{"refrec":{"BRefID":256500,"RR":"<b>Oberrecht, S.P.; Krysl, P.; Cranford, T.W.</b> (2016). Sound transmission validation and sensitivity studies in numerical models, <b><i>in</i></b>: Popper, A.N. <i>et al.</i> <i>The effects of noise on aquatic life II. Advances in Experimental Medicine and Biology,</i> 875: pp. 785-789. <a href=\"http://dx.doi.org/10.1007/978-1-4939-2981-8_96\" target=\"_blank\">http://dx.doi.org/10.1007/978-1-4939-2981-8_96</a>","BEntID":248509,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":", <b><i>in</i></b>: Popper, A.N. <i>et al.</i> <i>The effects of noise on aquatic life II. Advances in Experimental Medicine and Biology,</i> 875: pp. 785-789. <a href=\"http://dx.doi.org/10.1007/978-1-4939-2981-8_96\" target=\"_blank\">http://dx.doi.org/10.1007/978-1-4939-2981-8_96</a>","DocTypID":17,"DocType":"Book chapters","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Oberrecht, S.P.; Krysl, P.; Cranford, T.W.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Oberrecht, S.P. <i>et al.</i>","Englishabstract":"In 1974, Norris and Harvey published an experimental study of sound transmission into the head of the bottlenose dolphin. We used this rare source of data to validate our Vibroacoustic Toolkit, an array of numerical modeling simulation tools. Norris and Harvey provided measurements of received sound pressure in various locations within the dolphin’s head from a sound source that was moved around the outside of the head. Our toolkit was used to predict the curves of pressure with the best-guess input data (material properties, transducer and hydrophone locations, and geometry of the animal’s head). In addition, we performed a series of sensitivity analyses (SAs). SA is concerned with understanding how input changes to the model influence the outputs. SA can enhance understanding of a complex model by finding and analyzing unexpected model behavior, discriminating which inputs have a dominant effect on particular outputs, exploring how inputs combine to affect outputs, and gaining insight as to what additional information improves the model’s ability to predict. Even when a computational model does not adequately reproduce the behavior of a physical system, its sensitivities may be useful for developing inferences about key features of the physical system. 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