| 1. |
- Atzori, Marco, et al.
(författare)
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A new perspective on skin-friction contributions in adverse-pressure-gradient turbulent boundary layers
- 2023
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Ingår i: International Journal of Heat and Fluid Flow. - : Elsevier BV. - 0142-727X .- 1879-2278. ; 101
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Tidskriftsartikel (refereegranskat)abstract
- For adverse-pressure-gradient turbulent boundary layers, the study of integral skin-friction contributions still poses significant challenges. Beyond questions related to the integration boundaries and the derivation procedure, which have been thoroughly investigated in the literature, an important issue is how different terms should be aggregated. The nature of these flows, which exhibit significant in-homogeneity in the streamwise direction, usually results in cancellation between several contributions with high absolute values. We propose a formulation of the identity derived by Fukagata et al. (2002), which we obtained from the convective form of the governing equations. A new skin-friction contribution is defined, considering wall-tangential convection and pressure gradient together. This contribution is related to the evolution of the dynamic pressure in the mean flow. The results of the decomposition are examined for a broad range of pressure-gradient conditions and different flow-control strategies. We found that the new formulation of the identity allows to readily identify the different regimes of near-equilibrium conditions and approaching separation. It also provides a more effective description of control effects. A similar aggregation between convection and pressure-gradient terms is also possible for any other decomposition where in-homogeneity contributions are considered explicitly.
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| 2. |
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| 3. |
- Mallor, Fermin, et al.
(författare)
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Bayesian Optimisation of blowing and suction for drag reduction on a transonic airfoil
- 2023
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Ingår i: Proceedings of the 14th ERCOFTAC Symp. on Engineering Turbulence Modelling and Measurements (ETMM14), Barcelona, Spain. ; , s. 837-842
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Wall-normal blowing and suction has shown to be a promising active control method for friction drag reduction. In this work, we exploit a Bayesian optimization framework based on Gaussian process regression to find a configuration of non-homogeneous wall-normal blowing and suction capable of improving the aerodynamic efficiency of an RAE2822 airfoil in transonic conditions. The RANS simulations are carried out with the open-source solver SU2. During the optimization process, three different scenarios are considered: only the drag is minimized, the drag and the power needed to drive the control system are included, and the actuation power with a specified compressor efficiency are used for the calculation of the efficiency increase. Even in the most realistic case considering the actuation power and efficiencies an increase in the overall efficiency of 1.15% is reached.
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| 4. |
- Mallor, Fermin, et al.
(författare)
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Bayesian Optimization of Wall-Normal Blowing and Suction-Based Flow Control of a NACA 4412 Wing Profile
- 2023
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Ingår i: Flow Turbulence and Combustion. - : Springer Nature. - 1386-6184 .- 1573-1987.
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Tidskriftsartikel (refereegranskat)abstract
- Active flow-control techniques have shown promise for achieving high levels of drag reduction. However, these techniques are often complex and involve multiple tunable parameters, making it challenging to optimize their efficiency. Here, we present a Bayesian optimization (BO) approach based on Gaussian process regression to optimize a wall-normal blowing and suction control scheme for a NACA 4412 wing profile at two angles of attack: 5 and 11∘, corresponding to cruise and high-lift scenarios, respectively. An automated framework is developed by linking the BO code to the CFD solver OpenFOAM. RANS simulations (validated against high-fidelity LES and experimental data) are used in order to evaluate the different flow cases. BO is shown to provide rapid convergence towards a global maximum, even when the complexity of the response function is increased by introducing a model for the cost of the flow control actuation. The importance of considering the actuation cost is highlighted: while some cases yield a net drag reduction (NDR), they may result in an overall power increase. Furthermore, optimizing for NDR or net power reduction (NPR) can lead to significantly different actuation strategies. Finally, by considering losses and efficiencies representative of real-world applications, still a significant NPR is achieved in the 11∘ case, while net power reduction is only marginally positive in the 5∘ case.
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| 5. |
- Rosenberg, Emelie, et al.
(författare)
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Sentiment analysis on Twitter data towards climate action
- 2023
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Ingår i: Results in Engineering (RINENG). - : Elsevier BV. - 2590-1230. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the progress of the Sustainable Development Goals (SDGs) proposed by the United Nations (UN) is important, but difficult. In particular, policymakers would need to understand the sentiment within the public regarding challenges associated with climate change. With this in mind and the rise of social media, this work focuses on the task of uncovering the sentiment of Twitter users concerning climate-related issues. This is done by applying modern natural-language-processing (NLP) methods, i.e. VADER, TextBlob, and BERT, to estimate the sentiment of a gathered dataset based on climate-change keywords. A transfer-learning-based model applied to a pre-trained BERT model for embedding and tokenizing with logistic regression for sentiment classification outperformed the rule-based methods VADER and TextBlob; based on our analysis, the proposed approach led to the highest accuracy: 69%. The collected data contained significant noise, especially from the keyword 'energy'. Consequently, using more specific keywords would improve the results. The use of other methods, like BERTweet, would also increase the accuracy of the model. The overall sentiment in the analyzed data was positive. The distribution of the positive, neutral, and negative sentiments was very similar in the different SDGs.
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| 6. |
- Sanchez-Roncero, Alejandro, et al.
(författare)
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The Sustainable Development Goals and Aerospace Engineering : A critical note through Artificial Intelligence
- 2023
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Ingår i: Results in Engineering (RINENG). - : Elsevier BV. - 2590-1230. ; 17
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Tidskriftsartikel (refereegranskat)abstract
- The 2030 Agenda of the United Nations (UN) revolves around the Sustainable Development Goals (SDGs). A critical step towards that objective is identifying whether scientific production aligns with the SDGs' achievement. To assess this, funders and research managers need to manually estimate the impact of their funding agenda on the SDGs, focusing on accuracy, scalability, and objectiveness. With this objective in mind, in this work, we develop ASDG, an easy-to-use Artificial-Intelligence-based model for automatically identifying the potential impact of scientific papers on the UN SDGs. As a demonstrator of ASDG, we analyze the alignment of recent aerospace publications with the SDGs. The Aerospace data set analyzed in this paper consists of approximately 820,000 papers published in English from 2011 to 2020 and indexed in the Scopus database. The most-contributed SDGs are 7 (on clean energy), 9 (on industry), 11 (on sustainable cities), and 13 (on climate action). The establishment of the SDGs by the UN in the middle of the 2010 decade did not significantly affect the data. However, we find clear discrepancies among countries, likely indicative of different priorities. Also, different trends can be seen in the most and least cited papers, with apparent differences in some SDGs. Finally, the number of abstracts the code cannot identify decreases with time, possibly showing the scientific community's awareness of SDG.
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| 7. |
- Sirmacek, B., et al.
(författare)
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The Potential of Artificial Intelligence for Achieving Healthy and Sustainable Societies
- 2023
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Ingår i: The Ethics of Artificial Intelligence for the Sustainable Development Goals. - : Springer Nature. ; , s. 65-96
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter we extend earlier work (Vinuesa et al., Nat Commun 11, 2020) on the potential of artificial intelligence (AI) to achieve the 17 Sustainable Development Goals (SDGs) proposed by the United Nations (UN) for the 2030 Agenda. The present contribution focuses on three SDGs related to healthy and sustainable societies, i.e., SDG 3 (on good health), SDG 11 (on sustainable cities), and SDG 13 (on climate action). This chapter extends the previous study within those three goals and goes beyond the 2030 targets. These SDGs are selected because they are closely related to the coronavirus disease 19 (COVID-19) pandemic and also to crises like climate change, which constitute important challenges to our society.
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