Reflected shock tube experiments on aeroacoustic signature of hot jets

Journal of Mechanical Science and Technology - We used a reflected shock tube to investigate the acoustic signature of a hot jet at the far-field. Experiments were performed at Mach = 1.4 and a...     

Predicting the vapor-liquid equilibrium of carbon dioxide+alkanol systems by using an artificial neural network

A multi-layer feed-forward artificial neural network has been presented for accurate prediction of the vapor liquid equilibrium (VLE) of CO₂+alkanol mixtures. Different types of alkanols namely, 1-propaol, 2-propanol, 1-butanol, 1-pentanol, 2-pentanol, 1-hexanol and 1-heptanol, are used in this study. The proposed network is trained using the Levenberg-Marquardt back propagation algorithm, and the tan-sigmoid activation function is applied to calculate the output values of the neurons of the hidden layers. According to the network’s training, validation and testing results, a six layer neural network is selected as the best architecture. The presented model is very accurate over wide ranges of experimental pressure and temperatures. Comparison of the suggested neural network model with the most important thermodynamic correlations shows that the proposed neuromorphic model outperforms the other available alternatives. The predicted equilibrium pressure and vapor phase CO₂ mole fraction are in good agreement with experimental data suggesting the accuracy of the proposed neural network model for process design.     

A new correlation for predicting the minimum miscibility pressure regarding the enhanced oil recovery processes in the petroleum industry

Miscible gas injection is a promising technique to enhance the oil recovery from petroleum reservoirs. Natural gas is usually injected because of consistency between the gas and reservoir fluid composition and accessibility of the injected fluid. In this work a new correlation is presented for accurate prediction of the required minimum miscibility pressure (MMP) for multicontact miscible displacement of reservoir petroleum by hydrocarbon gas injection. Accurate estimation of the MMP is required for optimal design of recovery systems and optimization of the production economics. It is shown that the presented correlation is very accurate and reliable for predicting the MMP over wide ranges of oil and gas compositions. Comparison of the suggested correlation with the most important existing correlations shows that the presented correlation outperforms the other alternatives both in accuracy and generality.     

A multi-layer feed forward neural network model for accurate prediction of flue gas sulfuric acid dew points in process industries

Acidic combustion gases can cause rapid corrosion when they condense on pollution control or energy recovery equipments. Since the potential of sulfuric acid condensation from flue gases is of considerable economic significance, a multi-layer feed forward artificial neural network has been presented for accurate prediction of the flue gas sulfuric acid dew points to mitigate the corrosion problems in process and power plants. According to the network’s training, validation and testing results, a three layer neural network with four neurons in the hidden layer is selected as the best architecture for accurate prediction of sulfuric acid dew points. The presented model is very accurate and reliable for predicting the acid dew points over wide ranges of sulfur trioxide and water vapor concentrations. Comparison of the suggested neural network model with the most important existing correlations shows that the proposed neuromorphic model outperforms the other alternatives both in accuracy and generality. The predicted flue gas sulfuric acid dew points are in excellent agreement with experimental data suggesting the accuracy of the proposed neural network model for predicting the sulfuric acid condensation in stacks, pollution control devices, economizers and flue gas recovery systems in process industries.     

A practical algorithm for distribution state estimation including renewable energy sources

Renewable energy is energy that is in continuous supply over time. These kinds of energy sources are divided into five principal renewable sources of energy: the sun, the wind, flowing water, biomass and heat from within the earth. According to some studies carried out by the research institutes, about 25% of the new generation will be generated by Renewable Energy Sources (RESs) in the near future. Therefore, it is necessary to study the impact of RESs on the power systems, especially on the distribution networks. This paper presents a practical Distribution State Estimation (DSE) including RESs and some practical consideration. The proposed algorithm is based on the combination of Nelder–Mead simplex search and Particle Swarm Optimization (PSO) algorithms, called PSO-NM. The proposed algorithm can estimate load and RES output values by Weighted Least-Square (WLS) approach. Some practical considerations are var compensators, Voltage Regulators (VRs), Under Load Tap Changer (ULTC) transformer modeling, which usually have nonlinear and discrete characteristics, and unbalanced three-phase power flow equations. The comparison results with other evolutionary optimization algorithms such as original PSO, Honey Bee Mating Optimization (HBMO), Neural Networks (NNs), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) for a test system demonstrate that PSO-NM is extremely effective and efficient for the DSE problems.     

