Browse Topic: Risk management
Research into the feasibility of a scaled rim-drive propulsion product to enable ultra-heavy vertical lift (UHVL) is ongoing at the University of South Carolina in partnership with KRyanCreative, LLC, a start-up aerospace small business. The research team is advancing a superconductive design concept for a rotor system that delivers significant performance gains and flight envelope expansion disruptive to the vertical lift transportation sector. The team has conceived a novel electric tip-driven ducted propulsor to guide architectural and engineering investigations that improve hover and acoustic performance over current practice without penalty to weight and cost. This paper summarizes the data and assumptions that emerge from the systems engineering process of requirements decomposition for product realization. Requirements are categorized as to whether they are explicit (programs of record) or implied (comparable business case or as an alternative to a program of record). Risk
When the target value of functional geometrical specification is too tight, its cascade of tolerances is at the feasibility limit of production. In this case, the geometrical Tolerancing method loses its benefits and generates an excessive level of non-Conformity which induces additional costs that are not acceptable. The aim of this paper is first to introduce the background concerning chain of dimension method and tolerances capabilities based on test specimen results. Secondly, demonstrate ability to apply statistical calculation. Thirdly extend conventional chain of dimension in one dimension to multi-holes system installation. And, then analyze potential effect by stress evaluation. And confirm the demonstration of improvement on Tolerancing installation calculations, by onboarding all stakeholder (design, manufacturing, stress) early in design phase (interfaces maturation) and by analyzing more in detail installations constraints. This method should be applied first on "non
The Research Aircraft for eVTOL Enabling TechNologies (RAVEN) Subscale Wind-Tunnel and Flight Test (SWFT) model is a subscale aircraft built for flight dynamics and controls research demonstrated in wind-tunnel and flight-test experiments. The intent of this paper is to provide a summary of past, current, and future efforts being pursued by the RAVEN-SWFT project. Initially, vehicle development guidelines were crafted by a multidisciplinary team to ensure that the RAVEN-SWFT vehicle was well suited for research in multiple areas, including aero-propulsive modeling, flight controls, and autonomy, among others. The vehicle has been used to obtain extensive wind-tunnel data, enabling aero-propulsive model development across the transition flight envelope and validation of computational tools. The vehicle will be used to conduct flight testing in order to evaluate modeling strategies and flight control logic. The RAVEN-SWFT model also serves as a risk reduction activity for a conceptual
Automated vehicles, in the form we see today, started off-road. Ideas, technologies, and engineers came from agriculture, aerospace, and other off-road domains. While there are cases when only on-road experience will provide the necessary learning to advance automated driving systems, there is much relevant activity in off-road domains that receives less attention. Implications of Off-road Automation for On-road Automated Driving Systems argues that one way to accelerate on-road ADS development is to look at similar experiences off-road. There are plenty of people who see this connection, but there is no formalized system for exchanging knowledge. Click here to access the full SAE EDGETM Research Report portfolio.
The purpose of this Standard is to provide an integrated set of fundamental processes to aid a developer in the engineering or reengineering of a system. Use of this Standard is intended to help developers a) establish and evolve a complete and consistent set of requirements that will enable delivery of feasible and cost-effective system solutions; b) satisfy requirements within cost, schedule, and risk constraints; c) provide a system, or any portion of a system, that satisfies stakeholders over the life of the products that make up the system. NOTE—The term product is used in this standard to mean: a physical item, such as a satellite (end product), or any of its component parts (end products); a software item such as a stand-alone application to run within an existing system (end product); or a document such as a plan, or a service such as test, training, or maintenance support, or equipment such as a simulator (enabling products). d) provide for the safe and/or cost-effective
This SAE EDGE™ Research Report builds a comprehensive picture of the current state-of-the-art of human-robot applications, identifying key issues to unlock the technology’s potential. It brings together views of recognized thought leaders to understand and deconstruct the myths and realities of human- robot collaboration, and how it could eventually have the impact envisaged by many.Current thinking suggests that the emerging technology of human-robot collaboration provides an ideal solution, combining the flexibility and skill of human operators with the precision, repeatability, and reliability of robots. Yet, the topic tends to generate intense reactions ranging from a “brave new future” for aircraft manufacturing and assembly, to workers living in fear of a robot invasion and lost jobs.It is widely acknowledged that the application of robotics and automation in aerospace manufacturing is significantly lower than might be expected. Reasons include product variability, size, design
Quantitative Risk Assessment has become essential in rotorcraft safety risk management. Measures of risk include Cumulative Fleet Risk (also called Risk Factor), Risk per Flight, and Risk per Flight Hour. Each measure applies to a different situation and can produce the same or different predictions of future risk. Risk for a large fleet of aircraft might be accurately predicted by Cumulative Fleet Risk, whereas Risk per Flight or Risk per Flight Hour might be best for a small fleet of rotorcraft, a flight test program, or a fleet with low flight hours. Calculating risk per flight hour seems as simple as dividing the number of previous occurrences by the flight hours for the total fleet, but this is appropriate only in the case of random distribution. Most failures that lead to hazards are not random because the failure mechanism has a specific cause. A more appropriate method is to develop the future event forecast using Quantitative Risk Assessment, then divide that by the future
In the early days of quality management, prior to 1980s, the focus seemed to be on "Quality Control" or "Quality Assurance". Emphasis was placed on inspection and testing. Quality was about conformance to specification. Non-Conformance Reports were representative of quality control. Our understanding of quality management has evolved, largely based on the Toyota Quality and Concurrent Engineering Approach of moving it off the production line for Integrated Product and Process Development (IPPD) [1]. In the late 1980s industry experienced similar difficulties in understanding and adopting quality management. The ideas behind managing quality are quite abstract. Quality is primarily about understanding and satisfying a customer's expectations. This includes implicit expectations, as well as explicit expectations. The techniques of specification, inspection and testing only make sense in that wider context. Formal risk management was developed in the late 1980s and throughout the 1990s
Unsettled Topics in Automated Vehicle Data Sharing for Verification and Validation Purposes discusses the unsettled issue of sharing the terabytes of driving data generated by Automated Vehicles (AVs) on a daily basis. Perception engineers use these large datasets to analyze and model the automated driving systems (ADS) that will eventually be integrated into future “self-driving” vehicles. However, the current industry practices of collecting data by driving on public roads to understand real-world scenarios is not practical and will be unlikely to lead to safe deployment of this technology anytime soon. Estimates show that it could take 400 years for a fleet of 100 AVs to drive enough miles to prove that they are as safe as human drivers.Yet, data-sharing can be developed – as a technology, culture, and business – and allow for rapid generation and testing of the billions of possible scenarios that are needed to prove practicality and safety of an ADS – resulting in lower research
This document addresses measurement uncertainty and consumer risk as they relate to AS8879 thread inspection. It describes the rationale, theory and methodology used to generate the technical content of the AS5870. The document describes how to calculate measurement consumer risk. It documents all of the calculation methods which industry employs today to calculate what is commonly called measurement uncertainty (Appendices A, B, C, D, E and F). These, in turn, are used to calculate measurement uncertainty ratios which are required inputs to calculate measurement consumer risk. Users of this document can apply the information described herein for the evaluation of the capability of their measurements based on the measurement consumer risk. It involves the analysis of the measurement (product) distribution and biases of both the product and measurement system distributions. It protects the consumer from the worst case distribution results.
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