Wide Operating Conditions Research Articles

Multiphysics simulation explaining the behaviour of evaporation-driven nanoporous generators

Evaporation-induced electricity generation in porous nanomaterials has recently attracted considerable attention due to relatively high produced voltages and wide operating conditions. Here, we present a combined study of computational and experimental work exploiting finite-element method simulations to find the critical parameters influencing the performance of such generators. The simulated behaviour is found to agree with the experimental data within typical variation of the measurements. We find that the electrical power produced by the generator depends not only on the properties of the porous material, but also on the surrounding environment of the generator. Particularly, the pore size and geometry are found to have a significant influence on the output power, highlighting the importance of accurate characterization of the samples and careful control of the laboratory conditions when performing experimental work. Increasing the pore size from 5 to 20 nm improves the simulated output voltage from 0.12 to 0.47 V, while increasing the ambient humidity to 100% will prevent voltage generation completely. The obtained results can guide the future design of generators based on water evaporation induced capillary flow in a nanoporous carbon black film, leading to more efficient power production.

Monodispersed CuO nanoparticles supported on mineral substrates for groundwater remediation via a nonradical pathway

Nonradical oxidation based on singlet oxygen (1O2) has attracted great interest in groundwater remediation due to the selective oxidation property and good resistance to background constituents. Herein, recoverable CuO nanoparticles (NPs) supported on mineral substrates (SiO2) were prepared by calcination of surface-coated metal-plant phenolic networks and explored for peroxymonosulfate (PMS) activation to generate 1O2 for degrading organic pollutants in groundwater. CuO NPs with a close particle size (40 nm) were spatially monodispersed on SiO2 substrates, allowing highly exposure of active sites and consequently leading to outstanding catalytic performance. Efficient removal of various organic pollutants was obtained by the supported CuO NPs/PMS system under wide operation conditions, e.g., working pH, background anions and natural organic matters. Chemical scavenging experiments, electron paramagnetic resonance tests, furfuryl alcohol decay and solvent dependency experiments confirmed the formation of 1O2 and its dominant role in pollutants removal. In situ characterization with ATR-FTIR and Raman spectroscopy and computational calculation revealed that a redox cycle of surface Cu(II)-Cu(III)-Cu(II) was responsible for the generation of 1O2. The feasibility of the supported CuO NPs/PMS for actual groundwater remediation was evaluated via a flow-through test in a fixed-bed column, which manifested long-term durability, high mineralization ratio and low metal ion leaching.

Modelling two-phase Z factor of gas condensate reservoirs: Application of Artificial Intelligence (AI)

Accurate estimation of two-phase compressibility factor (Z factor) in gas condensate reservoirs is essential for understanding phase behaviour and reliable simulation studies. Experimental measurements of Z factor in wide operational conditions are cumbersome and costly. Hence, engineers rely on existing models such as cubic equation of states (EoSs) and empirical models for estimating such important property. In this study initially the accuracy of prevalent available Z factor models for prediction of two-phase gas condensate Z factor were examined. Then, several smart models including two multilayer perceptron neural networks known as feedforward neural network (FFNN) and cascade forward neural network (CFNN) optimized with Levenberg-Marquardt (LM) and Bayesian-Regularization (BR) algorithms and one Adaptive Neuro Fuzzy Inference System (ANFIS) optimized with Particle Swarm Optimization (PSO) were developed based on 19518 data points for the same task. The databank covers gas condensate two-phase Z factor, compositional variations, molecular weight of heptane plus (MWC 7+ ) and gas specific gravity in wide range of pressure and temperature. The results indicate that the FFNN-BR predicts all experimental data with high accuracy with an average absolute relative deviation of 0.321%. Furthermore, the accuracy of the developed model over two cubic EoSs and three empirical models from literature was confirmed. Finally, based on the sensitivity analysis, it was found that the pseudoreduced pressure (P pr ), MWC 7+ , and molar content of C 7+ in the mixture have the highest impact on prediction of two-phase Z factor. The proposed tools can be utilized for accurate prediction of gas condensate two-phase Z factor to ensure accurate simulation studies and better phase behaviour treatments. • Several artificial intelligence (AI) models were proposed for prediction of gas condensate two-phase Z factor. • The most comprehensive databank consists of 19518 data points were used for developing the models. The databank covers wide range of operational conditions. • The proposed models are feed forward neural networks (FFNN), cascade forward neural network (CFNN) and Adaptive Neuro Fuzzy Inference System (ANFIS) optimized with Levenberg-Marquardt (LM) and Bayesian-Regularization (BR) and particle Swarm Optimization (PSO). • The superiority of the FFNN, CFNN and ANFIS-PSO over empirical correlations and cubic equation of states have been confirmed. • The developed models could be used as effective tools for accurate prediction of gas condensate two-phase Z factor.

