Dependency of the impacts of geoengineering on the stratospheric sulfur injection strategy – Part 1: Intercomparison of modal and sectional aerosol modules

Injecting sulfur dioxide into the stratosphere with the intent to create an artificial reflective aerosol layer is one of the most studied options for solar radiation management. Previous modelling studies have shown that stratospheric sulfur injections have the potential to compensate for the greenhouse-gas-induced warming at the global scale. However, there is significant diversity in the modelled radiative forcing from stratospheric aerosols depending on the model and on which strategy is used to inject sulfur into the stratosphere. Until now, it has not been clear how the evolution of the aerosols and their resulting radiative forcing depends on the aerosol microphysical scheme used – that is, if aerosols are represented by a modal or sectional distribution. Here, we have studied different spatio-temporal injection strategies with different injection magnitudes using the aerosol–climate model ECHAM-HAMMOZ with two aerosol microphysical modules: the sectional module SALSA (Sectional Aerosol module for Large Scale Applications) and the modal module M7. We found significant differences in the model responses depending on the aerosol microphysical module used. In a case where SO2 was injected continuously in the equatorial stratosphere, simulations with SALSA produced an 88 %–154 % higher all-sky net radiative forcing than simulations with M7 for injection rates from 1 to 100 Tg (S) yr−1. These large differences are identified to be caused by two main factors. First, the competition between nucleation and condensation: while injected sulfur tends to produce new particles at the expense of gaseous sulfuric acid condensing on pre-existing particles in the SALSA module, most of the gaseous sulfuric acid partitions to particles via condensation at the expense of new particle formation in the M7 module. Thus, the effective radii of stratospheric aerosols were 10 %–52 % larger in M7 than in SALSA, depending on the injection rate and strategy. Second, the treatment of the modal size distribution in M7 limits the growth of the accumulation mode which results in a local minimum in the aerosol number size distribution between the accumulation and coarse modes. This local minimum is in the size range where the scattering of solar radiation is most efficient. We also found that different spatial-temporal injection strategies have a significant impact on the magnitude and zonal distribution of radiative forcing. Based on simulations with various injection rates using SALSA, the most efficient studied injection strategy produced a 33 %–42 % radiative forcing compared with the least efficient strategy, whereas simulations with M7 showed an even larger difference of 48 %–116 %. Differences in zonal mean radiative forcing were even larger than that. We also show that a consequent stratospheric heating and its impact on the quasi-biennial oscillation depend on both the injection strategy and the aerosol microphysical model. Overall, these results highlight the crucial impact of aerosol microphysics on the physical properties of stratospheric aerosol which, in turn, causes significant uncertainties in estimating the climate impacts of stratospheric sulfur injections.

Numerical investigation on the impact of multiple injection strategies and spray included angles on combustion and emissions in a marine diesel engine

The computational fluid dynamical software AVL-FIRE code was used for investigating the impact of multiply injection strategies and spray included angles on combustion and emissions in a marine diesel engine. The fuel injection parameters of spray included angle and pilot injection timing with pilot-main injection, as well as post injection ratio and post injection duration angle with pilot-main-post injection, were all investigated and optimized. The results indicate that retarding pilot injection timing with pilot-main injection declines high temperature region, resulting in a notable reduction in NOx emissions. Since fuel evaporation and burn are hampered by long spray penetration due to low temperature and pressure with pilot injection, a suitable spray included angle are used to offer more efficient air-fuel mixing process. A wider spray included angle simultaneously reduces soot emission and indicated specific fuel consumption (ISFC). Post injection fuel exerts impact on combustion process by causing a great disturbance to flow field during post combustion. Increasing post injection ratio from 4% to 10% at a small post injection duration angle great emission performance is achieved by simultaneous reduction in NOx and soot emissions while only using a slight consumption of ISFC. To summarize, the defeat of traditional NOx-soot trade-off occurs as both NOx and soot emissions are decreased with optimized multiple injection strategy and spray included angle. Particularly, there are respectively four cases with pilot-main injection and two cases with pilot-main-post injection, that achieve simultaneous reduction in NOx emissions, soot emission, and ISFC, compared to the prototype.

Rigorous Performance Evaluation of Stochastic Optimization for Water Injection Strategies

This paper presents an approach to optimize field water injection strategies using stochastic methods under uncertainty. For many fields, voidage replacement was the dictating factor of setting injection strategies. Determining the optimum injection-production ratio (IPR) requires extensive experience taking into consideration all the operational facility constraints. We present the outcome of a study, in which several optimization techniques were used to find the optimum field IPR values and then elaborate on the techniques? strengths and weaknesses. The synthetic reservoir simulation model, with millions of grid blocks and significant numbers of producers and injectors, was divided into seven IPR regions based on a streamline study. Each region was assigned an IPR value with an associated uncertainty interval. An ensemble of fifty probabilistic scenarios was generated by experimental design, using Latin Hypercube sampling of IPR values within tolerance limits. Scenarios were used as the main sampling domain to evaluate a family of optimization engines: population-based methods of artificial intelligence (AI), such as Genetic algorithms and Evolutionary strategies, Bayesian inference using sequential or Markov chain Monte Carlo, and proxy-based optimization. The optimizers were evaluated based on the recommended IPR values that meet the objective of minimizing the water cut by maximizing oil production and minimizing water production. The speed of convergence of the optimization process was also a subject of evaluation. To ensure unbiased sampling of IPR values and to prevent oversampling of boundary extremes, a uniform triangular distribution was designed. The results of the study show a clear improvement of the objective function, compared to the initial sampled cases. As a direct search method, the Evolutionary strategies with covariance matrix adaptation (ES-CMA) yielded the optimum IPR value per region. While examining the effect of applying these IPR values in the reservoir simulation model, a significant reduction of water production from the initial cases without an impact on the oil production was observed. Compared to ESCMA, other optimization methods have dem