The wind turbine power curve shows the relationship between the wind turbine power and hub height wind speed. It essentially captures the wind turbine performance. Hence it plays an important role in ...condition monitoring and control of wind turbines. Power curves made available by the manufacturers help in estimating the wind energy potential in a candidate site. Accurate models of power curve serve as an important tool in wind power forecasting and aid in wind farm expansion. This paper presents an exhaustive overview on the need for modeling of wind turbine power curves and the different methodologies employed for the same. It also reviews in detail the parametric and non-parametric modeling techniques and critically evaluates them. The areas of further research have also been presented.
CAD model quality in parametric design scenarios largely determines the level of flexibility and adaptability of a 3D model (how easy it is to alter the geometry) as well as its reusability (the ...ability to use existing geometry in other contexts and applications). In the context of mechanical CAD systems, the nature of the feature-based parametric modeling paradigm, which is based on parent–child interdependencies between features, allows a wide selection of approaches for creating a specific model. Despite the virtually unlimited range of possible strategies for modeling a part, only a small number of them can guarantee an appropriate internal structure which results in a truly reusable CAD model. In this paper, we present an analysis of formal CAD modeling strategies and best practices for history-based parametric design: Delphi’s horizontal modeling, explicit reference modeling, and resilient modeling. Aspects considered in our study include the rationale to avoid the creation of unnecessary feature interdependencies, the sequence and selection criteria for those features, and the effects of parent/child relations on model alteration. We provide a comparative evaluation of these strategies in the form of a series of experiments using three industrial CAD models with different levels of complexity. We analyze the internal structure of the models and compare their robustness and flexibility when the geometry is modified. The results reveal significant advantages of formal modeling methodologies, particularly resilient techniques, over non-structured approaches as well as the unexpected problems of the horizontal strategy in numerous modeling situations.
•We analyze three formal CAD modeling strategies for history-based parametric design.•User performance was studied using three industrial CAD models with different levels of complexity.•Formal modeling methodologies offer significant advantages over non-structured approaches.•Modeling methodologies significantly affect reusability.
To date, a large number of experiments are performed to develop a biochemical process. The generated data is used only once, to take decisions for development. Could we exploit data of already ...developed processes to make predictions for a novel process, we could significantly reduce the number of experiments needed. Processes for different products exhibit differences in behaviour, typically only a subset behave similar. Therefore, effective learning on multiple product spanning process data requires a sensible representation of the product identity. We propose to represent the product identity (a categorical feature) by embedding vectors that serve as input to a Gaussian process regression model. We demonstrate how the embedding vectors can be learned from process data and show that they capture an interpretable notion of product similarity. The improvement in performance is compared to traditional one‐hot encoding on a simulated cross product learning task. All in all, the proposed method could render possible significant reductions in wet‐lab experiments.
A novel algorithm is developed that allows the joint modeling of process data from different cell lines by associating each cell line with a point in a low dimensional embedding space. The embedding is learned end‐to‐end from data and thus provides interpretable information about cell line similarities through the distances between embedding points. In simulation experiments, the algorithm is shown to improve prediction accuracy while reducing data requirements because it can transfer knowledge across cell lines.
•Hybrid modeling has been attracting the interest of the scientific community for almost 30 years.•Big data and the industry 4.0 bring opportunities for new hybrid modeling solutions.•We review ...hybrid modeling schemes, their training, validation and applications.•Usually mechanistic models are improved by data-driven models.•There is the need for a generic framework balancing prior and data-driven knowledge.
The chemical processing industry has relied on modeling techniques for process monitoring, control, diagnosis, optimization, and design, especially since the third industrial revolution and the emergence of Process Systems Engineering. The fourth industrial revolution, connected to massive digitization, made it possible to collect and process large volumes of data triggering the development of data-driven frameworks for knowledge extraction. However, one must not leave behind the successful solutions developed over decades based on first principle mechanistic modeling approaches. At present, both industry and researchers are realizing the need for new ways to incorporate process and phenomenological knowledge in big data and machine learning frameworks, leading to more robust and intelligible artificial intelligence solutions, capable of assisting the target stakeholders in their activities and decision processes. In this article, we review hybrid modeling techniques, associated system identification methodologies and model assessment criteria. Applications in chemical and biochemical processes are also referred.
•A non-parametric model for short-term traffic forecast is proposed.•An enhanced K-nearest neighbors (K-NN) is developed and implemented.•The proposed non-parametric model outperformed advanced ...parametric models.•The model was applied on 36 datasets collected from different regions.
