Publications

Managing uncertainty in engineering design continues to challenge engineers. By combining the concept of regret from robust optimization with that of design space reduction from set-based design, a new complementary view on uncertainty referred to as Design Space Covering for Uncertainty (DSC-U) is introduced. This method develops a trade-space between reducing the design space and locking in sub-optimal performance while considering interval uncertainty that impacts the problem. A double-loop algorithm and a more efficient set covering algorithm are presented along with the structure of the method. These are demonstrated on three sample problems, a previously-studied cantilever tube optimization, the Rosenbrock function with different levels of coupling between the uncertain parameters, and a ship midship section design problem with thirty-six design variables and four uncertain terms. The DSC-U approach is shown to be practical in providing a complementary insight into the impact of uncertainty for a wide range of design techniques.

Digital twins have the potential to improve future health prognosis and operational safety of engineering structures. However, few studies have attempted to validate such twins with experimental data. In this work, two system-level digital twin models for structural capacity degradation are evaluated with experimental data from a four-crack structural fatigue specimen. Each twin model is based on a dynamic Bayesian network, but the two models differ in the complexity of the crack-to-crack interaction model. Using measured crack lengths from the experiment, the ability of each twin to predict the evolution of future crack size is assessed. For this specimen, a high level of crack-to-crack interaction must be captured for the twin to realistically trace the evolution of the system. The suitability of Bayesian network based twins for this type of problem is discussed.

The desire to operate crewless platforms for months autonomously requires platforms that can sense their current state, maintain themselves, and perform long-term mission planning tasks to optimize their effectiveness as they degrade. How these long-term tasks are currently organized, exe- cuted, and regulated on human-crewed vessels is unexplored compared to more immediate navigation and hazard avoidance. This paper presents a series of inter-related explorations of the issues of longer- term mission planning in a fully autonomous framework. Based on the current state-of-the-art, a new three-component ranking scale for crewless platforms is proposed. Semi-structured interviews with retired military crews and commercial mariners were used to identify what planning tasks crews were currently carrying out. At the end of the interview process, themes from all interviews were reviewed, and an affinity diagram was created from the themes. The interviews revealed a surprising diversity in approaches, especially for tasks beyond machinery health assessment. Complementing this bottom-up analysis, a top-down analysis via a modified STAMP/STPA framework identifies critical information paths and control structures surrounding these tasks. By integrating the results of these two comple- mentary analyses, gaps in our current ability to achieve long-term autonomous operations are identified. Three proposed demonstration cases are developed to help develop approaches to fill these gaps.

Path selection is a temporal undertaking characterized by the order design activities are performed and decisions are made. It may be considered arbitrary in the grand scheme of marine design, but it can have far-reaching impacts on required rework in the maturation of an acceptable design solution. Marine design is largely human-driven in the handling of interdependent vessel characteristics between analyses, so any simulation capturing the progression and iteration of design activities must account for actual human tendencies. The following research extends a previous study exploring the likelihood of design convergence based on path selection [1]. The new study assumes analyses cannot be executed in multiple directions nor combined, which introduces different types of rework. The updated model explores the same variables and paths as the previous study through two simulations differing in experience and rework while simultaneously acknowledging the independent variables a designer has influence over with each design activity. Both simulations highlight the path benefits and flaws pertaining to convergence probability, designer influence, and requisite rework. With these new understandings, designers can begin to make more well-informed decisions on how to efficiently approach their own design problem.

Weeks-to-months long crewless missions in the marine domain present unique challenges for autonomous systems. At these mission lengths, the platform must take on self-assessment and mission planning tasks as well as short-term autonomy tasks such as navigation. The results of a four-prong research program to explore these challenges are reported here. Existing vessels were reviewed and analyzed and a new rating system was constructed. Interviews with human crew members were carried out to identify assessment and planning approaches. A simplified simulation and an STPA-inspired analysis were used to identify areas where research would be beneficial. From these investigations, it is clear that existing planning systems rely heavily on humans to integrate different sources of information. Humans make decisions today based on shared experience and implicit criteria. Automating this process is a significant challenge. Three test cases are proposed to explore the identified research needs, one on fuel management, one on design and operational support of machinery systems, and one on adapting and updating risk criteria in service.

