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Abstract The field of energy harvesting holds the promise of making our buildings “smart” if effective energy sources can be developed for use in ambient indoor conditions. Photovoltaics (PV), especially in its thin flexible form for easy integration, become a prime candidate for the aim, if tailored for low-density artificial light. We designed a test system which enabled us to measure the performance of PV devices under compact fluorescent lamp (CFL) and light-emitting diode (LED) illumination at different illuminance levels and compared polycrystalline and amorphous silicon cells with our own flexible dye solar cells (DSCs). Whereas poly-Si cells, with 15% outdoor efficiency, delivered at 200 lux under CFL only 2.8 μW/cm2 power density (and an efficiency of 4.4%), a-Si specifically designed for indoors, gave 5.9 μW/cm2 and 9.2% efficiency under the same CFL conditions (and 7.5% efficiency under LED). However, we show that the customization of flexible DSCs, by simply formulating ad-hoc less-concentrated, more transparent electrolytes, enabled these devices to outperform all others, providing average power densities of 8.0 μW/cm2 and 12.4% efficiencies under 200 lux CFL (more than quadruple compared to those measured at 1 sun), and 6.6 μW/cm2 and 10% efficiency under 200 lux LED illumination.

Small scale (solar-) thermally driven cooling systems suffer from two important drawbacks: firstly, the systems usually offer no means of adapting the chilling capacity to the actual load; secondly constantly running pumps and fans lead to high auxiliary electricity consumption even when the available driving and cooling water temperatures only allow a reduced chilling capacity. To solve these problems a generic approach for controlling the main parasitic electrical devices – the cooling water pump and the heat rejection fan - as a function of the actual boundary conditions was developed. Different variants of control strategies are analyzed in different system configurations under a variety of climates and load conditions by means of dynamic system simulations in TRNSYS. The most typical combinations of ab- and adsorption chillers with dry cooler and wet cooling tower are covered. The results show that capacity modulation can be realized well by this approach. Additionally electricity savings of up to 25% can be achieved for reasonably sized systems compared to a reference control strategy with fixed pump speed and fixed cooling water set temperature. Yet it becomes obvious that the concrete savings depend strongly on the system configuration and boundary conditions.

A volume transmission phase holographic element was designed and constructed to perform as a building integrated photovoltaic concentrator. The holographic lens diffracts light in the spectral bandwidth to which the cell presents the highest sensitivity with a concentration factor of 3.6X. In this way, the cell is protected from overheating because the infrared for which the solar cell is not sensitive is not concentrated. In addition, based on the asymmetric angular selectivity of the volume hologram and based on the linear concentration, only single-axis tracking is needed. The use of the holographic element increases the efficiency of the PV cell by 3% and the fill factor by 8%.

The present paper is focused on the design, optimization and analysis of a complex parallel hybrid electric vehicle, equipped with two electric machines on both the front and rear axles, and on the evaluation of its potential to reduce fuel consumption and NOx emissions over several driving missions. The vehicle has been compared with two conventional parallel hybrid vehicles, equipped with a single electric machine on the front axle or on the rear axle, as well as with a conventional vehicle. All the vehicles have been equipped with compression ignition engines. The optimal layout of each vehicle was identified on the basis of the minimization of the overall powertrain costs during the whole vehicle life. These costs include the initial investment due to the production of the components as well as the operating costs related to fuel consumption and to battery depletion. Identification of the optimal powertrain control strategy, in terms of the management of the power flows of the engine and electric machines, and of gear selection, is necessary in order to be able to fully exploit the potential of the hybrid architecture. To this end, two global optimizers, one of a deterministic nature and another of a stochastic type, and two real-time optimizers have been developed, applied and compared. A new mathematical technique has been developed and applied to the vehicle simulation model in order to decrease the computational time of the optimizers. First, the vehicle model equations were written in order to allow a coarse time grid to be used, then, the control variables (i.e., power flow and gear number) were discretized, and the values of the main model variables were evaluated and stored in a matrix (referred to as configuration matrix), for all the possible combinations of control variables and for each time node, before the optimization process. In this way, the optimizers can read the actual values of the relevant variables from the pre-processed data, instead of calculating them iteratively during the optimization stage. The performance of the hybrid vehicles has been evaluated over several driving missions, including the NEDC, the FTP, the AUDC, the ARDR and the AMDC, and a detailed energetic analysis has been carried out in order to clearly identify the key operating modes that contribute most to the fuel consumption and NOx emission savings of the different hybrid architectures.

