Earthmoving trucks condition level prediction using neural networks

Purpose: The purpose of this paper is to present an artificial neural network (ANN) model that predicts earthmoving trucks condition level using simple predictors; the model’s performance is compared to the respective predictive accuracy of the statistical method of discriminant analysis (DA).

Design/methodology/approach: An ANN-based predictive model is developed. The condition level predictors selected are the capacity, age, kilometers travelled and maintenance level. The relevant data set was provided by two Greek construction companies and includes the characteristics of 126 earthmoving trucks.

Findings: Data processing identifies a particularly strong connection of kilometers travelled and maintenance level with the earthmoving trucks condition level. Moreover, the validation process reveals that the predictive efficiency of the proposed ANN model is very high. Similar findings emerge from the application of DA to the same data set using the same predictors.

Originality/value: Earthmoving trucks’ sound condition level prediction reduces downtime and its adverse impact on earthmoving duration and cost, while also enhancing the maintenance and replacement policies effectiveness. This research proves that a sound condition level prediction for earthmoving trucks is achievable through the utilization of easy to collect data and provides a comparative evaluation of the results of two widely applied predictive methods.

Modulational instability of dust acoustic waves in dusty plasmas: Modulation obliqueness, background ion nonthermality, and dust charging effects

The oblique modulational instability of dust acoustic (DA) waves in an unmagnetized warm dusty plasma with nonthermal ions, taking into account dust grain charge variation (charging), is investigated. A nonlinear Schrodinger-type equation governing the slow modulation of the wave amplitude is derived. The effects of dust temperature, dust charge variation, ion deviation from Maxwellian equilibrium (nonthermality) and constituent species' concentration on the modulational instability of DA waves are examined. It is found that these parameters modify significantly the oblique modulational instability domain in the k-theta plane. Explicit expressions for the instability rate and threshold have been obtained in terms of the dispersion laws of the system. The possibility and conditions for the existence of different types of localized excitations are also discussed. The findings of this investigation may be useful in understanding the stable electrostatic wave packet acceleration mechanisms close to the Moon, and also enhances our knowledge on the occurrence of instability associated to pickup ions around unmagnetized bodies, such as comets, Mars, and Venus.

HTS Machines as Enabling Technology for All-Electric Airborne Vehicles

Environmental protection has now become paramount as evidence mounts to support the thesis of human activity-driven global warming. A global reduction of the emissions of pollutants into the atmosphere is therefore needed and new technologies have to be considered. A large part of the emissions come from transportation vehicles, including cars, trucks and airplanes, due to the nature of their combustion-based propulsion systems. Our team has been working for several years on the development of high power density superconducting motors for aircraft propulsion and fuel cell based power systems for aircraft. This paper investigates the feasibility of all-electric aircraft based on currently available technology. Electric propulsion would require the development of high power density electric propulsion motors, generators, power management and distribution systems. The requirements in terms of weight and volume of these components cannot be achieved with conventional technologies; however, the use of superconductors associated with hydrogen-based power plants makes possible the design of a reasonably light power system and would therefore enable the development of all-electric aero-vehicles. A system sizing has been performed both for actuators and for primary propulsion. Many advantages would come from electrical propulsion such as better controllability of the propulsion, higher efficiency, higher availability and less maintenance needs. Superconducting machines may very well be the enabling technology for all-electric aircraft development.

