• Energy Research
• 2016-2025close
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• Polytechnic University of Milan close

Abstract The present work focuses on the numerical simulation of Moderate or Intense Low oxygen Dilution combustion condition, using the Partially-Stirred Reactor model for turbulence-chemistry interactions. The Partially-Stirred Reactor model assumes that reactions are confined in a specific region of the computational cell, whose mass fraction depends both on the mixing and the chemical time scales. Therefore, the appropriate choice of mixing and chemical time scales becomes crucial to ensure the accuracy of the numerical simulation prediction. Results show that the most appropriate choice for mixing time scale in Moderate or Intense Low oxygen Dilution combustion regime is to use a dynamic evaluation, in which the ratio between the variance of mixture fraction and its dissipation rate is adopted, rather than global estimations based on Kolmogorov or integral mixing scales. This is supported by the validation of the numerical results against experimental profiles of temperature and species mass fractions, available from measurements on the Adelaide Jet in Hot Co-flow burner. Different approaches for chemical time scale evaluation are also compared, using the species formation rates, the reaction rates and the eigenvalues of the formation rate Jacobian matrix. Different co-flow oxygen dilution levels and Reynolds numbers are considered in the validation work, to evaluate the applicability of Partially-Stirred Reactor approach over a wide range of operating conditions. Moreover, the influence of specifying uniform and non-uniform boundary conditions for the chemical scalars is assessed. The present work sheds light on the key mechanisms of turbulence-chemistry interactions in advanced combustion regimes. At the same time, it provides essential information to advance the predictive nature of computational tools used by scientists and engineers, to support the development of new technologies.

The use of liquid metals in solar power systems is not new. The receiver tests with liquid sodium in the 1980s at the Plataforma Solar de Almer a (PSA) already proved the feasibility of liquid metals as heat transfer fluid. Despite the high efficiency achieved with that receiver, further investigation of liquid metals in solar power systems was stopped due to a sodium spray fire. Recently, the topic has become interesting again and the gained experience during the last 30 years of liquid metals handling is applied to the concentrated solar power community. In this paper, recent activities of the Helmholtz Alliance LIMTECH concerning liquid metals for solar power systems are presented. In addition to the components and system simulations also the experimental setup and results are included.

Abstract Reliability analysis of IESs (Integrated Energy System) is complicated because of the complexity of system topology and dynamics and different kinds of uncertainties. Reliability is often calculated based on statistic methods, which always focus on historical performances and neglect the importance of their dynamics and structure. To overcome this problem, in this paper, a systematic framework for dynamically analysing the real-time reliability of IESs is proposed by integrating different machine learning methods and statistics. Firstly, the bootstrap-based Extreme Learning Machine is developed to forecast the conditional probability distributions of the productions of renewable energies and the energy consumptions. Then, the dynamic behaviour of IESs is simulated based on a stacked auto-encoder model, instead of using traditional mechanism-based simulation models, for improving computational efficiency. Besides, the variables representing the transient properties of natural gas pipeline networks, such as delivery pressures and flow rates, are taken as the indicators for quantifying the energy supply security in natural gas pipeline networks. The time-dependent relationships among these indicators and their statistic correlations are modelled for improving the effectiveness of the analysis results. Finally, the reliability assessment is performed by estimating the probability distribution of each functional state of the target IES. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed method. The results show that the method is able to effectively evaluate the reliability of IESs, which can provide useful information for system operation and management.

