Non-optimized Model Research Articles

An integrated model of predicting sand erosion in elbows for multiphase flows

Sand erosion is a serious problem to be solved in the oil and gas industry. During the transportation of oil and gas, different multiphase flow patterns may occur due to variations of factors, such as the conveying speed, fluid property and pipe geometry. The present work aims at developing a novel framework to predict sand erosion in elbows for multiphase flows. Firstly, the effects of flow patterns on sand erosion are analyzed. Based on these analyses, the erosion model for each specific flow pattern is established. Combining with the basic penetration ratio in gas flow and erosion coefficients, the erosion models of different flow patterns can be unified as an integrated model. Then, the accuracy of the integrated model is examined by 108 experimental cases. After the experimental validation, an optimal coefficient is proposed to further optimize the integrated model. A comparison between the optimized integrated model and the non-optimized integrated model is performed. The results demonstrate that the optimized integrated model performs well in improving the accuracy. Finally, the integrated model is compared with other existing erosion models. According to the comparative study, the non-optimized integrated model already owns the similar accuracy compared with existing erosion models, while the optimized integrated model performs best in accuracy and efficiency.

Setting Artificial Neural Network Hyperparameters for Mobile Platform Navigation

Purpose of reseach. The main purpose of this work is to increase the efficiency of a neural network model when navigating a mobile robotic platform in static and dynamically generated environments. Methods. To solve this problem, precise setting and optimization of neural network hyperparameters were proposed. In order to encourage agents to explore the environment, the reward system was adjusted to increase the reward when the distance from the agent to the target point was reduced, and the penalty increased when moving in the opposite direction to the end point and passing each subsequent scene. This distribution of rewards and penalties encourages agents to learn actively and helps to reduce the total number of scenes. In order to reduce the amount of data processed by a neural network, normalization of input vectors was introduced. The learning time of the neural network model was reduced due to the parallel training of agents and, consequently, increased experience as a result of the environmental research. Results. The proposed approach reduced the learning time by 30% and improved the navigation efficiency of the mobile platform by 10% in a dynamically generated environment and by 22% in a static environment compared to the non-optimized model. Conclusion. The proposed solution can be used in conjunction with other methods of tracing and navigation, when the taught neural network works simultaneously with the already developed and proven navigation algorithms, for example, if the mobile platform connects a taught neural network only to adjust the position in space and to prevent collisions with other objects.

Selecting Optimal Completion to Partial Matrix via Self-Validation

In many applications such as film recommendation, one often encounters the problem of estimating the unseen entries in a partially observed matrix, formally known as matrix completion. Over the past several decades, lots of effective methods have been established in the literature, and each method may contain several hyper-parameters. For a partial matrix, one can use those methods with certain parametric settings to obtain a large number of completions. Now, a critical question is, how to select the optimal completion from a number of candidates ? This question is indeed a hard to answer, because in practice the true values of the missing entries are unknown. Thus far, the only approach for dealing with the issue is through data-validation , which is to first split the observations into two subsets, a training set and a validation set, and then choose the model that performs best on the validation set as the winner to produce the final results. Though straightforward, this approach might fall in a non-optimal model that overfits the validation set. In this work, we shall suggest a different approach called self-validation , which accounts on a special metric that can evaluate the “goodness” of a completion without using any validation data. The metric is derived from the recently established isomeric condition , measuring the identifiable degree of the completion itself. Extensive experiments demonstrate that our self-validation approach is better than the commonly used data-validation.

Design optimisation of an automotive air filter model: a case approach to study the design complexity

The impact of design complexity on additive manufacturing (AM) process is a current research topic, intensified by familiar phrases such as 'complexity is for free'. The economic optimum for an AM process usually reflects a compromise between production costs and fulfilment of design for manufacturing (DfM) principles. The AM fraternity entails further investigation of the design complexity topic and its impact on production costs while targeting DfM as a holistic tool. In this paper a data-driven method was proposed which uses design complexity as a performance indicator for evaluation of specimens built using AM process. Further, this paper identifies five critical design features of an automotive air filter model to assess its optimal design complexity and manufacturability. Build time and dimensional accuracy were selected as the responses of the study. The research indicates that the proposed design complexity optimisation approach has potential to reduce build time and subsequent build cost by 10% as compared to a non-optimal model of the selected automotive case.

