| 1. |
- Nascimbeni, V., et al.
(författare)
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A new dynamical modeling of the WASP-47 system with CHEOPS observations
- 2023
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Ingår i: Astronomy & Astrophysics. - : EDP Sciences. - 1432-0746 .- 0004-6361. ; 673
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Tidskriftsartikel (refereegranskat)abstract
- Among the hundreds of known hot Jupiters (HJs), only five have been found to have companions on short-period orbits. Within this rare class of multiple planetary systems, the architecture of WASP-47 is unique, hosting an HJ (planet-b) with both an inner and an outer sub-Neptunian mass companion (-e and -d, respectively) as well as an additional non-transiting, long-period giant (-c). The small period ratio between planets -b and -d boosts the transit time variation (TTV) signal, making it possible to reliably measure the masses of these planets in synergy with the radial velocity (RV) technique. In this paper, we present new space- and ground-based photometric data of WASP-47b and WASP-47-d, including 11 unpublished light curves from the ESA mission CHaracterising ExOPlanet Satellite (CHEOPS). We analyzed the light curves in a homogeneous way together with all the publicly available data to carry out a global N-body dynamical modeling of the TTV and RV signals. We retrieved, among other parameters, a mass and density for planet -d of Md = 15.5 ± 0.8 M⊕ and ρd = 1.69 ± 0.22 g cm−3, which is in good agreement with the literature and consistent with a Neptune-like composition. For the inner planet (-e), we found a mass and density of Me = 9.0 ± 0.5 M⊕ and ρe = 8.1 ± 0.5 g cm−3, suggesting an Earth-like composition close to other ultra-hot planets at similar irradiation levels. Though this result is in agreement with previous RV plus TTV studies, it is not in agreement with the most recent RV analysis (at 2.8σ), which yielded a lower density compatible with a pure silicate composition. This discrepancy highlights the still unresolved issue of suspected systematic offsets between RV and TTV measurements. In this paper, we also significantly improve the orbital ephemerides of all transiting planets, which will be crucial for any future follow-up.
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| 2. |
- Amini, E., et al.
(författare)
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Design optimization of ocean renewable energy converter using a combined Bi-level metaheuristic approach
- 2023
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Ingår i: Energy Conversion and Management. - : Elsevier Ltd. - 2590-1745. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, there has been an increasing interest in renewable energies in view of the fact that fossil fuels are the leading cause of catastrophic environmental consequences. Ocean wave energy is a renewable energy source that is particularly prevalent in coastal areas. Since many countries have tremendous potential to extract this type of energy, a number of researchers have sought to determine certain effective factors on wave converters’ performance, with a primary emphasis on ambient factors. In this study, we used metaheuristic optimization methods to investigate the effects of geometric factors on the performance of an Oscillating Surge Wave Energy Converter (OSWEC), in addition to the effects of hydrodynamic parameters. To do so, we used CATIA software to model different geometries which were then inserted into a numerical model developed in Flow3D software. A Ribed-surface design of the converter's flap is also introduced in this study to maximize wave-converter interaction. Besides, a Bi-level Hill Climbing Multi-Verse Optimization (HCMVO) method was also developed for this application. The results showed that the converter performs better with greater wave heights, flap freeboard heights, and shorter wave periods. Additionally, the added ribs led to more wave-converter interaction and better performance, while the distance between the flap and flume bed negatively impacted the performance. Finally, tracking the changes in the five-dimensional objective function revealed the optimum value for each parameter in all scenarios. This is achieved by the newly developed optimization algorithm, which is much faster than other existing cutting-edge metaheuristic approaches.
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| 3. |
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| 4. |
- Lanzinger, S., et al.
(författare)
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A collaborative comparison of international pediatric diabetes registries
- 2022
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Ingår i: Pediatric Diabetes. - : Hindawi Limited. - 1399-543X .- 1399-5448. ; 23:6, s. 627-640
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Tidskriftsartikel (refereegranskat)abstract
- Background An estimated 1.1 million children and adolescents aged under 20 years have type 1 diabetes worldwide. Principal investigators from seven well-established longitudinal pediatric diabetes registries and the SWEET initiative have come together to provide an international collaborative perspective and comparison of the registries. Work Flow Information and data including registry characteristics, pediatric participant clinical characteristics, data availability and data completeness from the Australasian Diabetes Data Network (ADDN), Danish Registry of Childhood and Adolescent Diabetes (DanDiabKids), Diabetes prospective follow-up registry (DPV), Norwegian Childhood Diabetes Registry (NCDR), National Paediatric Diabetes Audit (NPDA), Swedish Childhood Diabetes Registry (Swediabkids), T1D Exchange Quality Improvement Collaborative (T1DX-QI), and the SWEET initiative was extracted up until 31 December 2020. Registry Objectives and Outcomes The seven diabetes registries and the SWEET initiative collectively show data of more than 900 centers and around 100,000 pediatric patients, the majority with type 1 diabetes. All share the common objectives of monitoring treatment and longitudinal outcomes, promoting quality improvement and equality in diabetes care and enabling clinical research. All generate regular benchmark reports. Main differences were observed in the definition of the pediatric population, the inclusion of adults, documentation of CGM metrics and collection of raw data files as well as linkage to other data sources. The open benchmarking and access to regularly updated data may prove to be the most important contribution from registries. This study describes aspects of the registries to enable future collaborations and to encourage the development of new registries where they do not exist.
