andC S(P',P")/C S(P',P") = y(P',P")Then from Equation (6.12)wheref fl - a(P')] 2 c 2 (P')L l21(P,P") = dD\ r ~ 7 IiP,P") 3F < s* i Uthat we are interested in the change of some reaction rate due to uV < i )1 'hof the material by a factor 1 — a (perturbed system). The scattering Kernels are identical
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Library of Congress Cataloging-in-P
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III. Statistical Considerations •
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IV. Further Generalizations — 183
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Appendix 6A:Unbiased Estimation of
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2 Monte Carlo Particle Transport Me
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Chapter 2INTRODUCTIONWhen we starte
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thus,As it is proved" — and is so
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9B. THE PROBABILITY MIXING METHODTh
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11It is easy to prove 15by summing
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13but the most probable values:x, =
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ISand give a random sign (by the us
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18 Monte Carlo Particle Transport M
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100 Monte Carlo Panicle Transport M
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102 Monte Carlo Particle Transport
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25!) Monte Carlo Particle Transport
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Page 274 and 275: 262 Monte Carlo Particle Transport
Page 317 and 318: 305Chapter 6SPECIAL GAMESIn the maj
Page 319 and 320: 3©7A. CORRELATED MOMENT EQUATIONSI
Page 321 and 322: 309Equation (6.1) will be referred
Page 323: 311The second moment of the score d
Page 327 and 328: 315introduced in Equation (6.1) for
Page 329 and 330: 317andL„(P 0,P;,...,Pi + 1) = L n
Page 331 and 332: 319whereB„(P„,P; P:, ,) = A*(P
Page 333 and 334: E. EXAMPLES AND SPECIAL TECHNIQUESL
Page 335 and 336: 323The weight factors associated wi
Page 337 and 338: 325It is seen from Equation (6.37)
Page 339 and 340: 327G. PARAMETRIC PERTURBATIONS: INT
Page 341 and 342: 129Hall 31 gave a constructive deri
Page 343 and 344: 331kernels, which remain unchanged
Page 345 and 346: 333and from Equation (6.51)w';,,(i)
Page 347 and 348: 335According to Equations (6,55) th
Page 349 and 350: 337Minimization is performed by set
Page 351 and 352: 339and letdsdaLet us consider a cor
Page 353 and 354: 341whereis the length of the flight
Page 355 and 356: and from Equation (6.79)I / ds \ 2
Page 357 and 358: 345Let us assume that we are intere
Page 359 and 360: 347Introduction of this factor also
Page 361 and 362: 3492. Let i|/ n (P) denote the solu
Page 363 and 364: 3SJNow, if ( "> > 0, then k = i an
Page 365 and 366: 353andk„ = S l "VS'" •" (6. if;
Page 367 and 368: 3SSLet k„ denote the normalizatio
Page 369 and 370: 3572. It is then proved that with t
Page 371 and 372: and start new histories until N new
Page 373 and 374: 361The application of source iterat
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363we haveD 2 [k] = < 2 (k, - k,) )
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365neutron density, S 0 for every
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367if the same relation holds for t
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369Then the multiplication factors
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371in the unperturbed system, the w
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373The perturbation of the effectiv
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375where P" = (r',E'). Multiplying
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377iterate of the derivative of the
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579where P* = (r*,w*,E) = (r*,E*).
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381This estimator, unlike f, in Equ
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383tends to some stable attracting
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3§SIo the case of the next-event e
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387LDFIGURE 6.1.Geometry in scoring
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389Let us denoteThen Equation (6.18
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391is to be increased in order to o
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393Now, since bl(P) = g(P) is bound
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395is scored at every collision wit
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397and its expectation isR — R 1n
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3994. If O > 0 m, then the particle
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401plays a distinguished role), and
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403Theorem 6.7 — If x and s denot
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this a priori information, the best
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40?Thus, the bias introduced into t
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409In view of Equation (6.226) and
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41.1This means that if we use the k
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413Then the whole-sample and rare-s
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41.5According to the results of Sec
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41?(Note that vD 2 [|i|v] is indepe
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419withkOn the other hand, § is an
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421Thus, the expected ratio reads
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423St follows from Equations (6.265
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425Wc start from the observation th
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427Nowi'6.77K)kj„,and therefore(V
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42*3Similarly, for the fourth momen
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43 sOn the other hand, multiplying
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43.¾This will be proven in the lem
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435ThenT.m = EJ.Iy.y.Rjeyiy,,,and s
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6. Brown. F. B. and Martin, W. R.,
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67. Rief, H. and Fioretti, A., Mont
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Q = iy.M 2 2 (1/T, - 1/1,..,)/1, £
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48© Monte Carlo Particle Transport
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a ( X506 Monte Carlo Particle Trans
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513INDEXAAbsorptiondefined, 25photo
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515score probability in general tim
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517path stretching, 447—455splitt
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Monte Carlo Particle Transport Methods: Neutron and Photon - gnssn

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