Advanced techniques have shown an effective approach to reducing nitrogen oxide (NOx) emissions from biodiesel-fueled engines. The primary concern is higher NOx emissions using biodiesel due to its different physio-chemical fuel properties and higher in-cylinder combustion temperatures than diesel. This study aims to scrutinize the existing literature on NOx forming origins from biodiesel and present the state-of-the-art literature on reduction technologies, identify research gaps and introduce further developments to the research opportunities. Accordingly, the present review has organized the technologies into three categories: pre-combustion, combustion, and post-combustion techniques. In addition, the pros and cons of each technique are presented by identifying their role using combustion characteristics. The study identifies that ignition delay and combustion duration are the major factors to consider in reducing NOx emissions. Applying innovative tactics to modify the fuel, chamber, and injection parameters prolongs the ignition delay and combustion duration, thereby reducing NOx emissions. Furthermore, exhaust gas after-treatment systems with novel catalytic convertors have shown a strong capability of reducing NOx emissions. The findings also indicate significant reductions in NOx emissions from biodiesel with provided technologies; however, a clear roadmap is needed to integrate these technologies for sustainable application. The study concludes that emulsion techniques, low-temperature combustion strategies, retarded injection timing, and exhaust gas after treatment have shown significant NOx reduction through reduced peak temperature rates. The study recommends further investigation on reactive controlled combustion techniques as they directly impact NOx emission, and their results comparisons will give a clear understanding for deriving better commercial options. 

Advanced strategies to reduce harmful nitrogen-oxide emissions from biodiesel fueled engine

Joint nonlinear-drift-driven Wiener process-Markov chain degradation switching model for adaptive online predicting lithium-ion battery remaining useful life

As a manufactured fuel, hydrogen can be produced in a decentralized way in most countries around the world. This means, even in a net zero economy, the global trade of hydrogen could look quite different to the current international trade in fossil fuels, including natural gas. With further declines in the costs of renewable electricity and electrolyzers, regions which have lower cost renewable electricity may develop an economic advantage in the production of low-cost hydrogen, but for hydrogen to become a globally traded commodity, the cost of imports needs to be lower than the cost of domestic production. Unlike oil or natural gas, transporting hydrogen over long distances is not an easy task. Hydrogen liquefaction is an extremely energy-intensive process, while maintaining the low temperature required for long-distance transportation and storage purposes results in additional energy losses and accompanying costs. The upside is that hydrogen can be converted into multiple carriers that have a higher energy density and higher transport capacity and can potentially be cheaper to transport over long distances. Among the substances currently identified as potential hydrogen carriers suitable for marine shipping, liquid ammonia, the so-called ‘liquid organic hydrogen carriers’ in general (toluene-methylcyclohexane (MCH) in particular), and methanol have received the most attention in recent years. This paper compares the key techno-economic characteristics of these potential carriers with that of liquified hydrogen in order to develop a better understanding of the ways in which hydrogen could be transported overseas in an efficient manner. The paper also discusses other factors, beyond techno-economic features, that may affect the choice of optimum hydrogen carrier for long distance transport, as well as the global trade, of hydrogen.

Global trade of hydrogen: what is the best way to transfer hydrogen over long distances?

For the accurate extraction of cavity decay time, a selection of data points is supplemented to the weighted least square method. We derive the expected precision, accuracy and computation cost of this improved method, and examine these performances by simulation. By comparing this method with the nonlinear least square fitting(NLSF) method and the linear regression of the sum(LRS) method in derivations and simulations, we find that this method can achieve the same or even better precision, comparable accuracy, and lower computation cost. We test this method by experimental decay signals. The results are in agreement with the ones obtained from the nonlinear least square fitting method.

