TY - CONF A1 - Kinne, Marko A1 - Farhan, Muhammad A1 - Schneider, Ronald A1 - Thöns, Sebastian T1 - Influence of the structural integrity management on the levelized cost of energy of offshore wind: a parametric sensitivity analysis N2 - The levelized cost of energy (LCoE) is an important measure to quantify the macro-economic efficiency of an offshore wind farm and to enable a quantitative comparison with other types of energy production. The costs of the structural integrity management - which is required to ensure an adequate lifetime reliability of the turbine support structures - are part of the operational expenditures of an offshore wind farm. An optimization of the structural integrity management may reduce the operational expenditures and consequently the LCoE. However, the effect of the structural integrity management on the LCoE is hardly known. To investigate this effect, this paper presents a sensitivity analysis of the LCoE of a generic offshore wind farm. The probabilistic models of the parameters influencing the LCoE are based on a literature study including an explicit model for the structural integrity management. The analysis reveals that LCoE may potentially be reduced if an optimization of the structural integrity management enables a service life extension. T2 - International Probabilistic Workshop 2022 CY - Stellenbosch, South Africa DA - 08.09.2022 KW - Structural integrity management KW - Levelized cost of energy KW - Sensitivity analysis PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-572724 DO - https://doi.org/10.14311/APP.2022.36.0090 VL - 36 SP - 90 EP - 98 PB - Acta Polytechnica CTU Proceedings AN - OPUS4-57272 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - Simulating the service life performance of an inspected group of jacket-type structures N2 - A novel method for risk-based optimization of inspection and repair strategies for deteriorating structural systems has recently been proposed. The method defines heuristics at the system level to reduce the number of possible strategies. For each defined strategy, it computes the updated system failure probability conditional on simulated inspection and repair histories, and evaluates the associated costs and risk. The expected total service life costs and risk for a strategy are finally determined using Monte Carlo simulation. The optimal strategy minimizes the expected total service life costs and risk. We intend to adopt this approach to optimize inspection, monitoring and repair activities for offshore wind park support structures. As a first step, we simulate – in analogy to an offshore wind park – the service life performance of an inspected group of jacket-type frames. The performance is quantified in terms of the group’s system failure probability conditional on simulated inspection and repair histories. The underlying system model accounts for the structural redundancy of the frames and the interdependence among their failure events due to similar loading conditions. The model also captures stochastic dependence among the deterioration states of the frames. As part of the simulation process the a-priori unknown outcome of any planned inspection is generated conditional on the outcome of all previous inspections. T2 - 12th International Conference on Structural Safety and Reliability CY - Vienna, Austria DA - 06.08.2017 KW - Structural reliability KW - Inspection KW - Fatigue KW - Inspection planning PY - 2017 AN - OPUS4-41346 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - Probabilistic fatigue crack growth modeling for reliability-based inspection planning T2 - Third International Symposium on Life-Cycle Civil Engineering (IALCCE'12) CY - Vienna, Austria DA - 2012-10-03 PY - 2012 AN - OPUS4-26634 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Farhan, Muhammad A1 - Schneider, Ronald A1 - Thöns, Sebastian T1 - Corrigendum to Predictive information and maintenance optimization based on decision theory: a case study considering a welded joint in an offshore wind turbine support structure N2 - This article has been revised and republished due to substantial changes to the text of the original article, as published Online First on January 31, 2021. Most of the change were minor grammatical changes. The following changes are more significant and will be highlighted below. PY - 2022 DO - https://doi.org/10.1177/14759217211040385 SN - 1475-9217 VL - 21 IS - 4 SP - 1956 EP - 1956 PB - Sage Publications CY - London AN - OPUS4-55165 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schneider, Ronald A1 - Thöns, Sebastian A1 - Straub, Daniel T1 - Reliability analysis and updating of deteriorating systems with subset simulation N2 - An efficient approach to reliability analysis of deteriorating structural systems is presented, which considers stochastic dependence among element deterioration. Information on a deteriorating structure obtained through inspection or monitoring is included in the reliability assessment through Bayesian updating of the system deterioration model. The updated system reliability is then obtained through coupling the updated deterioration model with a probabilistic structural model. The underlying high-dimensional structural reliability problems are solved using subset simulation, which is an efficient and robust sampling-based algorithm suitable for such analyses. The approach is demonstrated in two case studies considering a steel frame structure and a Daniels system subjected to high-cycle fatigue. KW - Structural reliability KW - Deterioration KW - Bayesian analysis KW - Inspection KW - Monitoring KW - Subset simulation PY - 2017 DO - https://doi.org/10.1016/j.strusafe.2016.09.002 SN - 0167-4730 SN - 1879-3355 VL - 64 SP - 20 EP - 36 PB - Elsevier AN - OPUS4-38218 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -