Cost-Effective Simulation and Prediction of Explosions for Military and Public Safety, and for Improved Oil Extraction
Ian G. Cullis () and
Mark A. Kelmanson ()
Additional contact information
Ian G. Cullis: QinetiQ Fort Halstead
Mark A. Kelmanson: University of Leeds, School of Mathematics
A chapter in UK Success Stories in Industrial Mathematics, 2016, pp 155-161 from Springer
Abstract:
Abstract An MoD-funded research programme based in Applied Mathematics at Leeds University has resulted in demonstrable long-term and ongoing benefits on diverse fronts for beneficiaries in a range of public and private sectors. First, by guaranteeing robustness and reliability of bespoke numerical methods for the MoD, the joint research led to substantial financial savings in ballistic-development programmes, thereby enabling the delivery of advanced research output cost-effectively under severe budgetary pressures. As a result, QinetiQ was placed as a world leader in the simulation of explosions, which supported the MoD to rapidly assess and develop countermeasures to the ever-changing threats faced by British Forces in Afghanistan and Iraq, and to reduce casualties. It also enabled government agencies to assess threats to transport and public-building infrastructure. Second, the joint research underpinned substantial recurrent income for QinetiQ, who has additionally developed the codes with the oil industry to develop a new explosive perforator for oil extraction that has not only led to demonstrable improvements in both extraction efficiency and research-and-development costs, but has also yielded recurrent licensing royalties.
Keywords: Cost-effective Simulation; QinetiQ; Substantial Financial Savings; Leeds University; Numerical Simulation Capabilities (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-25454-8_20
Ordering information: This item can be ordered from
http://www.springer.com/9783319254548
DOI: 10.1007/978-3-319-25454-8_20
Access Statistics for this chapter
More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().