La prévision précise de l'irradiance solaire (SI) est un aspect important de la collecte de l'énergie solaire et dépend de diverses caractéristiques météorologiques. De nombreux algorithmes de sélection de caractéristiques ont été mis en œuvre pour la sélection de paramètres météorologiques appropriés. Cependant, les algorithmes de stimulation ne sont pas largement explorés pour les applications de sélection de fonctionnalités. Par conséquent, dans cette étude, une nouvelle perspective est introduite en explorant l'efficacité des algorithmes d'amplification dans les applications de sélection de fonctionnalités. Dans l'étude proposée, nous effectuons une analyse comparative de différents algorithmes de boosting pour les applications de sélection de fonctionnalités, notamment Extreme Gradient Boosting (XgBoost), Categorical Boosting (CatBoost), Random Forest (RF) et Light Gradient Boosting Machine (LGBM). La nouveauté de cette approche réside dans l'utilisation de ces techniques d'amplification pour la sélection des caractéristiques les plus appropriées qui améliorent la performance prédictive du modèle. Les données SI de trois emplacements géographiques différents : Islamabad, Pakistan, Bâle, Suisse et Golden, Colorado, États-Unis sont obtenues à partir de la base de données nationale sur le rayonnement solaire (NSRDB) et utilisées dans l'étude proposée. Tout d'abord, les fonctionnalités appropriées sont sélectionnées séparément par quatre algorithmes d'amplification. Les caractéristiques sélectionnées sont ensuite transmises au réseau de mémoire bidirectionnelle à long terme et à court terme (BiLSTM) pour la prévision de l'irradiance horizontale globale (GHI) à une heure d'avance. L'erreur quadratique moyenne (RMSE), l'erreur quadratique moyenne (MSE), l'erreur absolue moyenne (MAE), l'erreur d'échelle absolue moyenne (MASE) et l'erreur quadratique moyenne normalisée (NRMSE) sont utilisées comme indicateurs de performance. Les résultats démontrent que le réseau BiLSTM formé sur des fonctionnalités sélectionnées, proposé par le modèle XgBoost, produit de meilleurs résultats de prévision. Dans le cas de l'ensemble de données de la ville d'Islamabad, le RMSE et le MAE de BiLSTM formés avec des fonctionnalités appropriées, par rapport au modèle conventionnel, sont améliorés de 29,92 % et 14,03 %, respectivement. Pour l'ensemble de données de Bâle, le RMSE et le MAE du réseau BiLSTM se sont améliorés de 14,43 % et 28,72 %, respectivement. De plus, pour l'ensemble de données de Golden City, le RMSE et le MAE de l'approche proposée sont améliorés de 10,5% et 17,38%, respectivement, par rapport au modèle conventionnel. El pronóstico preciso de la irradiancia solar (IS) es un aspecto importante de la recolección de energía solar y depende de varias características meteorológicas. Se han implementado numerosos algoritmos de selección de características para la selección de parámetros meteorológicos adecuados. Sin embargo, los algoritmos de refuerzo no se exploran ampliamente para las aplicaciones de selección de características. Por lo tanto, en este estudio, se introduce una perspectiva novedosa al explorar la eficacia de los algoritmos de impulso en las aplicaciones de selección de características. En el estudio propuesto, realizamos un análisis comparativo de diferentes algoritmos de refuerzo para aplicaciones de selección de características, incluyendo Extreme Gradient Boosting (XgBoost), Categorical Boosting (CatBoost), Random Forest (RF) y Light Gradient Boosting Machine (LGBM). La novedad de este enfoque está en la utilización de estas técnicas de potenciación para la selección de las características más adecuadas que mejoren el rendimiento predictivo del modelo. Los datos del SI de tres ubicaciones geográficas diferentes: Islamabad, Pakistán, Basilea, Suiza y Golden, Colorado, EE. UU., se obtienen