Browse Topic: Needs assessment
Civil vehicles, commonly seen as complex products, involve many high-tech aspects, several fields working together, many investments spent on projects, and challenging management. Through the entire life-cycle of aircraft development, the application of requirement-driven systems engineering methodologies helps to manage the aircraft development process while addressing the needs of the market and of stakeholders. The operational needs of an aircraft are design inputs for aircraft development, and the precision, authenticity, and comprehensiveness of these needs influence the efficiency of the development processes and the quality of the products. When the design and research-and-development activities are based on accurate and complete needs, the development interval for such projects can be shortened significantly, and the costs of R&D lowered. Especially because it is one of the fundamental phases of establishing whether aircraft meet the design requirements, design verification is becoming one of the most critical stages of civil aircraft development. Consequently, designing a more methodical and effective way of verifying designs becomes a central theme in current aviation technology development. There is a human-centered design verification approach that emphasizes requirement-oriented processes, using systematic requirement analysis and conceptual design procedures in order to maximize the congruency between design results and intended requirements. This study aims to perform a holistic analysis of the aircraft design validation process that is based on research, while offering some innovative techniques and strategies for civil aviation on effective validation practices.
Electric Vertical Takeoff and Landing (eVTOL) vehicles undergoing advanced air mobility (AAM) operations feature increasingly autonomous systems (IAS) with non-traditional role allocations. Ensuring the safety of these operations and their novel human–machine teaming (HMT) paradigms requires an appropriate body of knowledge created through relevant, reproducible research. In this paper, we briefly examine the meaning of teaming; current regulation, standards, and guidance; and the knowledge required to build resilient HMTs before turning our attention to how this knowledge is being created by recent research and what conclusions or recommendations can be made. We identify the need for further research into the holistic performance of HMTs, the effect of novel allocations of roles between humans and machines, the ability of humans to provide resilience to unforeseen dangers when acting as a part of these teams; and the characteristics required for clear, timely, and accurate communication between the humans and machines. This work is done in the context of eVTOL aircraft with an indirect flight control system (IFCS) undergoing urban air mobility operations.
Advancements in sensor technologies have led to increased interest in detecting and diagnosing “driver states”—collections of internal driver factors generally associated with negative driving performance, such as alcohol intoxication, cognitive load, stress, and fatigue. This is accomplished using imperfect behavioral and physiological indicators that are associated with those states. An example is the use of elevated heart rate variability, detected by a steering wheel sensor, as an indicator of frustration. Advances in sensor technologies, coupled with improvements in machine learning, have led to an increase in this research. However, a limitation is that it often excludes naturalistic driving environments, which may have conditions that affect detection. For example, reductions in visual scanning are often associated with cognitive load [1]; however, these reductions can also be related to novice driver inexperience [2] and alcohol intoxication [3]. Through our analysis of the research, we discover that the tendency to explore these singular driver states with only a comparison to “normal” driving is common. Additionally, research on interventions for these driver states is relatively scarce (fewer than 10% of cognitive load-related papers we examined assessed or discussed intervention solutions) and narrowly tailored to specific states [e.g., 4, vis-à-vis cognitive load]. States that share common behavioral and physiological markers tend to be explored independently when a more universal and integrated approach may be warranted. In this paper, we identify the need for a driver state and intervention framework that addresses these limitations by exploring state indicators and their overlap, interventions for one or multiple states, and major research gaps. Our framework offers practical approaches for handling one or many driver states, including interventions that may be deployed at different timings during a trip.
This chapter delves into the field of multi-agent collaborative perception (MCP) for autonomous driving: an area that remains unresolved. Current single-agent perception systems suffer from limitations, such as occlusion and sparse sensor observation at a far distance. To address this, three unsettled topics have been identified that demand immediate attention. First, it is crucial to establish normative communication protocols to facilitate seamless information sharing among vehicles. Second, collaboration strategies need to be defined, including identifying the need for specific collaboration projects, determining the collaboration partners, defining the content of collaboration, and establishing the integration mechanism. Finally, collecting sufficient data for MCP model training is vital. This includes capturing diverse modal data and labeling various downstream tasks as accurately as possible.