Screening versatile water/adsorbent working pairs for wide operating conditions of adsorption heat pumps

Computational screening of 231 adsorbent/water working pairs from experimental water adsorption isotherm database for AHPs is carried out based on effective coefficient of performance and effective temperature lift under practical working condition.

A study of oxygen-enriched reactivity-controlled compression ignition combustion in a diesel research engine under varying loadings and premixed ratios

ABSTRACT High-Efficiency Reactivity-Controlled Compression Ignition (RCCI) with ultra-low NOx and Soot emissions still needs to be investigated in terms of its operating conditions such as high load range and increasing CO and unburnt HC emissions, which are critical issues to be addressed before implementation for practical engines. Therefore, as the main factors affecting the RCCI combustion are the engine load and premixed ratio of low reactivity-fuel, in the present study, the effect of these parameters in a modified single-cylinder research engine equipped with common-rail direct injection (CRDI) on RCCI combustion are experimentally studied in detail along with oxygen-enrichment environment at two stages. At first, the effect of engine load (20%, 40%, and 60% of max.torque at 2400 rpm) and premixed ratio (Rp, 15% to 60% in increments of 15%) in enriched O2 environment to 29% was investigated while in second stage, increasing oxygen amount in intake air under low-mid load range (40% of max.torque) and premixed ratio (Rp, 30% replacement of total energy given to engine) was investigated, and compared with atmospheric air conditions. Following results were noted in the first part of the study when the engine load was increased at all premixed ratios; in-cylinder pressure and rate of heat release increased with shortening the ignition delay, IMEP increased up to 12% along with decrease in CO (by 38%), unburned HC (by 32%) and smoke opacity (by 24%) with the cost of increasing NOx emissions by 162% when compared to atmospheric air. In the second stage, increase in oxygen ratio resulted in decrease of ignition delay period and prolong combustion duration that have led to RCCI engine performance to be better and sharp reductions in the incomplete combustion emissions up to 36%. It was concluded that enriched oxygen is a beneficial way for reducing exhaust emissions during RCCI combustion at wider operating conditions.

Performance measurement and evaluation of an ionic liquid electrospray thruster

As a novel micro-propulsion system for small satellites (from micro to nano), the ionic liquid electrospray propulsion system is a promising candidate. However, performance measurement and evaluation of the Ionic Liquid Electrospray Thruster (ILET) is one of the most challenging issues for practical application, due to the difficulties in the development of a prototype and direct measurements of micro-thrust and small flow rate. To address this issue, a Modular Ionic Liquid Electrospray Thruster (MILET) prototype is constructed, and a diagnostic system for thrust and mass flow rate is specially developed based on an analytical balance method. With the ionic liquid 1-ethyl-3-methylimidazolium tetrafluoroborate as the propellant, a series of experiments is carried out on the MILET prototype under a wide operating condition through changing the applied voltage to control the thrust. Under different applied voltages, the thrust and the mass flow rate of the propellant are directly measured. The propulsive performance parameters of the thruster, such as thrust, specific impulse, thrust-to-power ratio, thruster efficiency, etc., are comprehensively analyzed. Then, a performance comparison is made between the MILET and other representative ILETs. With a relatively low applied voltage ranging from 1550 V to 2000 V, the MILET achieves a quasi-constant specific impulse of 1263 s with the averaged thrust-to-power ratio of 65.2 μN/W and thruster efficiency of 40.7%. The performance of ILET is also compared with other typical electric propulsions. The results demonstrate that the ILET exhibits an excellent ability of minimalization with high specific impulse and thruster efficiency, which guarantees a great superiority in micro propulsions. Finally, the ways to further improve the performance of ILET are discussed, which further confirms the potential prospect of ILET. The present result helps to advance the development and application of ILET.

