Accepted Manuscripts

David Eager and Hasti Hayati
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039999
More than four decades have passed since the introduction of safety standards for impact attenuation surfaces (IAS) used in playgrounds. Falls in children’s playground are a major source of injuries and IAS is one of the best method of preventing severe head injuries. However, the ability of IAS in prevention of other types of injuries such as upper limb fractures is unclear. Accordingly, in this paper 10 synthetic playground surfaces were tested to examine their performance beyond the collected Head Injury Criterion (HIC) and maximum G-force (Gmax) outputs recommended by ASTM F1292. The aim of this work was to investigate any limitations with current safety criteria and proposing additional criteria to filter hazardous IAS that technically comply with the current 1000 HIC and 200 Gmax thresholds. The proposed new criterion is called the impulse force criterion (If). If combines two important injury predictor characteristics, namely: HIC duration that is time duration of the most severe impact; and the change in momentum that addresses the IAS properties associated with bounce. Additionally, the maximum jerk (Jmax), the bounce and the IAS absorbed work are presented. HIC, Gmax, If and Jmax followed similar trends regarding material thickness and drop height. Moreover, the bounce and work done by the IAS on the falling missile at increasing drop heights was similar for all surfaces apart from one viscoelastic foam sample. The results presented in this paper demonstrate the limitations of current safety criteria and should therefore assist future research to reduce long-bone injuries in playgrounds.
TOPICS: Wounds, Risk, Safety, Fracture (Materials), Impulse (Physics), Bone, Fracture (Process), Filters, Missiles, Momentum, ASTM International
Zhangli Hu and Xiaoping Du
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4040000
In traditional reliability problems, the distribution of a basic random variable is usually unimodal; in other words, the probability density of the basic random variable has only one peak. In real applications, some basic random variables may follow bimodal distributions with two peaks in their probability density. When binomial variables are involved, traditional reliability methods, such as the First Order Second Moment (FOSM) method and the First Order Reliability Method (FORM), will not be accurate. This study investigates the accuracy of using the saddlepoint approximation for bimodal variables and then employs saddlepoint approximation based reliability methods with first order approximation to predict the reliability. A limit-state function is at first approximated with the first-order Taylor expansion so that it becomes a linear combination of the basic random variables, some of which are bimodally distributed. The saddlepoint approximation is then applied to estimate the reliability. Examples show that the saddlepoint approximation based reliability methods are more accurate than FOSM and FORM.
TOPICS: Reliability, Approximation, Risk, Density, Probability
Mohammad Al Sharman, Mohammad Al Jarrah and Mamoun Abdel-Hafez
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039943
The high estimated position error (EPE) in current commercial-off-the-shelf (GPS/INS) impedes achieving precise autonomous takeoff and landing flight operations. To overcome this problem, in this paper we propose an integrated GPS/INS/Optical Flow (OF) solution in which the OF provides an accurate augmentation to the GPS/INS. To ensure accurate and robust OF augmentation, we have used a robust modeling method to estimate OF based on a set of real-time experiments conducted under various simulated helicopter-landing scenarios. Knowing that the accuracy of the OF measurements are dependent on the accuracy of the height measurements; we have developed a real time testing environment to model and validate the obtained dynamic OF model at various heights. The performance of the obtained OF model matches the real OF sensor with 87.70 % fitting accuracy. An accuracy of 0.006 m/s mean error between the real OF sensor velocity and the velocity of the OF model is also achieved. The velocity measurements of the obtained OF model and the position of the GPS/INS are used in performing a dynamic model-based sensor fusion algorithm. In the proposed solution, the OF sensor is engaged when the vehicle approaches a landing spot that is equipped with a predefined landing pattern. The proposed solution has succeeded in performing a helicopter auto takeoff and landing with a maximum position error of 27 cm.
TOPICS: Flow (Dynamics), Risk, Sensors, Errors, Fittings, Velocity measurement, Flight, Algorithms, Modeling, Testing, Vehicles
Linjie Shen, Yugang Zhang, Xinchen Zhuang and Bozhi Guo
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039941
The gear door lock system (GDLS) is a hydraulic and mechatronic system with high degree of complexity and uncertainty, making the performance assessment of the system especially intractable. We develop copula models to estimate the reliability of GDLS with dependent failure modes. Based on the working principle of the GDLS, kinematic and dynamic model with imperfect joints is built, in which Latin hypercube sampling and kernel smoothing density are utilized to obtain the marginal failure probabilities. Then, copula models are utilized to describe the dependence between the two function failure modes. Furthermore, to be more accurate, mixed copula models are developed. The squared Euclidean distance is adopted to estimate the parameters of the above reliability models. Finally, the Monte Carlo simulation is conducted to evaluate the different reliability models.