As model-based systems engineering is proliferating throughout the aerospace industry as a method to manage the development of complex cyber-physical systems, opportunities to leverage formal methods for verification and validation purposes are significant. As a system model described in SysML can contain the level of semantics required to define strict system requirements, it is possible to create a translation tool to generate SRL (SADL (Semantic Application Design Language) Requirements Language) to leverage ASSERT™ (Analysis of Semantic Specifications and Efficient generation of requirements-based Tests) for verification and validation of the system requirements. SADL [13] is a controlled English grammar that translates directly into OWL (Web Ontology Language) [14]. As part of the validation of the SRL requirements, ASSERT™ leverages a theorem prover to look for conflict and completeness errors. For verification, ASSERT™ uses a Satisfiability Modulo Theories (SMT) solver for the generation of test cases and procedures. This paper extends the Braking System Control Unit (BSCU) portion of the Wheel Braking System (WBS) example within Appendix E of ARP4754B [2] described in [1] to demonstrate an example of capturing system requirements in Cameo using SysML, creating test cases and procedures from ASSERT™ exporting from SysML and translating to SRL/SADL, and exporting the Cameo system model data along with the test cases, test procedures, and requirements analysis data from ASSERT™ into the Rapid Assurance Curation Kit (RACK). RACK is a data curation platform which facilitates reporting on Development Assurance and safety assessments guidelines like those used for aircraft certification.
Historically, patch antennas have been used for SmallSat communications. While new antenna technologies are in development, some are not optimized for size, mass, and performance — especially beyond low-Earth orbit (LEO). Engineers at NASA’s Marshall Space Flight Center identified the need for a small form factor antenna to provide high data rate communications for such missions.
A digital twin is a virtual model that accurately imitates a physical asset. This can be as complex as an entire vehicle, a subsystem, and down to a small functioning component. The digital twin has a level of fidelity that aligns to the goals of the project team. The usage of a digital twin inside a digital engineering (DE) ecosystem permits architecture and design decisions for optimized product behavior, performance, and interactions. This paper demonstrates a methodology to incorporate the digital twin concept from requirement analysis, low fidelity feature level simulation, rapid prototypes running inside a System Integration Lab, and high fidelity virtual prototypes executing in an entirely virtual environment.
ABSTRACT This GVSETS paper outlines the strategy for integrating Digital Engineering (DE) practices into the Detroit Arsenal (DTA) acquisition, engineering, and sustainment communities. A DTA DE Community of Practice (CoP) is being led by Program Executive Office (PEO) Ground Combat Systems (GCS), PEO Combat Support & Combat Service Support (CS&CSS), Combat Capabilities Development Command (DEVCOM) Ground Vehicles Systems Center (GVSC), and Tank-Automotive & Armaments Command (TACOM). In addition, Program Management Offices (PMOs) will document their DE implementation plans as part of all planning documents per Assistant Secretary of the Army for Acquisition, Logistics & Technology (ASA[ALT]) guidance [1]. In this paper, each of the DTA organizations will address the following: Ongoing DE Related Efforts; Upcoming / Planned Efforts / Opportunities; Lessons Learned; and Challenges / Issues / Help Needed. Additionally, each DTA organization explains its current and future states along with its corresponding gap analysis which contains its respective near-, intermediate- and long-term DE goals. Citation: E. Alexander, G. Reilly, A. Kwietniewski, B. Berklich, “Detroit Arsenal Digital Engineering Implementation & Way Forward”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 16-18, 2022.
Non-exhaust and exhaust particles from traffic were evaluated to account for nearly equal proportions in traffic-related emissions. Among non-exhaust emissions, tyre wear has been a crucial contributor to Particulate matter (PM), with its mass contribution as high as 30% to non-exhaust emissions from traffic. As exhaust emissions control regulation becomes stricter, which leads to a substantial reduction in exhaust emissions from road traffic, currently relative contributions of non-exhaust particles generated from tyre wear to PM is becoming more important. Accordingly, possible regulatory requirement and effectively control strategy of tyre wear particles needs to be developed. This review paper covers the physical properties, chemical composition, emission rates, and mathematic model development of tyre wear particles. Three main methods, including the road simulation in the laboratory, the source analysis, and the on-road direct measurement under real driving conditions, were used to analyse the tyre wear particles in the existing literature. The particle number concentration presented primarily a unimodal distribution, while there was no consensus regarding the peak position of the distribution. The most important chemical components of tyre wear were within coarse and fine particles.
