Heavy Truck Fuel Storage System Design for Improved Impact Protection

Heavy (Class 8) truck fuel storage location and geometry has not significantly changed in several decades. Manufacturers have taken steps to improve their designs by eliminating cross over lines and making material property and thickness changes, among other changes, but there has been no mandate or significant effort to decrease the potential for post collision fuel fed fires in heavy trucks. Even with these design changes, FARS data indicates the number of fatal post-impact fires has not decreased over time. Several studies were conducted in the 1980’s and 1990’s that brought the unprotected design of the fuel storage on these vehicles to light. This paper combines these historical works with current FARS data on the subject and describes a different design approach that increases the impact protection of the fuel storage tank. This new approach uses the truck’s frame rails to guard the fuel storage tank and absorb and redirect impact energy. Currently, a heavy truck “saddle” mounted fuel tank’s integrity is tested through a 30 foot drop test prescribed by 49 CFR 393 and also listed in SAE Recommended Procedure J703. In this work, a crash test methodology used to test the integrity of a school bus side mounted fuel tank as prescribed in FMVSS 301S is discussed. Results of using this crash methodology on a current “saddle” tank design and a prototype of the new fuel storage system design are also presented.

Damage Classification of Composites Using Machine Learning

Composite materials have tremendous and ever-increasing applications in complex engineering systems; thus, it is important to develop non-destructive and efficient condition monitoring methods to improve damage prediction, thereby avoiding catastrophic failures and reducing standby time. Nondestructive condition monitoring techniques when combined with machine learning applications can contribute towards the stated improvements. Thus, the research question taken into consideration for this paper is “Can machine learning techniques provide efficient damage classification of composite materials to improve condition monitoring using features extracted from acousto-ultrasonic measurements?” In order to answer this question, acoustic-ultrasonic signals in Carbon Fiber Reinforced Polymer (CFRP) composites for distinct damage levels were taken from NASA Ames prognostics data repository. Statistical condition indicators of the signals were used as features to train and test four traditional machine learning algorithms such as K-nearest neighbors, support vector machine, Decision Tree and Random Forest, and their performance was compared and discussed. Results showed higher accuracy for Random Forest with a strong dependency on the feature extraction/selection techniques employed. By combining data analysis from acoustic-ultrasonic measurements in composite materials with machine learning tools, this work contributes to the development of intelligent damage classification algorithms that can be applied to advanced online diagnostics and health management strategies of composite materials, operating under more complex working conditions.

Distributed Training for Data Driven Models in Power Machinery Online Monitoring

Prognostics and Health Management (PHM) Systems have been widely applied in online monitoring of modern mechanical devices, and models in PHM systems are of vital importance. Traditional mechanism models are able to simulate parameters and evaluate the overall machine state accurately, but their defect is the high requirements in understanding the objects. Therefore, data driven models could be a suitable substitute. Data driven models take the advantage of machine learning technique and are able to be established only on the basis of past data. Models applied in this paper are based on Artificial Neural Network (ANN), but the complexity of network structures will occupy huge amount of computing resources and time in training. For online monitoring, spending too much time on training will postpone real time prediction and decrease the reaction speed of PHM systems in abnormal conditions. In this paper distributed training based on computer cluster is proposed to decrease training time. By dispatching computing task into several workers, the severs in the cluster will only undertake a small fraction of the total computing load and therefore accelerate the overall training process. On the other hand, there are some risks of losing prediction accuracy and stability because of the nonlinear gradient deviation in data parallelism. Aggregation period (AP) is an important factor in balancing the requirements of both ends. This paper analyzes the influence of AP on training speed, accuracy and stability, then proposes a novel distributed training algorism according to the regulations achieved. Then a distributed training and online monitoring process for a typical two-shaft gas turbine is taken as an example in the result and discussion section. It turns out that the experimental results fit the theoretical regulations well, and the revised distributed learning algorism is able to meet all the requirements of training speed, prediction accuracy and stability.