Data point selection for weighted least square fitting of cavity decay time constant

Structural reliability assessment has been widely recognized as vital in engineering product design and development [7]. In the context of structural reliability assessment, uncertainty propagation plays a significant role, which aims to quantify the uncertainties of input factors and calculate the overall uncertainty within the model response in reliability estimation [36]. Before propagating the structure’s uncertainty, a primary issue is to choose a reasonable mathematical theory related to the types of uncertainty, to quantify the uncertainty [12,38]. In practical structural engineering problems, uncertainty can be divided into two categories: aleatory uncertainty derived from inherent randomness of physical behavior, while the epistemic uncertainty arising out of lack of knowledge [10]. Probability theory is regarded as the most effective tools to describe aleatory uncertainty in structural reliability assessment. Over the last decades, numerous reliability assessment methods based on probability theory have been developed, including first-order reliability method (FORM) [23], second-order fourth moment [29] Monte Carlo simulation (MCS) [24], FORM-sampling simulation method [22], envelope function method [28], response surface method (RSM) [6], and Bayesian networks method [26]. Although these probabilistic methods typically make sense in uncertainty quantification and propagation when the structure is mainly affected by aleatory uncertainty, they do not work well in the scenarios involving great epistemic uncertainty [37]. For example, the distribution of input factors may not be precisely obtained due to insufficient sample data. Consequently, several alternative non-probabilistic theories have been developed to describe the epistemic uncertainty in reliability assessment. The general non-probabilistic structural reliability assessment theories consist of fuzzy set theory [9], fuzzy random theory [13], possibility theory [1], interval theory [5, 27], and evidence theory [39]. The fuzzy and possibility measures fail to satisfy the duality property, which will make it difficult for decision-makers to understand the results [33]. Moreover, interval and evidence theories will lead to an over-conservative result due to the interval extension problems [38]. To overcome the shortcomings of the above-mentioned theories, a new mathematical framework called uncertainty theory was introduced to deal with epistemic uncertainty. Uncertainty propagation plays a pivotal role in structural reliability assessment. This paper introduces a novel uncertainty propagation method for structural reliability under different knowledge stages based on probability theory, uncertainty theory and chance theory. Firstly, a surrogate model combining the uniform design and least-squares method is presented to simulate the implicit limit state function with random and uncertain variables. Then, a novel quantification method based on chance theory is derived herein, to calculate the structural reliability under mixed aleatory and epistemic uncertainties. The concepts of chance reliability and chance reliability index (CRI) are defined to show the reliable degree of structure. Besides, the selection principles of uncertainty propagation types and the corresponding reliability estimation methods are given according to the different knowledge stages. The proposed methods are finally applied in a practical structural reliability problem, which illustrates the effectiveness and advantages of the techniques presented in this work. Highlights Abstract

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Uncertainty propagation in structural reliability with implicit limit state functions under aleatory and epistemic uncertainties

Response surface method based on uniform design and weighted least squares for non-probabilistic reliability analysis

This paper proposes a revised variation disturbance method to provide valuable information and reference for fuel design or optimization of internal combustion engines to realize the comprehensive and quantitative evaluation of the effects of blending agents on the combustion performance of primary fuels. In this method, methanol and ethanol are blended into gasoline to form six kinds of alcohol–gasoline (E10, E20, E30, M10, M20, and M30). Then, the ignition delay, adiabatic flame temperature, component concentration, fuel-burning rate, extinction strain rate, and CO emission of gasoline and alcohol–gasoline are studied by system simulation in a wide range of operating conditions. Based on the new variation disturbance method, the effects of methanol and ethanol on the combustion performance of gasoline are next analyzed globally and characterized quantitatively. The comprehensive results of ethanol and methanol on the gasoline’s combustion are visually presented. The method proposed in this paper is preliminarily validated based on the analysis of the microscopic mechanism of combustion. The results show that the blending of ethanol and methanol has positive effects on gasoline combustion, and ethanol can rapidly ignite the gasoline in a wide range of operating conditions and is superior to methanol in terms of fuel combustion, stability, and pollutant discharge. Based on the treatment of simulated values of six combustion characteristics selected in this paper and the calculations of the variation disturbance method, the total disturbance values of ethanol and methanol to gasoline combustion are obtained as 0.8493 and 0.2605, respectively. That is, ethanol has a more significant effect on improving the combustion performance of gasoline than methanol. In addition, based on the analysis results of the combustion, it is found that the blending of ethanol enlarges the reaction of notable components in gasoline. This finding also proves the effectiveness and validity of the scientific method utilized in this paper.

Effects of Ethanol and Methanol on the Combustion Characteristics of Gasoline with the Revised Variation Disturbance Method

Flywheel energy storage system (FESS) has been regarded as the most promising hybrid storage technique to manage the battery charging process of electric vehicles. Thanks to properly regulating with the FESS, the battery life can be significantly prolonged. In order to ensure the safety of the hybrid storage system, it is imperative to monitor the mechanical operation condition of the FESS. Because the rolling bearing is a critical mechanical component in the FESS, the performance degradation monitoring and remaining useful life (RUL) prediction of the rolling bearing must be performed. This paper proposes a new machine learning method for the construction of a health indicator to quantitatively evaluate the bearing health status. In this new method, the original feature set is firstly selected through three feature evaluation indicators and the principle component analysis (PCA) is employed to fuse the original feature set as a new health indicator. Then the primary trend of the health indicator is extracted by the empirical mode decomposition (EMD); and the Kriging model-based prediction method is proposed to predict the bearing RUL. The feasibility and superiority of the proposed method is verified through experimental test and analysis result shows that the root mean square error (RMSE) of the prediction is as small as 0.0425. 

A novel machine learning model for safety risk analysis in flywheel-battery hybrid energy storage system

Accurate remaining useful life (RUL) prediction is helpful to improve the reliability and safety of complex systems. However, in practical engineering applications, it often occurs imperfect or scarce prior degradation information for the degradation system with measurement error (ME). In order to solve this problem, based on the implicit linear Wiener degradation process, a RUL prediction method which reasonably fuses failure time data or multi-source information is proposed in this paper. Firstly, based on the implicit linear Wiener degradation process, we obtain the relationship between the natures of parameters estimation and degradation data by theoretical derivation, which provides a theoretical basis regarding how to fuse multi-source information. Secondly, according to the natures of parameters estimation, we use field degradation data and historical degradation data to estimate the fixed parameters of the two prediction cases respectively, and fuse failure time data into the degradation model by the expectation maximization (EM) algorithm. Then, the Kalman filtering algorithm is used to online update the drift parameter based on field degradation data. Finally, we use some simulation experiments to further verify the natures of parameters estimation, and two practical case studies to verify the superiority of the proposed method.

Remaining useful life prediction of implicit linear Wiener degradation process based on multi-source information

Due to integrated structures and multiple functions, complex systems, such as large‐scale equipment and aerospace vehicle, faced prominent reliability problems. However, in real‐world applications, collecting sufficient reliability data is costly and time‐consuming. To overcome this difficulty, the information‐poor variables are modeled with interval models and the corresponding reliability analysis method for complex system is studied in this paper. First, we establish a multidisciplinary nonprobabilistic reliability model based on interval analysis, which is an optimization framework with an objective of nonprobabilistic reliability index and two constraints of interdisciplinary consistency equation and limit state equation. Second, two types of algorithms for the above model are studied. Based on typical methods of multidisciplinary design optimization (MDO), the direct algorithms including RA‐MDF and RA‐IDF are developed. Then, a decoupled reliability analysis algorithm is proposed to realize parallel multidisciplinary reliability analysis, in which two methods of multidisciplinary interval uncertainty analysis are formulated to decouple the coupling relationship between disciplines. Finally, three examples are employed to illustrate the validity and efficiency of the proposed methods.

Pengya Fang

Papers

Response surface method based on uniform design and weighted least squares for non-probabilistic reliability analysis

Effects of Ethanol and Methanol on the Combustion Characteristics of Gasoline with the Revised Variation Disturbance Method

A novel machine learning model for safety risk analysis in flywheel-battery hybrid energy storage system

Remaining useful life prediction of implicit linear Wiener degradation process based on multi-source information

Interval uncertainty‐based multidisciplinary reliability analysis method for information‐poor complex system