Flexibility oriented adaptive modeling of combined heat and power plant under various heat-power coupling conditions

The flexible operation of thermal power plant has become a research hotspot for its safety and validity in eliminating power grid fluctuation from large-scale integration of renewable energy. Heat-power decoupling is effective technique to promote flexibility of combined heat and power plant. To address the insufficient dynamics investigation of combined heat and power plant under different heat-power decoupling strategies, an adaptive modeling approach is proposed. This method systematically fuses the mechanism analysis and data-driven fuzzy modeling to prepare for the controller design for rapid and deep load regulation in flexible operation. Firstly, the model structures with unknown parameters are determined by mechanism analyses to reflect the effects of various decoupling techniques on the characteristics of plant. Secondly, in view of the on-site data under wide load operation conditions, corresponding model structure is adaptively matched, and a new Takagi–Sugeno fuzzy modeling scheme depend on biquantum pigeon optimization algorithm is constructed for model parameters acquisition. The effectiveness of the proposed approach is illustrated based on a 350 MW supercritical combined heat and power unit. Simulation results illustrate that the adopted method can rapidly characterize dynamics of plant under different heat-power coupling conditions. All the model identification and verification errors in wide load operation conditions are less than 3.1% plant output, which reveals high modeling precision more than 97%. Thus, the established model provides solid foundation on highly matched controller for ensuring operational flexibility.

Thermal Analysis of a Forced Flow Diffusion Absorption Refrigeration System for Fishing-Boat Exhaust Waste Heat Utilization

Ammonia water absorption refrigeration systems are effective in utilizing fishing-boat exhaust waste heat for cryopreservation. However, the liquid level control and the use of a solution pump characterized by small flowrate and high-pressure head result in poor reliability in the traditional system. Besides, the system must necessarily be designed anti-swaying and anti-corrosion. This paper proposes a forced flow diffusion absorption refrigeration system, in which an inherently leak-free canned motor pump and an ejector are employed to provide the driving forces of the gas and liquid loops. The approximate single pressure operation allows for a simple passive liquid sealing control without throttling valves. The system adopts an integrated cooling strategy which allows the system to operate under swaying conditions, and the external seawater cooled heat exchanger avoids internal corrosion and leakage. The thermal analysis shows the system is valid to be operated under wide operating conditions, and the coupled gas and solution circulation ratios determined the performance of the novel system. There is an optimal ammonia mass fraction difference in the gas loop to obtain the optimal COP. The COP reaches 0.4 when the temperatures at the outlets of the generator, evaporator, absorber, and condenser are 160, −15, 35, and 35°C, respectively. The novel system provides a reliable absorption refrigeration system design for fishing-boat applications.

Emission and performance characteristics of CRDI diesel engine using Quadruple injection strategy with different pilots and post injection timing

Multiple injections in association with suitable (High/heavy) exhaust gas recirculation (EGR) is a promising technique to achieve low temperature combustion (LTC) regime in modern CRDI (common rail direct injection) diesel engine. This helps for simultaneous reduction of NOx and Soot to meet the stringent emission norms without penalizing the fuel efficiency and overcome the drawbacks of EGR-LTC. There are limited insights available which discuss about the potentiality of latest Quadruple injection (early-pilot-main-post/after; epMa) strategy in combination with high EGR and different injection timing over triple injections (pilot-main-after; pMa) at wide operating conditions. This paper deals with the study on the effect of Quadruple injection (epMa) startegy on performance, emissions and noise with different Pilot injection timing and post injection dwell combinations (8 Nos) on a CRDI diesel engine with high EGR and fixed main injection schedule at 4 operating loads and 5 speeds. The study shows that Quadruple injection strategy with retarded early and advanced pilot (early 35° and pilot 19°CA BTDC) and advanced post injection dwell timing 1100 ms is superior in providing optimum results in emissions (NOx, PM, THC, CO) and combustion noise (CN)[1.55–2 dBA @Rig level and 0.7 dBA @ Vehicle level Pass by Noise (PBN)]. This gives the best results in brake specific fuel consumption (BSFC)[0.2 to 1.31%], Torque and brake thermal efficiency (BTE) performance w.r.t other combinations and base triple injections (pMa). In contrary, the quadruple injection strategy having advanced double pilots with delayed post injection dwell (early-39°, Pilot-19°CA BTDC and DtA 1300 ms); shows the best CN reduction. Best smoke results found with the combination of retarded pilots and advanced post injection dwell. This study shows the importance of injection timing specially the twin pilots (early, pilot) along with post injection dwell. Furthermore, it indicates the potentiality of newest Quadruple injection strategy over triple injection.

