ML18026A351
| ML18026A351 | |
| Person / Time | |
|---|---|
| Site: | Susquehanna  |
| Issue date: | 05/15/1981 |
| From: | Curtis N PENNSYLVANIA POWER & LIGHT CO. |
| To: | Youngblood B Office of Nuclear Reactor Regulation |
| References | |
| PLA-785, NUDOCS 8105190322 | |
| Download: ML18026A351 (27) | |
Text
REGULAT INrORHATIOi's DISTRIBUTION STEM (RIDS)
ACCESSIO4 NdR:8105190322 DuC.DATE: 81,/05/15 NOTARIZED:
NO FACIL:50-3o7 Susquehanna Steam Electric Station<
Unit 1R Pennsylva 50-388 Susquehanna Steam Electric Station~
Unit 2s Pennsylva AUTH'AidE AUTHuR Af FILIATIQN CURTIS T N ~ T'j ~
Pennsylvania Power 8, Light Co.
REC IP ~ NAIVE REC IPIci4l AFFILIATION YOUNGBLQODsf3.J.
Licensing Branch 1
SUGJECT; Provides justification for interchanqeability of temooral cnug strength prooauility distribution w/soacial variation of chuq strengths at faci 1 i ty ~ Comp l etes action on SER outstanding Issue 27.
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TWO NORTH NINTH STREET, ALLENTOWN, PA.
18101 PHONE:
(215) 821-5151 NORMAN W. CURTIS Vice President-Engrneering
& Construction 821-538I May 15, 1981 Mr. B. J. Youngblood, Chief Licensing Branch No.
1 U.S. Nuclear Regulatory Commission Washington, D.C.
20555 Docket Nos.
50-387 50-388 SUSQUEHANNA STEAM ELECTRIC STATION SER OUTSTANDING ISSUE 27 ER 100450 FILE 841-2 PLA-785
Dear Mr. Youngblood:
Attached is a justification for the interchangeability of the temporal chug strength probability distribution with the'pacial variation of chug strengths at SSES.
This discussion completes our action on SER Outstanding Issue 27.
N.
W. Curtis Vice President-Engineering and Construction-Nuclear CTC/mks Attachment cc:
R.
M. Stark - NRC pool PENNSYLVANIA" POWER IL LIGHT COMPANY
Provide!justification & the interchangeability of the&emporal chug strength probability distribution with the spacial variation of chug strengths at SSES
Response
The SSES LOCA steam condensation load definition assumes that the chugs occurring simultaneously at different vent'ipes of SSES have different intensities and follow the same distribution of chug amplitudes in time as in the GKM II-M single vent facitity.
This assumption forms the basis for two key elements of-the LOCA load definition.
The first element assumes that average of simultaneously occurring chugs at different vents in SSES is equivalent to the average of consecutive GKM II-M chugs.
- Thus, as documented in DAR Subsection 9.5. 3. 1.2, the random amplitude chugs at SSES were replaced with the same chug at every vent which represents the average of consecutive GDi II-M chugs or "mean value" chug.
The second element assuares tha't the chug amplitude or strength at the individual SSES vents are random variables which have the same probability distribution as the distribution of chug amplitudes at GKM II-M.
The GKM II-M probability distribution was then applied statistically to an analytical model of the SSES suppression pool to calculate the symmetric and asymmetric amplitude factors.
These factors were then applied to the selected mean value chug to achieve the desired exceedance probability prior to transportat<<<<o SSFS for containment analysis
( see DAR'ubsections
- 9. 5. 3.4. 1 and 9.5. 3.4.2).
These two elements infer that the multi-vent facility is composed of many "single cells" whose chug strengths vary stochastically and independently of each other.
The random nature of chugging is explained qualitatively by looking at the actual bubble collapsing mechanism.
The most plausible mechanism for bubble collapse at the individual vents appears to be the convection in the pool.
This means that bubble collapses at individual vents are triggered by the local.'turbulent convection at each vent.
Thus due to the stochastic.c nature of "turbulence, the time at which rapid condensation and henc bubble collapse is triggered varies from vent to vent.
This implies that the size of the bubble formed before collapse starts, will also vary from vent to vent.
Therefore, the chug strength will vary from vent to vent.
- Since, the GKM II-M tests were designed to be prototypical of SSES (i.e.,
same initial pool temperature, same steam flow, etc.), this random variation is expected to be the same for both the GKM II-M single vent facility and the SSES plant.
