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CFD predictions of laminar mixed convection of Al2O3–water nanofluids by single-phase and three different two-phase models (volume of fluid, mixture, Eulerian) are compared. The elliptical, coupled, steady-state, three-dimensional governing partial differential equations for laminar mixed convection in a horizontal tube with uniform heat flux are solved numerically using the finite volume approach. It is found that single-phase and two-phase models predict almost identical hydrodynamic fields but very different thermal ones. The predictions by the three two-phase models are essentially the same. For the problem under consideration the two-phase models give closer predictions of the convective heat transfer coefficient to the experimental data than the single-phase model; nevertheless, the two-phase models over-predict the enhancement of the convective heat transfer coefficient resulting from the increase of the alumina volume fraction. The results are calculated for two Reynolds numbers (1050 and 1600) and three nanoparticle volume concentrations (<2%). Although single-phase and two-phase models have been used before to analyze mixed convection of nanofluids, this is the first systematic comparison of their predictions for a laminar mixed convection flow which includes the hydrodynamic characteristics and the effect of temperature dependent properties.

The majority of local respondents in a large-scale survey were in favour of planned local wind farms on the Danish coast, despite these wind farm plans being the source of wider public and political contestation and opposition. Here we discuss results from the open-ended questions in the survey, specifically focusing on comments expressing how some respondents felt split in their views of these wind farms, accepting the need for renewable energy while at the same time being concerned about the potential local impact of the wind farms. Building on previous theoretical propositions relating to energy infrastructure opposition, here we apply the concept of cognitive polyphasia in some depth, providing a socio-cognitive account of the internal contradiction of being positively disposed to renewable energy in principle, but concerned about or opposed to specific de­velopments in localities. We distinguish a cognitive polyphasic account of such mixed feelings from cognitive dissonance accounts, and we identify several types of polyphasic representations, providing a basis for further work in other cases. The majority of local respondents in a large-scale survey were in favour of planned local wind farms on theDanish coast, despite these wind farm plans being the source of wider public and political contestation andopposition. Here we discuss results from the open-ended questions in the survey, specifically focusing oncommentsexpressinghowsomerespondentsfeltsplitintheirviewsofthesewindfarms,acceptingtheneedforrenewableenergywhileatthesametimebeingconcernedaboutthepotentiallocalimpactofthewindfarms.Building on previous theoretical propositions relating to energy infrastructure opposition, here we apply theconceptofcognitivepolyphasiainsomedepth,providingasocio-cognitiveaccountoftheinternalcontradictionof being positively disposed to renewable energy in principle, but concerned about or opposed to specific de-velopments in localities. We distinguish a cognitive polyphasic account of such mixed feelings from cognitivedissonanceaccounts,andweidentify severaltypesofpolyphasicrepresentations,providingabasisforfurtherworkinothercases

Numerical calculations of the two-phase flow in an experimentally well investigated research combustor are presented. The comparison between measurements and calculations demonstrates the possibilities of the state-of-the-art Euler/Lagrange method for calculating two-phase flows, when applied to a complex reacting liquid fueled combustor. The governing equations for gaseous and liquid phase are presented, with special emphasis on the control of the coupling process between the two phases. The employed relaxation method together with a protocol of convergence show a suitable way to achieve a fast and accurate solution for the strongly coupled two-phase flow under investigation. Furthermore methods are presented to simulate the stochastic behaviour of the atomization process that appears due to the use of an airblast atomizer. In addition to the numerical methods, experimental techniques are presented, which are used to achieve detailed information about droplet starting conditions.

Co-simulation of heterogeneous systems allows for in-depth analysis of various aspects of power systems’ operation while staying within the environments of the simulation tools that are best fit to represent their respective domains. Equipped with a proprietary co-simulation platform, the paper focuses on the issue of power-conjugate coupling between parts of power grids modeled in transient stability and electromagnetic transient simulation tools. The problems of co-simulation stability and precision in presence of delays are tackled by means of designing a proper coupling interface. It is shown that two established interface methods – the V-I method and the Transmission Line Interface – are special cases of a generalized interface framework proposed in the paper. Moreover, a new interface algorithm is described by parametrizing the generalized framework. Analytical tools are also formulated to aid in the analysis of interface stability and precision via the concepts of passivity and transparency. Simulation results of benchmark systems of various complexity demonstrate the application of the developed power coupling interface.