Optimality Verification of Tensor Completion Model via Self-Validation

Tensor completion (TC) attempts to estimate the missing tensor entries from partial observations. Over the past decade, a tremendous amount of work has focused on this problem, which has given rise to a few excellent TC methods. Thus, a critical question is how to verify the optimality of TC models, especially when the underlying tensor is unknown, which is a common scenario in practice. The only existing method available—data validation—addresses the problem based on the validation set. While straightforward, this method may lead to a nonoptimal model, especially in scenarios with high missing ratios, and noise. In this work, we propose a self-validation method, which accounts for a metric named the identifiability measure , defined based on the generalization of isomeric conditions in the tensor case. It is noteworthy that the identifiability measure can verify the optimality of TC models without the use of any validation set. Extensive numerical experiments conducted on both synthetic, and real-world datasets empirically validate the superiority of the proposed method over the data validation approach.

Design optimisation of an automotive air filter model: a case approach to study the design complexity

The impact of design complexity on additive manufacturing (AM) process is a current research topic, intensified by familiar phrases such as 'complexity is for free'. The economic optimum for an AM process usually reflects a compromise between production costs and fulfilment of design for manufacturing (DfM) principles. The AM fraternity entails further investigation of the design complexity topic and its impact on production costs while targeting DfM as a holistic tool. In this paper a data-driven method was proposed which uses design complexity as a performance indicator for evaluation of specimens built using AM process. Further, this paper identifies five critical design features of an automotive air filter model to assess its optimal design complexity and manufacturability. Build time and dimensional accuracy were selected as the responses of the study. The research indicates that the proposed design complexity optimisation approach has potential to reduce build time and subsequent build cost by 10% as compared to a non-optimal model of the selected automotive case.

Comparison of Power Converter Models with Losses for Hardware-in-the-Loop Using Different Numerical Formats

Nowadays, the Hardware-In-the-Loop (HIL) technique is widely used to test different power electronic converters. These real-time simulations require processing large data at high speed, which makes this application very suitable for FPGAs (Field Programmable Gate Array) as they are capable of parallel processing. This paper provides an analytical discussion on three HIL models for a full-bridge converter. The three models use different possible numerical formats, namely float and fixed-point, the latter with and without optimizing the width of signals to the embedded DSP (Digital Signal Processors) blocks of the FPGA. The optimized fixed-point model (OFPM) uses three and two times fewer DSP blocks or LUTs (Look Up Tables), and the maximum achievable clock frequency is also up to 35 % and 25 % higher than the float model and non-optimized fixed-point model (nOFPM), respectively. Furthermore, the models’ accuracy is proportional to the clock frequency, thus the OFPM is also the most accurate model. Finally, the paper shows the differences in the simulation when the models include or not losses, proving that not including losses leads to high errors, especially during transients.

Multi-Class Management With Sub-Class Service for Autonomous Electric Mobility On-Demand Systems

This paper aims to resolve a clear and unrealistic limitation found in recent works that have proposed solutions to the massive demand for the autonomous electric mobility-on-demand (AEMoD) services. Namely, this paper will focus on the matched charge-to-trip only service by enabling sub-class service; i.e., allowing vehicles to serve customer classes with trips needing less charge. The paper proposes a multi-class management system with a new queuing model for one city zone, and then derives the stability conditions of the system. Proportions of vehicles from each class that will immediately serve customers with sub-class service or full/partial charge are modeled, and the maximum expected response time of the system is then minimized by optimizing these vehicle service decisions. The advantages of the new model are then compared with previously proposed schemes and non-optimized models through simulations.

An optimization of artificial neural network modeling methodology for the reliability assessment of corroding natural gas pipelines

A fast calculation of the reliability is meaningful to the in-line inspection of corroding natural gas pipelines. However, the traditional Monte Carlo simulation(MCS) method is time consuming for the low possibilities of the pipeline failure. The artificial neural network(ANN) is preferable for the complex nonlinear situation. An optimization of artificial neural network modeling methodology for the reliability assessment of corroding natural gas pipelines is proposed in this paper. To reduce the influence of training sets random behaviors on the calculation results, some algorithms are used to optimize the sequence of the training samples and the initial parameters of ANN models. The optimized model is applied to the reliability assessment of a corroded pipe with two successive inline inspections. According to the physical parameters of the pipeline, the trend of corroding pipeline reliability in time is predicted. The comparison between the trained ANN model, the MCS method and non-optimized ANN model shows the advantages the proposed modeling process. The methodology given in this paper is general and it can be applied to evaluate the reliability of other kind of structure safeties in practical systems.

