41 Sustainable Marine Structures | Volume 01 | Issue 01 | January 2019 Sustainable Marine Structures http://ojs.nassg.org/index.php/sms Distributed under creative commons license 4.0 DOI: *Corresponding Author: S Surendran, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai, India; Emial: sur@iitm.ac.in AbstrAct Wind energy is considered one of the most promising alternative energy sources against the conventional fossil fuels. However, the deployment of these structures in deep-water for better power production is considered as a complex task. this also has raised the issue regarding selection of appropriate support structures for various sea conditions by considering environ- mental impact and carbon footprint. this paper considers a jacket like support structure as a case study for an intermediate water depth (50m). the jacket is considered to be located in North of Dutch Sea, and 100-extreme wave is applied as load condition. Here, the presented methodology provides an insight towards environmental/social impact made by the optimized designs in comparison with reference design. ArtIcLE INFO Article history: received: 26 December 2018 Accepted: 7 January 2019 Published: 18 January 2019 Keywords: Wind turbine support structure sustainable design Optimization Multi criteria decision making Non-linear based design Article A Practical Decision Making on Design of Fixed Offshore Wind turbine Support Structure considering Socio-economic impact M Vishnu Surendran Sankunny* Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai, India 1. introduction Fixed offshore structures are one of the most com-monly used offshore structures for intermediate water depths compared to monopile. these are technically feasible and economically viable in design but are complex to design in nature. this possess many chal- lenges in designing and execution of the project. More- over, offshore structures are designed to resist extreme wave loading but can succumb to collapse damage due to failure of multiple components members. One major challenges faced by industry is cost effec- tive design of structures under extreme and normal en- vironmental conditions. For a reliable and cost effective design under extreme loads, a non-liner static structural analysis always been a significant aspect. Computer aided structural optimization can assist in designing economical structure under various constraints like fatigue. Hence, optimization of structure has to fatigue and extreme loads under the target life. chew et al.[1] has considered gradient based optimization and reported the importance of buck- ling and fatigue load constraints over the design variables. Gentils et al.[2] integrated Genetic algorithm (GA) and FEA (Finite Element Analysis) to optimize support struc- ture under various constraints. the paper also reported the advantage of using meta-heuristic methods as compared to gradient based optimization. Gomes[3] has studied the truss optimization using particle swarm optimization (PsO)[4] based on the reported the well behavior of the algorithm. In most cases, API and ISO codes are used to design structures under elastic and component based design[5,6]. However, Nizamani[7] suggested the advantage of system based design considering structure as a whole component. 42 Sustainable Marine Structures | Volume 01 | Issue 01 | January 2019 Distributed under creative commons license 4.0 DOI: the stress re-distribution between the members can result in extended load capacity towards plastic stage based de- sign [8]. Hence the approach proposed by Ueda et. al [9] is used in this paper by using a finite element based code, UsFOs[10]. In 2007, as per major world leaders a 20% share of en- ergy from renewable sources by 2020, by making individ- ual targets for all EU Member States [11]. The UK targets to acquire 15% of its final energy intake from renewable sources by 2020 and to decrease cO2 emissions by a minimum of 26% by 2020 and 60% by 2050 [11] and also having the best geographically varied wind resources in Europe [12]. So, it is also worth considering the social im- pact made by a wind energy project at various stages from manufacturing, installation and decommission stages. Lozano-Minguez et al.