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Uncertainty analysis is useful in determining whether the results of life cycle assessment are sufficiently reliable and valid when making optimal decisions. However, only a few studies have measured carbon emissions by considering the inherent uncertainty during building construction phase that may result in the misinterpretation of critical parameters. To address such weakness, a multi-method-based uncertainty analysis framework was developed in view of the basic characteristics of the construction practice. This framework integrated the deterministic and probabilistic approaches to facilitate the uncertainty assessment in quantifying carbon emissions and to provide insights into the sensitive construction activities from the uncertainty perspective. The developed framework was examined through a mix-use project in Guangzhou China. Results showed that the uncertainties in the measurement method and geographic representativeness are the major uncertainty sources for the building construction phase. The total greenhouse gas emission for the target building was 8791.5 tonnes of carbon dioxide equivalent with a 9.8% coefficient of variation, which was in line with the result calculated by the deterministic method and with the result extrapolated based on the data collected from China. The results of the scenario analysis showed that the proportion of 1% in contribution analysis and the coefficient of variation of 18% in uncertainty analysis can be regarded as the baseline for determining the critical input parameters. This study lends a useful tool for monitoring the uncertainty of LCA studies in the construction practice. In addition, this framework can facilitate to avoid the misinterpretation of the final results during the decision-making process. Although this study focuses on Chinese construction industry, it also provides good references for measuring uncertainty of greenhouse gas emissions of construction industries around the world.

The offshore wells are subject to hostile environments of such areas as the North Sea, GOM and the high arctic. The strong loop ocean currents and induced eddies can pose significant problems for deep-water well. Broadly divided ocean currents, surface currents, bottom currents and vertical currents, interact with the deep water well structures as one of environmental forces. One of the engineering challenges in deep water drilling is temperature gradient. In the past the temperature in the wellbore was ignored and an isothermal system was assumed because no practical means existed to determine the well bore temperature profile. But the fact is that the negative thermal gradient exists between surface to seafloor and it becomes positive below the seafloor. The extreme values could be as low as 40°F and as high as 150∼200°F. In addition to low temperature condition, the significant heat exchange also occurs for high temperature and geothermal reservoirs. The universal matrix form of implicit finite differential equations is introduced to predict the temperature profile of the fluid in the well and near-wellbore formation. This paper is to combine various factors together to derive a solver for the transient temperature modeling during the dirculation of riserless drilling, which can be the basis to describe the near-wellbore well stability under geo-thermal stress and predict the annular pressure during HPHT injection or production, which can also be used to including but not limited to the dynamic temperature profile and bottom-hole temperature, improving cementing program design, casing thermal stresses to be determined.

Abstract The present study is aimed to investigate and compare effects of biodiesel-ethanol (BE) and biodiesel-n-butanol (BBu) blends on combustion, performance and emissions of a direct-injection diesel engine. Experiments were conducted on BE5 (5% ethanol and 95% biodiesel, v/v), BE10, BE15, BBu5, BBu10 and BBu15, at five engine loads and at 1800 rpm. In regard to combustion characteristics, effects on maximum heat release rate, maximum in-cylinder pressure, start of combustion, combustion duration and coefficients of variations (COVs) of IMEP and maximum increase rate of in-cylinder pressure were investigated. In regard to engine performance, effects on BSFC and BTE were investigated. The blended fuels have adverse effects on engine performance especially at low load, with the BE blends having more adverse effects than the BBu blends. Moreover, on average of the five engine loads, the BBu and BE blends increase CO emission by 13.7% and 22.8% and HC emission by 5.6% and 29.2%, respectively; but reduce NOx emission by 6.5% and 28.0%, particle mass concentration by 20.7% and 20.6% and particle number concentration by 22% and 21%, respectively. Overall, the BE blends are more effective in reducing particulate and NOx emissions but the BBu blends would lead to less increase in CO and HC emissions.

Abstract Driven by wind and buoyancy effects in the urban environment, ventilation performance and pollutant transmission are highly related to human health. In order to investigate characteristics of the single-sided natural ventilation and interunit dispersion problem, this study conducted scaled outdoor experiments in summer and winter periods in two-dimensional street canyons. Tracer gas method was adopted to predict the ventilation rate and simulate the pollutant dispersion. It was found the ventilation performance of windward and leeward rooms showed different trends with wind velocities. Archimedes number Ar was used to examine the interactions of the buoyancy and the wind forces. It revealed that the non-dimensional ventilation rates of all rooms were generally smaller than the results of buoyancy effect only. It indicates that interactions between the buoyancy and wind effects were destructive, which reduced the ventilation rates. The interunit dispersion characteristics with the wind effect were highly dependent on source locations. The results of the tracer gas concentrations of the reentered rooms were not showing simple increasing or decreasing trends. This study provides authentic and instant airflow and pollutant dispersion information in an urban environment. The dataset of this experiment can offer validations for further numerical simulations.

