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

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

Persuasive Technology for Learning in Business Context

"Persuasive Design is a relatively new concept which employs general principles of persuasion that can be implemented in persuasive technology. This concept has been introduced by BJ Fogg in 1998, who since then has further extended it to use computers for changing attitudes and behaviour. Such principles can be applied very well in learning and teaching: in traditional human-led learning, teachers always have employed persuasion as one of the elements of teaching. Persuasive technology moves these principles into the digital domain, by focusing on technology that inherently stimulates learners to learn more quickly and effectively. This is very relevant for the area of Business Management in several aspects: Consumer Behavior, Communications, Human Resource, Marketing & Advertising, Organisational Behavior & Leadership. The persuasive principles identified by BJ Fogg are: reduction, tunnelling, tailoring, suggestion, self-monitoring, surveillance, conditioning, simulation, social signals. Also relevant is the concept of KAIROS, which means the just-in-time, at the right place provision of information/stimulus. In the EuroPLOT project (2010-2013) we have developed persuasive learning objects and tools (PLOTs) in which we have applied persuasive designs and principles. In this context, we have developed a pedagogical framework for active engagement, based on persuasive design in which the principles of persuasive learning have been formalised in a 6-step guide for persuasive learning. These principles have been embedded in two tools – PLOTmaker and PLOTLearner – which have been developed for creating persuasive learning objects. The tools provide specific capability for implementing persuasive principles at the very beginning of the design of learning objects. The feasibility of employing persuasive learning concepts with these tools has been investigated in four different case studies with groups of teachers and learners from realms with distinctly different teaching and learning practices: Business Computing, language learning, museum learning, and chemical substance handling. These case studies have involved the following learner target groups: school children, university students, tertiary students, vocational learners and adult learners. With regards to the learning context, they address archive-based learning, industrial training, and academic teaching. Alltogether, these case studies include participants from Sweden, Africa (Madagascar), Denmark, Czech Republic, and UK. One of the outcomes of this investigation was that one cannot apply a common set of persuasive designs that would be valid for general use in all situations: on the contrary, the persuasive principles are very specific to learning contexts and therefore must be specifically tailored for each situation. Two of these case studies have a direct relevance to education in the realm of Business Management: Business Computing and language learning (for International Business). In this paper we will present the first results from the evaluation of persuasive technology driven learning in these two relevant areas.

Persuasive Technology for Learning and Teaching – The EuroPLOT Project

The concept of persuasive design has demonstrated its benefits by changing human behavior in certain situations, but in the area of education and learning, this approach has rarely been used. To change this and to study the feasibility of persuasive technology in teaching and learning, the EuroPLOT project (PLOT = Persuasive Learning Objects and Technologies) has been funded 2010-2013 by the Education, Audiovisual and Culture Executive Agency (EACEA) in the Life-long Learning (LLL) programme. In this program two tools have been developed (PLOTMaker and PLOTLearner) which allow to create learning objects with inherently persuasive concepts embedded. These tools and the learning objects have been evaluated in four case studies: language learning (Ancient Hebrew), museum learning (Kaj Munk Museum, Denmark), chemical handling, and academic Business Computing. These case studies cover a wide range of different learning styles and learning groups, and the results obtained through the evaluation of these case studies show the wide range of success of persuasive learning. They also indicate the limitations and areas where improvements are required

Adapting SAM for CDF

The CDF and D0 experiments probe the high-energy frontier and as they do so have accumulated hundreds of Terabytes of data on the way to petabytes of data over the next two years. The experiments have made a commitment to use the developing Grid based on the SAM system to handle these data. The D0 SAM has been extended for use in CDF as common patterns of design emerged to meet the similar requirements of these experiments. The process by which the merger was achieved is explained with particular emphasis on lessons learned concerning the database design patterns plus realization of the use cases.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 4 pages, pdf format, TUAT00

Arctic smoke - aerosol characteristics during a record smoke event in the European Arctic and its radiative impact

In early May 2006 a record high air pollution event was observed at Ny-Ålesund, Spitsbergen. An atypical weather pattern established a pathway for the rapid transport of biomass burning aerosols from agricultural fires in Eastern Europe to the Arctic. Atmospheric stability was such that the smoke was constrained to low levels, within 2 km of the surface during the transport. A description of this smoke event in terms of transport and main aerosol characteristics can be found in Stohl et al. (2007). This study puts emphasis on the radiative effect of the smoke. The aerosol number size distribution was characterised by lognormal parameters as having an accumulation mode centered around 165–185 nm and almost 1.6 for geometric standard deviation of the mode. Nucleation and small Aitken mode particles were almost completely suppressed within the smoke plume measured at Ny-Ålesund. Chemical and microphysical aerosol information obtained at Mt. Zeppelin (474 m a.s.l) was used to derive input parameters for a one-dimensional radiation transfer model to explore the radiative effects of the smoke. The daily mean heating rate calculated on 2 May 2006 for the average size distribution and measured chemical composition reached 0.55 K day−1 at 0.5 km altitude for the assumed external mixture of the aerosols but showing much higher heating rates for an internal mixture (1.7 K day−1). In comparison a case study for March 2000 showed that the local climatic effects due to Arctic haze, using a regional climate model, HIRHAM, amounts to a maximum of 0.3 K day−1 of heating at 2 km altitude (Treffeisen et al., 2005)