The poultry industry is one of the largest and fastest growing sectors of livestock production in the world. The estimated 2010 world flock was over 18 billion birds with a yearly manure output of 22 million tonnes. Storage and disposal of raw poultry manure has become an environmental problem because of the associated air, water and soil pollution. Environmental and health problems such as odor and pathogens that may arise during and after land application of raw manure can be eliminated by drying. Dried manure can be utilized as a soil conditioner to improve soil tilth and reduce the problems associated with soil compaction and as a feed for ruminants because of its high nitrogen content. The aim of this study was to investigate the kinetics of thin layer drying of poultry manure and evaluate the effects of drying with heated air on the chemical and biological properties of manure. The effects of temperature and depth of manure layer were evaluated. The profile of the moisture content of poultry manure followed an exponential decay curve. The moisture decay constant was affected by the drying temperature and the depth of the manure layer. At the three temperature levels studied, the time required to dry poultry manure in 1 cm-deep layer was the least, followed by 2 and 3 cm-deep layers, respectively. The diffusion coefficient increased with both temperature and depth of drying layer, but did not show a linear increase with either variable. The optimum depth for drying manure (at which the highest drying effectiveness occurred) was 3 cm. Drying manure at 40-60°C resulted in the loss of 44-55% of the total Kjeldahl nitrogen, with losses increasing with both the temperature and depth of manure. The pH of the manure decreased from the initial value of 8.4 before drying to about 6.6 after drying. The odor analysis indicated that dried poultry manure did not have an offensive odor. Drying achieved 65.3 and 69.3% reductions in odor intensity and offensiveness, respectively. Reductions in the number of bacteria, mold/yeast and E.coli were 65-99, 74-99 and 99.97% respectively. The greatest reductions in microbial population occurred at the highest temperatures (60°C) and the thinest manure depths (1 cm). Heated air drying of poultry manure at temperatures between 40 and 60°C was effective in killing pathogens and removing odor.

In recent years, prices for photovoltaics have fallen steadily and the demand for sustainable energy has increased. Consequentially, the assessment of roof surfaces in terms of their suitability for PV (Photovoltaic) installations has continuously gained in importance. Several types of assessment approaches have been established, ranging from sampling to complete census or aerial image analysis methodologies. Assessments of rooftop photovoltaic potential are multi-stage processes. The sub-task of examining the photovoltaic potential of individual rooftops is crucial for exact case study results. However, this step is often time-consuming and requires lots of computational effort especially when some form of intelligent classification algorithm needs to be trained. This often leads to the use of sampled rooftop utilization factors when investigating large-scale areas of interest, as data-driven approaches usually are not well-scalable. In this paper, a novel neighbourhood-based filtering approach is introduced that can analyse large amounts of irradiation data in a vectorised manner. It is tested in an application to the city of Giessen, Germany, and its surrounding area. The results show that it outperforms state-of-the-art image filtering techniques. The algorithm is able to process high-resolution data covering 1 km2 within roughly 2.5 s. It successfully classifies rooftop segments which are feasible for PV installations while omitting small, obstructed or insufficiently exposed segments. Apart from minor shortcomings, the approach presented in this work is capable of generating per-rooftop PV potential assessments at low computational cost and is well scalable to large scale areas.

Abstract Solutions containing hydrofluoric acid (HF), hydrochloric acid (HCl), and hydrogen peroxide (H2O2) were investigated as novel acidic, NOx-free etching mixtures for texturing of monocrystalline silicon wafers. High etch rates of up to 13.3 nm s−1 were observed at room temperature, which are comparable to the etch rates of KOH-IPA solutions. The silicon surface was investigated by scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM), indicating pyramidal textures for diamond wire and SiC-slurry sawn as well as saw-damage etched (polished) wafers. Non-stirred baths generate random pyramidal structures while constantly stirred solutions generate novel random inverted pyramidal surface structures. The light trapping efficiency of wafers etched by the HF-HCl-H2O2 solutions was compared by UV/vis-reflectivity measurements to KOH/i-propanol specimens indicating lower reflectivities for the HF-HCl-H2O2-treated samples. Using the ‘wafer ray tracer’ (pvlighthouse.com) the light absorption properties of monomodal and random inverted pyramid structures were simulated and compared to well-known random and monomodal textures for PERC solar cells, clearly indicating the best performance for random inverted pyramids. Besides, simulation of a PERC solar cell on a roof top at our university was performed, indicating improved performance, especially for random inverted pyramid textures.

Abstract In this paper, energy and exergy analyses of the geothermal-based hydrogen production via thermochemical water decomposition using a new, four-step copper–chlorine (Cu–Cl) cycle are conducted, and the respective cycle energy and exergy efficiencies are examined. Also, a parametric study is performed to investigate how each step of the cycle and its overall cycle performance are affected by reference environment temperatures, reaction temperatures, as well as energy efficiency of the geothermal power plant itself. As a result, overall energy and exergy efficiencies of the cycle are found to be 21.67% and 19.35%, respectively, for a reference case.

Smart charging of Electric Vehicles (EVs) reduces operating cost, allows more sustainable battery usage, and promotes the rise of electric mobility. In addition, bidirectional charging and improved connectivity enable efficient power grid support. Today, however, uncoordinated charging, e.g., governed by users’ habits, is still the norm. Thus, the impact of upcoming smart charging applications is mostly unexplored. We aim to estimate the expenses inherent with smart charging, e.g., battery aging costs, and give suggestions for further research. Using typical onboard sensor data we concisely model and validate an EV battery. We then integrate the battery model into a realistic smart charging use case and compare it with measurements of real EV charging. The results show that i) the temperature dependence of battery aging calls for precise thermal models for charging power greater than 7 kW, ii) disregarding battery aging underestimates EVs’ operating cost by approx. 30%, and iii) the profitability of Vehicle-to-Grid (V2G) services based on bidirectional power flow, e.g., energy arbitrage , depends on battery aging costs and the electricity price spread.