Construction and derivation of a series of skeletal chemical mechanisms for n-alkanes with uniform and decoupling structure based on reaction rate rules

A reliable reduced chemical model is crucial for the design and optimization of advanced combustion systems. At present, reduced mechanisms are usually obtained by directly reducing detailed mechanisms. Due to the limitations of computational methods and measurement techniques, the construction of detailed mechanisms for high-carbon fuels is still challenging, which hinders the development of the reduced mechanism for practical transportation fuels. To solve this problem, a systematic method is proposed in this work. The method includes two aspects. One is the construction of a skeletal mechanism for the reference fuel by integrating decoupling methodology, reaction class-based global sensitivity analysis method, and genetic algorithm. The other one is that a set of skeletal mechanisms with a uniform structure is extrapolated from the skeletal mechanism of the reference fuel through the reaction rate rules. In the present method, only the detailed mechanism of the reference fuel is required to construct a set of skeletal mechanisms for the fuels with a similar molecular structure but different size as that of the reference fuel. The performance of the present method is evaluated by building a set of skeletal mechanisms for normal alkanes of C5–C20 in this work. Based on extensive validations under wide operating conditions, satisfactory predictions are achieved by the skeletal mechanisms for n-alkanes, which illustrates the potential of the present method for the applications of other fuels.

Optimization of supercritical carbon dioxide based combined cycles for solid oxide fuel cell-gas turbine system: Energy, exergy, environmental and economic analyses

Among various supercritical carbon dioxide cycles, the supercritical recompression carbon dioxide cycle can well adapt to the high temperature of the exhaust gas of the solid oxide fuel cell-gas turbine system to augment power generation. Nevertheless, even after the recovery by the supercritical recompression carbon dioxide cycle, the exhaust gas still contains a large amount of unutilized waste energy. Few studies introduce low-temperature cycles to build cascade cycle systems, which are very likely to address this issue effectively. From the perspectives of energy, exergy, environmental and economic indexes, this article analyzes and compares the improvement potential of integrating four common low-temperature cycles, including organic Rankine cycle, transcritical carbon dioxide cycle, Kalina cycle, and organic flash cycle. Different key operating parameters are considered in-depth and optimized by a genetic algorithm. The results illustrate that in terms of efficiency, the introduction of the organic Rankine cycle is the most outstanding since it can reach the highest energy efficiency of 72.74–73.55% (exergy efficiency of 70.22–71.01%) across wide operation conditions. In terms of cost, the coupling of Kalina cycle is suggested due to the lowest capital cost of 19.94 $/h. The environmental penalty of the four systems all accounts for 14.73% of the total cost. As a consequence, the pros and cons of four common low-temperature cycles are fully demonstrated, which can provide references for the power plant planning.

Proposal, Robustness Analysis and Equivalent Implementation of Optimization Method for Row-by-Row Fin Distribution in Multi-Row Frosting Evaporator

The evaporator in a frost-free refrigerator typically has more tube rows, but frost deposition reduces along the airflow direction. Correspondingly, the evaporator fin distribution is thinner in the upstream rows but denser downstream, and a good match between frost and fin distribution is achieved to recover evaporator capacity loss. However, quantitative design principles of non-uniform fin distribution are lacking. A quasi-static frosting evaporator model is established and experimentally verified considering a three-dimensional (3D) evaporator, 1D frost growth and 1D non-uniform fin distribution. An optimization method for row-by-row fin distribution of a multi-row frosting evaporator is proposed based on the air pressure drop’s increase rate. When the increase rate in the air pressure drop of each row is almost equal, the smallest overall evaporator pressure drop is obtained, leading to the highest air flowrate and the greatest evaporator capacity. By applying the method, the air flowrate and the evaporator capacity increase by 5.5% and 4.6%, respectively, compared to the original fin distribution scheme. Moreover, the robustness of the optimization method is validated under wide temperature and humidity operating conditions. An equivalent implementation under an initial no-frost condition is also proposed to facilitate the optimization method without calculating the whole frosting process.

