Search

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. 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. We explore the application of MBSE to requirements engineering by extending the Model-Based Structured Requirement SysML Profile to comply with the INCOSE Guide to Writing Requirements while conforming to the ISO/IEC/IEEE 29148 standard requirement statement patterns. Rules, Characteristics, and Attributes were defined in SysML according to the Guide to facilitate requirements definition, verification & validation. The resulting SysML Profile was applied in two system architecture models at NASA Jet Propulsion Laboratory, allowing us to assess 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 in the system architecture modeling software., Comment: 19 pages; 9 figures; 3 tables; journal preprint

In the realm of document engineering and Natural Language Processing (NLP), the integration of digitally born catalogs into product design processes presents a novel avenue for enhancing information extraction and interoperability. This paper introduces CatalogBank, a dataset developed to bridge the gap between textual descriptions and other data modalities related to engineering design catalogs. We utilized existing information extraction methodologies to extract product information from PDF-based catalogs to use in downstream tasks to generate a baseline metric. Our approach not only supports the potential automation of design workflows but also overcomes the limitations of manual data entry and non-standard metadata structures that have historically impeded the seamless integration of textual and other data modalities. Through the use of DocumentLabeler, an open-source annotation tool adapted for our dataset, we demonstrated the potential of CatalogBank in supporting diverse document-based tasks such as layout analysis and knowledge extraction. Our findings suggest that CatalogBank can contribute to document engineering and NLP by providing a robust dataset for training models capable of understanding and processing complex document formats with relatively less effort using the semi-automated annotation tool DocumentLabeler., Comment: 8 pages, 6 figures

A promising direction towards improving the performance of wave energy converter (WEC) farms is to leverage a system-level integrated approach known as control co-design (CCD). A WEC farm CCD problem may entail decision variables associated with the geometric attributes, control parameters, and layout of the farm. However, solving the resulting optimization problem, which requires the estimation of hydrodynamic coefficients through numerical methods such as multiple scattering (MS), is computationally prohibitive. To mitigate this computational bottleneck, we construct data-driven surrogate models (SMs) using artificial neural networks in combination with concepts from many-body expansion. The resulting SMs, developed using an active learning strategy known as query by committee, are validated through a variety of methods to ensure acceptable performance in estimating the hydrodynamic coefficients, (energy-related) objective function, and decision variables. To rectify inherent errors in SMs, a hybrid optimization strategy is devised. It involves solving an optimization problem with a genetic algorithm and SMs to generate a starting point that will be used with a gradient-based optimizer and MS. The effectiveness of the proposed approach is demonstrated by solving a series of optimization problems with increasing levels of integration. For a layout optimization study, the framework offers a 91-fold increase in computational efficiency compared to MS. Previously unexplored investigations of much further complexity are also performed, leading to a concurrent geometry, control, and layout optimization of WEC devices in probabilistic irregular waves. The scalability of the method is evaluated by increasing the farm size to include 25 devices. The results indicate promising directions toward a practical framework for integrated WEC farm design with more tractable computational demands., Comment: 22 pages and 19 figures

As uncertainty considerations become increasingly important aspects of concurrent plant and control optimization, it is imperative to identify and compare the impact of uncertain control co-design (UCCD) formulations on their associated solutions. While previous work developed the theory for various UCCD formulations, their implementation, along with an in-depth discussion of the structure of UCCD problems, implicit assumptions, method-dependent considerations, and practical insights, is currently missing from the literature. Therefore, in this study, we address some of these limitations by focusing on UCCD formulations, with an emphasis on optimal control structures, and uncertainty propagation techniques. Specifically, we propose three optimal control structures for UCCD problems: (i) open-loop multiple-control (OLMC), (ii) multi-stage control (MSC), and (iii) open-loop single-control (OLSC). Stochastic in expectation UCCD (SE-UCCD) and worst-case robust UCCD (WCR-UCCD) formulations, which are motivated by probabilistic and crisp representations of uncertainties, respectively, are implemented for a simplified strain-actuated solar array case study. Solutions to the OLMC SE-UCCD problem are obtained using two uncertainty propagation techniques: generalized Polynomial Chaos expansion (gPC) and Monte Carlo simulation (MCS). The OLMC and MSC WCR-UCCD problems are solved by leveraging the structure of the linear program, leading to polytopic uncertainties. To highlight the importance of uncertainty in early-stage design, the closed-loop reference-tracking response of the systems is also investigated. Insights from such studies underscore the role of the control structure in managing the trade-offs between risk and performance, as well as meeting problem requirements. The results also emphasize the benefits of efficient uncertainty propagation techniques for dynamic optimization problems., Comment: 18 pages and 11 figures

