INTERNATIONAL JOURNAL OF INDUSTRIAL MATHEMATICS
Year:2022 | Volume:14 | Issue:3|Papers Count:7

Strong dependence of the humans daily life on the engine-powered vehicles, increasing environmental pollutions, especially in urban areas, and reduction of the world oil resources have challenged the energy researchers to make appropriate decisions at regional scale for selecting alternative fuels to improve the engine e, ciency and fuel economy. In the current work, the lean-burn idea jointed to dual (gasoline-NG) fuel has been considered to develop the SI engines. To investigate the performance and exhaust gas emissions, experimental tests were performed using a single-cylinder research engine with gasoline-NG dual fuels, including four mass-based blends of the two fuels (G100, G87. 5, G75, and G62. 5 containing gasoline at 100%, 87. 5%, 75%, and 62. 5%, respectively, with the remaining part being composed of NG), at 1800 rpm ES and 0. 9 ER under at least seven di, erent SAs. The result showed that increasing the NG fraction in the fuel blend, both the and COV of imep decreased. It was implicitly observed that, for blended fuels, non-uniform distribution of each fuel can serve as a new source for cyclic variations. Under the considered lean burning condition at optimal safe SA, the imep and output torque for the gas-contained fuel blends were comparable to those obtained by the pure gasoline. Also, it was detected that an increase in the CR reduced the emitted amounts of HC and CO pollutants.

Classic Data Envelopment Analysis expect that the values of inputs and outputs are precisely determined. However, in most real-life issues, the values of some of inputs and outputs are imprecise. One of them is ordinal data. In this article, a new model is presented for evaluating decision making units with ordinal data. The general idea of this model is assigning real values to ordinal data with new approach. Furthermore, another new model for ranking e, cient units is presented with the main idea of changes in controlled e, ciency. Then, the results with results Cooper's model are compared. Therefore, the e, ciency scores obtained from proposed model are more realistic and reasonable than the results obtained from the Cooper's model.

In this paper an improved version of the collocation method is proposed to solve ordinary di, erential equations with initial conditions. Our proposed algorithm is described by applying it to some well-known IVPs. The results are compared with basic collocation algorithms to show the advantages, applicability and e, ciency of the proposed method. Based on numerical results, the proposed algorithm has better accuracy and execution time.

BlockChain (BC) has the potential to address the Internet of Things' security and privacy issues (IoT). BC, on the other hand, is computationally expensive, has limited scalability, and incurs signi, cant bandwidth overheads and delays, making it unsuitable for use in the IoT. In this study, we have proposed a method that optimizes BC for use in IoT environment. We have presented a hierarchical structure that uses a private BC to increase scalability, reduce network overhead and delay. In the proposed method, devices with high-level resources build a network referred to as the overlay network on devices with low-level resources. The members of the overlay network manage the BC. We used trust technique and voting from direct neighbors to reduce network tra, c and overhead costs. The use of factors such as trust, encouragement and penalty of block managers in the overlay network ensures accurate transactions in IoT. We used a new authentication algorithm for authenticating block managers in the overlay network. The simulation results show that the proposed algorithm reduces packet overhead and delay in service delivery and increases the scalability of the BC in comparison to the system that uses the base BC. Furthermore, because in the proposed algorithm, the number of e, ective block managers in voting is limited to direct neighbors, the average time to con, rm a block is signi, cantly reduced.

Standard Data Envelopment Analysis (DEA) supposes that the performance measure status from the point of view of input or output is known. Nevertheless, in some situations, determining the status of a performance measure in two-stage network is not easy. Measures with unknown status of input/output are called exible measures. In all of the previous studies did not point to classify exible measures in two-stage network DEA. In this paper we propose FNDEA models based on the multiplier model under constant returns to scale (CRS) and variable returns to scale (VRS) in general two-stage network structures. Our models classify exible measures, in which each one of the exible measure is treated as either input or output to maximize the overall network e, ciency of the DMU under evaluation. The current paper develops an additive e, ciency decomposition approach wherein the overall e, ciency is expressed as a (weighted) sum of the e, ciencies of the individual stages. This approach can be applied under both CRS and (VRS) assumptions. Numerical examples are used to illustrate the procedures.

Increasing environmental pollution, which causes global warming and endangers human health and environmental degradation, has led to the concern of many supply chain managers and designers. The purpose of this research is to provide a mathematical model for designing the purchase, production, and distribution in a multi-level and multi-product supply chain network such that the environmental impact and total costs of supply chain is minimized and the customers' satisfaction level is maximized. Due to the unspeci, ed demands level, demand uncertainty has been considered in the problem. Regarding the complexity of the proposed mathematical model and di, culties in solving the problem with exact methods in large size, a NSGA II has been proposed. To evaluate the proposed NSGA II, , ve sample instances are generated in di, erent size and solved by Epsilon constraints method and NSGA II. According to the results, the proposed NSGAII is a reliable method to , nd e, cient Pareto frontiers in a reasonable time.

A conditional moment closure model is adopted together with a state-of-the-art LES approach to simulate the target ame of the TNF workshop, Delft piloted methane ame III. For modeling the sub-grid scales, the constant Smagorinky model is used in this study. First order conditional moment closure is used to model the turbulence-chemistry interaction. To calculate the conditional velocity and the conditional scalar dissipation, conditional volume averaging with smoothing is used. To study the e, ect of the chemistry, two di, erent methane reaction mechanisms are used. The , rst reaction mechanism is the one-step methane chemistry with 4 species and 1 reaction. The second reaction mechanism is the Smooke reaction mechanism which consists of 16 species and 25 reactions. A second order time accurate predictor-corrector method is used for time integration of the Favre , ltered Navier-Stokes equations. It is shown that the reaction mechanisms has almost no in uence on velocity and scalar , elds. However, the temperature , eld is in uenced by using a more complex reaction mechanism.