ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
ISSN / EISSN : 2332-9017 / 2332-9025
Published by: ASME International (10.1115)
Total articles ≅ 336
Latest articles in this journal
Published: 26 October 2021
ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg; https://doi.org/10.1115/1.4052823
Modern vehicles are connected to the network and between each other through smart sensors and smart objects commonly present on board. This situation has allowed manufacturers to send over-the-air updates, receive diagnostic information, and offer various multimedia services. More generally, at present, all this is indicated by the term 'Vehicle to Everything' (V2X), which indicates a system of communication between a vehicle to any entity that may influence the vehicle and vice versa. However, it introduces problems regarding the vehicle's IT security. It is possible, for example, by tampering with one of the Electronic Control Units (ECUs) to take partial or total control of the vehicle. In this paper, we introduce a preliminary study case to guarantee cybersecurity inside connected vehicles. In particular, an Intrusion Detection System over the CAN-Bus to allow the possible malicious massages. In particular, through the use of a two-step detection algorithm that exploits both the variation of the status parameters of the various ECUs over time and the Bayesian networks can identify a possible attack. The first experimental results seem encouraging.
Published: 14 October 2021
ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, Volume 8; https://doi.org/10.1115/1.4052270
Modeling and simulation for additive manufacturing (AM) are critical enablers for understanding process physics, conducting process planning and optimization, and streamlining qualification and certification. It is often the case that a suite of hierarchically linked (or coupled) simulation models is needed to achieve the above tasks, as the entirety of the complex physical phenomena relevant to the understanding of process-structure-property-performance relationships in the context of AM precludes the use of a single simulation framework. In this study using a Bayesian network approach, we address the important problem of conducting uncertainty quantification (UQ) analysis for multiple hierarchical models to establish process-microstructure relationships in laser powder bed fusion (LPBF) AM. More significantly, we present the framework to calibrate and analyze simulation models that have experimentally unmeasurable variables, which are quantities of interest predicted by an upstream model and deemed necessary for the downstream model in the chain. We validate the framework using a case study on predicting the microstructure of a binary nickel-niobium alloy processed using LPBF as a function of processing parameters. Our framework is shown to be able to predict segregation of niobium with up to 94.3% prediction accuracy on test data.
Published: 1 October 2021
ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, Volume 8; https://doi.org/10.1115/1.4051940
As autonomous vehicle (AV) intelligence for controllability continues to develop, involving increasingly complex and interconnected systems, the maturity level of AV technology increasingly depends on the systems reliability level, also considering the interactions among them. Hazard analysis is typically used to identify potential system risks and avoid loss of AV system functionality. Conventional hazard analysis methods are commonly used for traditional standalone systems. New hazard analysis methods have been developed that may be more suitable for AV system-of-systems complexity. However, a comprehensive comparison of hazard analysis methods for AV systems is lacking. In this study, the traditional hazard analysis methods, hazard and operability (HAZOP) and failure mode and effects analysis (FMEA), as well as the most recent methods, like functional resonance analysis method (FRAM) and system-theoretic process analysis (STPA), are considered for implementation in the automatic emergency braking system. This system is designed to avoid collisions by utilizing the surrounding sensors to detect objects on the road, warning drivers with alerts about any collision risk, and actuating automatic partial/full braking through calculated adaptive braking deceleration. The objective of this work is to evaluate the methods with the unified theory of acceptance and use of technology (UTAUT) approach, in terms of their applicability to AV technologies. The advantages of HAZOP, FMEA, FRAM, and STPA, as well as the possibility of combining them to achieve systematic risk identification in practice, are discussed.
Published: 30 September 2021
ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, Volume 7; https://doi.org/10.1115/1.4052269
Levelized cost of energy (LCoE) is the most common metric used in renewable energy assessments. However, this can be a very complex calculation with numerous methodologies depending on the perspective taken. Inputs including costs, energy production are generally forecasts and predictions based on publicly available information; therefore, they are key areas of uncertainty. Elements of the calculation are site or region specific such as the tax rate or inclusion of grid connection costs. The business case and financial assumptions applied will be very project specific, e.g., the discount rate applied. These numerous variables and uncertainties must be fully understood in order to effectively apply the metric or review and compare LCoEs. Therefore, this paper provides a comprehensive set of LCoE methodologies that provide a reference basis for researchers. A case study demonstrates the application of these methods and the variation in results illustrates the importance of correctly selecting the discount rate and cash flow based on the perspective and motivation of the user. Sensitivity studies further investigates the potential impact of key variables and areas of uncertainty on results. Analysis indicates that the energy production and discount rate applied will have the most significant impact on LCoE, followed by Capital Expenditure (CAPEX) costs. While the key areas of uncertainties cannot necessarily be solved, this paper promotes consistency in the application and understanding of the metric, which can help overcome its limitations.