Privately-owned vehicles were never so safe as they are today. Nor have they made so much sense. That thought hit me between the nostrils recently, as I sprayed a pungent disinfectant on the steering wheel of my family's B-segment runabout. Carefully wiping down the car's touch points with antibacterial cleaners has become a new pandemic ritual for us after returning home from food shopping and other essential missions. My little tribe generally keeps the interiors of our two vehicles tidy. Finding the occasional French fry buried in a seat track is, however, preferable to the dangerous microbes deposited every minute in the typical bus, train, or ride-share. The term “clean,” as understood by transit passengers, means a few spritzes by a custodial crew racing to meet uptime targets.
Note: On April 17, 2020, the European Parliament adopted the Commission proposal to postpone the Medical Devices Regulation until May 26, 2021, due to COVID-19.
The U.S. Engineer Research and Development Center (ERDC) Military Engineering Program on Remote Assessment of Infrastructure for Ensured Maneuver (RAFTER) Boreal Aspects of Ensured Maneuver (BAEM) identifies the need for modeling over-snow vehicle performance, as many factors related to vehicle setup and land surface condition contribute to vehicle efficiency. Accurately estimating snow macromechanical characteristics—such as elastic modulus, stiffness, and strength—is critical for understanding how effectively a vehicle will travel over snow-covered terrain.
No abstract. Part of Introduction: Helicopter rotor hubs are geometrically complex components that experience a wide rage of aerodynamic behaviors and flow physics. This includes strong unsteadiness, large amounts of separation, laminar-turbulent transition, and interactional aerodynamic behaviors (Ref. 1). At high forward flight speeds (high advance ratios), the parasitic drag of the hub accounts for O(30 percent) of the total power required to fly (Ref. 1). A common method for characterizing this contribution is the hub drag factor, Kf e, which correlates the flat-plate area of the hub with the helicopter gross weight and functions as a technology factor (Ref. 2). In a recent assessment of needs for future vertical lift systems, Ormiston suggested that the hub drag factor needs to be reduced from the current state of the art of Kf e = 0:5 down to a value 0.2 (Ref. 3).
During Operation Iraqi Freedom and Operation Enduring Freedom, improvised explosive devices were used strategically and with increasing frequency. To effectively design countermeasures for this environment, the Department of Defense identified the need for an under-body blast-specific Warrior Injury Assessment Manikin (WIAMan). To help with this design, information on Warfighter injuries in mounted under-body blast attacks was obtained from the Joint Trauma Analysis and Prevention of Injury in Combat program through their Request for Information interface. The events selected were evaluated by Department of the Army personnel to confirm they were representative of the loading environment expected for the WIAMan. A military case review was conducted for all AIS 2+ fractures with supporting radiology. In Warfighters whose injuries were reviewed, 79% had a foot, ankle or leg AIS 2+ fracture. Distal tibia, distal fibula, and calcaneus fractures were the most prevalent. The most common injury mechanisms were bending with probable vehicle contact (leg) and compression (foot). The most severe injuries sustained by Warfighters were to the pelvis, lumbar spine, and thoracic spine. These injuries were attributed to a compressive load from the seat pan that directly loaded the pelvis or created flexion in the lumbar spine. Rare types of injuries included severe abdominal organ injury, severe brain injury, and cervical spine injury. These typically occurred in conjunction with other fractures. Mitigating the frequently observed skeletal injuries using the WIAMan would have substantial long-term benefits for Warfighters.
Engine Stop/Start System (ESS) promises to reduce greenhouse emissions and improve fuel economy of vehicles. Previous work of the Authors was concentrated on bridging the gap of improvement in fuel economy promised by ESS under standard laboratory conditions and actual driving conditions. Findings from the practical studies lead to a conclusion that ESS is not so popular among the customers, due to the complexities of the system operation and poor integration of the system design with the driver behavior. In addition, due to various functional safety requirements, and traffic conditions, actual benefits of ESS are reduced. A modified control algorithm was proposed and proven for the local driving conditions in India. The ways in which a given driver behaves on the controls of the vehicles like Clutch and Brake Pedals, Gear Shift Lever were not uniform across the demography of study and varied significantly. In addition, Authors also discovered that some drivers also deployed the parking brake during an idle stop. Thus, a concept of autonomous learning algorithm was envisaged, which would learn the driver behavior on the controls which influence the functions of ESS and then adapt the same conditions to trigger the auto engine stop and restart. This was aimed at improving the user experience and yet ensure the benefits of the ESS. In this paper, the findings from previous works are analyzed to make grounds for the new submission and to identify the need for User Experience of ESS. The solution implemented to detect the driver behavior from the set of possible ways is discussed in detail and simulation case studies are discussed to ascertain the functions and benefits of the new algorithm.