[12] investigated regarding the influ- ence of environmental factors like carbon foot, noise, and vibration, water turbidity, etc. The authors also proposed the advantage of using tOPIs (technique for Order Pref- erence by similarity to Ideal solution) method as multi criteria decision making tool. From the above literature review, the authors under- stand the importance of considering socio-economic im- pact on decision making of offshore structural designs. Hence, this paper aims to provide an analytical method- ology for the selection of the most preferable fixed jacket like support structure for a typical 5 MW wind turbine in 50 m water depth. In this analysis; engineering, econom- ics, and environmental assessment will be considered to balance the socio-economic activities of the sustainable energy sector. Figure 1 below provides sketch of offshore wind turbine (OWt) support structure under environmen- tal loads and the methodology followed is given below table 1. Figure 1. Jacket model table 1. Methodology step 1 Selection of site, loading condition and structure step 2 Evaluating optimal designs under constraints step 3 Evaluate social impact for each design step 4 tOPIs method 2. Methodology 2.1 Step 1: case Study A fixed jacket structure was proposed by Vorpahl et al.[13] was designed to support an offshore wind turbine of 5 MW capacity. The height of the jacket structure is 66m and is placed at a water depth of 50m. the location to be installed is considered as the North of Dutch sea. the structure consists of 56 nodes and 104 beams of steel tubular cross section and used in this paper to perform structural optimization. the jacket structure is modelled using UsFOs as shown below Figure 1. the tubular members of the jacket are categorized into six groups to utilize them for the structural optimization, and the cross sectional details of the groups are given Table 2. Also, the structure can work with stand loads even if one member is failed under yield conditions and the force redistribution happens to other members. this is indicated by factor re- ferred as reserve strength ratio (rsr) and considers the nonlinear static capacity of the structure [14]. Ultimate collapse load RSR Design load = (1) table 2. Jacket reference design (continued) Design variable Description Diameter (mm) thickness (mm) Group 1- Dark blue Leg 1200 50 Group 2- red brace 800 20 Group 3- Yellow brace 800 20 Group 4-Green brace 800 20 Group 5-cyan brace 800 20 Group 6-blue brace 800 20 the jacket material properties are given in table 3. 2.1.1 Hydrodynamic loading the wave environment used is based on statistical wave description. Table 4 gives the significant wave height and the hydrodynamic forces acting on the tubular members are calculated using Morison's equation [15]. As per Equation (2), the relationship between wave height and return period was formulated as: ,3 ( ) 0.6127 ln( ) 7.042s hrs returnH T x= ⋅ + (2) 43 Sustainable Marine Structures | Volume 01 | Issue 01 | January 2019 Distributed under creative commons license 4.0 DOI: From the 100-year return period significant wave height, extreme design wave is calculated by the follow- ing relationship (16): He = 1.86Hs (3) For the given location, the shallow water depth allows the use of 1.86 as the factor and the loading condition shown below. table 4. Wave data Parameter [Unit] Description Value Hs,100 [m] Significant wave height in 100 year return period 9.90 He,100 [m] Extreme wave height in 100 year return period 18.41 V100 [m/s] Mean wind speed in 100 year return period 44.50 U100 [m/s] current speed in 100 year return period 1.20 A typical analysis for the considered reference case and given wave leading is shown below Figure 2. the load displacement curve indicates the maximum load factor or rsr. Here the rsr is evaluated as 3.6 and is over conser- vative as compared to minimum prescribed values of 1.58 and 1.85 provided by API and ISO respectively. However, there seems to be lack of knowledge on target rsr values for various site and loading conditions. Also, considering target values from code based methodology for offshore oil and gas structures for wind energy system may not be feasible approach. this demands multi criteria based de- cision making methodology in conjunction with optimiza- tion of structures to evaluate target load factor. Figure 2. Typical load deflection curve for reference case 2.2 Step 2: integrated USFOS-MAtlAB Optimi- zation In the present study, the evolutionary approach based particle swarm optimization (PsO) algorithm proposed by Kennedy and Eberhart is considered[17]. Perez[18] and Gomes[3] reported the robustness of PsO algorithm for truss optimization. Initially, the design variable and objec- tive functions are defined. Fitness values for each design is evaluated by integrating finite element structural analy- sis with PSO algorithm developed in MATLAB (Figure 3). (a) Problem formulation the optimization problem for minimizing structural weight with design variables, subject to sizing and ulti- mate collapse load factor as constraints, can be formulated as follows. the optimization problem can be formulated as given below: Minimize jacket mass: ( ) 1 ( ) en n n n i f x A x lρ = = ∑ (4) subjected to: (varied from1.6 to 3.2)tRSR RSR≥ L Ux x x≤ ≤ rsr is the collapse load factor for given wave load. Here x represents the vector of jacket member dimensions namely, diameter and thickness; A is the vector of cross section area, l represents the length of each member, ne represents the total number of members. this is a sim- plified representation of the cost function and other cost components that are incurred in the design life cycle of support structures, excluding manufacturing, installation and maintenance costs. (b) sizing constraints Sizing constraints define the lower and upper bounds of table 3. Jacket Properties Property Description Material used steel Elastic modulus 2.1 × 105 MPa Poisson's modulus 0.3 Yield strength 345 MPa Density 7850 Kg/m3 Dead load 350 ton X joints 16 K joints 24 t/Y joints 16 Height 66 m Mass 608 ton 44 Sustainable Marine Structures | Volume 01 | Issue 01 | January 2019 Distributed under creative commons license 4.0 DOI: design variables as well as the geometrical relationships among the variables. they can be expressed as 1 min maxg b b b= ≤ ≤ Here bmin and bmax are the lower and upper bounds of the design variables as shown in below table 5. table 5. Design bounds Member type (Group) Diameter bound (mm) thickness bound (mm) Legs (1) (600,1400) (30,60) Braces(2,3,4,5,6) (400,800) (10,30) Figure 3. Integrated USFOS-MATLAB optimization framework the results for the optimsation are shown in table 6 for various target rsr values. table 6. Optimal design vs. rsr Mass (Ton) rsr 285 1.6 324 1.8 365 2.0 398 2.2 430 2.4 447 2.6 480 2.8 525 3.0 538 3.2 2.3 Step 3: economic and environmental impact Assessment (eeiA) this section will describe about the various environmental and social impact made by installation of jacket structure. this mainly includes the following factors: (a) carbon footprint the equivalent amount of carbon di Oxide (cO2) can be expressed as following: 2 2 4270 24.5 1.4CO e N O CH CO= × + × + × (5) For steel structures, the emission unit per kg of total weight is 0.07, 0.04 and 0.93 g for N2O, CH4 and CO re- spectively (b) Noise and Vibration As the machinery used is the same and the duration of the work will not vary significantly, it can be assumed that the choice of foundation will not affect the impact. (c) Electromagnetic fields However, it is not yet known whether the fish will suffer any consequences caused by this interaction. the choice of foundation will not, therefore, be considered as affecting the impact. (d) Impact on birds the choice of foundation will not affect the impact on birds. (e) Net present value this parameter will convert the total cost of the service life of the structure to present value. 2.4 Step 4: Multi criteria based Decision Making (tOPiS) As given in publicly available literature by Lozano-Min- guez et al. [12], the basic steps of multi criteria based deci- sion making algorithm is given below. For more informa- tion, the readers are advised to refer the above paper. (a) Formulate initial design matrix (b) Normalized design matrix (c) construct weighing matrix from experts (d) Weighted normalized decision matrix (e) Derived PIs and NIs (f ) Evaluate relative closeness of each solution (g) ranking the solution Analysis of attributes for finding the optimal design from possible nine alternative designs, nine criteria has been considered as follows (table 7). table 7. Description of attributes sl. No Attribute Negative/ Positive Value significance 1 Artificial reefs Positive Higher the better 2 Certification Positive 1- If certified0.5- If not certified 3 CO2e Negative Lower the better 4 Depth compatibility Negative 1 for depth < 40 m 5 Durability Positive 5 for Jacket and 4 for monopile 6 Life cycle cost Negative CAPEX + OPEX 7 reserve strength ratio (rsr) Positive Higher the better 8 Water turbidity Negative 2356 for jacket and 1530 for monopile 45 Sustainable Marine Structures | Volume 01 | Issue 01 | January 2019 Distributed under creative commons license 4.0 DOI: the weights as shown in table 8 used for the study in- fluence the decision approaching and has been taken from experience of experts from Cranfield offshore renewable energy group[12]. table 8. Weight factor for each attribute Attribute 1 2 3 4 5 6 7 8 Expert weight 0.65 0.65 0.91 0.91 1.00 1.00 0.83 0.74 3. results and Discussion For the nine design combination corresponding to jacket based support structure, only the attribute 3, 6 and 7 are variables with no change for remaining. For evaluating the life cycle cost excluding risk expenditure, the capital cost (CAPEX) is evaluated considering 1000 € per Ton as material cost and Manufacturing cost as 400% of Material cost. For evaluating Operational cost (OPEX), it