Factors Affecting Car Fuel Consumption

1993 ◽  
Vol 207 (1) ◽  
pp. 1-22 ◽  
Author(s):  
M Redsell ◽  
G G Lucas ◽  
N J Ashford
Keyword(s):  
Fuel Consumption ◽  
Test Data ◽  
Regression Models ◽  
Ambient Pressure ◽  
Statistical Significance ◽  
Environmental Parameters ◽  
Independent Effect ◽  
Power Correction ◽  
Vehicle Speed ◽  
Gear Change

This paper describes a programme of statistical analyses on test data conducted to investigate the effects of a number of factors on car fuel consumption. The data used for the analyses were obtained from a comparative study of the fuel economy of petrol and diesel cars, conducted at the Department of Transport Technology, Loughborough University, on behalf of the Transport and Road Research Laboratory (TRRL). Three Vauxhall Cavalier cars (1300 cc, 1600 cc petrol and 1600 cc diesel models) and six drivers, categorized by sex and age, participated in the tests. Each test was completed over an extensive test route on public roads encompassing all types of driving environments. Factor analysis was used to determine the underlying relationships between a number of measured variables and fuel consumption. Under the ‘free-flowing’ environment of the motorway route section, fuel consumption was primarily influenced by ambient pressure, vehicle speed and frequency of gear change. In suburban conditions, only throttle velocity made any significant contribution to fuel consumption. In the tests over urban sections, the significant variables were found to be vehicle speed and deceleration, frequency of gear change and ambient pressure. Of the ambient factors examined, only pressure had any significant independent effect. The associations derived from factor analyses point the way to realistic correction techniques for vehicle test data. Regression models were constructed to fit the measured fuel consumption data to an expression derived to give fuel consumption as a function of engine, vehicle and environmental parameters. For purely urban, suburban and motorway travel and an overall journey based on a 50/40/10 per cent split of urban, suburban and motorway road types, 93.8, 92.4, 88.2 and 95 per cent of fuel consumption variance was explained by the respective regression models. Regression variables based on engine power correction factors for ambient temperature and humidity showed a degree of statistical significance in all models.

scholarly journals Effect of Speed and Boot Opening on Aerodynamics, Fuel Consumption, and CO2 Emission of Minibus

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Olusola A. Oloruntoba ◽  
Adebunmi P. Okediji
Keyword(s):  
Fuel Consumption ◽  
Co2 Emission ◽  
Regression Models ◽  
Viscous Drag ◽  
Vehicle Speed ◽  
Road Accidents ◽  
Total Drag ◽  
Pressure Drag ◽  
Regulatory Bodies ◽  
Excess Load

Overspeeding   and overloading contribute to road accidents. In developing countries, overloading is often indicated by open boot due to commercial transporters’ motivation to carry an excess load to boost revenue. Therefore, there is a need to provide measures to control or eliminate the practice of overspeeding and overloading. This study aims to conduct a parametric study to determine the effect of vehicle speed and boot opening on the aerodynamics of airflow around a typical minibus, fuel consumption, and CO2 emission, and recommend optimum boot opening. Computational Fluid Dynamics is employed using the FLUENT™ program. Results show the existence of a wavy pattern for drag coefficient, fuel consumption, and CO2 emission concerning boot opening. Furthermore, two boot opening regions exist:  and . The first region exhibits low prediction error (maximum of 7.25%) and better fit of regression model to FLUENT™ data. The first region also has lower susceptibility to exhibit handling instability. Therefore, boot opening around  is recommended as the optimum boot opening, to ensure minimum fuel consumption and CO2 emissions, improve handling and safety. The developed regression models could inform regulatory bodies’ formulation and implementation of policies to mitigate road accidents. Keywords—Boot Opening, CO2 emission, Fuel Consumption, Pressure drag, Total Drag, Minibus, Viscous Drag

scholarly journals New polyp image classification technique using transfer learning of network-in-network structure in endoscopic images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Young Jae Kim ◽  
Jang Pyo Bae ◽  
Jun-Won Chung ◽  
Dong Kyun Park ◽  
Kwang Gi Kim ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Transfer Learning ◽  
Test Data ◽  
State Of The Art ◽  
Early Stage ◽  
Statistical Significance ◽  
Recall Rate ◽  
Training Data ◽  
Fine Tuning ◽  
Accuracy Evaluation