Abstract Renewable energy utilisation, latent energy storage, optimal system design, and robust system operation are critical elements for carbon-free buildings and communities. Machine learning methods are effective to assist the energy-efficient renewable systems during multi-criteria design and multi-level uncertainty-based operation periods. However, the current literature provides little knowledge on this topic. In this study, a state-of-the-art-review on phase change materials for cooling applications is presented, in terms of smart ventilations, intelligent PCMs charging/discharging, deterministic parametrical analysis, stochastic uncertainty-based performance prediction and optimisation. Furthermore, technical effectiveness of machine learning methods in single and multi-objective optimisations has been presented, through hybrid PCMs integrated renewable systems. Multivariables involved in the review include thermo-physical, geometrical and operating parameters of PCMs. Multi-criteria employed in the review include heat transfer rate, cooling energy storage density, heat storage and release efficiency, and indoor thermal comfort. The literature review presents technical challenges, such as tradeoff solutions between computational accuracy and efficiency, generic methods for effective selection amongst multi-diversified optimal solutions along the Pareto front, the general methodology for multi-level uncertainty quantification, smart controllers with accurate predictions under high-level parameters’ uncertainty and stochastic occupants’ behaviors. The future outlook and recommendations of machine learning methods in PCMs integrated cooling systems have also been presented as avenues for upcoming research.

Socio-technical or strategic approach to renewable energy deployment all suggests that the uptake of renewable energy technology such as solar photovoltaic is as much a social issue as a technical issue. Among social issues, one most direct and immediate component is the cost of the renewable energy technology. Because renewable electricity provides no new functionality—a clean electron does the same work as a dirty electron does—but is relatively expensive compared with fossil fuel based electricity, there is currently an under-supply of renewable electricity. Policy instruments based on economics approaches are therefore developed to encourage the production and consumption of renewable electricity, aiming to remediate the market inefficiencies that stem from the failure in internalizing the environmental or social costs of fossil fuels. In this vein, the most discussed instruments are renewable portfolio standard or quota based system and the general category of feed-in tariff. Feed-in tariff is to support output or generation of the renewable electricity by subsidizing revenues. The existing discussions have all concerned about the relative effectiveness of these two instruments in terms of cost, prices and implementation efficiency. This paper attempts a different basis of evaluation of these two instruments in terms of cost and (network) externality effects. The cost effect is driven by deploying the renewable as a manufactured technology, and the network externality effect is driven by deploying the renewable as an information technology. The deployment instruments are studied in terms of how these two effects are leveraged in the deployment process. Our formulation lends itself to evolutionary policy interpretation. Future research directions associated with this new energy policy framework is then suggested.

To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (TVDI) using data from the Project for On-Board Autonomy (PROBA-V). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.

Abstract Ground source heat pump (GSHP) systems can provide cost effective and sustainable heating and cooling for buildings while using considerably less fossil fuel than conventional heating and cooling systems. This benefit can be further enhanced by adopting hybrid GSHP (HGSHP) systems wherein the GSHP component provides the baseload thermal energy with the balance provided by conventional systems. This study compares the costs of GSHP and HGSHP systems against conventional systems under a variety of climatic conditions, exemplified by those encountered across Australia, but including conditions encountered elsewhere. The results indicate that the comparative performance of GSHP and HGSHP systems depend on many parameters including climatic conditions, ground conditions, drilling prices and prices of electricity and gas in the regions where the systems are installed. Here we show that in general, adopting GSHP or HGSHP systems over conventional systems allow property owners to pay lower lifetime costs under most climatic conditions and gas to electricity energy price ratios. The results also indicate that conventional systems may be preferred in highly heating or cooling dominant climates and at locations with high drilling costs or low energy prices. In contrast, GSHP and HGSHP systems are preferred in locations with a more balanced climate, lower drilling costs and/or higher energy prices. There is no “one size fits all” rule given the many factors that can influence the lifetime costs. The paper shows that climatic conditions, ground conditions, drilling and energy costs must all be carefully considered when assessing the most cost effective sustainable energy technologies for space heating and cooling.