A promising direction towards reducing the levelized cost of energy for wave energy converter (WEC) farms is to improve their performance. WEC design studies generally focus on a single design domain (e.g., geometry, control, or layout) to improve the farm's performance under simplifying assumptions, such as regular waves. This strategy, however, has resulted in design recommendations that are impractical or limited in scope because WEC farms are complex systems that exhibit strong coupling among geometry, control, and layout domains. In addition, the location of the candidate site, which has a large impact on the performance of the farm, is often overlooked. Motivated by some of the limitations observed in WEC literature, this study uses an integrated design framework, based on simultaneous control co-design (CCD) principles, to discuss the impact of site selection and wave type on WEC farm design. Interactions among plant, control, and layout are also investigated and discussed using a wide range of simulations and optimization studies. All of the studies were conducted using frequency-domain heaving cylinder WEC devices within a farm with a linear reactive controller in the presence of irregular probabilistic waves. The results provide high-level guidelines to help the WEC design community move toward an integrated design perspective., Comment: 12 pages and 7 figures

Design of wave energy converter farms entails multiple domains that are coupled, and thus, their concurrent representation and consideration in early-stage design optimization has the potential to offer new insights and promising solutions with improved performance. Concurrent optimization of physical attributes (e.g., plant) and the control system design is often known as control co-design or CCD. To further improve performance, the layout of the farm must be carefully optimized in order to ensure that constructive effects from hydrodynamic interactions are leveraged, while destructive effects are avoided. The variations in the joint probability distribution of waves, stemming from distinct site locations, affect the farm's performance and can potentially influence decisions regarding optimal plant selection, control strategies, and layout configurations. Therefore, this paper undertakes a concurrent exploration of control co-design and layout optimization for a farm comprising five devices, modeled as heaving cylinders in the frequency domain, situated across four distinct site locations: Alaskan Coasts, East Coast, Pacific Islands, and West Coast. The challenge of efficiently and accurately estimating hydrodynamic coefficients within the optimization loop was mitigated through the application of surrogate modeling and many-body expansion principles. Results indicate the optimized solutions exhibit variations in plant, control, and layout for each candidate site, signifying the importance of system-level design with environmental considerations from the early stages of the design process., Comment: 9 pages, 9 figures

Integration of various electricity-generating technologies (such as natural gas, wind, nuclear, etc.) with storage systems (such as thermal, battery electric, hydrogen, etc.) has the potential to improve the economic competitiveness of modern energy systems. Driven by the need to efficiently assess the economic feasibility of various energy system configurations in early system concept development, this work outlines a versatile computational framework for assessing the net present value of various integrated storage technologies. The subsystems' fundamental dynamics are defined, with a particular emphasis on balancing critical physical and economic domains to enable optimal decision-making in the context of capacity and dispatch optimization. In its presented form, the framework formulates a linear, convex optimization problem that can be efficiently solved using a direct transcription approach in the open-source software DTQP. Three case studies demonstrate and validate the framework's capabilities, highlighting its value and computational efficiency in facilitating the economic assessment of various energy system configurations. In particular, natural gas with thermal storage and carbon capture, wind energy with battery storage, and nuclear with hydrogen are demonstrated., Comment: 16 pages, 10 figures

This paper is based on a case study of an IT organization in a large, US-based healthcare provider, and develops simluation models to identify areas for performance improvement. These organizations are often grouped into departments by technical skill and support both operational work (tickets) and project work (tasks) of various priorities. From a practical standpoint, resource managers and staff regularly manage all work as queued and assign / complete it based on the priorities of the day. Using project and operational metrics from the case study organization, the hybrid model using both system dynamics and discrete event simulation developed through this research depicts the flow of work through a skill-based team as well as many of the key factors that influence that workflow, both positive and negative. Experience indicates that the interaction between project and operational work -- as well as between teams with differing skills -- entangles work queues and wait times within those queues in a way that rapidly scales in complexity as the number of interacting individuals and teams increases. Results from model simulation bear out this intuition. Scaling the models to accommodate multiple teams is a topic of future research., Comment: 8 pages, 2 figures

Digital Engineering currently relies on costly and often bespoke integration of disparate software products to assemble the authoritative source of truth of the system-of-interest. Tools not originally designed to work together become an acknowledged system-of-systems, with their own separate feature roadmaps, deprecation, and support timelines. The resulting brittleness and conglomeration of disparate interfaces in the Digital Engineering Ecosystem of an organization drains resources and impairs efficiency and efficacy. If Model-Based Systems Engineering were applied to this problem, a complete system architecture model would be defined, and a purpose-built computing system-of-systems would be constructed to satisfy stakeholder needs. We have decades of research in computer science, cybersecurity, software and systems engineering, and human-computer interaction from which to draw that informs the design of a Seamless Digital Engineering tooling system, but it would require starting from a clean slate while carefully adopting existing standards. In this paper, this problem space and solution space are characterized, defining and identifying Seamless Digital Engineering as a grand challenge in Digital Engineering research., Comment: 17 pages, 7 figures, 4 tables, to be published in AIAA SciTech Forum 2024 conference proceedings