Effect of Particle Breakage on Explosibility of Coal/Rock Dust Mixtures due to Dispersion in 20-L Chambers

Research has shown that particle size has a significant impact on the explosibility of coal dust/rock dust mixtures. Previous explosion studies conducted using the U.S. Bureau of Mines’ (BOM) 20-L explosion chamber tend to show a difference in the amount of inerting material needed to prevent an explosion when compared to the 20-L Siwek chamber. To reconcile these differences, samples were comparatively tested in the historic BOM 20-L chamber and the new NIOSH 20-L Siwek chamber with an emphasis on particle breakage. Rock dust and coal dust samples were dispersed in the chambers without ignitors and their specific surface areas were compared to the undispersed specific surface areas in order to quantify the breakage. Then, rock dust and coal dust mixtures were prepared, dispersed using the Siwek chamber, collected and tested for explosibility in the BOM 20-L chamber to see if the severe particle breakage in the Siwek chamber would influence the inerting limits of the BOM chamber. Results indicate that the particle breakage of friable brittle materials during explosion testing should be considered when evaluating the explosion risks in the process industries.

Review of Design Techniques of Armored Vehicles for Protection Against Blast From Improvised Explosive Devices

This review paper covers the subject of vehicle design for protection against the blast of improvised explosive devices. It summarizes the most recent techniques in the design of vehicle structures for blast protection. This review pinpoints the challenges in designing a vehicle for blast protection and maintaining acceptable mobility at the same time. In addition, this paper reveals and summarizes key design principles based on the critical assessment of the published literature. This review covers the state of the art design methods for each individual part of vehicle structure. It also identifies the role of human protective gear and its effectiveness in mitigating blast related injuries. This review suggests future trends in developing innovative protective structures that are inspired by nature or manufactured. The aim of this review is to guide and advance the research of vehicle design for blast protection.

Risk Assessment of Escalator Sidewall Entrapment Between the Years 1990 and 2017

In previous work, hazards related to escalator sidewall entrapment were examined between the years 1998 and 2017 [1]. This prior analysis included entrapment of the escalator riders’ fingers, hands, toes, or feet. Data were analyzed from the U.S. Consumer Product Safety Commission National Electronic Injury Surveillance System (NEISS) to better understand the trends associated with escalator sidewall entrapment since the introduction of the step/skirt performance index in the 2000 edition of ASME A17.1 Safety Code for Elevators and Escalators. This current effort extends the analysis to include injury data as early as 1990 to discern the trends in sidewall entrapment hazards during the decade prior to the introduction of the step/skirt performance index. The 1990 to 1997 data set was statistically adjusted to account for changes in the sampling frame of the NEISS data set between that period and the 1998 to 2017 data that were analyzed in the previous work [2,3]. This new work analyzes data from the entire time period, 1990 to 2017, to better understand the injury data trends and risks associated with sidewall entrapment. The resulting trends are then compared to the Code modifications introduced to mitigate the hazards of sidewall entrapment. The statistical grouping analysis used in the previous work is improved and extended in this work. This grouping analysis presents an estimate of the number of sidewall entrapment incidents per day and overall incident percentage for contiguous date ranges covering the years 1990–2017. In all, the work presented herein will allow for a comparison across date ranges for both hand/finger and foot/toe injuries as it relates to modifications to ASME A17.1 such as the addition of the step/skirt performance index.

Design, Analysis and Test of Multi-Stage Crashworthiness Energy Absorbing Device for Railway Vehicles

An anti-collision and energy absorbing device is generally placed at the front end of railway vehicles to provide controlled collapse and sufficient energy absorbing capacity. In the conventional way, the energy absorbing device is usually designed as an integrated part of the carbody that can’t be easily replaced nor maintained after crash. To increase the maintainability and energy absorbing capacity of the energy absorbing device, a Multi-stage Crashworthiness Energy Absorbing Device has been developed, in which the first-stage and the second-stage energy absorbing units can be replaced or repaired under the collision that is not severe enough to initiate the third-stage energy absorbing unit. The Crashworthiness Energy Management (CEM) has four stages: coupler energy absorbing components (stage 1), honeycomb in the sliding center anti-climber (stage 2), metal peeling tubes mounted at the back of the fixed anti-climber (stage 3) and the structural components in the cab area (stage 4). By comparing the simulation results and test results, it is concluded that the finite element simulation model can provide dependable and accurate prediction for collision behaviors. Based on the design, simulation and test data, a safe, reliable and maintainable Multi-stage Crashworthiness Energy Absorbing Device has been verified and validated, which can provide valuable reference for researchers and engineers in the crashworthiness and railway vehicle industry.

