Pub Date : 1900-01-01DOI: 10.7712/120221.8042.19074
M. Baudin, R. Lebrun
{"title":"LINEAR ALGEBRA OF LINEAR AND NONLINEAR BAYESIAN CALIBRATION","authors":"M. Baudin, R. Lebrun","doi":"10.7712/120221.8042.19074","DOIUrl":"https://doi.org/10.7712/120221.8042.19074","url":null,"abstract":"","PeriodicalId":444608,"journal":{"name":"4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121079834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120221.8022.18997
Corentin Houpert, J. Garnier, P. Humbert
. Fissile matter detection and characterisation are crucial issues; especially in nuclear safety, safeguards, matter comptability, reactivity measurements. In this context, we want to identify a source of fissile matter knowing external measures such as instants of detection of neutrons during an interval of measure. Thus we observe the neutrons detection times emitted by the fissile matter and going through the detector, then we compute the moments of the empirical distribution of the number of neutrons detected during a time gate T. In order to identify the source we have to get the following parameters: the multiplication factor k of the system, the intensity of the source S , the fission efficiency ε F . Given the parameters of the source there are some models that allow us to predict the moments of counted number of neutrons during a time gate T. We consider a point model stating monokinetic neutrons are moving in an infinite, isotropic and homogeneous medium. The method makes it possible to compute the first moments of the count number distribution. Then, given the moments of counted number of neutrons during a time gate T we want to get the parameters of the fissile source. In order to achieve this goal, we will use the following method
{"title":"INVERSE PROBLEMS FOR STOCHASTIC NEUTRONICS","authors":"Corentin Houpert, J. Garnier, P. Humbert","doi":"10.7712/120221.8022.18997","DOIUrl":"https://doi.org/10.7712/120221.8022.18997","url":null,"abstract":". Fissile matter detection and characterisation are crucial issues; especially in nuclear safety, safeguards, matter comptability, reactivity measurements. In this context, we want to identify a source of fissile matter knowing external measures such as instants of detection of neutrons during an interval of measure. Thus we observe the neutrons detection times emitted by the fissile matter and going through the detector, then we compute the moments of the empirical distribution of the number of neutrons detected during a time gate T. In order to identify the source we have to get the following parameters: the multiplication factor k of the system, the intensity of the source S , the fission efficiency ε F . Given the parameters of the source there are some models that allow us to predict the moments of counted number of neutrons during a time gate T. We consider a point model stating monokinetic neutrons are moving in an infinite, isotropic and homogeneous medium. The method makes it possible to compute the first moments of the count number distribution. Then, given the moments of counted number of neutrons during a time gate T we want to get the parameters of the fissile source. In order to achieve this goal, we will use the following method","PeriodicalId":444608,"journal":{"name":"4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121326831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120221.8044.19131
W. Chuang, S. Spence
{"title":"SOFTWARE FOR UNCERTAINTY PROPOGATION AND RELIABILITY ASSESSMENT OF INELASTIC WIND EXCITED SYSTEMS","authors":"W. Chuang, S. Spence","doi":"10.7712/120221.8044.19131","DOIUrl":"https://doi.org/10.7712/120221.8044.19131","url":null,"abstract":"","PeriodicalId":444608,"journal":{"name":"4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126141616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120221.8036.18925
M. Fischer, C. Proppe
{"title":"A SEQUENTIAL MULTI-POINT SAMPLING PROCEDURE FOR SURROGATE MODELS","authors":"M. Fischer, C. Proppe","doi":"10.7712/120221.8036.18925","DOIUrl":"https://doi.org/10.7712/120221.8036.18925","url":null,"abstract":"","PeriodicalId":444608,"journal":{"name":"4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128415452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120221.8043.19104
E. Papoutsis‐Kiachagias, V. Asouti, K. Giannakoglou
{"title":"ASSESSMENT OF VARIANTS OF THE METHOD OF MOMENTS AND POLYNOMIAL CHAOS APPROACHES TO AERODYNAMIC UNCERTAINTY QUANTIFICATION","authors":"E. Papoutsis‐Kiachagias, V. Asouti, K. Giannakoglou","doi":"10.7712/120221.8043.19104","DOIUrl":"https://doi.org/10.7712/120221.8043.19104","url":null,"abstract":"","PeriodicalId":444608,"journal":{"name":"4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129241950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120221.8019.18856
O. Kosheleva, V. Kreinovich
. In many practical situations, the only information that we know about the measurement error is the upper bound Δ on its absolute value. In this case, once we know the measurement result (cid:21) x , the only information that we have about the actual value x of the corresponding quantity is that this value belongs to the interval [ (cid:21) x − Δ , (cid:21) x +Δ] . How can we estimate the accuracy of the result of data processing under this interval uncertainty? In general, computing this accuracy is NP-hard, but in the usual case when measurement errors are relatively small, we can linearize the problem and thus, make computations feasible. This problem is well studied when data processing results in a single value y , but usually, we use the same measurement results to compute the values of several quantities y 1 , . . . , y n . What is the resulting set of tuples ( y 1 , . . . , y n ) ? In this paper, we show that this set is a particular case of what is called a zonotope, and that we can use known results about zonotopes to make the corresponding computational problems easier to solve.