Multiobjective Optimization Design and Experimental Investigation on the Axial Flow Pump with Orthogonal Test Approach

A multiobjective optimization technique based on the computational fluid dynamics (CFD) simulations and the orthogonal test is proposed to reduce the pressure pulsation in this paper. Three levels of four well‐known performance factors L9 (34) were considered in the orthogonal test scheme: the number of blades, the blade setting angle, the hub ratio, and the distance between the blade and the guide vane. The evaluation indexes corresponded to the head, efficiency, shaft power, and pressure pulsation, respectively. An optimal configuration A2B1C2D3 was obtained by comprehensive frequency analysis method, after intuitive and range analysis. In comparison with the nonoptimized model, the new design’s head and efficiency increased by 17.8% and 4.26%, whilst the shaft power and the pressure pulsation coefficient reduced by 1.22% and 11%, respectively. Experiments conducted on the optimized pump were consistent with the CFD model. Six different rotational speed conditions in the optimal operating points were numerically calculated in order to explore the internal hydraulic characteristics of the optimized axial flow pump. It is verified that the comprehensive frequency analysis method based on the orthogonal test approach is effective for the multiobjective optimization of the axial flow pump.

Optimal Performance of Doubly Fed Induction Generator Wind Farm Using Multi-Objective Genetic Algorithm

The main purpose of this paper is allowing doubly fed induction generator wind farms (DFIG), which are connected to power system, to effectively participate in feeding electrical loads. The oscillation in power system is one of the challenges of the interconnection of wind farms to the grid. The model of DFIG contains several gains which need to be achieved with optimal values. This aim can be accomplished using an optimization algorithm in order to obtain the best performance. The multi-objective optimization algorithm is used to determine the optimal control system gains under several objectives. In this paper, a multi-objective genetic algorithm is applied to the DFIG model to determine the optimal values of the gains of DFIG control system. In order to point out the contribution of this work; the performance of optimized DFIG model is compared with the non-optimized model of DFIG. The results show that the optimized model of DFIG has better performance over the non-optimized DFIG model.

A Parallel Mode Optimized GPU Accelerated Monte Carlo Model for Light Propagation in 3-D Voxelized Bio-Tissues

Monte Carlo simulation is a precise method to model light propagation in bio-tissues and has been considered the golden standard to estimate the result of other computation methods. But the huge computation burden limited the application. In this paper, we propose a parallel computing model using graphic card to accelerate the Monte Carlo simulation in 3-D voxelized media with the consideration of internal refraction. Optimization of the parallel mode is made by using segmentations and offered an extra boost of simulation speed. The acceleration efficiency affecting factors are investigated and the acceleration rate of the five segmented model is 32.6 times higher than non-GPU model and 1.66 times higher than non-optimized model for a real human head 3-D structure simulation.

Preservation of local high-resolution data in a regional low-resolution dataset: A ‘nested’ 3D modeling approach using an example from a Quaternary glacial stratigraphy (Ontario, Canada)

Regional scale 3D models provide insight into general unit geometries and stratigraphic relationships; however, potentially significant local scale data and unit geometries are rarely preserved. Accurate modeling of local scale unit geometries and stratigraphic relationships is important for understanding aquifer-aquitard connectivity, estimation of aquifer volume, and prediction of contaminant migration pathways; this is particularly important in previously glaciated areas because of the high degree of lithological heterogeneity and stratigraphic variation in glaciogenic deposits. This study presents a case study (Ontario, Canada) that explores the impacts of an optimized model (in which high-resolution data are preserved in local scale models as ‘golden blocks’) and non-optimized regional scale 3D models on the predicted geometries and stratigraphic relationships of coarse- and fine-grained units (lithofacies associations) within a Quaternary stratigraphy. Each of the regional scale models include high quality data (outcrop and cored borehole logs) and low quality data (water well lithology logs); however, the optimized model was constructed by seamlessly integrating the golden blocks into the regional scale model using an algorithm (producing a ‘nested’ model), whereas the non-optimized model was built using a global modeling approach. By way of comparison to unit geometries previously delineated in outcrops and surficial geology maps and hydraulic responses from pumping tests, the results of this study indicate that coarse-grained units, which host significant municipal aquifers, and fine-grained units were more realistically and accurately represented in the optimized model as a result of the nested 3D modeling approach. This case study demonstrates a relatively simple and effective method for preserving high quality data in a regional scale 3D model without sacrificing model accuracy in key areas of interest (e.g. aquifer well fields).