de la Base de Datos Nacional de Radiación Solar (NSRDB) y se utilizan en el estudio propuesto. En primer lugar, las características apropiadas son seleccionadas por cuatro algoritmos de refuerzo por separado. Las funciones seleccionadas se alimentan a la red de memoria bidireccional a largo y corto plazo (BiLSTM) para pronosticar la irradiancia horizontal global (GHI) con una hora de antelación. El error cuadrático medio (RMSE), el error cuadrático medio (MSE), el error absoluto medio (MAE), el error escalado absoluto medio (MASE) y el error cuadrático medio normalizado (NRMSE) se utilizan como indicadores de rendimiento. Los hallazgos demuestran que la red BiLSTM capacitada en características seleccionadas, propuesta por el modelo XgBoost, produce mejores resultados de pronóstico. En el caso del conjunto de datos de la ciudad de Islamabad, el RMSE y el MAE de BiLSTM entrenados con las características adecuadas, en comparación con el modelo convencional, mejoran en un 29,92% y un 14,03%, respectivamente. Para el conjunto de datos de Basilea, el RMSE y el MAE de la red BiLSTM mejoraron en un 14,43% y un 28,72%, respectivamente. Además, para el conjunto de datos de Golden City, el RMSE y el MAE del enfoque propuesto mejoran en un 10,5% y un 17,38%, respectivamente, que el modelo convencional. Accurate Solar Irradiance (SI) forecasting is an important aspect of solar energy harvesting and it depends on various meteorological features. Numerous feature selection algorithms have been implemented for the selection of suitable meteorological parameters. However, boosting algorithms are not explored widely for feature selection applications. Therefore, in this study, a novel perspective is introduced by exploring the efficacy of boosting algorithms in feature selection applications. In the proposed study, we perform a comparative analysis of different boosting algorithms for feature selection applications including Extreme Gradient Boosting (XgBoost), Categorical Boosting (CatBoost), Random Forest (RF) and Light Gradient Boosting Machine (LGBM). The novelty of this approach is in utilizing these boosting techniques for the selection of the most appropriate features that improve the predictive performance of the model. The SI data of three different geographical locations: Islamabad, Pakistan, Basel, Switzerland and Golden, Colorado, USA are attained from the National Solar Radiation Database (NSRDB) and used in the proposed study. First, the appropriate features are selected by four boosting algorithms separately. The selected features are then fed to the Bidirectional Long-Short-Term Memory (BiLSTM) network for forecasting hour-ahead Global Horizontal Irradiance (GHI). The Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE) and Normalized Root Mean Square Error (NRMSE) are used as performance indicators. Findings demonstrate that the BiLSTM network trained on selected features, proposed by the XgBoost model, produces better forecasting results. In the case of the Islamabad city dataset, the RMSE and MAE of BiLSTM trained with appropriate features, as compared to the conventional model, are improved by 29.92% and 14.03%, respectively. For the dataset of Basel, the RMSE and MAE of BiLSTM network improved by 14.43% and 28.72%, respectively. Moreover, for the Golden city dataset, the RMSE and MAE of the proposed approach are improved by 10.5% and 17.38%, respectively than the conventional model. يعد التنبؤ الدقيق بالإشعاع الشمسي (SI) جانبًا مهمًا من حصاد الطاقة الشمسية ويعتمد على ميزات الأرصاد الجوية المختلفة. تم تنفيذ العديد من خوارزميات اختيار الميزات لاختيار معلمات الأرصاد الجوية المناسبة. ومع ذلك، لا يتم استكشاف خوارزميات التعزيز على نطاق واسع لتطبيقات اختيار الميزات. لذلك، في هذه الدراسة، يتم تقديم منظور جديد من خلال استكشاف فعالية تعزيز الخوارزميات في تطبيقات اختيار الميزات. في الدراسة المقترحة، نقوم