Improving the Fuel Economy of a Tuned 600cc FSAE Engine

To maintain its relevance, motorsport cannot be exempt from
the trend of increasing fuel economy. This bears obvious
competitive benefits as well, either in decreasing the
frequency of pit stops or the mass of fuel carried. Given the
increased points weighting of fuel economy for the Formula
Student (FS) competition, a complete analysis was performed
on the Queen's Formula Racing 600cc motorcycle engine in
preparation for the 2011 competition.
The criteria for such high performance fuel economy differ to
a degree from most mass transportation counterparts and were
divided into three distinct regimes; full load, part load and no
load conditions.
Full load positions naturally demand maximum torque for
performance but that does not imply that fuel savings cannot
be made whilst preserving this. The point at which maximum
torque is produced with minimum air -fuel ratio, Leanest
mixture for Best Torque (LBT), was therefore sought and
mapped for full load.
At part load, torque is less of a concern, and maintaining a
sustainable engine temperature and transient response become
more important. With decreasing AFR, engine temperatures
can rise dramatically so temperatures were measured close to
the exhaust port for a wide range of air-fuel ratios.
Competition track data was analysed to highlight key part load
operating regions and these were mapped according to
measured safe temperature limits. Torque response to a step
throttle change was also measured to ensure suitable engine
transient performance was maintained.
At no load conditions, with low engine speed only idle
conditions need to be satisfied. In the situation where the
engine is still at high speed without load, the engine is being
motored and no fuel is required. An overrun fuel cut was
employed to reflect this giving significant fuel savings. The
effect on torque and engine pickup was measured.
Modifications were also made to the fuel injector location to
improve fuel mixing and evaporation at this lower air flow
condition.
These mapping regimes were implemented and tested using
fully transient lap simulations using competition track data
and a four quadrant AC engine dynamometer. The experiment
indicated a reduction in fuel consumption for 22 laps of the FS
track from 5.08litres to 3.67litres, around 27% in total. The
actual fuel used at the 2011 competition was 3.6 litres while
placing 8th in the endurance event, further validating the
benefits of these mapping regimes.

A Novel Approach for Axle Detection in Bridge Weigh-In-Motion Systems

Bridge weigh-in-motion (B-WIM), a system that uses strain sensors to calculate the weights of trucks passing on bridges overhead, requires accurate axle location and speed information for effective performance. The success of a B-WIM system is dependent upon the accuracy of the axle detection method. It is widely recognised that any form of axle detector on the road surface is not ideal for B-WIM applications as it can cause disruption to the traffic (Ojio & Yamada 2002; Zhao et al. 2005; Chatterjee et al. 2006). Sensors under the bridge, that is Nothing-on-Road (NOR) B-WIM, can perform axle detection via data acquisition systems which can detect a peak in strain as the axle passes. The method is often successful, although not all bridges are suitable for NOR B-WIM due to limitations of the system. Significant research has been carried out to further develop the method and the NOR algorithms, but beam-and-slab bridges with deep beams still present a challenge. With these bridges, the slabs are used for axle detection, but peaks in the slab strains are sensitive to the transverse position of wheels on the beam. This next generation B-WIM research project extends the current B-WIM algorithm to the problem of axle detection and safety, thus overcoming the existing limitations in current state-of–the-art technology. Finite Element Analysis was used to determine the critical locations for axle detecting sensors and the findings were then tested in the field. In this paper, alternative strategies for axle detection were determined using Finite Element analysis and the findings were then tested in the field. The site selected for testing was in Loughbrickland, Northern Ireland, along the A1 corridor connecting the two cities of Belfast and Dublin. The structure is on a central route through the island of Ireland and has a high traffic volume which made it an optimum location for the study. Another huge benefit of the chosen location was its close proximity to a nearby self-operated weigh station. To determine the accuracy of the proposed B-WIM system and develop a knowledge base of the traffic load on the structure, a pavement WIM system was also installed on the northbound lane on the approach to the structure. The bridge structure selected for this B-WIM research comprised of 27 pre-cast prestressed concrete Y4-beams, and a cast in-situ concrete deck. The structure, a newly constructed integral bridge, spans 19 m and has an angle of skew of 22.7°.

Climate change and the long-term viability of the World’s busiest heavy haul ice road

Climate models project that the northern high latitudes will warm at a rate in excess of the global mean. This will pose severe problems for Arctic and sub-Arctic infrastructure dependent on maintaining low temperatures for structural integrity. This is the case for the economically important Tibbitt to Contwoyto Winter Road (TCWR)—the world’s busiest heavy haul ice road, spanning 400 km across mostly frozen lakes within the Northwest Territories of Canada. In this study, future climate scenarios are developed for the region using statistical downscaling methods. In addition, changes in lake ice thickness are projected based on historical relationships between measured ice thickness and air temperatures. These projections are used to infer the theoretical operational dates of the TCWR based on weight limits for trucks on the ice. Results across three climate models driven by four RCPs reveal a considerable warming trend over the coming decades. Projected changes in ice thickness reveal a trend towards thinner lake ice and a reduced time window when lake ice is at sufficient thickness to support trucks on the ice road, driven by increasing future temperatures. Given the uncertainties inherent in climate modelling and the resultant projections, caution should be exercised in interpreting the magnitude of these scenarios. More certain is the direction of change, with a clear trend towards winter warming that will reduce the operation time window of the TCWR. This illustrates the need for planners and policymakers to consider future changes in climate when planning annual haulage along the TCWR.