Challenges in nuclear data evaluation of actinide nuclei
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1 Challenges in nuclear data evaluation of actinide nuclei 1 Roberto Capote NAPC - Nuclear Data Section, IAEA, Vienna, Austria
2 Thanks to: Organizers for the invitation/support All collaborators o A. Trkov (IAEA) o M. Sin (Univ. Bucharest) o D. Bernard and G. Noguere (CEA) o Y. Danon and A. Daskalakis (RPI) o M.W. Herman (BNL) o B.V.Carlson (ITA) 2
3 OUTLOOK Introduction and motivation Experimental challenges (from an evaluator point of view) Modelling challenges Evaluation methodology challenges Challenges in use of integral data 3
4 Why we need ADS/fast reactors? FERTILE ( 235 U, 233 U ) FISSILE 4
5 Neutron induced reactions on U-238 (n,el) (n,tot) (n,g) (n,inel) (n,f) (n,2n) 5
6 Experimental challenges (biased) 1. Quasi-differential experiments (~RPI) for 235 U, 239 Pu, 232 Th, 237 Np Goal: better elastic an inelastic scattering from 0.5 to 10 MeV 2. SPA cross sections measurements in 235 U and 239 Pu PFNS fields ( inelastic and n,2n reactions) Goal: better PFNS below 700 kev and above 8 MeV, PFNS validation 6
7 RPI quasidifferential data measured on Zr (published) and 238 U 7
8 8
9 RPI experimental setup 9
10 RPI benchmark:
11 Angle dependent cross sections (n,f) (n,n ) cont (n,2n) 11
12 SPA cross sections measurements in 235 U and 239 Pu PFNS thermal fields 12
13 C/E Cf(sf) PFNS Average: / E50%-response [MeV] 13
14 C/E ENDF/B-VII U(n th,f) PFNS Average: / (E50% < 11 MeV) E50%-response in [MeV] 14
15 (Nuclear reaction) modelling challenges 15
16 total (and 1 st lev ) inelastic cross sections 16
17 cross sections [mb] new physics: DIR-CN interference Engelbrecht-Weidenmuller transformation [1] (n,el) dir-cn interference elastic 1 st level (44.9) 2 nd level (148) no interference elastic 1 st level (44.9) 2 nd level (148) 1000 (n,n 1 ) energy [MeV] [1] C.A. Engelbrecht, H.A. Weidenmuller, Hauser--Feshbach theory and Ericson fluctuations in the presence of direct reactions, Phys.Rev. C8 (1974)
18 MODELLING CHALLENGES 238 U(n,n g) (gsb) kev 5/ /2 (gs) 235 U(n,n g) Bacquias/Kerveno et al (prelim., unpublished) Kerveno et al, PRC87(2013) Better structure models => CC OMP 18
19 Rigid rotor with soft-rotor corrections U-238 Yu.V.Porodzinkij and E. Soukhovitdkii, Phys. At. Nuclei 59 (1996)
20 Evaluation methodology challenges 20
21 Evaluation Methodology Challenges Use nuclear reaction modelling code (e.g EMPIRE) Choose adequate model options (HF+fluct., direct, PE) Determine needed input parameters (starting from RIPL) Calculate cross sections and other quantities Compare calculated values to selected measured data (after correcting for new stds, discarding discrepant, etc) Fine-tune the input model parameters Loop-1 From model parameter uncertainties and model uncertainties generate covariance matrix prior Do the GLSQ fit to combine the EXP and model data 21
22 Evaluation Methodology Challenges 1) MC modeling (EMPIRE, TALYS, CCONE, CoH, ) {σ i } exp 2) For each random set {σ i } we calculate L(y E,V E σ i ) L(y E,V E σ i ) = exp{-(½)[(f (σ i ) y E ) T ( V E ) -1 (f (σ i ) y E )]} N i 1 w N i 1 exp w ( ) exp OUTPUT: 1) i ( ) i, i, w ( i) cov(, i Unified Monte Carlo cov(, i j UMC-B ) j ) i j i j 22 2) Stochastic set {σ i } (e.g. to be used in TMC)
23 Evaluation Methodology Challenges ALTERNATIVES to GLSQ: MC methods: No covariance generation UMC-B : Solution is obtained as a linear combination of model results However: The solution may not be reproducible by model calculations (more information is derived from experimental data => overcome model defects) 23
24 Challenges in use of integral data JEZEBEL Compensating errors!! LANL, USA INDC(NDS)-0597 A.J. Plompen et al 24
25 Challenges in use of integral data Compensating errors: Elastic vs Inelastic Cross-correlations among isotopes: e.g. Flattop-25 critical assembly 235 U (PFNS) and 238 U (elastic and inelastic) RELATED 25
26 elastic -bar (P1 component) 26
27 Elastic angular distribution 720 kev impact on reflectors beyond -bar (P1)! 27
28 CONCLUSIONS Experimental challenges New quasi-diff. and SPA cross-section meas. Modelling challenges Better structure in reaction calcs (CC OMPs) Evaluation methodology challenges Going beyond model calcs (e.g. UMC-B) Challenges in use of integral data Cross-isotope correlations 28
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