The submarine H. L. Hunley conducted the first successful submarine attack on an enemy vessel, USS Housatonic, during the American Civil War but was lost with all hands because of unknown circumstances. The submarine has been recovered, and recent archeological findings have uncovered that a spar torpedo was used as opposed to a standoff torpedo that was commonly assumed to have been used. As a result, the submarine would have been in close proximity to the weapon when it exploded than previously thought. A multipart investigation has been conducted with the goal of determining if this reduced standoff distance could explain the mysterious loss of the vessel in the minutes or hours after the attack. Here, the results of a bottom-up naval architectural and weapons-effects analysis are reported. Together, the experimental, computational, and analytical results provide new insight to the vessel’s stability characteristics, propulsion, and dynamic loading environment during the attack. In addition, a discussion of possible loss scenarios, informed by both calculation results and inspections of vessel’s hull, is presented. Although the story of what happened to H. L. Hunley that night remains shrouded in mystery after this work, several important new research questions emerge.

Path selection is defined as the order in which design activities are chosen to be performed, and it plays a critical role in determining a project’s need for rework as well as the effectiveness of the final design. In this paper, a four-step, polynomial model is introduced to demonstrate the impact that different solution paths have on meeting design requirements. Quantifiable path knowns are then contemplated with the goal of guiding path selection before beginning any design activities or making any decisions. For the explored paths and inputs, the results showcase the frequency of individual and collective variable success. With the initial findings, plans are set to expand the polynomial model to include more maritime design tools.

Machine learning approaches, onboard measurements, and widely available wave forecast and hindcast data present an opportunity to develop predictive models for vessel motion forecasting. Detailed vessel motion forecasts would support underway and deployment decisions for safer and more efficient vessel operation. To demonstrate this application, ridge regression and neural network models for heave, pitch, and roll prediction were trained and tested using time-and-place specific, multidirectional wave model parameters as input. Additionally, the performance benefits of providing these predictive models with computationally efficient, physics-based model predictions (PBMPs) of heave, pitch, and roll as additional inputs were examined. Data from approximately 13,500 30-minute windows, measured aboard an operational research vessel, were used to train and test the data-driven models. Data from over 2,500 additional 30-minute windows, measured aboard a sister vessel, were also used to test the versatility of the trained models. The results of this study showed effective reduction of motion amplitude mean-squared error (MSE) values on multiple test datasets relative to the PBMPs alone. The results also showed that inclusion of PBMPs as input to the data-driven models was typically beneficial in terms of MSE reduction, stressing the importance of retaining physics-based information in data-driven models.

This report was prepared for the Office of Naval Research in fulfillment of Task 1 of contract N00014-20-C-1099, titled “Data-Model Fusion for Naval Platforms and Systems.” The objective of Task 1 is to develop a rigorous, generic framework for naval applications of Data-Model Fusion. Data-Model Fusion (DMF) is a concept developed at Martin Defense Group (legacy Navatek), in conjunction with the University of Michigan, to describe: 1. Data: information from sensors, expert knowledge, reports, inspections, surveys, or other sources regarding physical components, systems, platforms or fleets within available operating conditions 2. Model: digital representations (e.g., empirical equations, physics-based models, networks, ontological characterizations, etc) of those components, systems, platforms or fleets within simulated operating conditions. 3. Fusion: the integration of said data and models to bring both into agreement. Our data- model fusion approach uses data science techniques and machine learning methods to improve state estimates, update model parameters, identify operational areas or anomalies, and inform decision-making. Our integration approach utilizes and expands upon the state-of-the-art in data science and Artificial Intelligence (AI)-based decision support to provide real-time actionable diagnostic and/or prognostic information on the state of real-world physical platforms. When a digital model is operationally coupled via sensors to a specific real-world component, system, platform, or fleet, we refer it as a digital twin. Use cases for twins involve managing degrading systems, improving performance, updating design approaches, and optimal planning for a fleet of similar platforms. Digital twins are not a necessary component in data-model fusion, but they are frequently used as a basis for analysis and decision-making in our real-world system applications. This report is a compilation of six separate reports, with an overall focus on defining a fundamental framework for data-model fusion in the naval domain. We start in Chapter 1 with a literature survey of approaches, gaps, and opportunities in data-model fusion. Next, we define in Chapter 2 a unified theory of digital twins, followed in Chapter 3 by a delineation of digital twin types in the naval domain. In Chapter 4 we discuss techniques for data persistence that enable storage of the geometry models, measurements, and environmental data needed by twins. In Chapter 5 we shift our focus back to data-model fusion, providing a survey of tools and techniques that can be used to inform naval systems design and operation. We develop methods for understanding and managing the implications, risks, and opportunities of digital naval engineering with respect to the design and operation of autonomous naval platforms and systems. Chapter 6 was written to serve as a primer on AI-based decision support methodologies, tools, and techniques for practicing naval research engineers and scientists. Finally, we conclude this report with a discussion on technology transfer, capability gaps, and opportunities for further research.