Additional qualitative data verifying the random nature of chugging is provided by numerous multi-vent test programs.
Specifically, the VK multi-vent concrete cell tests in Karlstein, Creare subscale multi-vent tests and JAERI f:ull scale multi-vent tests provide multi.-vent data of.the chugging phenomena.
The Karlstein facility investigated the chugging phenomena for 2, 6, and 10 vents at subscale.
Each vent in the concrete cell was instrumented with a pressure transducer in such a way that it was ind9cative of the chug strength for its respective vent.
Figures 1, 2, and.3 illistrate these vent transducers and the remaining transducers for the 10, 6,
and 2 vent facilities.
Figures 4 and 5 show typical pressure time histories for the pressure, transducers mounted near the vent pipes for the six vent configuration.
These pressure transducers were all exposed to steam environment and clearly indicate that the chug strengths differ by up to a factor of 10.
Page 2
In addition, Figure 6
the measured wall pressures creases from 2 to 6 to 10.
in a lower overall pressure shows that the distribution of relative frequencies of becomes narrower as the number of vent pipes in-Again, the variation in chug strengths results amplitude with increasing number of vents.
This variation in chug strengths was also observed in the Creare subscale multi-vent test program.
This observation was obtained by examining the pool o
wall pressures measured at the three different circumferential logations at the vent exit.
All test geometries had three transducers located 120 apart circumferentially at the vent exit elevation.
In the multi-vent geometries, each of these pressure transducers was located close to a particular vent in the multi-vent geometry.
Therefore,
.the amplitude of the POP measured at each cir-cumferential location reflects to a large extent the chug strength at the vent closest to i.t (since pressure amplitude varies inversely with the distance between the vent and wall pressure measurement location).
For example, only if the chug strengths at all vents were identical, would the peak over-pressure (POP) measured at each of these three circumferential locations be identical.
Figure 7 shows the pool wall pressures at the three circumferential vent exit locations in the 1/6 scale 3 vent geometry.
The steam mass flux was 8 ibm/sec ft and as determined from the vent static pressures over 80% of the chugs shown had all these vents participating.
This figure shows that the POP's at the three locations are different for individual chugs.
Therefore, it can be concluded that the chug strength varies from vent to vent.
Simi/ar data from the 1/10 scale 19 vent geometry at a steam mass flux of 8 ibm/sec ft are shown in Figure 8.
Again, from vent static pressure data for vents closest to each circumferenti.al wall pressure measurement location
, it was determined that all three vents participated in the chugs shown.
The POP's at the three different circumferential locations are".seen"as being different for individual chugs.
Note that the variation of chug strength from vent to vent is expected to be stochastic to a large extent.
Therefore, it is expected that for some chugs, the chug strength at the three vents would be similar.
Additional proof that the chug strengths in a multi-vent facility behave stochastically is given by the JAERI multi-vent test data.
There are several pool wall pressure transducers that are located near the exits of different vents in the JAERI facility.
Speci.fically, transducers MWPF-202,
- 302, 602, and 702 are located at the vent exit elevation next to vents 2,
3, 4, and 7, respectively (See Figures 9 and 10),
The pressure amplitudes measured by these transducers reflect the chug strengths at vents closest to them.
" The variation of chug strengths at individual vents is shown in Figure 11.
'The pool wall pressures at the vent exit elevation for a chug occur at 62.S seconds in JAERI test 0002.
In this chug event, a high amplitude chug occurred
- at vent 7 as indicated by the large pressure spike at MWPF702.
The other vents had relatively smaller chugs.
Keep in mind that the variation of chug strengths from vent to vent is stochastic in nature and that not all pool chugs will exhibit the large variation seen in Figure 11.
Nonetheless, varying degrees of variation in chug strengths from vent to vent" were found in all the chugs from Tests
- 0002,
'101, and 3102 for which expanded time traces are available.
So far, we have stated that chugging is stochastic in nature, and as such the chug strengths are expected to vary, even though the same thermodynamic conditions exist at each vent (i.e.,
steam air content, mass flux, bulk pool
Page 3
temperature, etc.).
As presented above, this phenomena has been observed in numerous multi-vent test facilities.
- However, we have not quantitatively verified ourassumption of the interchangeability of the temporal chug strength variations at GIQf II-~i( with the spacially varying chug strengths at SSES.
Again, the Creare subscale multi-vent test data and JAERI test data provide information verifying the conservatism of this assumption.
Each will be presented below.
