Autoignition and preliminary heat release of gasoline surrogates and their blends with ethanol at engine-relevant conditions: Experiments and comprehensive kinetic modeling

This work utilizes a rapid compression machine (RCM) to experimentally quantify autoignition and preliminary heat release characteristics for blends of 0 to 30% ethanol by volume into two surrogates (FGF-LLNL and FGF-KAUST) that represent a full boiling range gasoline (FACE-F). Experimental conditions cover pressures from 15 to 100 bar, temperatures from 700 to 1000 K, and diluted/stoichiometric and undiluted/lean fuel loading conditions representative of boosted spark-ignition and advanced compression ignition engines, respectively. Direct comparison is made with previously reported results for FACE-F/E0–E30 blends. A detailed gasoline surrogate chemistry model is also proposed, and chemical kinetic modeling is undertaken using the proposed model to generate chemical insights into the compositional effects and ethanol blending effects. Although experiments show similar qualitative trends between the surrogates, quantitative differences between the surrogates are obvious, where FGF-LLNL displays greater low-temperature reactivity and faster evolution of low-temperature heat release (LTHR) than FGF-KAUST, with such differences being significantly muted by ethanol blending. Flux analyses reveal the compositional effects on surrogate reactivity at the diluted/stoichiometric condition, where n-heptane facilitates first-stage ignition reactivity for FGF-LLNL/E0 by initiating earlier and more rapid ȮH branching than n-butane for FGF-KAUST/E0. Sensitivity analyses highlight the importance of non-fuel-specific interactions between ethanol and surrogate sub-chemistries in controlling the reactivity of ethanol-blended surrogates. Direct experimental comparisons between the surrogates and FACE-F, as well as between the surrogate/EtOH and FACE-F/EtOH blends highlight the need of high-fidelity surrogates that can fully replicate the target gasoline in properties including ignition reactivity and LTHR characteristics at extended conditions, as well as their response to ethanol blending. Overall, the model captures the experiments reasonably well. Nevertheless, the model displays increasing disagreement with experiments for the two surrogates at higher levels of ethanol blending, and this is found to be caused primarily by non-fuel-specific interactions between ethanol and surrogate component sub-chemistries. Futhermore, the inadequacy of the kinetic model to capture surrogate-to-surrogate differences at the diluted/stoichiometric condition suggests more physical testing is needed to facilitate more extensive model validation

Linear robust models for international logistics and inventory problems under uncertainty

Globalisation presents business organisations with some special challenges that they have never met before; they have to manage their activities in the ambit of global supply chain networks. Traditional managerial approaches, techniques and principles are no longer effective in dealing with these challenges. This article examines logistics and inventory problems in a supply chain operating in two countries where decisions have to be made with uncertain customer information. There are some differentials between two countries in terms of vehicle operation cost and capacity, labour cost, warehousing cost, etc. This article proposes three different types of robust models to integrate logistics and inventory processes between the two counties for coping with uncertain customer shipment information and the risk it entails. The first model is called the robust optimisation model with solution robustness, which provides an integrated logistics and inventory solution that is less sensitive to realisations of stochastic parameters. The second type of model is called the robust optimisation model with model robustness allowing late delivery (if it is profitable). The third type of model is called the robust optimisation model with trade-off between solution robustness and model robustness. It provides a direct way to measure the trade-off between risk and cost during the international transportation process. A series of experiments demonstrate that the proposed robust models can provide effective integrated logistics and inventory systems between two countries, which is important in today's highly competitive and dynamic business environmen