The nature of the ocean is random and, as a result, marine structures face extreme, non-linear load effects. Estimating extreme responses of said structures often reduces to purely probabilistic approaches involve a considerable amount of conjecture. A method is developed in this paper to conserve original system information without the need for high fidelity Monte Carlo Simulations. Here, an iterative linearization of a non-linear system is developed to estimate extremes for the non-linear system. The Matched Upcrossing Equivalent Linear System (MUELS) method finds linear systems with zero-upcrossing periods equivalent to that of the non-linear system under a specified forcing spectrum. These linear systems are used as input into the Design Loads Generator, where ensembles of extreme linear realizations at the exposure period of interest, and the input time series that lead to those extremes, are generated. These input time series are valid inputs into the non-linear system and create a set of non-linear realizations that approximate the set of non-linear extremes at the exposure period of interest. As an example, this paper introduces and applies this method to the Duffing Oscillator at various levels of increasing cubic stiffness, forced by an ITTC wave spectrum, for exposure periods as long as 10textasciicircum 8 hours representing O(10textasciicircum10) zero-crossing maxima. Quantitative comparisons are made with Monte Carlo simulations, GEVD estimates, and expected values of time series near extreme local maxima.

Few experimental data sets exist in the literature to support the development and evaluation of digital twins predicting structural degradation. The literature is especially sparse for system tests where multiple failures occur and interact. In this work, a laboratory-level experiment is conducted to mimic many of the properties of larger and more complex marine structures with redundant load paths, failure interaction, and component-to-system level integration. In the experiment, such properties are reflected by a hexagon tension specimen with four propagating fatigue cracks tested under displacement-controlled loading. The applied loading cycles and corresponding crack lengths are recorded as the major time-varying data of degradation, with the resisting force at maximum extension used as the system capacity. A novel computer vision method is used to measure the crack length. Strain gauges are also used to monitor the structure’s status. The experimental data is presented and analyzed in this paper. The resulting data sets can be used to evaluate the performance of different digital twin updating approaches.

Predicting rare events of a non-linear process can prove to be a difficult challenge. In this paper, the linearization of a non-linear process, namely impact loading, was completed by performing a gaussianization of a sample non-linear, non-gaussian time series to predict the probability of human injury in a specified sea state. The gaussianized time series was then input into the Design Loads Generator (DLG) to estimate a lower bound to the non-gaussian extrema as well as providing an ensemble of extreme time series of the gaussianized process. The DLG takes an input spectrum, transfer function, and associated exposure time to optimize phase sets of a modified gaussian distribution such that the extreme realizations with return periods equal to the exposure time of the desired process, as well as the input that leads to those realizations, can be generated. In the present application, the input to the corresponding gaussian extreme time series was used as input in the non-linear, non-gaussian model to estimate the extremes of the non-linear process, conditioned on the gaussian process being extreme. The process of gaussianizing the non-linear time series, entering into the DLG, and using the resulting input time series as input into the non-linear model was iterated to further develop the conditional extreme pdf. The relationship between these conditional, developed extrema and the observed extrema of the process was applied to determine the probability of human injury. Here, injury is assumed to be related to two possibly correlated random variables: the magnitude and duration of an extreme acceleration event.

Ships and marine structures are subjected to fatigue damage from repeated fluctuating loads. Predicting crack growth is important for increasing structural safety and service life, however few approaches currently deal with multiple cracks in a complex structural system. To mimic many of the properties of larger and more complex marine structures in a laboratory-level experiment, a hexagon tension specimen with four propagating fatigue cracks on each corner is designed and tested. This specimen replicates the properties of a marine structure with redundant load paths, crack interaction and component to system level integration. The applied loading cycles and corresponding crack lengths are recorded as major time-varying data of degradation state which is used to evaluate the performance of a dynamic Bayesian network for predicting crack growth. The dynamic Bayesian network models the time-varying process with sequential slices. The dependence among components are controlled by hyperparameters and are integrated into complex system behavior to reflect the structure from components level to system level. The ability of network to predict the experiment is assessed.

