Simulation-Based Design Optimization in Marine Engineering: Trends, Best Practices, and Research Gaps

Conceptos Básicos

Simulation-based design optimization (SBDO) has emerged as a critical tool in marine engineering, enabling the optimization of marine structures, vessels, and renewable energy systems to enhance performance, cost-efficiency, and sustainability.

Resumen

This scoping review assesses the current state of SBDO in marine engineering, identifying research trends, methodologies, and application areas. The review analyzes 277 studies from Scopus and Web of Science, finding that SBDO is predominantly applied to optimizing marine vessel hulls, including both surface and underwater types, as well as key components like bows, sterns, propellers, and fins. It also covers marine structures and renewable energy systems.
The review highlights a notable trend towards the preference for deterministic single-objective optimization methods, indicating potential growth areas in multi-objective and stochastic approaches. It points out the necessity of integrating more comprehensive multidisciplinary optimization methods to address the complex challenges in marine environments. Despite the extensive application of SBDO in marine engineering, the review identifies a need for enhancing the methodologies' efficiency and robustness. The review offers a critical overview of SBDO's role in marine engineering and highlights opportunities for future research to advance the field.

Estadísticas

"SBDO is predominantly applied to optimizing marine vessel hulls, including both surface and underwater types, and extends to key components like bows, sterns, propellers, and fins."
"A notable trend is the preference for deterministic single-objective optimization methods, indicating potential growth areas in multi-objective and stochastic approaches."
"The review points out the necessity of integrating more comprehensive multidisciplinary optimization methods to address the complex challenges in marine environments."
"Despite the extensive application of SBDO in marine engineering, there remains a need for enhancing the methodologies' efficiency and robustness."

Citas

"SBDO addresses these challenges by enabling a more nuanced exploration of design possibilities, leveraging computational power to identify optimal solutions that balance performance, cost, and environmental considerations."
"Looking ahead, the field of SBDO in marine engineering is poised for significant advancements. Emerging trends like the integration of machine learning algorithms and the incorporation of real-time data analytics are expected to further revolutionize SBDO applications."
"This scoping review aims to present a comprehensive, current overview of SBDO in marine engineering, highlighting its applications and pointing to future research directions within marine and ocean engineering contexts."

Ideas clave extraídas de

A Scoping Review on Simulation-based Design Optimization in Marine Engineering: Trends, Best Practices, and Gaps

by Andrea Seran... a las arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18654.pdf

Consultas más profundas

How can the integration of machine learning algorithms and real-time data analytics further revolutionize SBDO applications in marine engineering?

In the realm of Simulation-Based Design Optimization (SBDO) in marine engineering, the integration of machine learning algorithms and real-time data analytics holds immense potential for revolutionizing the optimization process. Machine learning algorithms, such as neural networks, support vector machines, and deep learning models, can enhance the efficiency and accuracy of surrogate modeling in SBDO. By leveraging historical simulation data and real-time performance metrics, these algorithms can learn complex patterns and relationships within the design space, leading to more accurate surrogate models.
Real-time data analytics, on the other hand, enable continuous monitoring of system performance and environmental conditions. By integrating real-time data streams into the optimization process, engineers can adapt and adjust design parameters dynamically, responding to changing operational requirements and environmental factors. This real-time feedback loop enhances the agility and adaptability of the optimization process, allowing for rapid adjustments and improvements based on live data.
Furthermore, the combination of machine learning algorithms and real-time data analytics can facilitate predictive maintenance strategies, anomaly detection, and optimization of operational parameters in marine systems. By analyzing large volumes of data in real-time, anomalies or inefficiencies can be identified promptly, leading to proactive maintenance and optimization actions. This proactive approach can significantly improve the reliability, safety, and performance of marine systems, ultimately leading to cost savings and enhanced operational efficiency.

What are the potential drawbacks or limitations of the predominant deterministic single-objective optimization methods, and how can multi-objective and stochastic approaches address these limitations?

The predominant use of deterministic single-objective optimization methods in marine engineering may have certain drawbacks and limitations. One key limitation is the oversimplification of design problems, as single-objective optimization focuses on optimizing a single performance metric without considering trade-offs with other objectives. This narrow focus can lead to suboptimal solutions that do not account for the complex interdependencies between different design parameters and objectives.
Multi-objective optimization approaches, on the other hand, address this limitation by considering multiple conflicting objectives simultaneously. By optimizing across multiple objectives, engineers can explore the trade-offs between different performance metrics and identify a set of Pareto-optimal solutions that represent the best compromise between competing objectives. This enables a more comprehensive exploration of the design space and provides decision-makers with a range of design options to choose from based on their priorities.
Stochastic optimization approaches can further enhance the robustness and reliability of optimization solutions by incorporating uncertainty and variability into the optimization process. Unlike deterministic methods, stochastic approaches account for randomness and variability in input parameters, environmental conditions, and model uncertainties. By considering these uncertainties, stochastic optimization methods can generate solutions that are more resilient to variations and disturbances, leading to more robust designs in marine engineering applications.
In summary, while deterministic single-objective optimization methods offer simplicity and efficiency, they may overlook important trade-offs and uncertainties in the design process. Multi-objective and stochastic approaches address these limitations by considering multiple objectives and uncertainties, leading to more robust, flexible, and comprehensive optimization solutions in marine engineering.

What are the key challenges in developing and implementing comprehensive multidisciplinary optimization methods for marine engineering, and how can researchers and practitioners overcome these challenges?

Developing and implementing comprehensive multidisciplinary optimization methods for marine engineering poses several challenges that researchers and practitioners need to address:

Complexity and Interdisciplinary Nature: Marine engineering projects involve a wide range of disciplines, including hydrodynamics, structural engineering, materials science, and environmental considerations. Integrating these diverse disciplines into a unified optimization framework can be challenging due to the complexity and interdependencies between different subsystems.

Data Integration and Model Coupling: Combining data from various sources and integrating different simulation models can be complex. Researchers need to develop robust data integration strategies and ensure seamless communication between different modeling tools to enable multidisciplinary optimization.

Computational Resources: Multidisciplinary optimization often requires computationally intensive simulations and optimization algorithms. Researchers need to optimize computational workflows, leverage high-performance computing resources, and develop efficient algorithms to handle the computational demands of multidisciplinary optimization.

Uncertainty and Variability: Marine environments are inherently uncertain, with variability in environmental conditions, material properties, and operational parameters. Researchers need to account for this uncertainty in optimization models and develop robust optimization strategies that can handle variability and ensure the reliability of optimized designs.

To overcome these challenges, researchers and practitioners can:

Foster interdisciplinary collaboration and communication to ensure a holistic approach to optimization.
Develop advanced data integration techniques and model coupling strategies to enable seamless information exchange between different disciplines.
Invest in high-performance computing resources and optimization algorithms to handle the computational demands of multidisciplinary optimization.
Incorporate uncertainty quantification methods and robust optimization techniques to account for variability and ensure the reliability of optimized designs.
By addressing these challenges and implementing comprehensive multidisciplinary optimization methods, researchers and practitioners can enhance the efficiency, performance, and sustainability of marine engineering projects.