The ability to timely and accurately forecast the evolution of traffic is very important in traffic management and control applications. This paper proposes a non-parametric and data-driven methodology for short-term traffic forecasting based on identifying similar traffic patterns using an enhanced K-nearest neighbor (K-NN) algorithm. Weighted Euclidean distance, which gives more weight to recent measurements, is used as a similarity measure for K-NN. Moreover, winsorization of the neighbors is implemented to dampen the effects of dominant candidates, and rank exponent is used to aggregate the candidate values. Robustness of the proposed method is demonstrated by implementing it on large datasets collected from different regions and by comparing it with advanced time series models, such as SARIMA and adaptive Kalman Filter models proposed by others. It is demonstrated that the proposed method reduces the mean absolute percent error by more than 25%. In addition, the effectiveness of the proposed enhanced K-NN algorithm is evaluated for multiple forecast steps and also its performance is tested under data with missing values. This research provides strong evidence suggesting that the proposed non-parametric and data-driven approach for short-term traffic forecasting provides promising results. Given the simplicity, accuracy, and robustness of the proposed approach, it can be easily incorporated with real-time traffic control for proactive freeway traffic management.
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•A parametric model of intake and exhaust phases of Wankel rotary engine is developed.•Optimized intake and exhaust phases increase the indicated mean effective ...pressure.•Multi-objective particle swarm optimization algorithm is enhanced by Sobol sequence.•Sensitivity of performance and emissions to intake and exhaust phases is evaluated.•A parallel computing optimization platform based on MOPSO is established.
Focusing on performance and emissions optimization, a novel parallel computing optimization platform was implemented to optimize the intake and exhaust phases of a hydrogen Wankel rotary engine (WRE). An improved multi-objective particle swarm algorithm implemented with the Sobol sequence was introduced in this study, which makes it superior in global search. A one-dimensional model integrating the leakage models was built and validated under various excess air ratios. The parametric control variables of the intake and exhaust phases were defined as rise stage, main stage, and decline stage. The indicated mean effective pressure (IMEP), indicated specific fuel consumption (ISFC), and nitrogen oxide (NOx) were used as evaluation objectives.
The optimization results showed that there was a quadratic relationship between ISFC and IMEP, and the ISFC decreased with increasing IMEP. The relationship between NOx and IMEP was closer to linear, and the NOx increased with the increase of IMEP. The timing of intake port full closing (IPFC) contributed the most influence to IMEP and NOx, and a delayed IPFC resulted in a lower IMEP. The timing of exhaust port start opening (EPSO) significantly affected the ISFC, and an earlier EPSO resulted in a higher ISFC. In the optimal case, the IMEP was increased by 2.0%, ISFC was reduced by 1.1%, and NOx was only increased by 0.1%. It is a prospective approach to further improve performance and emissions simultaneously using parallel computing optimization platform.
•BIM-based performance optimization (BPOpt) framework is developed.•Open-source Optimo is developed to serve as a multi-objective optimization engine.•Optimo utilizes the visual programming method ...integrated with BIM for BPOpt.•The performance of BPOpt is evaluated with a case study of a design project.•BPOpt works on the top of a widely used BIM-platform for sustainable design.
The increase in global environmental concerns as well as the advancement of computational tools and methods have had significant impacts on the way in which buildings are being designed. Building professionals are increasingly expected to improve energy performance of their design. To achieve a high level of energy performance, multidisciplinary simulation-based optimization can be utilized to help designers in exploring more design alternatives and making informed decisions. Because of the high complexity in setting up a building model for multi-objective design optimization, there is a great demand of utilizing and integrating the advanced modeling and simulation technologies, including BIM, parametric modeling, cloud-based simulation, and optimization algorithms, as well as a new user interface that facilitates the setup of building parameters (decision variables) and performance fitness functions (design objectives) for automatically generating, evaluating, and optimizing multiple design options. This paper presents an integrated framework for building information modeling (BIM)-based performance optimization, BPOpt. This framework enables designers to explore design alternatives using an open-source, visual programming user interface on the top of a widely used BIM platform, to generate models of building design options, assess the environmental performance of the models through cloud-based simulation, and search for the most appropriate design alternatives. This paper details the process of the development of BPOpt and also provides a case study to show its application. The case study demonstrates the use of BPOpt in minimizing the energy consumption while maximizing the appropriate daylighting level for a residential building. Finally, strengths, limitations, current adoption by academia and industry, and future improvements of BPOpt for high-performance building design are discussed.
Next-generation deformable mirrors are envisaged to exhibit low-frequency flexible dynamics and to contain a large number of spatially distributed actuators due to increasingly stringent performance ...requirements. The increasingly complex system characteristics necessitate identifying the flexible dynamic behavior for design validation and next-generation control. The aim of this paper is to develop a unified approach for the identification of mechanical systems with a large number of spatially distributed actuators and a limited number of sensors. A frequency domain-based approach using local modeling techniques is developed. The modal modeling framework is employed to analyze the design and create outputs that were not measured. The proposed approach is applied to an experimental deformable mirror case study that illustrates the effectiveness of the proposed approach.
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