Despite the hype surrounding digital twin technology and its implementation in other fields, the marine industry has published very few successful, full-scale applications of this technology to date. The development of useful digital twin technology within the industry requires fundamental exploration of data fusion techniques in the marine context. Future development of a useful surface platform digital twin that incorporates advanced machine learning techniques requires preliminary experimentation with more basic, well-understood learning models using full-scale, real-world data. The goal of this work was to lay a foundation for addressing the current knowledge gap through fusion of seakeeping predictions with full-scale measured motions using linear least-squares and neural network corrections. Motion data were obtained from an oceanographic research cruise and these correction methods were applied to frequency-domain response predictions. The success of these relatively simplistic correction models provided insight for the next steps of surface platform data fusion algorithm development.

Modern ship design often involves the automated creation of thousands of design alternatives; even when provided a Pareto front of optimal solutions designers may struggle to understand the differences in designs and the relationships of design variables. Machine learning Bayesian networks from automatically developed design data can allow us to analyze the designs, understand the variable relationships that drive their differences and optimalities, and lead engineers to better designs. However, the information about variable relationships in Bayesian network are encoded in difficult to interpret conditional probability tables (CPTs). Translation of a Bayesian network’s CPTs into simpler edge weights defining the strength of relationship between nodes allows engineers to more easily interpret and use the complex information encoded in the network through standard network analysis techniques. Bayesian networks developed from a multi-objective bulk carrier design problem developed by Sen are transformed to network adjacency matrices for such analysis in this work.

The availability of detailed environmental hindcast data opens the door for virtual structural health monitoring; however, the impact of hindcast wave data selection on the results of such an approach have not been explored. While studies on the differences between wave models have been conducted in the past, extensions of these comparisons to resultant vessel response predictions and fatigue damage estimates are limited. At three separate geographical locations, this work compared hindcast wave data from NOAA’s WAVEWATCH III (NWW3) Multigrid Production Hindcast, the EU’s CMEMS Global Ocean Waves Analysis and Forecasting Product, and National Data Buoy Center buoys. In addition to comparisons of wave parameters at each location, the resultant heave, pitch, and vertical bending moment responses and fatigue damage of a destroyer-sized naval combatant, the DTMB 5415, were compared. The novelty of this work lies in its large scope, which calculated responses every 3 h for all of 2017 in 32 speed and heading combinations for each location and wave data source. The results show that differences between wave data sources propagated to the vessel response predictions. These differences were then amplified in the calculation of fatigue damage causing significant discrepancies between data sources after just one year.

With harshening ocean environments and the push to extend the service life of vessels and platforms, lifetime structural design performance is an increasingly important factor to differentiate between design options. But despite many advancements in structural modeling, reliability analysis may still be too time consuming and computationally expensive to be employed for local design choices. Reliability analysis for stiffened ship panels is additionally complicated as the collapse mechanism is due to combined stochastic lateral and in-plane loading effects [1]. This paper employs the non-linear Design Loads Generator (NL-DLG) process [2] to efficiently estimate the probability of stiffened ship panel collapse while retaining the wave profiles which lead to lifetime responses. This information allows a direct comparison between panel options and highlights critical panel parameters which are strongly related to design robustness. The resulting ensemble of short NL-DLG wave profiles lead to similar statistical characteristics of the panel designs as do brute-force Monte Carlo Simulations, but reduce the required simulation time by a factor of nearly 87,000 for the same number of simulations. These directed wave profiles offer naval architects the opportunity to efficiently examine rare complex structural responses by linking high-fidelity structural and hydrodynamic models. The potential of the NL-DLG process is that relevant information about complex marine structures, even those with limit surfaces excited by combined non-Gaussian loading, can be obtained efficiently and in earlier stages of the design process via low-order surrogate models.

This case study presents a novel insight into the design of a codesigned planing craft with an active control system (ACS), along with its potential advantages and disadvantages when compared with a traditionally designed vessel (i.e., a vessel whose geometry is first selected, and then its ACS is implemented). This work has three purposes:present tools a designer can use to codesign a planing craft with its ACS,use these tools to expand the design space and further explore the potential of codesign, andinvestigate the feasibility of having a planing craft with ACS designed to the codesign results found in 2) and compare it with a traditionally designed vessel.The vessel particulars that are numerically optimized are the beam, dead rise, longitudinal center of gravity (lcg), and two tuning parameters for the ACS’s linear quadratic regulator. In the case study, the codesigned vessel had 4% lower drag at the design speed and Sea State (SS) 3, but on lower SS’s it had drag savings of 10% and seakeeping improvements of around 40% for the investigated seakeeping metric. The case study suggests that although the codesigned vessel is technically feasible, it would require unconventional hull/deck design—a result which emphasizes the importance of considering the coupling between a planing craft and its ACS early in the concept design.In search for a better performing planing craft, a naval architect could consider using an active control system (ACS) on their designs. Although they will encounter published research confirming performance improvements when an ACS is used (Wang 1985; Savitsky 2003; Xi & Sun 2006; Kays et al. 2009; Engle et al. 2011; Hughes & Weems 2011; Rijkens et al. 2011; Shimozono & Kays 2011; Rijkens 2013), literature addressing the concept design process of a planing craft that will have an ACS is, to the best of the authors’ knowledge, limited only to the previous work by the authors (Castro-Feliciano et al. 2016, 2018). The work by Castro-Feliciano et al. (2016, 2018) suggests that the benefit from codesigning (as opposed to sequentially designing the vessel geometry and later adding an ACS) can be significant and should be the design methodology followed when designing a planing craft that will have an ACS.