AbstractWhile colorectal cancer is known to occur in the gastrointestinal tract. It is the third most common form of cancer of 27 major types of cancer in South Korea and worldwide. Colorectal polyps are known to increase the potential of developing colorectal cancer. Detected polyps need to be resected to reduce the risk of developing cancer. This research improved the performance of polyp classification through the fine-tuning of Network-in-Network (NIN) after applying a pre-trained model of the ImageNet database. Random shuffling is performed 20 times on 1000 colonoscopy images. Each set of data are divided into 800 images of training data and 200 images of test data. An accuracy evaluation is performed on 200 images of test data in 20 experiments. Three compared methods were constructed from AlexNet by transferring the weights trained by three different state-of-the-art databases. A normal AlexNet based method without transfer learning was also compared. The accuracy of the proposed method was higher in statistical significance than the accuracy of four other state-of-the-art methods, and showed an 18.9% improvement over the normal AlexNet based method. The area under the curve was approximately 0.930 ± 0.020, and the recall rate was 0.929 ± 0.029. An automatic algorithm can assist endoscopists in identifying polyps that are adenomatous by considering a high recall rate and accuracy. This system can enable the timely resection of polyps at an early stage.

Adaptive calibration of Diesel engine injection for minimising fuel consumption with constrained NOx emissions in actual driving missions

2020 ◽  
pp. 146808742091880
Author(s):  
José Manuel Luján ◽  
Benjamín Pla ◽  
Pau Bares ◽  
Varun Pandey
Keyword(s):  
Diesel Engine ◽  
Fuel Consumption ◽  
Time Window ◽  
Transition Probability ◽  
Estimation Method ◽  
Engine Performance ◽  
Driving Cycle ◽  
Vehicle Speed ◽  
Adaptive Calibration ◽  
Emission Limits

This article proposes a method for fuel minimisation of a Diesel engine with constrained [Formula: see text] emission in actual driving mission. Specifically, the methodology involves three developments: The first is a driving cycle prediction tool which is based on the space-variant transition probability matrix obtained from an actual vehicle speed dataset. Then, a vehicle and an engine model is developed to predict the engine performance depending on the calibration for the estimated driving cycle. Finally, a controller is proposed which adapts the start-of-injection calibration map to fulfil the [Formula: see text] emission constraint while minimising the fuel consumption. The calibration is adapted during a predefined time window based on the predicted engine performance on the estimated cycle and the difference between the actual and the constraint on engine [Formula: see text] emissions. The method assessment was done experimentally in the engine test set-up. The engine performace using the method is compared with the state-of-the-art static calibration method for different [Formula: see text] emission limits on real driving cycles. The online implementation of the method shows that the fuel consumption can be reduced by 3%–4% while staying within the emission limits, indicating that the estimation method is able to capture the main driving cycle characterstics.

scholarly journals Updated definitions on piezophily as suggested by hydrostatic pressure dependence on temperature

2020 ◽  
Author(s):  
Alberto Scoma
Keyword(s):  
Hydrostatic Pressure ◽  
Deep Sea ◽  
Ambient Pressure ◽  
Environmental Parameters ◽  
Microbial Life ◽  
Deep Waters ◽  
Microbial Response ◽  
Definition Of ◽  
Substrate Ph ◽  
Cell Functionality

AbstractMicrobial preference for elevated hydrostatic pressure (HP) is a recognized key feature of environmental and industrial processes. HP effects on macromolecules and, consequently, cell functionality has been accurately described in the last decades. While there is little debate about the importance of HP in shaping microbial life, a systematic definition of microbial preference for increased HP is missing. The lack of a consensus about ‘true’ piezophiles, and ‘low’ or ‘high’ HP levels, has deleterious repercussions on microbiology and biotechnology. As certain levels are considered ‘low’ they are not applied to assess microbial activity. Most microorganisms collected in deep waters or sediments have not been tested (nor isolated) using the corresponding HP at which they were captured. Microbial response to HP is notoriously dependent on other environmental parameters, most notably temperature, but also on availability of nutrients, growth substrate, pH and salinity. This implies that countless isolates retrieved from ambient pressure conditions may very well require increased HP to grow optimally, as already demonstrated in both Archaea and Bacteria.In the present study, I collected the data from described piezophilic isolates and used the fundamental correlation existing between HP and temperature, as first suggested in seminal works by Yayanos, to update the definition of piezophiles. Thanks to the numerous new piezophilic isolates available since such seminal studies, the present analysis brings forward updated definitions which concern 1) the actual beginning of the piezosphere, the area in the deep sea where piezophiles thrive; 2) the HP thresholds which should be considered low, medium and high HP, and their implications for experimental design in Microbiology; and 3) the nature of obligate piezophiles and their location in the deep sea.