As previously stated, one element of our LOCA load definition replaces the random amplitude chugs at SSES with the same chug at every vent, which is re-presentative of the mean value data at GKM II-H.
The Creare test data coupled with the accepted acoustic methodology provides verification of this assumption.
Creare has acoustically modeled the 1/10 - scale single and multi-vent geometries and using an acoustic
- model, they have derived a source which represents the mean value chug in the 1/10 scale single vent geometry.
They then placed this mean value chug source at each vent location of their acoustic model for the 1/10 scale 3,
7, and 19 vent geometries.
For each of the three multi-vent geometries, the pressure time history at the pool bottom elevation (same as the transducer location at this elevation in the test geometries) was computed for 20 chug events.
Each chug event involved selecting start times for individual vents randomly within a 20 msec time window.
The multi-vent multiplier was then computed based on the mean POP at the pool bottom elevation for the 20 computed chugs.
The predicted multi-vent multipliers compared quite favorably with the measured values.
Subsection A S.2.2 of NEDE-24360-P gives a detailed description of the analysis and results.
Thus, for subscale multi-vent geometries, the first element of our LOCA load definition is verified.
Final quantitative justification for our key assumption is provided by comparing the available JAERI full-scale multi-vent data with the Gk~f II-H single vent data.
There are two sets of JAERI data available that can be used to infer chug strengths at individual vents in a given multi-vent chug event.
The first set is the pool wall pressure data from the pool wall transducers located at the vent exit elevation.
In the JAERI test geometry, there were four pool wall pressure transducers-MPF202,
- 302, 602, and 702-located such that each of these trans-ducers is very near the exits of four individual vents.
Therefore, the pressure data from a given transducer reflects the chug strength at the vent closest to that tranducer.
As previously stated, the data from these wall pressure transducers were used to qualitatively show that the chug strengthsvary significantly from vent to vent in a JAERI multi-vent chug event.
Unfortunately, due to the fact that a pool transducer "sees" pressures due to chugs at all vents to varying extents, the data from such transducers are not suitable for quantitative evaluation of vent to vent chug strength variations.
The other set of JAERI data that providesa measure of chug strengths at individual vents are the vent static pressure measurements.
Five of the seven vents in the JAERI test facility are instrumented with vent exit static pressure transducers.
II II The vent static pressure is a direct measure of the vent component of the chug-induced pool wall pressure.
- Further, due to desynchronization in a multi-vent geometry, the "vent component" is the dominant component of the chug induced pool pressures observed in multi-vent chugging.
Therefore, the spatial (vent to vent) variation of the vent static pressures
Page 4
in the JAERI multi-vent geometry should provide a reliable estimate of the vent to vent chug strength variation in a multi-vent geometry.
Individual vent exit static pressures of 1. 125 sec periods are available for 38 chug events from six JAERI tests-eight chugs from Test 0002, seven chugs from Test 0003, six chugs from Test 0004, five chugs from Test l101, five chugs from Test
- 1201, and seven chugs from Test 2101.
These chugs were selected rom periods of high amplitude chugging in each test.
Therefore, this data base covers the worst chugging regions observed in these JAERI tests.
The individual vent exit static pressures for a given chug event were processed in the following manner.
First, the rms pressure P.
wa~ computed for each vent static pressure trace.
- Next, the" average rms pressure P was computed.
For example, if vent static pressures were available for all the five instrumented
- vents, the average rms vent static pressure for that chug is:
P +P +P +P4+P 1
2 3
4 5
p Since we are interested in the relative variation in chug strengths between individual vents, the individual rms vent static pressures were normalized by the average rms pressure p.
The normalized individual rms vent static pressures
- p. for the 38 chugs analyzed are given in Table l.
Also shown are the values of the normalized th individual vent rms pressures for individual chug events.
JAERI Note that due to instrumentation malfunctions, for all except one
- test, vent exit static pressure data are not available for all five instrumented vents.
Due to small number of vents (at most five) for which vent static pressure data are available, it is difficult to draw meaningful statistical inferences for vent to vent chug strength variations from any one individual chug event.
Therefore, it is necessary to make an assumption that allows the use o
the data from all 38 chug events such that meaningful statistical inferences can be drawn.
This assumption is that the normalized statistical distribution of chug strengths from vent to vent is independent of blowdown conditions.
at th normalized vent to vent chug strength for all 38 chug events are samples selected from the same statistical population.