TOPICS: Locks (Waterways), Doors, Reliability, Gears, Modeling, Failure, Risk, Gas diffusion layers, Failure mechanisms, Uncertainty, Density, Kinematics, Probability, Dynamic models, Simulation
Fazel Khayatian, Maryam MeshkinKiya, Piero Baraldi, Francesco Di Maio and Enrico Zio
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039784
Optimal sizing of peak loads has proven to be an important factor affecting the overall energy consumption of heating ventilation and air-conditioning (HVAC) systems. Uncertainty quantification of peak loads enables optimal configuration of the system by opting for a suitable size factor. However, the representation of uncertainty in HVAC sizing has been limited to probabilistic analysis and scenario-based cases, which may limit and bias the results. This study provides a framework for uncertainty representation in building energy modeling, due to both random factors and imprecise knowledge. The framework is shown by a numerical case study of sizing cooling loads, in which uncertain climatic data is represented by probability distributions and human-driven activities are described by possibility distributions. Cooling loads obtained from the hybrid probabilistic-possibilistic propagation of uncertainty are compared to those obtained by pure probabilistic and pure possibilistic approaches. Results indicate that a pure possibilistic representation may not provide detailed information on the peak cooling loads, whereas a pure probabilistic approach may underestimate the effect of uncertain human behavior. The proposed hybrid representation and propagation of uncertainty in this paper can overcome these issues by proper handling of both random and limited data.
TOPICS: Stress, Cooling, Uncertainty, Risk, HVAC equipment, Peak load, Ventilation, Modeling, Energy consumption, Statistical distributions, Heating, Uncertainty quantification, Air conditioning
Vicente J. Romero, Benjamin Schroeder, J. Franklin Dempsey, Nicole Breivik, George Orient, Bonnie Antoun, John Lewis and Justin Winokur
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039558
This paper examines the variability of predicted responses when multiple stress-strain curves (reflecting variability from replicate material tests) are propagated through a finite element model of a ductile steel can being slowly crushed. Over 140 response quantities of interest (including displacements, stresses, strains, and calculated measures of material damage) are tracked in the simulations. Each response quantity's behavior varies according to the particular stress-strain curves used for the materials in the model. We desire to estimate response variability when only a few stress-strain curve samples are available from material testing. Propagation of just a few samples will usually result in significantly underestimated response uncertainty relative to propagation of a much larger population that adequately samples the presiding random-function source. A simple classical statistical method, Tolerance Intervals, is tested for effectively treating sparse stress-strain curve data. The method is found to perform well on the highly nonlinear input-to-output response mappings and non-Normal response distributions in the can-crush problem. The results and discussion in this paper support a proposition that the method will apply similarly well for other sparsely sampled random variable or function data, whether from experiments or models. The simple Tolerance Interval method is also demonstrated to be very economical.
TOPICS: Uncertainty, Risk, Stress-strain curves, Engineering simulation, Finite element model, Damage, Steel, Materials testing, Simulation, Stress
Harshini Devathi and Sunetra Sarkar
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039471
A novel uncertainty quantification routine in the genre of adaptive sparse grid stochastic collocation has been proposed in the present study to investigate the propagation of parametric uncertainties in a stall flutter aeroelastic system. In a hypercube stochastic domain, presence of strong nonlinearities can give way to steep solution gradients that can adversely affect the convergence of non-adaptive sparse grid collocation schemes. A new adaptive scheme is proposed here that allows for accelerated convergence by clustering more discretization points in regimes characterized by steep fronts, using hat-like basis functions with non-equidistant nodes. The proposed technique has been applied on a nonlinear stall flutter aeroelastic system to quantify the propagation of multi-parametric uncertainty from both structural and aerodynamic parameters. Their relative importance on the stochastic response is presented through a sensitivity analysis.
TOPICS: Uncertainty, Risk, Flutter (Aerodynamics), Sensitivity analysis, Uncertainty quantification
Atsutaka Tamura, Junji Hasegawa and Takao Koide
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039464
A series of pedestrian sideswipe impacts were computationally reconstructed; a fast-walking pedestrian was collided laterally with the side of a moving vehicle at 25 or 40 km/h, which resulted in rotating the pedestrian’s body axially. Potential severity of traumatic brain injury (TBI) was assessed using linear and rotational acceleration pulses applied to the head and by measuring intracranial brain tissue deformation. We found that TBI risk due to secondary head strike with the ground can be much greater than that due to primary head strike with the vehicle. Further, an ‘effective’ head mass, meff, was computed based upon the impulse and vertical velocity change involved in the secondary head strike, which mostly exceeded the mass of the adult head-form impactor (4.5 kg) commonly used for a current regulatory impact test for pedestrian safety assessment. Our results demonstrated that an SUV is more aggressive than a sedan due to the differences in frontal shape. Additionally, it was highlighted that a striking vehicle velocity should be lower than 25 km/h at the moment of impact to exclude the potential risk of sustaining TBI, which would be mitigated by actively controlling meff, because meff is closely associated with a rotational acceleration pulse applied to the head involved in the final event of ground contact.