Many seakeeping analyses are performed using idealized wave statistics from a vessel’s expected operation area; however, widely available wave forecast and hindcast data present an opportunity for digitalization in the marine industry through time-and-place specific response predictions. To better understand the effect of wave data source selection on these predictions, quantification of the differences between results generated using different sources is required. Two locations off the coast of Alaska were selected for analysis over a one-year period. Data were used from the NOAA WAVEWATCH III Production Hindcast, the EU’s Copernicus Marine Environment Monitoring Service Ocean Waves Analysis and Forecasting Product, and nearby National Data Buoy Center buoys. Frequency-domain predictions of a ship’s heave, pitch, and vertical bending moment responses revealed significant disagreement between wave data sources. More pronounced disagreement was found for the vessel’s cumulative fatigue damage, highlighting the importance of accurate wave data for time-and-place specific analyses.

Measuring crack length is critical for data acquisition in fatigue tests. Especially for large, complex marine structural tests with multiple cracks, efficient and inexpensive crack length measurement techniques are a major advantage. A computer vision based method for measuring the crack length on the surface of complex vessel structures is developed with the goals of reasonable accuracy with extreme ease-of-use. The method involves digital image acquisition with automated crack detection and then dimension measurement. In order to capture a clear image by a GoPro camera with a 12.5X macro lens, UV dyes are applied to increase contrast between cracks and structures surfaces. The images are transferred to grey scale and the crack is detected by an edge filter. Noise points are filtered out and the crack is simulated and measured on pixel level. A MATLAB code is created for the proposed method to automate the processing of images. To evaluate the method, a laboratory experiment is conducted on a hexagon tension specimen with four cracks. Details of the experimental testing, figure capturing, figure processing and accuracy are presented.

This paper proposes a wireless hull monitoring system that is quick to install as a short-term monitoring solution. Hull measurements have the potential to increase the accuracy of ship response predictions at a lower cost than computer simulation or towing tank models. The performance of the wireless monitoring system is validated on the all-aluminium United States Coast Guard Response Boat-Medium. The system is designed to measure ship motions and hull strain responses during high-speed operations and in harsh weather conditions. An analytical framework is developed to extract sea states from inertial measurements recorded at the ship centre-of-gravity and in a bow compartment. To assess the fatigue life of the hull during harsh weather operations, response amplitude operators (RAOs) are empirically derived to map sea states to root mean square accelerations and strain cycles measured from a high-stress hull element. A RAO that maps sea state to consumed fatigue in the hull, so termed a consumed fatigue operator (CFO), is presented which can assist in the management of an asset over its life cycle. The study reveals reliable hull monitoring during a one-week sea trial. RAO and CFO fit to hull response data are proven to provide accurate estimates.

Current in situ strain sensing techniques focus on determining accurate, time-histories of strain utilizing fairly complex sensing, compensation, data processing, and powering arrangements. A simpler and lower-cost strain sensing approach would open up more opportunities to use strain measurements to support engineering decision making. This work explores a fully mechanical, ultra-low cost strain sensor printed using additive manufacturing techniques. The accuracy of current additive manufacturing techniques are discussed, and the performance of the sensor in terms of accuracy, measurement repeatability, and batch-to-batch manufacturing variability are studied. An example of using the proposed sensor to measure transient thermal weld stresses is presented. Overall, the key challenge to such a sensor is shown to be the accuracy of pin and slot print features and the resulting slip and friction introduced into the sensor. A properly calibrated design printed with current state-of-the-art machines is shown to be capable of resolving strain changes on the order of one micro strain with good repeatability.

