What We Publish

On this page, you can find links to our internal MSDL report publications, as well as abstracts and bibliographic information for publications in peer-reviewed journal articles and conferences. Older publications by Dr. Collette before he founded the MSDL are also included. Where allowed by copyright, full text downloaded or pre-prints of articles are also provided. Papers where source code and data is provided for provenance will have a link below the abstract to access the code or data. Zotero metadata is also provided for the publications. 

Zotero

Department of Naval Architecture 
and Marine Engineering
210 Naval Architecture and Marine Engineering
2600 Draper Dr., Ann Arbor MI 48109
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20172016
2015 selected

Dylan Temple; Chair: Matthew D. Collette. "A Multi-Objective Collaborative Optimization Framework to Understand Trade-offs Between Naval Lifetime Costs Considering Production, Operation, and Maintenance". P.h.D University of Michigan-Ann Arbor. 2015
Hodapp_thesis

The lifetime cost of naval vessels is an increasingly important factor to ship owners and, subsequently, to ship designers. A vessel's lifetime cost is composed of various cost categories such as production, operation, and maintenance. The impact of each of these categories is important and in many instances they may be competing with each other. Design decisions regarding the hull form and structure will dictate these costs, however, in what way decisions will impact them is difficult to understand. This is especially true for naval vessels as their service life is uncertain, and changes to the operational life of a vessel can have significant unforeseen costs with respect to maintenance and operation. In order to reduce the overall lifetime cost the tradeoffs between these different categories must be understood. This thesis explores a linked resistance, production, and maintenance costing model and develops a novel enhanced multi-disciplinary optimizer capable of solving the resulting problem. Most work in cost optimization has focused on reducing a single category of cost and considering other disciplines operational constraints at best. This type of sequential or single-discipline optimization does not reveal the trade-space to the designer and may result in non-optimal designs being developed when considering the full lifecycle cost of the vessel. Unfortunately understanding these trade-offs is difficult and traditional multi-objective optimization algorithms are unable to resolve the Pareto-fronts effectively. Presented here is a framework to aid designers in finding these trade spaces using a multi-disciplinary optimization environment. In order to realistically represent the problem being solved a maintenance costing algorithm is developed that tracks physical damage throughout a ship's lifetime. Given that the design life of a vessel may be prolonged a probabilistic service life is implemented to account for this uncertainty. A hydrodynamic search method is also developed that facilitates efficiently searching large design spaces using a minimal number of design variables. These models allow for the development of trade-spaces that reflect the nuances of the naval design problem. In order to utilize these models to understand the trade-offs in lifetime cost an enhanced multi-disciplinary optimization framework is developed. This algorithm uses novel techniques to facilitate solving this difficult design problem. The algorithm (eMOCO) is adopted from a multi-objective collaborative optimization framework with two enhancements. The first is the use of a decision support process, goal programming, at the sub-system level in order to allow the discipline optimizers to reduce objective functions local to that discipline. This means that the discipline-level solutions that returned to the system-level optimizers are minimized with respect to their local variables. Secondly, a new single-objective genetic algorithm is developed specifically as a discipline-level optimizer in distributed MDO architectures. This novel GA, called the locally-elitist genetic algorithm (LEGA,) allows the discipline problem to be solved in a single execution of the discipline-level optimizer. These enhancements, tailored specifically to the naval design problem, facilitate solving for these difficult and unique trade-spaces. This model is used to develop trade spaces between production, maintenance, and resistance in order to understand the interaction between the different categories of cost. The results show that the trade-spaces are difficult to fully resolve and the use of a multi-disciplinary environment is necessary. They also show that by developing the trade-spaces unique understanding into the interaction between cost categories can be found that allow an engineer to design ships that have minimal lifetime cost and are robust to changes in operation or service life.

Zhu, J., M. Collette. "A dynamic discretization method for reliability inference in Dynamic Bayesian Networks." Reliability Engineering and System Safety (June 2015 Volume 138):242-52
link_to_journal

The material and modeling parameters that drive structural reliability analysis for marine structures are subject to a significant uncertainty. This is especially true when time-dependent degradation mechanisms such as structural fatigue cracking are considered. Through inspection and monitoring, information such as crack location and size can be obtained to improve these parameters and the corresponding reliability estimates. Dynamic Bayesian Networks (DBNs) are a powerful and flexible tool to model dynamic system behavior and update reliability and uncertainty analysis with life cycle data for problems such as fatigue cracking. However, a central challenge in using DBNs is the need to discretize certain types of continuous random variables to perform network inference while still accurately tracking low-probability failure events. Most existing discretization methods focus on getting the overall shape of the distribution correct, with less emphasis on the tail region. Therefore, a novel scheme is presented specifically to estimate the likelihood of low-probability failure events. The scheme is an iterative algorithm which dynamically partitions the discretization intervals at each iteration. Through applications to two stochastic crack-growth example problems, the algorithm is shown to be robust and accurate. Comparisons are presented between the proposed approach and existing methods for the discretization problem.

Temple, D.W., M. Collette. "Minimizing lifetime structural costs: Optimizing for production and mantenance under service life uncertainty." Marine Structures (January 2015 Volume 40):60-72
link_to_journal

Current naval shipowners are being forced to extend the service lives of their aging vessels from budgetary and political constraints. This is causing them to incur significant costs due to maintaining the structure of these older ships to keep the ships in operation. These increasing costs make it desirable to design new naval structures with their minimization in mind, as well as ensuring that such vessels are robust to changes in expected service life with respect to their total lifetime cost. However, such structures will necessarily have higher production costs, therefore, an optimization framework is presented to estimate both production and maintenance costs for a naval vessel's internal structure and develop trade-spaces between these two competing objectives in order to find designs that represent a balance of both.

Hodapp, D., M. Collette and A.W. Troesch. 2015. "Stochastic nonlinear fatigue crack growth predictions for simple specimens subject to representative ship strucutral loading sequences." International Journal of Fatigue 70 (January):38-50
link_to_journal

Recent work by the authors investigated an extension of the finite element analysis of plasticity-induced crack closure to non-stationary, ship structural loading sequences by taking advantage of their inherent time-dependent nature in which the larger loading cycles tend to be clustered together. In doing so, first-order load interactions are presumed to arise from the random occurrence and severity of physical storms encountered by ships and offshore structures throughout their service lives. This material hysteresis is captured through a time-dependent crack “opening” level (Kop) which is based on the evolution of a rate-independent, incremental plasticity model simulating combined nonlinear kinematic and isotropic hardening. The result is a mechanistic rather than phenomenological numerical model requiring only experimentally measured fatigue crack growth rates under constant amplitude, cyclic loading (e.g., ASTM E647-13) and a full material constitutive model defined through experimental push–pull tests for the same material. This approach permits a consideration of material behaviors which are physically relevant to structural steels, yet necessarily omitted in the similar application of a strip-yield model. The present paper generalizes the model originally proposed by the authors to now consider arbitrary storm model loading sequences taken from high-fidelity, time-domain seakeeping codes. To predict the fatigue fracture induced by variable amplitude stress records with upwards of 5×106 time-dependent cycles, a consistent modeling reduction is applied based on the Ordered Overall Range (OOR) or racetrack counting method. The resultant crack growth behavior is demonstrated to converge remarkably well for sufficiently small refined mesh sizes. Using this model, and by considering different arrangements of the same stress record, the importance of nonlinearities (i.e., those associated with ship response as well as material hysteresis) are emphasized.

2014 unselected
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2006_2003 unselected