A novel method for parameter identification and performance estimation of PV module under varying operating conditions

In this paper, a novel and adaptive method is proposed for parameter identification and performance estimation of a photovoltaic (PV) module under varying operating conditions. By comprehensively considering the irradiance and temperature dependence of physical parameters in a single-diode model, a set of new transforming equations is proposed to model and calculate parameters under varying operating conditions. This set is developed to calculate physical parameters without setting any reference condition, thereby eliminating the effect of such reference condition on the accuracy of parameter identification and performance estimation. The coefficients of the transforming equations are calculated by fitting experimental I–V data under a wide range of irradiance and temperature conditions through optimization technology. To reduce the complexity of multi-parameter optimization, three operating points (short circuit, open circuit, and maximum power) provided by the manufacturers are used to reduce the dimensions of the search space. Moreover, a new objective function is proposed for parameter optimization by considering five key operating points for each I–V curve. This improves the accuracy and reduces the computational cost. In contrast with existing methods using parameters under a reference condition, the proposed method estimates the output performance of the PV module under different operating conditions with high accuracy and efficiency. This was validated by experimental data for six different types of PV modules under wide operating conditions.

Feasibility study of an integrated COGES-DF engine power plant in LNG propulsion for a cruise-ferry

The feasibility study of an integrated COmbined Gas Electric and Steam-reciprocating engine propulsion plant for a Liquified Natural Gas (LNG) fueled cruise-ferry is assessed in this paper. Specifically, the purpose of this work is to respond to GNV shipowners’ need to equip ships with a propulsion system highly efficient in a wide ship speed range. The issue was addressed through the repowering study of the GNV La Suprema ship. Several power plant configurations based on different dual-fuel (DF) engine and gas turbine combinations have been tested in a cogeneration efficiency maximization study performed over a wide ship speed range. Afterwards, the best power plant was identified. Analysis showed that COmbined Gas Electric and Steam (COGES) plants have a great potential for marine propulsion application due to their benefits in cogeneration efficiency, emissions and required volumes. Furthermore, integrating a COmbined Gas Electric and Steam plant with small size dual-fuel engines revealed high cogeneration efficiency over wide ship operating conditions. The propulsion plant comprising a COmbined Gas Electric and Steam plant based on GE25.1 gas turbine and small size dual-fuel engines was proved to guarantee high cogeneration efficiency (47–52%) over a wide ship speed range (16–26 knots). In particular, a 51% cogeneration efficiency is provided for the current operating condition of GNV La Suprema. Moreover, the energy efficiency design index (EEDI) for the different investigated plants has been also evaluated, to verify their compliance with IMO limits. The selected propulsion plant provides an EEDI margin of 1.42–2.89 gCO2/(t*nm) with respect to the 2025 requirements. Finally, economic analysis was performed for four different ship routes and capital/operational costs have been computed. Overall, the proposed COmbined Gas Electric and Steam-reciprocating engine power plant revealed to be economically feasible over all of the operating conditions accounted for.