A personalized mandibular implant with supporting and porous structures designed with topology optimization – a case study of canine

PurposeThe purpose of this study is to obtain a titanium mandibular implant that possesses a personalized external shape for appearance recovery, a supporting structure for physiological loading and numerous micro-pores for accelerating osseointegration.Design/methodology/approachA three-dimensional intact mandibular model of a beagle dog was created from cone-beam computerized tomography scans. A segment of the lower jaw bone was resected and replaced by a personalized implant with comprehensive structures including a customized external shape, supporting structures and micro-pores, which were designed by topology optimization. Then with FEM analysis, the stress, displacement distribution and compliance of the designed implant were compared with the non-optimized model. The weight of the optimized implant that was fabricated by SLM with titanium alloy powder was measured and contrasted with the predicted non-optimized model for evaluating the viability of the design.FindingsThe FEM results showed the peaks of von Mises stress and displacement on the optimized implant were much lower than those of the implant without optimization. With topology optimization, the compliance of the implant decreased significantly by 53.3 per cent, and a weight reduction of 37.2 per cent could be noticed.Originality/valueA design strategy for personalized implant, with comprehensive structures and SLM as the fabrication method, has been developed and validated by taking a canine mandible as the case study. With comprehensive structures, the implant presented good biomechanical behaviors thanks to the most appropriate supporting structures obtained by optimal design. The topological optimal design combined with SLM printing proved to be an effective method for the design and fabrication of personalized implant with complex structures.

A Simple Adaptation of Variable Selection Software for Regression Models to Select Variables in Nested Error Regression Models

Data users often apply standard regression model selection criteria to select variables in nested error regression models, which are widely used in small area estimation. We demonstrate through a Monte Carlo simulation study that this practice may lead to selection of a non-optimal or incorrect model. To assist data users who wish to use standard regression software, we propose a transformation of the data so that transformed data follow a standard regression model. Thus, variable selection software available for the standard regression model can be directly applied to the transformed data. We illustrate our methodology using survey and satellite data for corn and soybeans in 12 Iowa counties.

Hydrologic impacts of changing land use and climate in the Veneto lowlands of Italy

Abstract The Po valley in northern Italy is one of Europe’s largest and most anthropogenically-modified lowland areas, where intensifying climate and land transformation are increasingly causing water management problems. In this study, the Wageningen Lowland Runoff Simulator (WALRUS) is calibrated, validated, and applied to a reclaimed basin in the Veneto region (Italy) in order to assess the hydrologic impacts of land use and climate change scenarios. First-time model calibration for Mediterranean lowlands resulted in reasonable performance during the training year (NSE 0.77), but lower validation performance (NSE 0.53), while potential for improved calibration was limited by data availability. Scenario analysis covers the historical and future changes in land cover and climate throughout a century (1951–2060), based on aerial imagery analysis, hydrologic measurements, COSMO-CLM regional climate projections and demographics. WALRUS simulations illustrate how land use transformation (i.e. expanded built-up zones and a diminished drainage network) have a strong potential to increase discharge intensities from the catchment, mostly evident in summer peak flow (past −34%; future +48%). A historical scenario of combined land use and climate shows even stronger deviations from the present (annual discharge −19%; summer peak flow −45%), resulting from an observed increase in rainfall intensity and seasonality over the past 50 years. With drier future climate projections, however, the discharge response is moderate in the combined future scenario. Despite the non-optimal model calibration, the presented work in the Veneto region illustrates the directional impact of processes typical of anthropogenic lowlands. Particularly, the impact of observed land transformation seems to diminish the buffering and storage capacity of the catchment, thereby enhancing the hydrologic risks in modern times.

Social Incentive Mechanism Based Multi-User Sensing Time Optimization in Co-Operative Spectrum Sensing with Mobile Crowd Sensing.

Co-operative spectrum sensing emerging as a significant method to improve the utilization of the spectrum needs sufficient sensing users to participate. Existing related papers consider only the limited secondary users in current sensing system and assume that they will always perform the co-operative spectrum sensing out of obligation. However, this assumption is impractical in the realistic situation where the secondary users are rational and they will not join in the co-operative sensing process without a certain reward to compensate their sensing energy consumption, especially the ones who have no data transmitting in current time slot. To solve this problem, we take advantage of the mobile crowd sensing to supply adequate co-operative sensing candidates, in which the sensing users are not only the secondary users but also a crowd of widely distributed mobile users equipped with personal spectrum sensors (such as smartphones, vehicle sensors). Furthermore, a social incentive mechanism is also adapted to motivate the participations of mobile sensing users. In this paper, we model the interactions among the motivated sensing users as a co-operative game where they adjust their own sensing time strategies to maximize the co-operative sensing utility, which eventually guarantees the detection performance and prevents the global sensing cost being too high. We prove that the game based optimization problem is NP-hard and exists a unique optimal equilibrium. An improved differential evolution algorithm is proposed to solve the optimization problem. Simulation results prove the better performance in our proposed multi-user sensing time optimization model and the proposed improved differential evolution algorithm, respectively compared with the non-optimization model and the other two typical equilibrium solution algorithms.