TOPICS: Accidents, Traumatic brain injury, Risk, Vehicles, Brain, Impact testing, Shapes, Biological tissues, Deformation, Safety, Impulse (Physics)
Arun Veeramany, James T. Woodward and Donald J. Hammerstrom
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039465
Valuation of transactive energy systems should be supported by a structured and systematic approach to uncertainty identification, assessment, and treatment in the interest of risk-informed decision-making. The proposed approach, a variation of fault tree analysis, is anticipated to support valuation analysts in analyzing conventional and transactive system scenarios. This approach allows for expanding the entire tree up to the level of minute details or collapsing them to a level sufficient enough to get an overview of the problem. Quantification scheme for the described approach lends itself for valuation. The method complements value exchange analysis, simulation, and field demonstration studies. The practicality of the proposed approach is demonstrated through uncertainty assessment of the smart grid interoperability panel peak heat day scenario.
TOPICS: Energy / power systems, Uncertainty, Risk, Valuation, Risk-based decision making, Smart grids, Heat, Simulation
Aimee Cloutier and James Yang
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039467
The development of robust and adaptable methods of grasping force optimization is an important consideration for robotic devices, especially those which are designed to interact naturally with a variety of objects. Along with considerations for the computational efficiency of such methods, it is also important to ensure that a grasping force optimization approach chooses forces which can produce a stable grasp even in the presence of uncertainty. This paper examines the robustness of three methods of grasping force optimization in the presence of variability in the contact locations and in the coefficients of friction between the hand and the object. A Monte Carlo simulation is used to determine the resulting probability of failure and sensitivity levels when variability is introduced. Two numerical examples representing two common grasps performed by the human hand are used to demonstrate the performance of the optimization methods. Additionally, the method which yields the best overall performance is also tested to determine its consistency when force is applied to the object’s center of mass in different directions. The results show that both the nonlinear and linear matrix inequality methods of grasping force optimization produce acceptable results, whereas the linear method produces unacceptably high probabilities of failure. Further, the nonlinear method continues to produce acceptable results even when the direction of the applied force is changed. Based on these results, the nonlinear method of grasping force optimization is considered to be robust in the presence of variability in the contact locations and coefficients of friction.
TOPICS: Grasping, Optimization, Robustness, Risk, Uncertainty analysis, Probability, Failure, Friction, Simulation, Center of mass, Robotics, Linear matrix inequalities, Uncertainty
Nick Kloppenborg, Tara Amenson, Jacob Wernik and John F. Wiechel
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039357
Go-karts are a common amusement park feature enjoyed by people of all ages. While intended for racing, contact between go-karts does occur. To investigate and quantify the accelerations and forces which result from contact, 44 low-speed impacts were conducted between a stationary (target) and a moving (bullet) go-kart. The occupant of the bullet go-kart was one of two human volunteers. The occupant of the target go-kart was a Hybrid III 50th percentile male anthropomorphic test device (ATD). Impact configurations consisted of rear-end impacts, frontal impacts, side impacts, and oblique impacts. Results demonstrated high repeatability for the vehicle performance and occupant response. Go-kart accelerations and speed changes increased with increased impact speed. Impact duration and restitution generally decreased with increased impact speed. All ATD acceleration, force, and moment values increased with increased impact speed. Common injury metrics such as the Head Injury Criterion (HIC), N_ij, and N_km were calculated and were found to be below injury thresholds. Occupant response was also compared to published activities of daily living data.
TOPICS: Vehicles, Wounds, Bullets, Risk
Kasmet T. Niyongabo and Scott B. Nokleby
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4032634
A proof-of-concept detector prototype capable of collecting and storing radiometric data in the Jet Boring System (JBS) during pilot hole drilling at the Cigar Lake uranium mine is presented. Cigar Lake is the world’s second highest known grade uranium mine and is located in northern Saskatchewan, Canada. Variant design is used to design, develop, test and implement the detector’s firmware, software and hardware. The battery powered detector is attached inside a JBS drill rod to collect radiometric data through the drilling cycle. A readout box is used to initiate the detector, recharge the battery and download radiometric data afterthe pilot hole drilling cycle is complete.Functional testing results are presented and comparative test results between the JBS gamma probe and the AlphaNUCLEAR Hi-Flux probe are evaluated. Field data collected from a pilot hole is plotted against the pilot hole’s driving layout and jetting recipe to show the accuracy of the readings collected.

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