Computational fluid dynamics-based parametric study on the performance of solar air heater channel

Computational fluid dynamics (CFD) plays a prominent role in the design and development of solar air heaters. The previous investigations have lagged in using a radiation model for the solar heat input; instead, most of the researchers simulated a constant heat flux model. Moreover, an extensive study on the geometrical and boundary conditions like confinement and transition length, suction, and blowing effects has not been studied. The present investigation deals with the aforementioned effects on the flow and heat transfer characteristics of the SAH channel, which is designed for residential space heating. The finite volume-based solver Ansys Fluent is used for finding the field variables. The confinement height is varied from 25 to 150 mm, and the transition length is varied from 250 to 1000 mm. The suction and blowing effect is investigated by changing the flow direction across the channel. Even though the temperature rise is less significant with respect to confinement height and transition length, the effective efficiency increases with decreasing channel height and increasing transition length. In general, blowing of air across the channel gives better performance than suction. When comparing them, the influence is less in temperature rise and more in pressure drop for the channel height of 25 mm, whereas the channel height of 150 mm has better influence in temperature rise and less influence in pressure drop.

Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study

Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases. We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest. Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.

A comparative study of radiation models on propane jet fires based on experimental and computational studies

Radiation as a consequence of jet fires is one of the significant parameters in process industry events. In the present work, the open field vertical propane jet fire was studied via experimental and computational fluid dynamics (CFD). The predicted values of radiation were verified at three locations in the horizontal direction from the jet fire. In the simulation section, four radiation models of Monte Carlo (MC), P-1, Discrete Transfer (DT), and Rosseland were applied to find the fine model for simulating the jet fire. Shear Stress Transport (SST) and Eddy Dissipation Concept (EDC) models are employed for combustion and turbulence, respectively. The estimated data by the simulation demonstrated that the MC radiation is better than the other models with an average error of 5% for predicted incident radiation from the jet flame axis. Also, the P-1 radiation model had an above 65% error at around the jet fire, but due to the error of less than 15% estimated by MC and DT models, these radiation models could simulate the jet flame radiation. The simulation outcomes proved that the Rosseland radiation model is not applicable owing to a lack of accurate temperature prediction.

Influence of semi-arid environment on radiation use efficiency and other growth attributes of lentil crop

Solar radiation (SR) is essential for yield improvement in lentil, which is a crop of marginal environments. Herein, experiments were conducted over 2 years under a semi-arid environment to study the radiation interception (RI), efficiency, growth, and development of three lentil genotypes (Punjab Masoor-2009 (PM-2009), NIAB Masoor-2006 (NM-2006), and NIAB Masoor-2002 (NM-2002)) in relation to three nitrogen rates (13, 19, and 25 kg ha^-1). Seasonal dynamics of intercepted photoactive radiation (IPAR) and cumulated photosynthetic photon flux density were highly associated with seasonal dynamics of leaf area index (LAI), with a high value of R² (0.93 and 0.89) across all nitrogen rates and genotypes in both years. Nitrogen application promoted growth, and maximum LAI (3.97 and 3.57) and RI (324 and 301 MJ m^-2) were attained for the first and second years of study, respectively. Biomass and yield were positively associated with IPAR. Variation in radiation absorption (RA) among genotypes was due to different patterns of LAI development. In both years, yield (23% and 25%) and radiation use efficiency (RUE) for grain yield (0.44 and 0.37 g MJ^-1) were respectively higher for PM-2009 than for the other genotypes. Genotype PM-2009 had 15 days shorter crop cycle than others while 14% higher GDDs accumulated in the first year compared with the second due to the higher temperature. High nitrogen (25 kg ha^-1) application resulted in higher dry matter (DM), and grain yield (GY), while RUE and PAR were not statistically different under 19 kg N ha^-1 application across years. Genotypes PM-2009 and NM-2006 may perform reasonably well under arid to semi-arid regions at farmer field. These findings may assist researchers and crop modelers to optimize the lentil ideotype for efficient light utilization.

Inferences about spatiotemporal variation in dengue virus transmission are sensitive to assumptions about human mobility: a case study using geolocated tweets from Lahore, Pakistan

Billions of users of mobile phones, social media platforms, and other technologies generate an increasingly large volume of data that has the potential to be leveraged towards solving public health challenges. These and other big data resources tend to be most successful in epidemiological applications when utilized within an appropriate conceptual framework. Here, we demonstrate the importance of assumptions about host mobility in a framework for dynamic modeling of infectious disease spread among districts within a large urban area. Our analysis focused on spatial and temporal variation in the transmission of dengue virus (DENV) during a series of large seasonal epidemics in Lahore, Pakistan during 2011-2014. Similar to many directly transmitted diseases, DENV transmission occurs primarily where people spend time during daytime hours, given that DENV is transmitted by a day-biting mosquito. We inferred spatiotemporal variation in DENV transmission under five different assumptions about mobility patterns among ten districts of Lahore: no movement among districts, movement following patterns of geo-located tweets, movement proportional to district population size, and movement following the commonly used gravity and radiation models. Overall, we found that inferences about spatiotemporal variation in DENV transmission were highly sensitive to this range of assumptions about intra-urban human mobility patterns, although the three assumptions that allowed for a modest degree of intra-urban mobility all performed similarly in key respects. Differing inferences about transmission patterns based on our analysis are significant from an epidemiological perspective, as they have different implications for where control efforts should be targeted and whether conditions for transmission became more or less favorable over time.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (10.1140/epjds/s13688-018-0144-x) contains supplementary material.

Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model

Background

Mathematical models of human mobility have demonstrated a great potential for infectious disease epidemiology in contexts of data scarcity. While the commonly used gravity model involves parameter tuning and is thus difficult to implement without reference data, the more recent radiation model based on population densities is parameter-free, but biased. In this study we introduce the new impedance model, by analogy with electricity. Previous research has compared models on the basis of a few specific available spatial patterns. In this study, we use a systematic simulation-based approach to assess the performances.

Methods

Five hundred spatial patterns were generated using various area sizes and location coordinates. Model performances were evaluated based on these patterns. For simulated data, comparison measures were average root mean square error (aRMSE) and bias criteria. Modeling of the 2010 Haiti cholera epidemic with a basic susceptible–infected–recovered (SIR) framework allowed an empirical evaluation through assessing the goodness-of-fit of the observed epidemic curve.

Results

The new, parameter-free impedance model outperformed previous models on simulated data according to average aRMSE and bias criteria. The impedance model achieved better performances with heterogeneous population densities and small destination populations. As a proof of concept, the basic compartmental SIR framework was used to confirm the results obtained with the impedance model in predicting the spread of cholera in Haiti in 2010.

Conclusions

The proposed new impedance model provides accurate estimations of human mobility, especially when the population distribution is highly heterogeneous. This model can therefore help to achieve more accurate predictions of disease spread in the context of an epidemic.

Electronic supplementary material

The online version of this article (10.1186/s12942-017-0115-7) contains supplementary material, which is available to authorized users.