Diesel Engine Performance Improvement for Constant Speed Application Using CFD

2017 ◽  
Author(s):  
Balasaheb S. Dahifale ◽  
Anand S. Patil
Keyword(s):  
Combustion Chamber ◽  
Fuel Consumption ◽  
Performance Improvement ◽  
Test Data ◽  
Engine Performance ◽  
Exhaust Valve ◽  
Fine Tuning ◽  
Intake Valve ◽  
Valve Seat ◽  
Load Conditions

The detailed investigation of flow behavior inside the combustion chamber and performance of engine is most challenging problem due to constraints in Experimental Data collection during testing; However, Experimental testing is essential for establishment of correlation with CFD Predictions. Hence, the baseline engine was tested at different load conditions and validated with CFD results, before it was optimized for performance improvement. The objective of the CFD Prediction was not only to optimize performance (Fuel Efficiency, Power, Torque, etc.) & Emissions Reduction, but also to assess feasibility of Performance Upgrade Potential. In the present CFD study, surface mesh and domain was prepared for the flame face, intake valve, intake valve seat, exhaust valve, exhaust valve seat and liner for closed volume cycle, between IVC and EVO using CFD code VECTIS. Finally simulations for three different load conditions were conducted using VECTIS solver. Initially, in-cylinder pressure vis a vis crank angle prediction was carried out for 100%, 75% and 50% load conditions. Then the fine tuning of (P-ϴ) diagram for different load conditions was conducted by varying different combustion parameters. Further, the engine performance validation was carried out for rated and part load conditions in terms of, IMEP, BMEP, break specific fuel consumption and power output, while NOx mass fractions were used to convert the NOx to g/kWh for comparison of emission levels with the test data. Finally optimized re-entrant combustion chamber and modified valve timing with optimum fuel injection system simulation was carried out to achieve target performance with reduced fuel consumption. A 3D CFD result showed reduction in BSFC and was in close agreement with the test data.

Predicting Dangerousness of Maximum Security Forensic Mental Patients

1982 ◽  
Vol 10 (2) ◽  
pp. 223-231 ◽  
Author(s):  
James M. Mullen ◽  
Robert C. Reinehr
Keyword(s):  
Psychiatric Hospital ◽  
Violent Crime ◽  
Test Data ◽  
Adult Male ◽  
Clinical Judgment ◽  
Statistical Significance ◽  
Separate Sample ◽  
Forensic Patients ◽  
Maximum Security ◽  
State Psychiatric

Demographic and psychological test data were collected for 269 adult male forensic patients upon admission to the forensic unit of a state psychiatric hospital. Each individual was judged to be dangerous or not dangerous by a panel of three judges. A separate sample of 135 patients was then collected and an attempt made to predict clinical judgment of dangerousness by use of a weighted formula previously derived. Predictions obtained did not attain statistical significance. A subsample of 165 patients was followed up after discharge. There were 15 arrests for violent crime recorded for these individuals subsequent to discharge. Implications of these findings are discussed.

scholarly journals Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2782 ◽  
Author(s):  
Amith Khandakar ◽  
Muhammad E. H. Chowdhury ◽  
Monzure- Khoda Kazi ◽  
Kamel Benhmed ◽  
Farid Touati ◽  
...  
Keyword(s):  
Feature Selection ◽  
Output Power ◽  
Regression Models ◽  
Prediction Models ◽  
Gaussian Process Regression ◽  
Environmental Parameters ◽  
Ann Model ◽  
Power Prediction ◽  
Pv System ◽  
Energy Evaluation

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.

Evaluating the Statistical Significance of Models Developed by Stepwise Regression

1983 ◽  
Vol 20 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Shelby H. McIntyre ◽  
David B. Montgomery ◽  
V. Srinivasan ◽  
Barton A. Weitz
Keyword(s):  
Monte Carlo ◽  
Null Hypothesis ◽  
Regression Models ◽  
Stepwise Regression ◽  
Statistical Significance ◽  
Selection Procedure ◽  
Coefficient Of Determination ◽  
Forward Selection ◽  
Monte Carlo Simulation Study ◽  
Independent Variables

Information for evaluating the statistical significance of stepwise regression models developed with a forward selection procedure is presented. Cumulative distributions of the adjusted coefficient of determination ([Formula: see text]) under the null hypothesis of no relationship between the dependent variable and m potential independent variables are derived from a Monté Carlo simulation study. The study design included sample sizes of 25, 50, and 100, available independent variables of 10, 20, and 40, and three criteria for including variables in the regression model. The results reveal that the biases involved in testing statistical significance by two well-known rules are very large, thus demonstrating the desirability of using the Monté Carlo cumulative [Formula: see text] distributions developed by the authors. Although the results were derived under the assumption of uncorrelated predictors, the authors show that the results continue to be useful for the correlated predictor case.

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