Effect of prompt dissociation of formyl radical on 1,3,5-trioxane and CH2O laminar flame speeds with CO2 dilution at elevated pressure

Many studies of the flame speed of hydrocarbon and oxygenated fuels show that flame speed is very sensitive to formyl radical (HCO) reactions with small species, such as HCO+M=H+CO+M (R1), HCO+O2=HO2+CO (R2) and HCO+X=CO+XH (X=H, OH) (R3). Through comparison among experimental measurements and kinetic model predictions, this paper investigates CH2O flame speed sensitivities to the effects of HCO prompt dissociation and CO2 third-body participation in R1. The conditions considered include atmospheric and elevated pressures as well as lean, ultra lean, and rich fuel mixtures using 1,3,5-trioxane as the CH2O precursor. The experimental results provide key validation targets for CH2O and HCO chemistry and the R1 third-body coefficient of CO2 in flames. Five mechanisms, GRI Mech 3.0 (Smith et al., 1999), Li Mech (Li et al., 2007), USC Mech II (Wang et al., 2007), HP Mech (Shen et al., 2015), and Aramco Mech 1.3 (Metcalfe et al., 2013) are compared against the experimental data. Model predictions indicate that the prompt reaction pathway has a significant effect on the flame speed. With an increase in pressure or the addition of CO2, the kinetic between the prompt reaction and R1 slightly reduces the prompt radical dissociation effect. On the other hand, an increase of O2 mole fraction enhances the prompt effect on the flame speed. Comparisons among experiments and model predictions show that the HP Mech with the prompt reactions, USC Mech, and Li Mech have better predictions of the flame speed at lean, ultra-lean, rich, and lean with CO2 conditions than GRI Mech and Aramco Mech. However, the predictions of USC Mech and Li Mech with prompt reactions show increased discrepancy between experiments and predictions. This result indicates that by including a new reaction pathway, an optimized model may fail beyond the validated experimental conditions. On the other hand, an elementary rate-based, non-optimized model like HP Mech can improve the prediction by directly adding the missing prompt reaction pathway.

MaxEnt's parameter configuration and small samples: are we paying attention to recommendations? A systematic review.

Environmental niche modeling (ENM) is commonly used to develop probabilistic maps of species distribution. Among available ENM techniques, MaxEnt has become one of the most popular tools for modeling species distribution, with hundreds of peer-reviewed articles published each year. MaxEnt’s popularity is mainly due to the use of a graphical interface and automatic parameter configuration capabilities. However, recent studies have shown that using the default automatic configuration may not be always appropriate because it can produce non-optimal models; particularly when dealing with a small number of species presence points. Thus, the recommendation is to evaluate the best potential combination of parameters (feature classes and regularization multiplier) to select the most appropriate model. In this work we reviewed 244 articles published between 2013 and 2015 to assess whether researchers are following recommendations to avoid using the default parameter configuration when dealing with small sample sizes, or if they are using MaxEnt as a “black box tool.” Our results show that in only 16% of analyzed articles authors evaluated best feature classes, in 6.9% evaluated best regularization multipliers, and in a meager 3.7% evaluated simultaneously both parameters before producing the definitive distribution model. We analyzed 20 articles to quantify the potential differences in resulting outputs when using software default parameters instead of the alternative best model. Results from our analysis reveal important differences between the use of default parameters and the best model approach, especially in the total area identified as suitable for the assessed species and the specific areas that are identified as suitable by both modelling approaches. These results are worrying, because publications are potentially reporting over-complex or over-simplistic models that can undermine the applicability of their results. Of particular importance are studies used to inform policy making. Therefore, researchers, practitioners, reviewers and editors need to be very judicious when dealing with MaxEnt, particularly when the modelling process is based on small sample sizes.

Short-run price forecast performance of individual and composite models for 496 corn cash markets

ABSTRACTUsing daily prices from 496 corn cash markets for July 2006–February 2011, this study investigates short-run forecast performance of 31 individual and 10 composite models for each market at horizons of 5, 10, and 30 days. Over the performance evaluation period September 2010–February 2011, two composite models are optimal across horizons for different markets based on the mean-squared error. For around half of the markets at the horizon of 5 days and most of them at 10 and 30 days, the mean-squared error of a market's optimal model is significantly different from those of at least other 23 models evaluated for it. Root-mean-squared error reductions through switching from non-optimal models to the optimal are generally around 0.40%, 0.55%, and 0.87% at horizons of 5, 10, and 30 days.