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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.

AbstractA comparison of several incrementally complex methods for predicting wind turbine performance, aeroelastic behavior, and wakes is provided. Depending on a wind farm's design, wake interference can cause large power losses and increased turbulence levels within the farm. The goal is to employ modeling methods to reach an improved understanding of wake effects and to use this information to better optimize the layout of new wind farms. A critical decision faced by modelers is the fidelity of the model that is selected to perform simulations. The choice of model fidelity can affect the accuracy, but will also greatly impact the computational time and resource requirements for simulations. To help address this critical question, three modeling methods of varying fidelity have been developed side by side and are compared in this article. The models from low to high complexity are as follows: a blade element‐based method with a free‐vortex wake, an actuator disc‐based method, and a full rotor‐based method. Fluid/structure interfaces are developed for the aerodynamic modeling approaches that allow modeling of discrete blades and are then coupled with a multibody structural dynamics solver in order to perform an aeroelastic analysis. Similar methods have individually been tested by researchers, but we suggest that by developing a suite of models, they can be cross‐compared to grasp the subtleties of each method. The modeling methods are applied to the National Renewable Energy Laboratory Phase VI rotor to predict the turbine aerodynamic and structural loads and then also the wind velocities in the wake. The full rotor method provides the most accurate predictions at the turbine and the use of adaptive mesh refinement to capture the wake to 20 radii downstream is proven particularly successful. Though the full rotor method is unmatched by the lower fidelity methods in stalled conditions and detailed prediction of the downstream wake, there are other less complex conditions where these methods perform as accurately as the full rotor method. Copyright © 2014 John Wiley & Sons, Ltd.

This paper illustrates the main results of an expert elicitation survey on advanced (second and third generation) biofuel technologies. The survey focuses on eliciting probabilistic information on the future costs of advanced biofuels and on the potential role of Research, Development and Demonstration (RD&D) efforts in reducing these costs and in supporting the deployment of biofuels in Organisation for Economic Co-operation and Development (OECD) and non-OECD countries. Fifteen leading experts from different EU member states provide insights on the future potential of advanced biofuel technologies both in terms of costs and diffusion. This information results in a number of policy recommendations with respect to public RD&D strategies and is an important contribution to the integrated assessment modelling community.

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.

This paper presents an extension of a Model Predictive Control (MPC) approach for microgrid energy management which takes into account electricity costs, power consumption , generation profiles, power and energy constraints as well as uncertainty due to variations in the environment. The approach is based on a coherent framework of control tools, like mixed-integer programming and soft constrained MPC, for describing the microgrid components dynamics and the overall system control architecture. Fault tolerant strategies are inserted in order to ensure the proper amount of energy in the storage devices such that (together with the utility grid) the essential consumer demand is always covered. Simulation results on a particular microgrid architecture validate the proposed approach.

Abstract The pressure waves generated by a train entering and running through a tunnel are studied experimentally and numerically with the aim of gaining a solid understanding of the flow in the standard tunnel geometry and in the configuration with airshafts along the tunnel surface. Laboratory experiments were conducted in a scaled facility where train models travelled at a maximum velocity of about 150 km/h through a 6-m-long tunnel. The flow was simulated by a one-dimensional numerical code modified to include the effect of the separation bubble forming near the train head. The numerical simulations reproduced well the experimental results. We tested the influence of the train cross-sectional shape and length on the compression wave produced by the vehicle entering the confined area. The cross-sectional shape was not found to be influential as long as the blockage ratio, namely the ratio between the train and tunnel cross-sectional areas, is constant. The pressure waves are one-dimensional sufficiently downwind of the tunnel mouth, which validates the comparison between the experimental and computational results. It is further shown that the numerical code can satisfactorily reproduce the pressure variations for the case with airshaft apertures along the tunnel surface.

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.

Abstract The degradation process of lithium-ion batteries has memory, i.e. it has long-range dependence (LRD). In this paper, an iterative model of the generalized Cauchy (GC) process with LRD characteristics is proposed for the remaining useful life (RUL) prediction of lithium-ion batteries. The GC process uses two independent parameters, fractal dimension and Hurst exponent, to measure the LRD of the degradation process. The diffusion term of the GC iterative model is replaced by the increment of the GC time sequences, constructed via the autocorrelation function (ACF) to describe uncertainty and the LRD characteristics of the lithium-ion batteries capacity degradation. Linear and nonlinear drift terms are used to explain the degradation trend of the lithium-ion batteries capacity. A comparison is made with fractional Brownian motion (FBM) and long-short-term memory (LSTM) network models to show how the GC iterative model has the best performance in RUL prediction of lithium-ion batteries.