Concurrent Probabilistic Control Co-Design and Layout Optimization of Wave Energy Converter Farms using Surrogate Modeling

Wave energy converters (WECs) are a promising candidate for meeting the increasing energy demands of today's society. It is known that the sizing and power take-off (PTO) control of WEC devices have a major impact on their performance. In addition, to improve power generation, WECs must be optimally deployed within a farm. While such individual aspects have been investigated for various WECs, potential improvements may be attained by leveraging an integrated, system-level design approach that considers all of these aspects. However, the computational complexity of estimating the hydrodynamic interaction effects significantly increases for large numbers of WECs. In this article, we undertake this challenge by developing data-driven surrogate models using artificial neural networks and the principles of many-body expansion. The effectiveness of this approach is demonstrated by solving a concurrent plant (i.e., sizing), control (i.e., PTO parameters), and layout optimization of heaving cylinder WEC devices. WEC dynamics were modeled in the frequency domain, subject to probabilistic incident waves with farms of $3$, $5$, $7$, and $10$ WECs. The results indicate promising directions toward a practical framework for array design investigations with more tractable computational demands.Comment: 14 pages, 7 figure

Using High-fidelity Time-Domain Simulation Data to Construct Multi-fidelity State Derivative Function Surrogate Models for use in Control and Optimization

Models that balance accuracy against computational costs are advantageous when designing dynamic systems with optimization studies, as several hundred predictive function evaluations might be necessary to identify the optimal solution. The efficacy and use of derivative function surrogate models (DFSMs), or approximate models of the state derivative function, have been well-established in the literature. However, previous studies have assumed an a priori state dynamic model is available that can be directly evaluated to construct the DFSM. In this article, we propose an approach to extract the state derivative information from system simulations using piecewise polynomial approximations. Once the required information is available, we propose a multi-fidelity DFSM approach as a predictive model for the system's dynamic response. This multi-fidelity model consists of summation between a linear-fit lower-fidelity model and an additional nonlinear error corrective function that compensates for the error between the high-fidelity simulations and low-fidelity models. We validate the model by comparing the simulation results from the DFSM to the high-fidelity tools. The DFSM model is, on average, five times faster than the high-fidelity tools while capturing the key time domain and power spectral density~(PSD) trends. Then, an optimal control study using the DFSM is conducted with outcomes showing that the DFSM approach can be used for complex systems like floating offshore wind turbines~(FOWTs) and help identify control trends and trade-offs.Comment: 14 pages,45 figure

On the Use of Geometric Deep Learning for the Iterative Classification and Down-Selection of Analog Electric Circuits

Many complex engineering systems can be represented in a topological form, such as graphs. This paper utilizes a machine learning technique called Geometric Deep Learning (GDL) to aid designers with challenging, graph-centric design problems. The strategy presented here is to take the graph data and apply GDL to seek the best realizable performing solution effectively and efficiently with lower computational costs. This case study used here is the synthesis of analog electrical circuits that attempt to match a specific frequency response within a particular frequency range. Previous studies utilized an enumeration technique to generate 43,249 unique undirected graphs presenting valid potential circuits. Unfortunately, determining the sizing and performance of many circuits can be too expensive. To reduce computational costs with a quantified trade-off in accuracy, the fraction of the circuit graphs and their performance are used as input data to a classification-focused GDL model. Then, the GDL model can be used to predict the remainder cheaply, thus, aiding decision-makers in the search for the best graph solutions. The results discussed in this paper show that additional graph-based features are useful, favorable total set classification accuracy of 80\% in using only 10\% of the graphs, and iteratively-built GDL models can further subdivide the graphs into targeted groups with medians significantly closer to the best and containing 88.2 of the top 100 best-performing graphs on average using 25\% of the graphs.Comment: Draft, 14 pages, 8 figures, Submitted to ASME Journal of Mechanical Design Special Issue IDETC202

An Agile Model-Based Software Engineering Approach Illustrated through the Development of a Health Technology System

Model-Based Software Engineering (MBSE) is an architecture-based software development approach. Agile, on the other hand, is a light system development approach that originated in software development. To bring together the benefits of both approaches, this article proposes an integrated Agile MBSE approach that adopts a specific instance of the Agile approach (i.e., Scrum) in combination with a specific instance of an MBSE approach (i.e., Model-Based System Architecture Process—“MBSAP”) to create an Agile MBSE approach called the integrated Scrum Model-Based System Architecture Process (sMBSAP). The proposed approach was validated through a pilot study that developed a health technology system over one year, successfully producing the desired software product. This work focuses on determining whether the proposed sMBSAP approach can deliver the desired Product Increments with the support of an MBSE process. The interaction of the Product Development Team with the MBSE tool, the generation of the system model, and the delivery of the Product Increments were observed. The preliminary results showed that the proposed approach contributed to achieving the desired system development outcomes and, at the same time, generated complete system architecture artifacts that would not have been developed if Agile had been used alone. Therefore, the main contribution of this research lies in introducing a practical and operational method for merging Agile and MBSE. In parallel, the results suggest that sMBSAP is a middle ground that is more aligned with federal and state regulations, as it addresses the technical debt concerns. Future work will analyze the results of a quasi-experiment on this approach focused on measuring system development performance through common metrics

Co-Design of Strain-Actuated Solar Arrays for Precision Pointing and Jitter Reduction

Many important spacecraft operations require precision pointing such as space astronomy and high-rate communications. Traditionally, reaction wheels have been used for this purpose but they have been considered unreliable for many missions. This work presents the use strain-actuated solar arrays (SASA) for precision pointing and jitter reduction. Piezoelectric actuators can achieve higher precision and bandwidth than reaction wheels, and they can also provide quiet operation for sensitive instruments. The representation of the array dynamics in the studies presented here is based on Euler-Bernoulli beam theory for high-fidelity simulations. This work also presents a methodology for the combined design of distributed structural geometry for the arrays and distributed control system design. The array geometry design allows for a distributed thickness profile, and the control design determines the distributed moment on the array. Fundamental limits on slew magnitude are found using pseudo-rigid body dynamic model (PRBDM) theory. A parametric study based on a representative spacecraft model demonstrates the validity of the proposed approach and illustrates optimal design trends

Co-Design of Strain-Actuated Solar Arrays for Precision Pointing and Jitter Reduction

Many important spacecraft operations require precision pointing such as space astronomy and high-rate communications. Traditionally, reaction wheels have been used for this purpose but they have been considered unreliable for many missions. This work presents the use strain-actuated solar arrays (SASA) for precision pointing and jitter reduction. Piezoelectric actuators can achieve higher precision and bandwidth than reaction wheels, and they can also provide quiet operation for sensitive instruments. The representation of the array dynamics in the studies presented here is based on Euler-Bernoulli beam theory for high-fidelity simulations. This work also presents a methodology for the combined design of distributed structural geometry for the arrays and distributed control system design. The array geometry design allows for a distributed thickness profile, and the control design determines the distributed moment on the array. Fundamental limits on slew magnitude are found using pseudo-rigid body dynamic model (PRBDM) theory. A parametric study based on a representative spacecraft model demonstrates the validity of the proposed approach and illustrates optimal design trends