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| 5. |
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| 6. |
- Majidi Nezhad, Meysam, et al.
(författare)
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Marine energy digitalization digital twin's approaches
- 2024
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Ingår i: Renewable & sustainable energy reviews. - : Elsevier Ltd. - 1364-0321 .- 1879-0690. ; 191
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Tidskriftsartikel (refereegranskat)abstract
- Digital twins (DTs) promise innovation for the marine renewable energy sector using modern technological advances and the existing maritime knowledge frameworks. The DT is a digital equivalent of a real object that reflects and predicts its behaviours and states in a virtual space over its lifetime. DTs collect data from multiple sources in pilots and leverage newly introduced low-cost sensor systems. They synchronize, homogenize, and transmit the data to a central hub and integrate it with predictive and learning models to optimize plant performance and operations. This research presents critical aspects of DT implementation challenges in marine energy digitalization DT approaches that use and combine data systems. Firstly, the DT and the existing framework for marine knowledge provided by systems are presented, and the DT's main development steps are discussed. Secondly, the DT implementing main stages, measurement systems, data harmonization and preprocessing, modelling, comprehensive data analysis, and learning and optimization tools, are identified. Finally, the ILIAD (Integrated Digital Framework for Comprehensive Maritime Data and Information Services) project has been reviewed as a best EU funding practice to understand better how marine energy digitalization DT's approaches are being used, designed, developed, and launched.
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| 7. |
- Majidi Nezhad, Meysam, et al.
(författare)
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Offshore wind farm layouts designer software's
- 2023
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Ingår i: e-Prime - Advances in Electrical Engineering, Electronics and Energy. - : Elsevier Ltd. - 2772-6711. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Offshore wind energy can be considered one of the renewable energy sources with high force potential installed in marine areas. Consequently, the best wind farm layouts identified for constructing combined offshore renewable energy farms are crucial. To this aim, offshore wind potential analysis is essential to highlight the best offshore wind layouts for farm installation and development. Furthermore, the offshore wind farm layouts must be designed and developed based on the offshore wind accurate assessment to identify previously untapped marine regions. In this case, the wind speed distribution and correlation, wind direction, gust speed and gust direction for three sites have been analyzed, and then two offshore wind farm layout scenarios have been designed and analyzed based on two offshore wind turbine types in the Northwest Persian Gulf. In this case, offshore wind farm layouts software and tools have been reviewed as ubiquitous software tools. The results show Beacon M28 and Sea Island buoys location that the highest correlation between wind and gust speeds is between 87% and 98% in Beacon M28 and Sea Island Buoy, respectively. Considerably, the correlation between wind direction and wind speed is negligible. The Maximum likelihood algorithm, the WAsP algorithm, and the Least Squares algorithm have been used to analyze the wind energy potential in offshore buoy locations of the Northwest Persian Gulf. In addition, the wind energy generation potential has been evaluated in different case studies. For example, the Umm Al-Maradim buoy area has excellent potential for offshore wind energy generation based on the Maximum likelihood algorithm, WAsP algorithm, and Least Squares algorithm.
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| 8. |
- Neshat, M., et al.
(författare)
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Enhancing the performance of hybrid wave-wind energy systems through a fast and adaptive chaotic multi-objective swarm optimisation method
- 2024
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Ingår i: Applied Energy. - : Elsevier Ltd. - 0306-2619 .- 1872-9118. ; 362
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Tidskriftsartikel (refereegranskat)abstract
- Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model's parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms.
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| 9. |
- Neshat, M., et al.
(författare)
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Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy
- 2023
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Ingår i: Energy. - : Elsevier Ltd. - 0360-5442 .- 1873-6785. ; 278
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Tidskriftsartikel (refereegranskat)abstract
- Developing an accurate and robust prediction of long-term average global solar irradiation plays a crucial role in industries such as renewable energy, agribusiness, and hydrology. However, forecasting solar radiation with a high level of precision is historically challenging due to the nature of this source of energy. Challenges may be due to the location constraints, stochastic atmospheric parameters, and discrete sequential data. This paper reports on a new hybrid deep residual learning and gated long short-term memory recurrent network boosted by a differential covariance matrix adaptation evolution strategy (ADCMA) to forecast solar radiation one hour-ahead. The efficiency of the proposed hybrid model was enriched using an adaptive multivariate empirical mode decomposition (MEMD) algorithm and 1+1EA-Nelder–Mead simplex search algorithm. To compare the performance of the hybrid model to previous models, a comprehensive comparative deep learning framework was developed consisting of five modern machine learning algorithms, three stacked recurrent neural networks, 13 hybrid convolutional (CNN) recurrent deep learning models, and five evolutionary CNN recurrent models. The developed forecasting model was trained and validated using real meteorological and Shortwave Radiation (SRAD1) data from an installed offshore buoy station located in Lake Michigan, Chicago, United States, supported by the National Data Buoy Centre (NDBC). As a part of pre-processing, we applied an autoencoder to detect the outliers in improving the accuracy of solar radiation prediction. The experimental results demonstrate that, firstly, the hybrid deep residual learning model performed best compared with other machine learning and hybrid deep learning methods. Secondly, a cooperative architecture of gated recurrent units (GRU) and long short-term memory (LSTM) recurrent models can enhance the performance of Xception and ResNet. Finally, using an effective evolutionary hyper-parameters tuner (ADCMA) reinforces the prediction accuracy of solar radiation.
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