37 research outputs found
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 , , ,
and 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
Digital requirements engineering with an INCOSE-derived SysML meta-model
Traditional requirements engineering tools do not readily access the
SysML-defined system architecture model, often resulting in ad-hoc duplication
of model elements that lacks the connectivity and expressive detail possible in
a SysML-defined model. Without that model connectivity, requirement quality can
suffer due to imprecision and inconsistent terminology, frustrating
communication during system development. Further integration of requirements
engineering activities with MBSE contributes to the Authoritative Source of
Truth while facilitating deep access to system architecture model elements for
V&V activities. The Model-Based Structured Requirement SysML Profile was
extended to comply with the INCOSE Guide to Writing Requirements updated in
2023 while conforming to the ISO/IEC/IEEE 29148 standard requirement statement
templates. Rules, Characteristics, and Attributes were defined in SysML
according to the Guide to facilitate requirements definition and requirements
V&V. The resulting SysML Profile was applied in two system architecture models
at NASA Jet Propulsion Laboratory, allowing us to explore its applicability and
value in real-world project environments. Initial results indicate that
INCOSE-derived Model-Based Structured Requirements may rapidly improve
requirement expression quality while complementing the NASA Systems Engineering
Handbook checklist and guidance, but typical requirement management activities
still have challenges related to automation and support with the system
architecture modeling software.Comment: 10 pages; 4 figures; 2 tables; to appear in Conference on Systems
Engineering Research (CSER) 202
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