ABSTRACT A digital twin is a dynamic virtual representation of a physical system that may be used to provide support for life-cycle management decisions. Fundamental exploration of algorithms and approaches to link, compare, and fuse numerical models used in digital twins with real-world sensor data is still needed for ship performance prediction, condition assessment, and ultimately, significant implementation of digital twin technology in the marine industry. In this work, a preliminary digital twin framework for surface ships has been developed to yield time-and-place specific predictions of vessel motions and structural responses given weather forecast or hindcast data for a selected route. Cumulative fatigue damage was predicted and compared for four simulated routes in the Pacific Ocean, and the predicted motions for the simulation with the greatest fatigue damage were analyzed to investigate the possible causes of this increased damage. The notable increase in cumulative damage seen for this route stresses the importance of the ability to track and balance fatigue damage among ships in a fleet. Moreover, this information would further support educated maintenance and deployment scheduling decisions to ensure fatigue damage equality among ships in a fleet, while real-time implementation of this digital twin technology would furnish operators with a greater understanding of a weather forecast’s implications, and thereby provide in-mission decision support.

The distortion incurred along a shipyard panel line is a costly factor of shipbuilding influenced by design parameters and manufacturing operations. By identifying the most impactful parameters, design for production can effectively mitigate panel line distortion. Bayesian networks can model the potential for distortion as a result of specific design parameters and process operations. Bayesian networks capture a complex web of cause-effect relationships, such as the mechanics of a structural panel’s production, and break them down into a series of smaller conditional relationships that are more easily established. By breaking down a panel line and creating a series of connected networks modeling each step, the potential for distortion can be predicted from design parameters such as plate thickness and cutout locations and manufacturing decisions such as the use of automated seam welding. The series of networks allows the distortion measured upstream to update the probabilities of downstream nodes. Determining the effect of panel parameters on the distortion generated along a panel line allows informed detail design decisions that could reduce costly distortion in the shipyard. This paper explores the capability of Bayesian networks to predict such distortion through demonstration on an active shipyard panel line.

Early stage design is marked by a low level of design definition, leading to high levels of uncertainty around design decisions made at this stage. Many techniques have been proposed to make better decisions given this uncertainty, including robust optimisation approaches and reliability-based design optimisation. Drawing inspiration from set-based design, this work presents a different approach. Instead of making a final decision with a margin for uncertainty, the procedure allows for a gradual reduction of the design space in a manner that maximises the designer’s remaining flexibility. Two measures are first defined—the complexity of the remaining design space, and the regret, or potential loss of performance resulting from deciding at that time. The procedure solves for the Pareto front in the trade space between complexity and regret. To generate the Pareto front, the method uses two optimisers with one nested inside of the other; both the inner and outer optimisation problems are solved using a genetic algorithm. The outer optimiser is multi-objective with the complexity, or size, of the reduced design space and the resulting regret as the two objective functions. This method was used to solve a structural design problem, and the results are presented here.

Current optimisers struggle to explore the multi-disciplinary trade-space that defines vessel lifecycle cost. This paper tests an enhanced multi-objective collaborative optimiser on a three-objective hull and structural lifecycle costing problem. The optimiser extends multi-disciplinary collaborative optimisers by including goal-programming and a novel genetic algorithm at the discipline level. The optimiser finds the trade-space between vessel resistance, production cost, and structural maintenance cost. Simultaneous changes to the hullform geometry and structural scantlings are used to explore this design space. The approach is demonstrated on a naval optimisation problem. With fixed maintenance schedules, the trade-space is shown to be steeply walled. This topology indicates that single-discipline optimisation for lifecycle cost may lead to large increases in lifecycle cost for the disciplines not considered. In conclusion, multi-disciplinary optimisation is shown to be a useful tool for addressing lifecycle costing during design.

Correctly estimating future failures in aging ship structures is a significant challenge. This manuscript explores a model updating approach based on fusing different visually observable physical records of structural degradation: permanent set of plating and fatigue crack initiation. A probabilistic S-N fatigue model is coupled to a permanent set model via a Bayesian network. Observations of permanent set and fatigue cracks are entered as evidence into this network. Through Bayesian inference, the network updates underlying loading and fatigue capacity models. These updated models are then used to forecast future failures. The proposed model is tested against Monte Carlo simulated service history data on five vessels from a larger fleet. The fusion of permanent set and fatigue together produces a more accurate estimate of future failures than using either failure mode alone. The benefit of fusing multiple visually observable measurements to update underlying structural models to provide fleet-wide prognosis appears promising, with further improvements possible with additional refinement.