is con- sidered as 10% of the CAPEX. However, a present worth factor should be considered to take care of the economic parameters during the life span of 20 years. (1 ) 1 / (1 ) LS w LS d P LS d d + − = + (6) below is tabulated results of Lcc and rsr for each design index (table 9). table 9. Lcc vs. rsr for each optimal design Design Index Lcc (106 Euro) rsr 1 2.92 1.6 2 3.13 1.8 3 3.34 2.0 4 3.45 2.2 5 3.62 2.4 6 3.75 2.6 7 3.90 2.8 8 4.10 3.0 9 4.21 3.2 (a) Based on the attributes (1-8), the initial decision matrix is given below table 10 table 10. Initial design matrix (continued) 1 2 3 4 5 6 7 8 8787 1 6036 1 5 2.92 1.6 2356 8787 1 6863 1 5 3.13 1.8 2356 8787 1 7730 1 5 3.34 2.0 2356 8787 1 8492 1 5 3.45 2.2 2356 8787 1 9107 1 5 3.62 2.4 2356 8787 1 9465 1 5 3.75 2.6 2356 8787 1 10166 1 5 3.90 2.8 2356 8787 1 11200 1 5 4.10 3.0 2356 8787 1 11395 1 5 4.21 3.2 2356 (b) the normalized decision matrix is as follows (table 11). table 11. Normalized design matrix 1 2 3 4 5 6 7 8 0.58 0.58 0.22 0.58 0.58 0.26 0.21 0.58 0.58 0.58 0.25 0.58 0.58 0.28 0.24 0.58 0.58 0.58 0.28 0.58 0.58 0.30 0.27 0.58 0.58 0.58 0.30 0.58 0.58 0.31 0.29 0.58 0.58 0.58 0.33 0.58 0.58 0.33 0.32 0.58 0.58 0.58 0.34 0.58 0.58 0.34 0.35 0.58 0.58 0.58 0.37 0.58 0.58 0.35 0.38 0.58 0.58 0.58 0.40 0.58 0.58 0.37 0.40 0.58 0.58 0.58 0.41 0.58 0.58 0.38 0.43 0.58 (c) the average normalized weight matrix is given in table 8 (d) And the weighted normalized matrix is obtained is given as table 12 table 12. Weighted normalized design matrix 1 2 3 4 5 6 7 8 0.38 0.38 0.20 0.53 0.58 0.26 0.17 0.43 0.38 0.38 0.23 0.53 0.58 0.28 0.20 0.43 0.38 0.38 0.25 0.53 0.58 0.30 0.22 0.43 0.38 0.38 0.27 0.53 0.58 0.31 0.24 0.43 0.38 0.38 0.30 0.53 0.58 0.33 0.27 0.43 0.38 0.38 0.31 0.53 0.58 0.34 0.29 0.43 0.38 0.38 0.34 0.53 0.58 0.35 0.32 0.43 0.38 0.38 0.36 0.53 0.58 0.37 0.33 0.43 0.38 0.38 0.37 0.53 0.58 0.38 0.36 0.43 (f) the positive and negative ideal solution (PIs and NIs) as given in table 13. table 13. PIs and NIs matrix 1 2 3 4 5 6 7 8 0.38 0.38 0.20 0.53 0.58 0.26 0.36 0.43 0.38 0.38 0.37 0.53 0.58 0.38 0.17 0.43 After evaluating the decision matrix using tOPIs method, design index four was found to be best option (0.69). the selected index has reserve strength ratio of 2.2 with mass of 398 ton. the rsr value is found to be well above the minimum prescribed value of 1.58 and 1.85 as per API and IsO studies respectively. 46 Sustainable Marine Structures | Volume 01 | Issue 01 | January 2019 Distributed under creative commons license 4.0 DOI: 4. conclusion the study provides a multi criteria based decision making methodology for design of offshore structures. this meth- odology not only considers technical feasibility, but social and economic factors for selection of optimal design. the optimal design provides technically safe and sustainable design. Further, this methodology can be extended to de- sign of floating structures for deep water also. A sensitiv- ity study can also be performed for change is water depth and environmental conditions. Author contributions: The first author Mr. Vishnu Murali (Ph.D. scholar) has de- veloped the methodology for design of offshore structures considering sustainability. His work includes scripting in MATLAB and FE analysis is USFOS. He has also inte- grated USFOS-MATLAB for seamless working of optimi- zation methodology. the second author Prof. surendran sankunny has provided the necessary inspiration and motivation for the work. He has contributed significantly to the research work in detailed correction and providing technical support for numerical analysis. Conflict of Interest: No conflict of interest was reported by the authors. Acknowledgments: the first author greatly acknowledge the research grant from the Ministry of Human Resources Development, Government of India. the authors also want to thank Dr. tore Holmas of www.usfos.no for his support that im- proved the quality of the paper. references [ 1 ] Chew K-H, Tai K, Ng EYK, Muskulus M. Analytical gradient-based optimization of offshore wind turbine sub- structures under fatigue and extreme loads. Mar Struct. 2016, 47, 23–41. [ 2 ] Gentils T, Wang L, Kolios A. Integrated structural optimi- sation of offshore wind turbine support structures based on finite element analysis and genetic algorithm. Appl Energy. Elsevier Ltd; 2017, 199, 187–204. Available from: http://dx.doi.org/10.1016/j.apenergy.2017.05.009. [ 3 ] Gomes HM. Truss optimization with dynamic constraints using a particle swarm algorithm. Expert syst Appl. Else- vier Ltd; 2011;38(1):957–68. Available from: http://dx.doi. org/10.1016/j.eswa.2010.07.086. [ 4 ] Coello C a C, Pulido GT, Lechuga MS. 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