| 1. |
- Hasanuzzaman, Gazi, et al.
(författare)
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Enhancement of PIV measurements via physics-informed neural networks
- 2023
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Ingår i: Measurement science and technology. - : IOP Publishing. - 0957-0233 .- 1361-6501. ; 34:4
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Tidskriftsartikel (refereegranskat)abstract
- Physics-informed neural networks (PINN) are machine-learning methods that have been proved to be very successful and effective for solving governing equations of fluid flow. In this work we develop a robust and efficient model within this framework and apply it to a series of two-dimensional three-component stereo particle-image velocimetry (PIV) datasets, to reconstruct the mean velocity field and correct measurements errors in the data. Within this framework, the PINNs-based model solves the Reynolds-averaged-Navier-Stokes equations for zero-pressure-gradient turbulent boundary layer (ZPGTBL) without a prior assumption and only taking the data at the PIV domain boundaries. The turbulent boundary layer (TBL) data has different flow conditions upstream of the measurement location due to the effect of an applied flow control via uniform blowing. The developed PINN model is very robust, adaptable and independent of the upstream flow conditions due to different rates of wall-normal blowing while predicting the mean velocity quantities simultaneously. Hence, this approach enables improving the mean-flow quantities by reducing errors in the PIV data. For comparison, a similar analysis has been applied to numerical data obtained from a spatially-developing ZPGTBL and an adverse-pressure-gradient TBL over a NACA4412 airfoil geometry. The PINNs-predicted results have less than 1% error in the streamwise velocity and are in excellent agreement with the reference data. This shows that PINNs has potential applicability to shear-driven turbulent flows with different flow histories, which includes experiments and numerical simulations for predicting high-fidelity data.
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| 2. |
- 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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| 3. |
- 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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| 4. |
- 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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