A group of rotorcraft original equipment manufacturers (OEMs) and military and commercial operators have come together to review the current state of mechanical diagnostics (MD) for on board rotorcraft Health and Usage Monitoring Systems (HUMS). HUMS has become an integral part of the modern rotorcraft both in commercial and military operations to enhance safety and enable Condition-Based Maintenance (CBM). Commercial oil and gas operators depend on the HUMS vibration monitoring and MD to comply with regulations and customer requirements for ensured safety of off-shore transportation. Under the auspices of the HUMS Technical Committee within the American Helicopter Society (AHS), the authors have assessed the performance of HUMS MD through both quantitative and qualitative means. First, results from the U.S. Army fleet, which comprises thousands of deployed HUMS on multiple aircraft models, were examined. Second, qualitative surveys of both commercial/military operators and rotorcraft/HUMS original equipment manufacturers (OEMs) were completed. Finally, a literature survey focused on HUMS research and development (R&D) and operational analysis was conducted. Based on this assessment, gaps in the performance of current HUMS MD, needs for future R&D, and challenges to closing those gaps are identified. Collaborative, pre-competitive efforts are also recommended to help close the gaps and generally raise the performance of HUMS MD to enable further enhancements to safety and to support expanded CBM initiatives.
The Deep Orange framework is an integral part of the graduate automotive engineering education at Clemson University International Center for Automotive Research (CU-ICAR). The initiative was developed to immerse students into the world of an OEM. For the 6th generation of Deep Orange, the goal was to develop an urban utility/activity vehicle for the year 2020. The objective of this paper is to describe the development of a multimaterial lightweight Body-in-White (BiW) structure to support an all-electric powertrain combined with an interior package that maximizes volume to enable a variety of interior configurations and activities for Generation Z users. AutoPacific data were first examined to define personas on the basis of their demographics and psychographics. The resulting market research, benchmarking, and brand essence studies were then converted to consumer needs and wants, to establish vehicle target and subsystem requirement, which formed the foundation of the Unique Selling Points (USPs) of the concept. The various sub-systems within the vehicle were then developed; a systems integration approach was used to balance design, engineering, and project (cost, weight, and timing) compromises. The paper discusses the BiW as an enabler of the vehicle USPs, including an very low, flat floor, a utility-oriented asymmetric door concept, and an integrated hatch and rear bumper which create a low lift-over height for loading and unloading. The development of the topology, geometry, and properties of the BiW structure in relation to the chassis, powertrain, and occupant packaging elements required balancing design space, functionality, cost, and weight. Novel manufacturing processes, materials, and joining techniques are described in addition to elaborations on the final realization of the BiW concept.
The Deep Orange framework is an integral part of the graduate automotive engineering education at Clemson University International Center for Automotive Research (CU-ICAR). The initiative was developed to immerse students into the world of an OEM. For the 6th generation of Deep Orange, the goal was to develop an urban utility/activity vehicle for the year 2020. The objective of this paper is to explain the interior concept that offers a flexible interior utility/activity space for Generation Z (Gen Z) users. AutoPacific data were first examined to define personas on the basis of their demographics and psychographics. The resulting market research, benchmarking, and brand essence studies were then converted to consumer needs and wants, to establish technical specifications, which formed the foundation of the Unique Selling Points (USPs) of the concept. Then the various sub-systems within the vehicle were developed; a systems integration approach was used to balance design, engineering and project (cost, weight and timing) compromises. The vehicle provides a flexible interior concept designed to support the active lifestyles of Gen Z that enables a broad range of use cases including stationary activities. The paper discusses the occupant packaging, seating, personalization and customization of the interior, power supply, infotainment, color selection, and interior lighting concepts which provide novel ways to support users in urban environments.
