Studies on damage aggregation for weapons salvos

dc.contributor.authorEsary, James Daniel
dc.contributor.corporateOperations Research (OR)
dc.contributor.corporateGraduate School of Operational and Information Sciences (GSOIS)
dc.contributor.departmentOperations Research
dc.date1990-07
dc.date.accessioned2013-03-07T21:54:25Z
dc.date.available2013-03-07T21:54:25Z
dc.date.issued1990-07
dc.description.abstractThis document records three working studies from an ongoing investigation of models and methods for the prediction of the cumulative effect of weapons salvos. The first paper is about an extant formula for estimating the expected proportion of damage to an area target, which proves to be optimistic when compared to a plausible model for the effect of the salvo. The second paper describes an alternate formula which is conservative when compared to the same model. The third paper describes a basic case of an emerging family of target configuration and weapons impact scenarios which lead to the plausible modelen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.
dc.description.funderO&MN, Direct Fundineen_US
dc.description.recognitionNAen_US
dc.description.sponsorshipPrepared for Naval Weapons Center, China Lake, CA, and funded by the Naval Postgraduate School.en_US
dc.description.urihttp://archive.org/details/studiesondamagea00esar
dc.identifier.npsreportNPS-55-90-16
dc.identifier.oclcNA
dc.identifier.urihttps://hdl.handle.net/10945/30219
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.subject.authorWeapons salvos; salvo; damage; cumulative damage; proportional effects; target; area target; cellular targeten_US
dc.subject.lcshDAMAGE.en_US
dc.titleStudies on damage aggregation for weapons salvosen_US
dc.typeTechnical Reporten_US
dspace.entity.typePublication
relation.isOrgUnitOfPublication58745961-c46a-45ad-ae9c-d139d1ba1041
relation.isOrgUnitOfPublicationdd7f1b97-9c92-402d-b910-27f080946cde
relation.isOrgUnitOfPublication.latestForDiscoverydd7f1b97-9c92-402d-b910-27f080946cde
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