While platform structures do not receive the same degree of attention as novel combat systems, propulsion devices, or design methods, structural performance shortfalls continue to bedevil naval architects working on naval vessels. Such shortfalls range from production-phase structural distortion and miss-alignment to in-service damage, fatigue cracking, and corrosion. Resolving such issues add to the vessel’s total ownership costs and the associated downtime also reduces the vessel’s operational effectiveness. This paper argues that development in two areas is needed to resolve this shortfall: development of complete lifecycle analysis tools, and development of tools enabled to work with the Navy’s new set-based design approaches. The paper presents two examples of such tools, including a life-cycle cost tradespace development algorithm and a set-based, or path-locating, design exploration algorithm. The types of advances required for these approaches to succeed are discussed based upon the two examples. Such new approaches to structural design would benefit the Navy as it plans the design process for the next generation of surface acquisition programs, including the Future Surface Combatant.

A novel interval uncertainty formulation for exploring the impact of epistemic uncertainty on reliability-constrained design performance is proposed. An adaptive surrogate modeling framework is developed to locate the lowest reliability value within a multi-dimensional interval. This framework is combined with a multi-objective optimizer, where the interval width is considered as an objective. The resulting Pareto front examines how uncertainty reduces performance while maintaining a specified reliability threshold. Two case studies are presented: a cantilever tube under multiple loads and a composite stiffened panel. The proposed framework demonstrates its ability to resolve the Pareto front in an efficient manner.

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.

Despite growing computational and sensing power, naval structural design and analysis remains focused minimizing structural system weight while ensuring that estimated stress levels remain below allowable thresholds. While this approach allows the rapid design of new vessels that meet existing structural requirements, it struggles to fulfill the U.S. Navy’s growing need for lifecycle support of aging assets. To address these new tasks, extensions of current structural analysis and design tools are required. This paper presents a comprehensive framework for managing fatigue cracks on aging assets by extending traditional design-stage approaches with Bayesian network-based model updating. A methodology of updating structural load estimates that is able to account for changing operational profiles is presented, allowing future fatigue loading to be predicted with increased confidence. A Dynamic Bayesian Network (DBN) approach is taken to represent the time-varying growth of fatigue cracks, including both shipboard inspection data and the updated loading. A robust reliability formulation is used to predict future crack growth risks based on the DBN formulation and the inspection data-to-date. An example is presented for a hypothetical sealift vessel. Finally, a discussion of the implications of such models on Navy design practice, operations, and maintenance is presented.

In this paper, a new type of real options analysis is used to evaluate the worth of an option to extend the service life (ESL options) of an aluminum structure from twenty to twenty-five years. It is an early application of prospect theory-based real options analysis (PB-ROA) in naval design. PB-ROA abstracts the principles of real options analysis to suit naval design applications where the assets do not generate cash flows, and therefor one cannot define value in monetary terms. Instead, the example in this paper defines the utility of a structural design based on three components: structural availability, cargo capacity, and producibility. The utility is contingent on risk factors like the time to crack initiation of a welding detail which is included using stochastic fatigue analysis. From an entire Pareto front of optimal structural designs, the options analysis exposes a partition in the design space which could be valuable in a design setting. The partitioning reveals the conditions in which certain candidate designs maximize the present value of future flexibility. Ultimately, this paper demonstrates a new approach to valuing flexibility in preliminary structural design that may generate useful insight for early stage decision makers.

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 ( K op ) 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 × 10 6 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.

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.

Surrogate-assisted evolutionary optimization has proved to be effective in reducing optimization time, as surrogates, or meta-models can approximate expensive fitness functions in the optimization run. While this is a successful strategy to improve optimization efficiency, challenges arise when constructing surrogate models in higher dimensional function space, where the trade space between multiple conflicting objectives is increasingly complex. This complexity makes it difficult to ensure the accuracy of the surrogates. In this article, a new surrogate management strategy is presented to address this problem. A k-means clustering algorithm is employed to partition model data into local surrogate models. The variable fidelity optimization scheme proposed in the author’s previous work is revised to incorporate this clustering algorithm for surrogate model construction. The applicability of the proposed algorithm is illustrated on six standard test problems. The presented algorithm is also examined in a three-objective stiffened panel optimization design problem to show its superiority in surrogate-assisted multi-objective optimization in higher dimensional objective function space. Performance metrics show that the proposed surrogate handling strategy clearly outperforms the single surrogate strategy as the surrogate size increases.