The Deep Orange framework is an integral part of the graduate automotive engineering education at Clemson University International Center for Automotive Research (CU-ICAR). The initiative was developed to immerse students into the world of an OEM. For the sixth generation of Deep Orange, the goal was to develop an urban utility/activity vehicle for the year 2020. The objective of this paper is to describe the development and implementation of a dual-purpose powertrain system enabling vehicle propulsion as well as stationary activities of the Deep Orange 6 vehicle concept. AutoPacific data were first examined to define personas on the basis of their demographics and psychographics. The resulting market research, benchmarking, and brand essence studies were then converted to consumer needs and wants, to establish vehicle target and subsystem requirement, which formed the foundation of the Unique Selling Points (USPs) of the concept. The Deep Orange 6 vehicle contains a very low floor supporting the active lifestyles of the target consumers through re-configurability of the interior, which enables a broad range of use cases including invehicle stationary activities. This concept required the development of a shallow dual-purpose electrical energy storage and power conversion system for the purpose of propelling the vehicle as well as powering various 110 VAC in-vehicle stationary activities for an extended period of time, at low noise levels and with zero local emissions. The paper explains simulation based sub-system sizing and component selection to meet the overall vehicle performance and fuel economy targets. Furthermore, special attention will be paid to the development of a control logic keeping functional safety in mind especially related to the two modes of operation of the vehicle’s energy supply and power conversion system. The paper concludes with a description of the subsystem testing, including driveline and vehicle integration as part of the vehicle performance validation process.
ABSTRACT System complexity continues to grow, creating many new challenges for engineers and decision makers. To maximize value delivery, amidst this complexity, “both” Systems Engineering and Decision Analysis capabilities are essential. For well over a decade the systems engineering profession has had a significant focus on improving systems engineering processes. While process plays an important role, the focus on process was often at the expense of foundational engineering axioms and their contribution to system value. As a consequence, Systems Engineers were viewed as process shepherds which diluted their technical influence on programs. With the recent shift toward Model Based Systems Engineering (MBSE) the Systems Engineering discipline is “getting back to basics,” focusing on value delivery via foundational engineering axioms built upon first principles, using established laws of engineering and science. This paper will share how Pattern Based Systems Engineering (PBSE), as outlined within INCOSE’s Model Based Systems Engineering (MBSE) initiative, is a methodology which explicates system value through an understanding and explicit modeling of first principles, better re-uniting Systems Engineering and Decision Analysis capabilities.
ABSTRACT This presentation shows the process a team should use to initiate a design project based on the needs of the customer. The VRS project supports the future integration and development needs of four combat platforms (Abrams, AMPV, Bradley, and Stryker) and TARDEC’s PM CVP. For this presentation, and to simplify the explanation, the TRADOC developed capability for Silent Watch is used to demonstrate the processes of analyzing Capability Description Documents (CDD), creating and deriving good requirements, allocating them to specific functions and activities, describing those activities to the lowest level, designing, building, and eventually testing.
Car infotainment systems feature an increasing number of functions to keep pace with consumer needs. The GENIVI Alliance aims to facilitate this evolution of infotainment systems by developing a common baseline where services of different suppliers can easily be integrated on a single hardware platform. Since the huge number of services creates more dependencies and interactions, more effort is required to ensure the same level of quality. We present a novel approach and effective tooling to reduce the effort for the interface verification of in-vehicle software components. Our models create different views of the system. Consistency checks and automated transformations between the views reduce the modeling effort and ensure compatible interactions of distributed software components. Layered reference models separate the description of the structure and the behavior of the services' communication. This simplifies the behavior descriptions and facilitates the usage of different communication technologies, e.g., D-Bus or CAN. Since the reference models are executable specifications, they can be used to verify the communication of the modeled services. This can be tested live or from a trace. In case of required changes to an interface, regression testing can be performed automatically using only the model. We evaluate the benefits and implications of our approach and tool with a case study of an in-vehicle audio function.
The automotive industry strives to develop high quality vehicles in a short period of time that satisfy the consumer needs and stand out in the competition. Full exploitation of simulation and Computer-Aided Engineering (CAE) tools can enable quick evaluation of different vehicle concepts and setups without the need of building physical prototypes. Addressing the aforementioned statements this paper presents a method for optimising the Electric Power-Assisted Steering (EPAS) ECU parameters employing solely CAE. The objective of the optimisation is to achieve a desired steering response. The developed process is tested on three specific steering metrics (friction feel, torque build-up and torque deadband) for two function parameters (basic steering torque and active return) of the EPAS. The optimisation method enabled all metrics to fall successfully within the target range.
The U.S. National Research Council recently identified the need for a near-term space mission of Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS). The primary objective of the ASCENDS mission is to make CO2 column measurements across the troposphere during the day and night over all latitudes and all seasons, and in the presence of scattered clouds. These measurements would be used to significantly reduce the uncertainties in global estimates of CO2 sources and sinks, provide an increased understanding of the connection between climate and CO2 exchange, improve climate models, and close the carbon budget for improved forecasting and policy decisions.
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