Published: 24 September 2021
ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, Volume 8; https://doi.org/10.1115/1.4052039
Tremendous efforts have been made to use computational and simulation models of additive manufacturing (AM) processes. The goals of these efforts are to better understand process complexities and to realize better high-quality parts. However, understanding whether any model is a correct representation for a given scenario is a difficult proposition. For example, when using metal powders, the laser powder-bed fusion (L-PBF) process involves complex physical phenomena such as powder morphology, heat transfer, phase transformation, and fluid flow. Models based on these phenomena will possess different degrees of fidelity since they often rely on assumptions that may neglect or simplify process physics, resulting in uncertainties in their prediction accuracy. Prediction accuracy and its characterization can vary greatly between models due to their uncertainties. This paper characterizes several sources of L-PBF model uncertainty for low, medium, and high-fidelity thermal models including modeling assumptions (model-form uncertainty), numerical approximations (numerical uncertainty), and input parameters (parameter uncertainty). This paper focuses on the input uncertainty sources, which we model in terms of a probability density function (PDF), and its propagation through all other L-PBF models. We represent uncertainty sources using the webontologylanguage, which allows us to capture the relevant knowledge used for interoperability and reusability. The topology and mapping of the uncertainty sources establish fundamental requirements for measuring model fidelity and for guiding the selection of a model suitable for its intended purpose.
Published: 24 September 2021
ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, Volume 7; https://doi.org/10.1115/1.4052359
Published: 16 September 2021
ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg; https://doi.org/10.1115/1.4052461
An essential step in the safe design of systems is choosing the system configuration that will maximize the overall availability of the system and minimize its overall cost. The main objective of this paper is to propose an optimization method of multi-state system availability in the presence of both aleatory and epistemic uncertainties, to choose the best configuration for the system in terms of availability, cost, and imprecision. The problem is formulated as follows: let us consider several configurations of a system, with each configuration consisting of components with different working states, and imprecise failure and repair rates provided in the form of intervals. The aim is to find the best configuration regarding the system's imprecise availability, cost, and imprecision. First, the imprecise steady availability of each configuration is computed by using an original method based on Markovian approaches combined with interval contraction techniques. Then an objective function incorporating cost, the lower and upper bounds of availability, and imprecision is defined and computed to provide the best configuration. To illustrate the proposed method, a use case is discussed.
Published: 10 September 2021
ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg; https://doi.org/10.1115/1.4052423
One of many barriers to the deployment of floating offshore wind turbines is the high cost of vessel time needed for soil investigations and anchor installation. A multiline anchor system is proposed in which multiple floating offshore wind turbines (FOWTs) are connected to a single caisson. The connection of multiple FOWTs to a single anchor introduces interconnectedness throughout the wind farm. Previous work by the authors has shown that this interconnectedness reduces the reliability of the FOWT below an acceptable level when exposed to survival loading conditions. To combat the reduction in system reliability an overstrength factor (OSF) is applied to the anchors functioning as an additional safety factor. For a 100 turbine wind farm, single-line system reliabilities can be achieved using the multiline system with an OSF of 1.10, a 10% increase in multiline anchor safety factors for all anchors in a farm.
Published: 1 September 2021
ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, Volume 8; https://doi.org/10.1115/1.4052228
Light detection and ranging (lidar) imaging systems are being increasingly used in autonomous vehicles. However, the final technology implementation is still undetermined as major automotive manufacturers are only starting to select providers for data collection units that can be introduced in commercial vehicles. Currently, testing for autonomous vehicles is mostly performed in sunny environments. Experiments conducted in good weather cannot provide information regarding performance quality under extreme conditions such as fog, rain, and snow. Under extreme conditions, many instances of false detection may arise because of the backscattered intensity, thereby reducing the reliability of the sensor. In this work, lidar sensors were tested in adverse weather to understand how extreme weather affects data collection. Testing setup and algorithms were developed for this purpose. The results are expected to provide technological validation for the commercial use of lidar in automated vehicles. The effective ranges of two popular lidar sensors were estimated under adverse weather conditions, namely, fog, rain, and snow. Results showed that fog severely affected lidar performance, and rain too had some effect on the performance. Meanwhile, snow did not affect lidar performance.
Published: 1 September 2021
ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, Volume 8; https://doi.org/10.1115/1.4051779
Six years (2015–2020) of autonomous vehicle (AV Level 3) crash data from California's (CA) OL 316 collision reports of AV crashes while in the autonomous mode (AM) or disengaged from AM just before the collision, divided by the associated CA AV make and mileage driven in the AM, are compared with the University of Michigan Transportation Research Institute (UMTRI) corrected human driver crash frequency. AV test drivers in CA mandatorily self-report every crash, whereas average drivers underreport minor accidents, so the UMTRI reporting correction factor permits comparison. CA's AV AM mileage is only a few million miles over the last few years, with virtually no police-reported crash data yet available. OL 316 crash consequence data (e.g., damage, injuries, etc.) is anecdotal and inconsistently self-reported. The CA collision report data indicate the CA AV test fleet exhibits multiples of the human crash frequency. Invariably, the AV accidents are the human driver's fault, with a majority being rear collisions. The human drivers appear less able to anticipate the AV's more conservative driving. CA's AV experience predicts more widespread deployment of existing AV technologies is not likely to reduce vehicle crash frequency, at least in the short term, and might well increase it.