Abstract A new methodology for estimation of the key characteristics of commercial scale Vanadium Redox Flow Battery (VRFB) at different operating conditions is proposed. The method is based on a set of simplified correlations that allow estimating VRFB rated power, capacity and operation time directly from the geometry of stack and tank without detailed numerical simulation of the battery. The study is focused on investigation of a kilo-watt class VRFB system (5 kW/15kWh) considering a wide range of current densities (40–100 mA cm−2). The proposed simplified approach is validated considering the most representative cases of battery operation strategies related to slow and fast modes. It demonstrated high accuracy for the estimation of rated power and operation time (average error below 3%) as well as stored energy (average error below 6%) compare to results of detailed numerical simulation. As a result, the proposed methodology can be used as a simple tool for development of proper battery usage protocol (a schedule for battery usage), which could allow avoiding over/underestimation of committed battery energy and power during battery operation. In addition, the obtained results can be also used in order to improve the accuracy of techno-economic studies determining the most economically attractive cases for application of VRFB systems.

Abstract Lately, the exhaust gas recycle usage, widely diffused in Diesel engines, has been adopted in SI engines as well. It is a cheap technique which allows a sound control of NO formation meanwhile it can improve engine thermodynamics. In this paper, the influence of EGR on the operation of a turbocharged spark ignition engine has been evaluated by using both experimental and numerical techniques. In particular, since knock occurrence is a crucial point in the optimization of a turbocharged SI engine, the improvement in knock resistance, at high load operation, has been assessed. First, a method for knock detection and quantification has been illustrated. Then, the influence of EGR on engine performance, octane requirement and exhaust gas temperature was measured at two different rotational speed values and WOT operation. Since EGR has produced a drop in engine performance (between 10% and 13%) and an increase in knock resistance, a new set of main control variables has been determined in order to restore the original torque level while achieving a significant decrease in specific fuel consumption (between 6% and 11%). At the end, numerical analyses of engine combustion, aimed to explain the results of experimental investigations, have been carried out and a summary is reported in the paper.

Energy production from clean sources is mandatory to reduce pollutant emissions. Among different options for hidden hydropower potential exploitation, Pump-as-Turbine (PaT) represents a viable solution in pico- and micro-hydropower applications for its flexibility and low-cost. Pumps are widely available in the global market in terms of both sizes and spare parts. To date, there are several PaTs’ performance prediction models in the literature, but very few of them use optimization algorithms and only for specific and limited prediction goals. The present work proposes evolutionary Artificial Neural Networks (ANNs) based on JADE, which is a typology of differential evolution algorithm, to forecast Best Efficiency Point (BEP) and performance curves of a PaT starting from the pump operational data. In this model, JADE is employed as optimizer of basic ANNs to upgrade parameter values of the learning rate, weights, and biases. The accuracy of the proposed model is evaluated through experimental data available from the literature and compared to a basic ANN and two versions of the differential evolution algorithm. Results are also validated with experiments on a PaT showing that the proposed method can achieve an average R2-value of 0.97, which is 5% higher than the one obtained with a basic ANN.

Abstract Carbon capture and storage is considered as promising technique by the scientific community to reduce CO2 emissions and is composed by three different stages: CO2 capture, transportation and storage. The capturing plant has to be installed where processes involving large CO2 emissions, as power generation from fossil fuel, concrete and iron plants are present. The captured CO2 in real applications will be likely a CO2 dominated mixture with a content of several impurities, depending on the capture process which presence affects the thermo-physical properties of the pure CO2, especially increasing the vapour pressure curve. Such behaviour plays an important role in the control of the ductile fracture, whenever the CO2 is transported using pipelines. In the unfortunate event of a pipeline failure, the study of the pipeline decompression can be useful to estimate whether the fracture will remain confined or will propagate. The residual pressure acting on the crack tip of the pipeline represents the energy source governing the fracture’s propagation. The pressure profile is given by the expansion wave, which propagates along the pipeline during the fracture. In the literature, it is common practice to model the expansion wave by assuming one-dimensional isentropic decompression. In this study, a new code developed by the authors for modelling the decompression behaviour in the fluid is presented. The code is based on the Peng-Robinson equation of state with the Peneloux correction correlation matching the density of pure components to experimental data. The sound velocity in the two-phase region has been modelled with the method proposed by Nichita et al., which increase the accuracy of prediction with respect to the most popular Wood’s method. The code was validated against experimental and numerical data relating to hydrocarbon gas mixtures and CO2-rich mixtures. In particular, the results obtained for the hydrocarbon gas mixture decompression have been compared with experimental data and the predictions calculated by the software GASDECOM, in order to assess the accuracy of the code, that was then utilized to calculate the expansion wave curves for CO2-rich mixtures. The results obtained have been compared with experimental data and with predictions generated by the GERG2008 equation of state, found in literature. The simulated expansion wave curves show a good agreement with the experimental data for the tested compositions, especially for the plateau characterising the two-phase transition of rich gas compositions. The plateau corresponds to the saturation pressure, which is a key parameter for the fracture propagation control in CO2 pipelines, since the material toughness required to prevent a propagating ductile fracture is determined by applying the condition that the fracture propagation curve does not cross this value.