بإجراء تحليل مقارن لخوارزميات التعزيز المختلفة لتطبيقات اختيار الميزات بما في ذلك تعزيز التدرج الشديد (XgBoost)، التعزيز الفئوي (CatBoost)، الغابات العشوائية (RF) وآلة تعزيز التدرج الخفيف (LGBM). تكمن حداثة هذا النهج في استخدام تقنيات التعزيز هذه لاختيار الميزات الأكثر ملاءمة التي تحسن الأداء التنبؤي للنموذج. يتم الحصول على بيانات SI لثلاثة مواقع جغرافية مختلفة: إسلام أباد، باكستان، بازل، سويسرا وغولدن، كولورادو، الولايات المتحدة الأمريكية من قاعدة البيانات الوطنية للإشعاع الشمسي (NSRDB) واستخدامها في الدراسة المقترحة. أولاً، يتم اختيار الميزات المناسبة من خلال أربع خوارزميات معززة بشكل منفصل. ثم يتم تغذية الميزات المحددة إلى شبكة الذاكرة طويلة الأجل ثنائية الاتجاه (BiLSTM) للتنبؤ بالإشعاع الأفقي العالمي قبل ساعة (GHI). يتم استخدام الخطأ التربيعي لمتوسط الجذر (RMSE)، والخطأ التربيعي لمتوسط الجذر (MSE)، والخطأ المطلق لمتوسط الجذر (MAE)، والخطأ المطلق لمتوسط الجذر التربيعي لمتوسط الجذر (MASE)، والخطأ التربيعي لمتوسط الجذر التربيعي لمتوسط الجذر (NRMSE) كمؤشرات أداء. تُظهر النتائج أن شبكة BiLSTM المدربة على ميزات مختارة، والتي اقترحها نموذج XgBoost، تنتج نتائج تنبؤ أفضل. في حالة مجموعة بيانات مدينة إسلام أباد، تم تحسين RMSE و MAE من BiLSTM المدربين بميزات مناسبة، مقارنة بالنموذج التقليدي، بنسبة 29.92 ٪ و 14.03 ٪ على التوالي. بالنسبة لمجموعة بيانات بازل، تحسنت RMSE و MAE لشبكة BiLSTM بنسبة 14.43 ٪ و 28.72 ٪ على التوالي. علاوة على ذلك، بالنسبة لمجموعة بيانات المدينة الذهبية، تم تحسين RMSE و MAE للنهج المقترح بنسبة 10.5 ٪ و 17.38 ٪ على التوالي من النموذج التقليدي.

Abstract Thermal engines, particularly closed power cycles, are currently a focus of many studies mainly because they represent the only way to exploit renewable thermal energy. To increase the exploitation of available thermal sources, this work investigates the higher potential offered by a complementary technology based on the use of reactive working fluids instead of inert fluids: the here-called “thermo-chemical” engine. Such a power cycle enables the simultaneous conversion of thermal and chemical energy into work. Based on a theoretical approach, this paper explores engine performance considering different stoichiometries and thermodynamic characteristics of reactive fluids and different operating conditions. It is shown that the use of specific equilibrated reactions occurring in the gaseous phase might lead to extremely powerful and highly efficient energy conversion systems in the whole current domain of the application of power cycles. Moreover, it is demonstrated that, unlike classical thermal machines, a thermo-chemical engine allows efficient and powerful exploitation of low-temperature heat sources and high-temperature cold sinks, which in general, characterize renewable thermal energy.

Wind energy is gaining special interest worldwide due to the necessity of reducing pollutant emissions and employ renewable resources. Traditionally, horizontal axis wind turbines have been employed but certain situations require vertical axis wind turbines. With a view to improve the efficiency of a vertical axis wind turbine Savonius type, the present work proposes a bioinspired design blade profile relying on the Fibonacci spiral. This shape is repeatedly presented in nature and thus it leads to a bio-inspired blade profile. A numerical model was carried out and it was found that the Fibonacci shape improves the performance of the original Savonius shape, based on semicircular blade profiles. Particularly, the Fibonacci blade profile increases around 14% the power in comparison with the Savonius blade profile. Besides this comparison between Savonius and Fibonacci, a research study was carried out to improve the efficiency of the Fibonacci turbine. To this end, the effect of several parameters was analyzed: number of blades, aspect ratio, overlap, separation gap, and twist angle. Improvements on the average power greater than 30% were obtained.