The design of many real-life engineering systems involves optimization according to multiple, often conflicting, objectives. In this paper, an algorithm called spreading multi-objective particle swarm optimizer (SMOPSO) is developed and tested for optimization problems with two objectives. The motivation for SMOPSO is to promote a high diversity of solutions found in two-objective particle swarm optimization. This is attempted through the use of a spreading function based on neighboring particle positions and an archive controller which discriminates based on particle spacing. The spreading function directs non-dominated particles away from their nearest neighbor, aiming for evenly-spaced solutions as particles “spread out”. To test if such an approach can indeed improve Pareto front diversity, a performance comparison of SMOPSO is made to two benchmark algorithms. Preliminary results suggest the proposed algorithm may improve the diversity of solutions for a limited selection of optimization problems, but at the expense of other important measures of performance which is discussed in this paper. SMOPSO’s performance degrades for more difficult optimization problems, such those with multiple fronts and narrow global minima. An example application of SMOPSO to a theoretical, two-objective high-speed planing craft design problem is also given.

In traditional naval architecture design methodologies optimization of the hull and propeller are done in two separate phases. This sequential approach can lead to designs that have sub-optimal fuel consumption and, thus, higher operational costs. This work presents a method to optimize the propeller–hull system simultaneously in order to design a vessel to have minimal fuel consumption. The optimization uses a probabilistic mission profile, propeller–hull interaction, and engine information to determine the coupled system with minimum fuel cost over its operational life. The design approach is tested on a KCS SIMMAN container ship using B-series propeller data and is shown to reduce fuel consumption compared to an optimized traditional design approach.

In this paper a metric for assessing the producibility of a stiffened grillage structure in the early stage design is presented. This metric is comprised of seven separate producibility drivers that are dependent on the properties of the panel. The new producibility metric is compared with a traditional costing method in a two-objective genetic algorithm to show that there is a competition between the two methods, meaning that the new metric gives a designer additional information at the early design stage. The estimated costs versus producibility scores for several grillage sizes show that large gains in producibility can be made without large increases in estimated costs up until a critical point where a knee in the Pareto front occurs. Furthermore, it was found that the individual producibility elements are highly dependent on the size of the panel tested and the constraints the panel must meet. In general, producibility is a key factor to be considered in structural design and should be accounted for in any structural optimization.

As modular weapon systems allow cost-effective upgrades of a vessel’s war-fighting capability, the degradation of the difficult-to-upgrade structure of the vessel may soon become one of the key drivers of vessel retirement and lifecycle maintenance costing. Existing structural design approaches are reviewed, along with recent developments in this field. It is argued that recent research has produced a number of ad hoc metrics for structural design, such as producability; however, to truly address the needs of future ship design teams it is necessary to integrate several such metrics in a systems-engineering view to evaluate how the structural system contributes to the overall capabilities and costs of a proposed vessel. Potential architectures for this approach are discussed, along with key shortcomings. A comparative example is given for structural fatigue of a strength deck under global bending loading, comparing the traditional design approach with a systems-oriented view.

Fatigue cracks are an ongoing problem for aluminum high-speed vessels, and preventing fatigue cracks caused by wave loading is expected to be a significant challenge for future aluminum high-speed ferries and military vessels. To aid in this effort, a hot-spot fatigue design approach using first-order reliability methods (FORM) is constructed. Two different limit state functions are investigated, and the accuracy and consistency of the FORM method for the highly nonlinear fatigue limit state equations are evaluated through a comparison with Monte Carlo simulation results. The sensitivity of the resulting safety index to changes in the input variables, and their uncertainties, are presented graphically. The method is compared to existing design standards for four simple structural details.

The high strength-to-weight advantage of aluminum alloys has made it the material of choice for building airplanes and sometimes for the construction of land-based structures. For marine applications, the use of high-strength, weldable and corrosion resistant aluminum alloys have made it the material of choice for weight sensitive applications such as fast ferries, military patrol craft, luxury yachts and to lighten the top-sides of offshore structures and cruise ships. And while, over the last two decades, the ultimate limit state (ULS) design approach has been widely adopted in the design of aerospace and land-based (steel) structures, it is just recently being considered as a basis for the structural design and strength assessment of ships and offshore structures. Practical ULS methods or design codes are available in the aerospace and civil engineering industries, but they are just now being developed for use by the marine industry. The present paper compares some useful ULS methods adopted for the design of aerospace, marine and land-based aluminum structures. A common practice for aerospace, marine and civil engineering welded stiffened panel applications is discussed. [All rights reserved Elsevier]