Complementary of Ferroelectric and Floating Gate Structure for High Performance Organic Nonvolatile Memory

AbstractOrganic thin film transistor (OTFT) based nonvolatile memory has made significant progress due to its biocompatibility, flexibility, and low cost, in which ferroelectric transistor memory and floating gate transistor memory play the main roles in organic nonvolatile transistor memory. Here, a novel layered hybrid structure OTFT nonvolatile memory is invented by combining ferroelectric poly(vinylidene fluoride‐co‐trifluoroethylene) P(VDF‐TrFE) with a floating gate layer utilizing CdSe/ZnS quantum dots (QDs), which integrates the advantages of ferroelectric memory and floating gate memory. The core–shell structured CdSe/Zns QDs are acted as robust charge trapping centers due to their band structure similar to a quantum well, preventing the back diffuse of trapped charges, while P(VDF‐TrFE) provides additional polarized electric field to modulate the capture of charge. The resultant devices exhibit high on‐state current (≈10−5 A), low off‐state current (≈10−10 A), excellent switch ratio (≈105), and retention characteristic (>104 s). Furthermore, a superior memory window, more than 85.6% of scanning voltage range, higher than most reported organic transistor memories, is achieved, which endows the device wide operating condition and significant discrimination between on and off state. The fine‐structured OTFT memory opens up a unique path for desirable memory to meet the growing demand of microelectronic industry.

High-efficiency solar heat storage enabled by adaptive radiation management

Solar heat storage technology is urgently needed to harness intermittent solar energy to directly drive widespread heat-related applications. However, achieving high-efficiency solar heat storage remains elusive due to the loss of heat to the surroundings, especially through radiative processes. Here, we present a bioinspired light-adaptive shutter (LAS) with a multi-layer architecture that autonomously switches between open and closed states according to solar illumination fluctuations because of a photothermal-expansion mismatch effect. As a result, in a LAS-integrated solar heat storage system, the LAS governs the incident and dissipated radiation, suppresses the radiative heat dissipation by 20 times, and achieves high-efficiency solar heat storage with a near-zero net radiative heat dissipation. Furthermore, a LAS is demonstrated to enhance the temperature by >20°C in a 3-day field test. Given excellent responsiveness, scalable manufacturing, and feasibility under wide operating conditions, the LAS provides a promising radiation management strategy for high-efficiency solar heat storage. Light-adaptive shutter enable autonomous radiation management with solar fluctuations LASs suppress radiative heat dissipation by 20 times for efficient solar heat storage LASs are promising for applications, including smart windows, autonomous robots, etc. The efficiency of solar heat storage is limited by radiative heat dissipation. Liu et al. present a light-adaptive shutter (LAS) that autonomously governs incident solar radiation and dissipated heat radiation according to solar illumination fluctuations, which may provide a promising radiation management strategy.

An evaluation of critical heat flux prediction methods for the upward flow in a vertical narrow rectangular channel

Narrow rectangular channels have widespread application in various domains owing to their significant enhancement in boiling heat transfer. In the present work, the CHF (critical heat flux) prediction method has been comprehensively evaluated and analyzed by the experimental data covering wide operating conditions in narrow rectangular channels. The Bubble Crowding Model can predict the high critical heat flux region. The prediction deviation of ANN (Artificial Neural Network) with two hidden layers and five neurons to predict CHF can reach 20 %, then the influences of thermal-hydraulic parameters on prediction of CHF were obtained based on this constructed ANN. Among the tens of CHF correlations, Tong's, Sudo's, and Mudawar's correlations were selected, and the results indicate that Sudo's correlation can forecast well in 1–4 MPa and Mudawar's correlation had relatively low prediction errors for a wide pressure range. The LUT (look-up table) needs proper correction factors to accurately predict the CHF in narrow rectangular channels.

The correlation between intake fluctuation and combustion CCV (cycle-to-cycle variations) on a high speed gasoline engine: A wide range operating condition study

In this work, the cycle-to-cycle variations (CCV) and their correlations to intake charge fluctuation of a motorcycle engine under wide operating conditions were investigated by experiment. Several key engine operating parameters (engine speed/load, air/fuel ratio, compression ratio and ignition mode) were considered and analyzed thoroughly. A new concept of intake charge time-pressure value (TPV) was proposed to estimate its charge capacity, and its fluctuation’s contribution to CCV was also assessed. Some important findings include: 1) the CCV goes up with engine speed. Engine speed is also the most important factor to affect intake charge fluctuation, coefficient of variation (COV) of TPV is only 3.38% at 3000 rpm under 60% load, but it rises up to 13.41% at 5000 rpm; 2) the contribution of charge cyclic fluctuation to CCV decreases with engine speed. At 3000 rpm, over 80% of CCV are caused by TPV’s fluctuation for all engine loads, but its contribution drops to about 30% at 5000 rpm under medium to full load; 3) engine load has moderate impact on intake charge fluctuation’s contribution to engine CCV within test range. This depends on the competition between intake pressure loss and trapped ability of residual gas; 4) the three factors of air/fuel ratio, compression ratio and ignition mode do not affect much the air exchange process, but largely influence the combustion process. The more they improve combustion process, the larger degree of participation in engine CCV by intake charge fluctuation.