Configurable three-way model merging

Software development is a collaborative activity that requires teams of software engineers to cooperate and work in parallel on versions of models. However, model management techniques such as model differencing, merging, and versioning have turned out to be difficult challenges, due to the complexity of operations and graph-like nature of models. Therefore, a well-developed support for model merging process, as well as conflict management, is highly desired. This paper presents a novel process for model merging, called the Epsilon-based Three-way Merging Process (E3MP) process. Model merging is a significant problem where there are different versions of a system model amongst modeler teams. E3MP includes three components implemented into the Epsilon framework. First, modelers can define domain-specific rules that customize the merging process. Second, E3MP enables an automated method for syntactic and semantic conflict detection amongst different versions of the system model. Third, E3MP puts forward a pattern-based approach for conflict resolution. We applied two generic benchmarks to assess conflict detection and resolution capabilities of our approach and carried out an initial scalability evaluation for the model merge with large models and large change sets. The results of our experiments revealed that the proposed process allows generating consistent and semantically correct merged models.     

Particle Deposition in a Multiple-Path Model of the Human Lung

Predicting the amount of particle deposition in the human lung following exposure to airborne particulate matter is the first step toward evaluating risks associated with exposure to airborne pollutants. Realistic deposition models are needed for accurate predictions of deposition in the lung, but a major limitation is the degree to which the lung geometry can be accurately reconstructed. Morphometric data for the entire airway tree of the human lung are not available. So far, idealistic lung structures have been used for deposition calculations. In this study, 10 statistical lung structures based on morphometric measurements of Raabe et al. (1976) were generated for the conducting airways of the human lung. A symmetric, dichotomous branching alveolar airway structure was attached to the end of the conducting airway tree of each lung structure. The total volume of the alveolar region was the same among the lung geometries. Using a mathematical scheme developed previously (Anjilvel and Asgharian 1995), regional, lobar, and per-generation depositions of particles were calculated in these geometries. The results were compared to deposition predictions using typical-path and five-lobe symmetric lung geometry models. All three lung models showed very similar regional and generation-by-generation deposition results. Lobar deposition was found to strongly depend on the detailed morphometry of the lung structure that was used.     

Stress test optimization using an integrated production test and field reliability model

Numerous papers and texts have been written in the reliability literature regarding the determination of the optimum test duration for a production stress or a burn‐in test. The techniques presented have largely been based on the identification of the change point at which infant mortality has largely been removed from the units. The time‐on‐test is typically the only factor that influences this decision. Few of these models have attempted to integrate the field performance or the influence of warranty costs into this decision. This paper proposes and validates a methodology that integrates the influence of the production test failures and the field performance including their respective costs into a single unified model. The objective is to identify a production test duration that minimizes the overall cost. A Weibull model is initially developed for the production test that incorporates the failure observations in different time segments of the test based on the ability to detect latent defects in the product. A separate Weibull model is then developed for the product's performance in the field that includes the lifetime of the unit. This paper identifies how both these Weibull models can be combined into a single model including both test and field costs with the objective of minimizing the overall cost. The advantage of the proposed technique is that it does not require one to track individual units from production through to the field in order to develop an integrated test and field cost model. Copyright © 2009 John Wiley & Sons, Ltd.     

A hybrid evolutionary algorithm for distribution feeder reconfiguration

Distribution feeder reconfiguration (DFR) is formulated as a multi-objective optimization problem which minimizes real power losses, deviation of the node voltages and the number of switching operations and also balances the loads on the feeders. In the proposed method, the distance (λ ₂ norm) between the vector-valued objective function and the worst-case vector-valued objective function in the feasible set is maximized. In the algorithm, the status of tie and sectionalizing switches are considered as the control variables. The proposed DFR problem is a non-differentiable optimization problem. Therefore, a new hybrid evolutionary algorithm based on combination of fuzzy adaptive particle swarm optimization (FAPSO) and ant colony optimization (ACO), called HFAPSO, is proposed to solve it. The performance of HFAPSO is evaluated and compared with other methods such as genetic algorithm (GA), ACO, the original PSO, Hybrid PSO and ACO (HPSO) considering different distribution test systems.