global energy balance
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2021 ◽  
pp. 146808742110342
Author(s):  
Francisco Payri ◽  
Jaime Martín ◽  
Francisco José Arnau ◽  
Sushma Artham

In this work, the Global Energy Balance (GEB) of a 1.6 L compression ignition engine is analyzed during WLTC using a combination of experimental measurements and simulations, by means of a Virtual Engine. The energy split considers all the relevant energy terms at two starting temperatures (20°C and 7°C) and two altitudes (0 and 1000 m). It is shown that reducing ambient temperature from 20°C to −7°C decreases brake efficiency by 1% and increases fuel consumption by 4%, mainly because of the higher friction due to the higher oil viscosity, while the effect of increasing altitude 1000 m decreases brake efficiency by 0.8% and increases fuel consumption by 2.5% in the WLTC mainly due to the change in pumping. In addition, GEB shows that ambient temperature is affecting exhaust enthalpy by 4.5%, heat rejection to coolant by 2%, and heat accumulated in the block by 2.5%, while altitude does not show any remarkable variations other than pumping and break power.


2021 ◽  
pp. 105-133
Author(s):  
R. van Dorland ◽  
H.M. ten Brink ◽  
R. Guicherit

2021 ◽  
Author(s):  
Martin Wild

<p>A plausible simulation of the global energy balance is a first-order requirement for a credible climate model. In the present study I investigate the representation of the global energy balance in 40 state-of-the-art global climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). In the CMIP6 multi-model mean, the magnitudes of the energy balance components are often in better agreement with recent reference estimates compared to earlier model generations  such as CMIP5 on a global mean basis. However, the inter-model spread in the representation of many of the components remains substantial, often on the order of 10-20 Wm<sup>-2</sup> globally,  except for aspects of the shortwave clear-sky budgets, which are now more consistently simulated by the CMIP6 models. The substantial inter-model spread in the simulated global mean latent heat fluxes in the CMIP6 models, exceeding 20% (18 Wm<sup>-2</sup>),  further implies also large discrepancies in their representation of the global water balance. From a historic perspective of model development over the past decades, the largest adjustments in the magnitudes of the simulated present-day global mean energy balance components occurred in the shortwave atmospheric clear-sky absorption and the surface downward longwave radiation. Both components were gradually adjusted upwards over several model generations, on the order of 10 Wm<sup>-2</sup>, to reach 73 and 344 Wm<sup>-2</sup>, respectively in the CMIP6 multi-model means. Thereby, CMIP6 has become the first model generation that largely remediates long-standing model deficiencies related to an overestimation in surface downward shortwave and compensational underestimation in downward longwave radiation in its multi-model mean (Wild 2020).</p><p>Published in: Wild, M., 2020: The global energy balance as represented in CMIP6 climate models. Clim Dyn <strong>55, </strong>553–577. https://doi.org/10.1007/s00382-020-05282-7</p><p> </p>


2020 ◽  
Vol 23 (14) ◽  
pp. 2647-2653 ◽  
Author(s):  
Paulo Serodio ◽  
Gary Ruskin ◽  
Martin McKee ◽  
David Stuckler

AbstractObjective:We evaluate the extent to which Coca-Cola tried to influence research in the Global Energy Balance Network, as revealed by correspondence between the company and leading public health academics obtained through Freedom-of-Information (FOI) requests.Design:US state FOI requests were made in the years 2015–2016 by US Right to Know, a non-profit consumer and public health group, obtaining 18 030 pages of emails covering correspondence between The Coca-Cola Company and public health academics at West Virginia University and University of Colorado, leading institutions of the Global Energy Balance Network. We performed a narrative, thematic content analysis of 18 036 pages of Coca-Cola Company’s emails, coded between May and December 2016, against a taxonomy of political influence strategies.Results:Emails identified two main strategies, regarding information and messaging and constituency building, associated with a series of practices and mechanisms that could influence public health nutrition. Despite publications claiming independence, we found evidence that Coca-Cola made significant efforts to divert attention from its role as a funding source through diversifying funding partners and, in some cases, withholding information on the funding involved. We also found documentation that Coca-Cola supported a network of academics, as an ‘email family’ that promoted messages associated with its public relations strategy, and sought to support those academics in advancing their careers and building their affiliated public health and medical institutions.Conclusions:Coca-Cola sought to obscure its relationship with researchers, minimise the public perception of its role and use these researchers to promote industry-friendly messaging. More robust approaches for managing conflicts of interest are needed to address diffuse and obscured patterns of industry influence.


2020 ◽  
Vol 269 ◽  
pp. 115139 ◽  
Author(s):  
S. Ezzitouni ◽  
P. Fernández-Yáñez ◽  
L. Sánchez ◽  
O. Armas

2019 ◽  
Vol 8 (3) ◽  
pp. 8220-8227

Weather Monitoring, surveillance of enemy vehicles, sensed data delivery are few of the applications of Wireless Sensor Networks. All the applications want the nodes to spend their energy in the critical activities. Lifetime depends on the residual energy of the nodes in the network. In this work we modify the Global Energy Balance [1] algorithms to have better network lifetime by making use of fixed relay nodes at various positions in the network. The selection of relay node is based on the distance and residual energy of the relay node all through the route discovery practice. The FRNS scheme is compared with existing algorithms for diverse parameters like End to End Delay, Overall Hops Count, Overall Alive nodes and Dead nodes, Residual energy, Lifetime ratio, Energy Consumption, Throughput and Routing Overhead.


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