Construction of reduced oxidation mechanisms of polyoxymethylene dimethyl ethers (PODE1–6) with consistent structure using decoupling methodology and reaction rate rule

Due to high oxygen content and lack of CC bond, polyoxymethylene dimethyl ethers (PODEn) have become desired fuel additives to simultaneously reduce nitrogen oxides (NOx) and soot emissions. However, there is a vacancy of reduced reaction mechanisms to describe the long-chain PODEn (n>3) combustion for engine simulations. This study aims at the construction of reduced mechanisms for the series of PODE1–6 with a consistent structure based on the decoupling methodology and reaction rate rule. First, the reduced mechanisms of PODE1–2 are constructed using the reaction class-based global sensitivity analysis and the decoupling methodology. In the reduced mechanisms of PODE1–2, the skeletal fuel-related sub-mechanism is integrated with a detailed H2/CO/C1 sub-mechanism and a reliable reduced C2–C3 sub-mechanism. Then, due to the similar molecular structure, the skeletal fuel-specific sub-mechanisms of the long-chain PODE3–6 are constructed based on the benchmark fuel of PODE2. The kinetic parameters of the reactions in the skeletal fuel-specific sub-mechanism of PODE3–6 are obtained using the modified linear lumping method and the reaction rate rule assumption. Through the approach proposed in this study, the reduced mechanisms of long-chain PODEn can be effectively constructed based on the chemistry of short-chain PODEn, especially for PODE5–6 with no available detailed mechanisms so far. The final reduced mechanism for the complete series of PODE1–6 only consists of 92 species and 389 reactions. To validate the predictions of the reduced mechanism, extensive comparisons are carried out including ignition delay times in shock tubes, major species concentrations in jet-stirred reactors, and laminar flame speeds and flame species concentrations in premixed laminar flames. Moreover, the measurements of homogeneous charge compression ignition (HCCI) engines fueled with PODE2–4 and PODE3–6 are used for the validations of in-cylinder pressure, heat release rate, and emissions. The overall performance of the reduced mechanism of PODE1–6 is satisfactory under wide operating conditions. This present approach can also be extended to build compact-size reduced mechanisms for the fuels with similar molecular structures, even though no detailed mechanism is available.

Probing the effect of various water fractions on methane (CH4) hydrate phase equilibria and hydrate inhibition performance of amino acid L-proline

To develop an effective hydrate risk mitigation strategy for offshore flow assurance its essential to understand the effect of different water fractions on the rate of hydrate formation and study the effect of various water fractions on inhibition effectiveness of eco-friendly gas hydrate inhibitors like amino acids [AA]. This work examines the effect of different water fractions on methane [CH4] hydrate phase equilibria and the effect of various water fractions on the hydrate inhibition strength of AA L-proline at wide operating conditions [40–120 bars]. The experimental results indicate that different water fractions do not have a significant effect on the CH4 hydrate vapor-liquid equilibrium [HLVE] data or hydrate phase equilibria. The 5 wt% L-proline at water fraction 50 vol% shifted the CH4 HLVE by 0.9 ± 0.05 °C (40 bars) and at water fraction 75 vol% it shifted the CH4 HLVE by 1.3 ± 0.05 °C (40 bars). Furthermore, at the same dosage (5 wt%), the THI effect of proline was compared with other amino acids glycine, alanine, and choline based ionic liquids reported in the literature previously. The results indicate that at low dosage [5 wt%], the amino acids glycine and L-alanine tend to slightly more effective thermodynamic hydrate inhibitors compared to the selected choline based ionic liquids. The computer-generated molecular models were used to probe the likely hydrate inhibition mechanism in the presence of amino acids and ionic liquids.