Teemu Manderbacka

Dr. Teemu Manderbacka

orcid

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 August 2015 in Journal of Fluids and Structures
Verified
Journal Article
Closed access
TMTeemu Manderbacka
Teemu Manderbacka
Tommi Kristian MikkolaTommi Kristian Mikkola
Tommi Kristian Mikkola
PRPekka Ruponen
Pekka Ruponen
JMJerzy Matusiak
Jerzy Matusiak

1 August 2015 in Journal of Fluids and Structures
Verified
Journal Article
Closed access
TMTeemu Manderbacka
Teemu Manderbacka
PRPekka Ruponen
Pekka Ruponen
JKJakke Kulovesi
Jakke Kulovesi
JMJerzy Matusiak
Jerzy Matusiak

1 August 2019 in Ocean Engineering
Verified
Journal Article
Closed access
TMTeemu Manderbacka
Teemu Manderbacka
Nikos ThemelisNikos Themelis
Nikos Themelis
IBIgor Bačkalov
Igor Bačkalov
Evangelos BoulougourisEvangelos Boulougouris
Evangelos Boulougouris
EEEleftheria Eliopoulou
Eleftheria Eliopoulou
DTDaisuke Terada
Daisuke Terada

1 July 2016 in Ocean Engineering
Verified
Journal Article
Closed access
TMTeemu Manderbacka
Teemu Manderbacka
PRPekka Ruponen
Pekka Ruponen

1 February 2018 in Ocean Engineering
Verified
Journal Article
Open access
PRPekka Ruponen
Pekka Ruponen
TMTeemu Manderbacka
Teemu Manderbacka
DLDaniel Lindroth
Daniel Lindroth

Verified
Journal Article
Open access
PDF file
JHJanne Huotari
Janne Huotari
TMTeemu Manderbacka
Teemu Manderbacka
ARAntti Ritari
Antti Ritari
Kari TammiKari Tammi
Kari Tammi
We present a novel convex optimisation model for ship speed profile optimisation under varying environmental conditions, with a fixed schedule for the journey. To demonstrate the efficacy of the proposed method, a combined speed profile optimisation model was developed that employed an existing dynamic programming approach, along the novel convex optimisation model. The proposed model was tested with 5 different ships for 20 journeys from Houston, Texas to London Gateway, with differing environmental conditions, which were retrieved from actual weather forecasts. As a result, it was shown that the combined model with both dynamic programming and convex optimisation was approximately 22% more effective in developing a fuel saving speed profile compared to dynamic programming alone. Overall, average fuel savings for the studied voyages with speed profile optimisation was approximately 1.1% compared to operation with a fixed speed and 3.5% for voyages where significant variance in environmental conditions was present. Speed profile optimisation was found to be especially beneficial in cases where detrimental environmental conditions could be avoided with minor speed adjustments. Relaxation of the fixed schedule constraint likely leads to larger savings but makes comparison virtually impossible as a lower speed leads to lower propulsion energy needed.