． 在许多实际情况下，我们所知道的关于测量误差的唯一信息是其绝对值的上界Δ。在这种情况下，一旦我们知道了测量结果(cid:21) x，我们所拥有的关于对应量的实际值x的唯一信息是该值属于区间[(cid:21) x−Δ， (cid:21) x +Δ]。在这种区间不确定性下，如何估计数据处理结果的准确性?一般来说，计算这种精度是np困难的，但在通常情况下，当测量误差相对较小时，我们可以将问题线性化，从而使计算可行。当数据处理结果为单个值y时，这个问题得到了很好的研究，但通常，我们使用相同的测量结果来计算多个量y的值1，…n .; n .;元组(y1，…)的结果集是什么?n) ?在本文中，我们证明了这个集合是一个特殊的情况下，什么被称为分区，我们可以使用已知的结果分区，使相应的计算问题更容易解决。
{"title":"LOW-COMPLEXITY ZONOTOPES CAN ENHANCE UNCERTAINTY QUANTIFICATION (UQ)","authors":"O. Kosheleva, V. Kreinovich","doi":"10.7712/120221.8019.18856","DOIUrl":"https://doi.org/10.7712/120221.8019.18856","url":null,"abstract":". In many practical situations, the only information that we know about the measurement error is the upper bound Δ on its absolute value. In this case, once we know the measurement result (cid:21) x , the only information that we have about the actual value x of the corresponding quantity is that this value belongs to the interval [ (cid:21) x − Δ , (cid:21) x +Δ] . How can we estimate the accuracy of the result of data processing under this interval uncertainty? In general, computing this accuracy is NP-hard, but in the usual case when measurement errors are relatively small, we can linearize the problem and thus, make computations feasible. This problem is well studied when data processing results in a single value y , but usually, we use the same measurement results to compute the values of several quantities y 1 , . . . , y n . What is the resulting set of tuples ( y 1 , . . . , y n ) ? In this paper, we show that this set is a particular case of what is called a zonotope, and that we can use known results about zonotopes to make the corresponding computational problems easier to solve.","PeriodicalId":444608,"journal":{"name":"4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114874586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120221.8018.19129
F. Genovese, G. Muscolino, A. Palmeri
{"title":"INFLUENCE OF DIFFERENT FULLY NON-STATIONARY ARTIFICIAL TIME HISTORIES GENERATION METHODS ON THE SEISMIC RESPONSE OF FREQUENCY-DEPENDENT STRUCTURES","authors":"F. Genovese, G. Muscolino, A. Palmeri","doi":"10.7712/120221.8018.19129","DOIUrl":"https://doi.org/10.7712/120221.8018.19129","url":null,"abstract":"","PeriodicalId":444608,"journal":{"name":"4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132666857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120221.8028.18904
J. Kullaa
Measuring structural vibrations with a large sensor network results in lots of data in structural health monitoring applications. A large number of sensors is advantageous for damage detection and localization. By storing only a few selected Bayesian virtual sensors it is possible to decrease the amount of data and reconstruct the discarded sensor data even with higher accuracy than the original measurements. A method is proposed, in which the stored and reconstructed data are used for damage detection and localization in the time domain. A numerical experiment was performed with a structure having a large number of sensors. The excitation and environmental conditions were variable and unknown. An optimal sensor placement algorithm was applied individually to each measurement to select the appropriate virtual sensors for storage. Less than ten percent of the data were stored, and the signals of all the reconstructed sensors were still more accurate than the actual measurements. The stored and reconstructed data outperformed the actual measurement data in damage detection and localization. Surprisingly, damage detection was also more successful with the stored and reconstructed data than with the full set of virtual sensors.
{"title":"OPTIMAL SELECTION OF BAYESIAN VIRTUAL SENSORS FOR DAMAGE DETECTION UNDER VARIABLE ENVIRONMENTAL CONDITIONS","authors":"J. Kullaa","doi":"10.7712/120221.8028.18904","DOIUrl":"https://doi.org/10.7712/120221.8028.18904","url":null,"abstract":"Measuring structural vibrations with a large sensor network results in lots of data in structural health monitoring applications. A large number of sensors is advantageous for damage detection and localization. By storing only a few selected Bayesian virtual sensors it is possible to decrease the amount of data and reconstruct the discarded sensor data even with higher accuracy than the original measurements. A method is proposed, in which the stored and reconstructed data are used for damage detection and localization in the time domain. A numerical experiment was performed with a structure having a large number of sensors. The excitation and environmental conditions were variable and unknown. An optimal sensor placement algorithm was applied individually to each measurement to select the appropriate virtual sensors for storage. Less than ten percent of the data were stored, and the signals of all the reconstructed sensors were still more accurate than the actual measurements. The stored and reconstructed data outperformed the actual measurement data in damage detection and localization. Surprisingly, damage detection was also more successful with the stored and reconstructed data than with the full set of virtual sensors.","PeriodicalId":444608,"journal":{"name":"4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133981032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120221.8041.19047
Ngọc Trình Trần, M. Staat
{"title":"FEM SHAKEDOWN ANALYSIS OF KIRCHOFF-LOVE PLATES UNDER UNCERTAINTY OF STRENGTH","authors":"Ngọc Trình Trần, M. Staat","doi":"10.7712/120221.8041.19047","DOIUrl":"https://doi.org/10.7712/120221.8041.19047","url":null,"abstract":"","PeriodicalId":444608,"journal":{"name":"4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114726812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120221.8024.19110
M. M. Dannert, Johannes L. Häufler, U. Nackenhorst
{"title":"LIMIT REPRESENTATIONS OF IMPRECISE RANDOM FIELDS","authors":"M. M. Dannert, Johannes L. Häufler, U. Nackenhorst","doi":"10.7712/120221.8024.19110","DOIUrl":"https://doi.org/10.7712/120221.8024.19110","url":null,"abstract":"","PeriodicalId":444608,"journal":{"name":"4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127049081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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