In this paper, we present Iterative Classification of Graph-Set-Based Design (IC-GSBD), a framework utilizing graph-based techniques with geometric deep learning (GDL) integrated within a set-based design (SBD) approach for the classification and down-selection complex engineering systems represented by graphs. We demonstrate this approach on aircraft thermal management systems (TMSs) utilizing previous datasets created using an enumeration or brute-force graph generation procedure to represent novel aircraft TMSs as graphs. However, as with many enumerative approaches, combinatorial explosion limits its efficacy in many real-world problems, particularly when simulations and optimization must be performed on the many (automatically-generated) physics models. Therefore, the approach uses the directed graphs representing aircraft TMSs and GDL to predict on a subset of the graph-based dataset through graph classification. This paper's findings demonstrate that incorporating additional graph-based features using principle component analysis (PCA) enhances GDL model performance, achieving an accuracy of 98% for determining a graph's compilability and simulatability while using only 5% of the data for training. By applying iterative classification methods, we also successfully segmented the total set of graphs into more specific groups with an average inclusion of 75.5 of the top 100 highest-performing graphs, achieved by training on 40% of the data., Comment: 31 pages, 12 figures, Updated version of the original conference paper. This version is a preprint, intended for journal submission

Symmetry and non-Hermiticity play pivotal roles in photonic lattices. While symmetries such as parity-time ($\mathcal{PT}$) symmetry have attracted ample attention, more intricate crystalline symmetries have been neglected in comparison. Here, we investigate the impact of the 17 wallpaper space groups of two-dimensional crystals on non-Hermitian band structures. We show that the non-trivial space group representations enforce degeneracies at high symmetry points and dictate their dispersion away from these points. In combination with either $\mathcal{T}$ or $\mathcal{PT}$, the symmorphic p4mm symmetry, as well as the non-symmorphic p2mg, p2gg, and p4gm symmetries, protect novel exceptional chains intersecting at the pertinent high symmetry points., Comment: 6 pages, 2 figures

This paper discusses a framework to design elements of the plant and control systems for floating offshore wind turbines in an integrated manner using linear parameter-varying models. Multiple linearized models derived from aeroelastic simulation software in different operating regions characterized by the incoming wind speed are combined to construct an approximate low-fidelity model of the system. The combined model is then used to generate open-loop, optimal control trajectories as part of a nested control co-design strategy that explores the system's power production and stability using the platform pitch tilt as a proxy in the context of crucial plant and control design decisions. The radial distance between the central and outer columns and the diameter of the outer columns of the semisubmersible platform are the plant design variables. The platform stability and power production are studied for different plant design decisions. The effect of plant decisions on subsequent power production and stability response of the floating wind turbine is quantified in terms of the levelized cost of energy. The results show that the inner-loop constraints and the plant design decisions affect the turbine's power and, subsequently, the cost of the system., Comment: 16 pages 47 figures

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 figures

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 figures

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 IDETC2023

This article explores various uncertain control co-design (UCCD) problem formulations. While previous work offers formulations that are method-dependent and limited to only a handful of uncertainties (often from one discipline), effective application of UCCD to real-world dynamic systems requires a thorough understanding of uncertainties and how their impact can be captured. Since the first step is defining the UCCD problem of interest, this article aims at addressing some of the limitations of the current literature by identifying possible sources of uncertainties in a general UCCD context and then formalizing ways in which their impact is captured through problem formulation alone (without having to immediately resort to specific solution strategies). We first develop and then discuss a generalized UCCD formulation that can capture uncertainty representations presented in this article. Issues such as the treatment of the objective function, the challenge of the analysis-type equality constraints, and various formulations for inequality constraints are discussed. Then, more specialized problem formulations such as stochastic in expectation, stochastic chance-constrained, probabilistic robust, worst-case robust, fuzzy expected value, and possibilistic chance-constrained UCCD formulations are presented. Key concepts from these formulations, along with insights from closely-related fields, such as robust and stochastic control theory, are discussed, and future research directions are identified., Comment: 20 pages; 8 Figures

A Belyi map $\beta: \mathbb{P}^1(\mathbb{C}) \to \mathbb{P}^1(\mathbb{C})$ is a rational function with at most three critical values; we may assume these values are $\{ 0, \, 1, \, \infty \}$. Replacing $\mathbb{P}^1$ with an elliptic curve $E: \ y^2 = x^3 + A \, x + B$, there is a similar definition of a Belyi map $\beta: E(\mathbb{C}) \to \mathbb{P}^1(\mathbb{C})$. Since $E(\mathbb{C}) \simeq \mathbb T^2(\mathbb {R})$ is a torus, we call $(E, \beta)$ a Toroidal Belyi pair. There are many examples of Belyi maps $\beta: E(\mathbb{C}) \to \mathbb P^1(\mathbb{C})$ associated to elliptic curves; several can be found online at LMFDB. Given such a Toroidal Belyi map of degree $N$, the inverse image $G = \beta^{-1} \bigl( \{ 0, \, 1, \, \infty \} \bigr)$ is a set of $N$ elements which contains the critical points of the Belyi map. In this project, we investigate when $G$ is contained in $E(\mathbb{C})_{\text{tors}}$. This is work done as part of the Pomona Research in Mathematics Experience (NSA H98230-21-1-0015).