Assessment of Elevator Risk and Code Requirements to Address These Hazards

The ASME A17.1 Safety Code for Elevators and Escalators establishes safe practices in the design, construction, installation, operation, testing, inspection, and maintenance of elevators. This Code continues to be updated with new regulations to improve safety for elevator riders. The estimated number of elevators in service in the U.S. is approximately 1.1 million.1 Two of the most common injuries for elevator riders are associated with unexpected door closures and trips and falls when entering or leaving an elevator. A recent study reported that door-related incidents accounted for 40% of the total elevator related injuries [1]. This same study reported that another 40% of the total injuries were associated with trips and falls caused by elevator cars that were not level with the landing. These two hazards result in approximately 80% of the elevator-related injuries occurring at the entrance of the elevator. Other recent studies have concluded that older adults [2] and young children [3] are particularly impacted by these elevator-related hazards. This paper will focus on identifying and analyzing the hazards associated with elevator door closures. In this study, the National Electronic Injury Surveillance System (NEISS) database of the U.S. Consumer Product Safety Commission (CPSC) is reviewed from 1990 to 2017 to better understand the trends associated with door strikes, the affected body part and the age of those involved in the incidents. This study also explores and discusses the safety mechanisms currently available to address these hazards. An examination of updates to the ASME A17.1 Code along with improvements in door reopening technologies will be presented to guide the discussion.

Uncertainty Quantification of Wind Turbine Wakes Under Random Wind Conditions

Understanding and minimizing the uncertainties in the wind energy field is of high importance to reduce the reliability risks and financial risks of wind farm projects. The present work aims to observe the levels of uncertainty in modeling the wake effect by attempting to perform statistical inference of a wake parameter, the wind speed deficit. For this purpose, an uncertainty propagation framework is presented. The framework starts by randomly sampling mean wind speed data from its probability density function (PDF), that is fed an inflow model (TurbSim), resulting in random full-flow fields that are integrated into an aeroelastic model (FAST), which results in the variability of the power and thrust coefficients of a wind turbine. Such coefficients and wind data, finally, fed the wake engineering model (FLORIS). The framework ends with the determination of the 95% coefficient intervals of the time-averaged wind speed deficit. The results obtained for the near and far wake regions introduce fundamentals in estimate the uncertainty in wind speed deficit of a single wind turbine wake and concludes that a systematic uncertainty quantification (UQ) framework for wind turbine wakes may be a useful tool to wind energy projects.

Insight Into the Design of Blast-Mitigating Floor Mats Using Design of Experiments

Recently, Improvised explosive devices (IEDs) have evolved into a major and significant threat inflicting substantial human casualties and property damage. The majority of injuries are to the lower extremities since they are in close contact to vehicle floor. Floor mats have been developed to mitigate the effects of IEDs blasts. This paper reports a computational study on the energy absorbing behavior of a novel commercial floor mat — Skydex — for foot protection. The design of experiments (DOE) approach was applied to investigate the effect of shape variations on the dynamic performance of a finite element model of Skydex. The FE model was verified using experimental tests on samples produced using 3D printing technique. The DOE approach revealed significant insight into the design of Skydex. It confirmed that shape variables have strong effect on the amount of energy absorbed and the transmitted load. DOE specifically identified the radius of the mid-section of Skydex as the most influential factor in controlling the mode of deformation under compression. In addition, it uncovered the interaction effect between the radius of curvatures of the two hemispheres and upper and lower radii. Finally, DOE revealed the bi-trade-off relations between energy absorbed, transmitted load and mass. These were shown in meaningful and helpful plots which will help the development of Skydex design.