Note that this is prec e y is l the same assumotion made in analyzing the temporal statistical properties of the GKM II-M single vent data (see DAR Subsection 9.5.3.2. 1).
m onent of the Te
-i aa h
GKM II-8 d t that providesa direct measure of the vent component th are the ool wall pressure data band pass filtered between 0..5-13 Hz.
chug strengt are e po u
d are due to the vent In this frequency range, the pool wall pressures measured are ue o
e pressure oscillations produced by the chug (see Subsection 9.4.2.1.2).
As described in DAR Subsection 9.5.3.2.1, the pressure amplitudes of individual chugs were normalized by the sliding mean value over a given interval.
In this way, a normalized data base reflecting the temporal variations of chug strengths was obtained for all the GM II-M tests.
Note that again implicit in this procedure is the assumption a
e th t th statistics of the variation of the normalized s
As reviousl mentioned, chug strengths is independent of system conditions.
As previously mentione this assumption was also used for combining the JAERI data for 38 chug events into a sin'gle statistical data base.
Page 5
The histograms of the normalized chug strengths for the various GKM II-M tests are given in DAR Figures 9-181, 9-182, and 9-183.
At this point, we now have a normalized vent to vent chug strength variation data base from the JAERX multi-vent tests and a corresponding normalized chug to chug strength variation data base from the GKM II-M single vent tests.
Table 2 shows the variance for the JAERX and GKM II-M data bases.
The variance for the JAERX data base is the average value of the individual vari.ances shown in Table 1 for each of the 38 chug events.
The variance oi the GKM XI-M data was calculated for the 0.5-13 Hz band passed data plotted in DAR Figures 9-181, 9-182, and 9-183.
It is seen that the average variance from the JAERI tests is virtually identical to the variance from the GKM II-M Full MSL tests* and is somewhat greater than the variances from the 1/3 and 1/6 MSL GKM II-M tests.
This implies that.the variation of vent to vent chug strengths in the JAERX multi-vent tests is equal to or greater than the chug to chug strength variation observed in the GKM II-M single vent tests.
Figures 12 through 14 show the comparison of the probability density histograms of the JAERI data and the GKM II-M Full iMSL, 1/3 MSL and 1/6 MSL data, respectively.
Again, the JAERI and GKM II-M data histograms are quite similar.
From the above comparisons it can be again concluded that the assumption that the chug to chug variation in chug strengths in a single vent geometry is equivalent to the vent to vent chug strength variation in a multi-vent
- geometry, used in developing the SSES chugging load definition from the GKM II-M single vent test data is quite reasonable.
Additional verification of the conservatism of the SSES LOCA load definition is provided by comparing the wall loads at JAERI calculated with the SSES LOCA load definition with the available JAERI wall load data (see DAR Subsection 9.5.3.5.1).
DAR Figures 9-268 and 9-269 show that the SSES LOCA load definition bounds the available JAERI data by a substantial margin.
Please note that the wall loadhcalculated by the SSES LOCA load definition do, not incl ude the symmetric amplitude factor and thus represent",mean value" chugs.
The Full MSL break, chug strength statistics were used to develop the SSES probabilistic load multipliers.
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TABLF ?
<p,pgy/QY~KN COMPARING OM gggMA~iZ ED
@[~AM VAR,LR hlCE
Provide justification for not considering CO
Response
The i)ark ZI Owners have specified two different CO loads for containment analysis.
The first CO load (CO 1) corresponds to the CO occurring at the beginning of a postulated LOCA and the second CO load (CO 2) corresponds to the reduced CO load occurring later in the blowdown.
For containment
~
The LOCA load comprises the envelop of the responses due to both chugging and CO.
SRV (ADS) considers both CO and chugging and is more conservative than the Owner's combination of a reduced CO load (CO 2) with SRV (ADS).
Document the comparison of the CO.measured at 4T-CO with the CO observed at GKM II-M.
Response
The SSES LOCA load definition selected one CO pressure time history (PTH No.
- 14) from GKM II-M as representative and bounding of the CO at GKM II-M (see DAR Figure 9-177a~b).
Subsequently, this CO PTH was sourced and applied to the IMEGS/MARS acoustic model for containment analysis.
SSES DAR'Figure 9-264 represents the enveloping PSD of the PTH No.
14.
Figure 2-1 of iKDE 24288-P presents the envelop of PSD values observed for CO in the 4T-CO tests.
These two figures ind'cate that the PSb
'f the PTH iNo.
14 from GKM II-M compares favorably..with the enveloping PSD of the CO in 4T-CO.