Browse Topic: Analysis methodologies
ABSTRACT High life cycle costs coupled with durability and environmental challenges of tracked vehicles in South West Asia (SWA) have focused R&D activities on understanding failure modes of track components as well as understanding the system impacts on track durability. The durability limiters for M1 Abrams (M1, M1A1, and M1A2) T-158LL track systems are the elastomeric components. The focus of this study is to review test methodology utilized to collect preliminary data on the loading distribution of a static vehicle. Proposed design changes and path forward for prediction of durability of elastomers at the systems level from component testing will be presented
ABSTRACT FBS Inc. is working with the TARDEC Electrified Armor Lab to develop a nondestructive structural health monitoring technology for composite armor panels that utilizes an array of embedded ultrasonic sensors for guided wave tomographic imaging. This technology would allow for periodic or real-time monitoring of armor integrity while being minimally intrusive and adding negligible weight. The technology is currently being developed and tested in pseudo composite armor panels and efforts are focused on reducing sensor array density, improving sensor integration procedures, and maximizing system sensitivity to damage. In addition to experimental testing and development, FBS is developing a highly-automated finite element model generation and analysis program to be used in conjunction with Abaqus/Explicit commercial finite element software. This program is specifically dedicated to modeling guided wave propagation in pseudo composite armor panels between embedded ultrasonic sensors
ABSTRACT In today’s competitive market, OEMs are racing towards developing more efficient vehicles without sacrificing on its performance. In this process, they’re forced to evaluate new technologies and designs in various subsystems. Most of the sub-systems today have become “intelligent”, which means that the controllers have become quintessential for the system’s behavior. Equally important are the physical behavior of the plant that needs to be controlled. These two independent groups have their own design and development cycle and the challenge for the companies have been in bridging the gap so as to identify potential failure modes. This paper discusses an Architecture-driven Model Based Development process that can address the challenges posed during the development. Three key enabling technologies – Imagine.Lab System Synthesis, Imagine.Lab SysDM & Imagine.Lab AMESim are leveraged in this process
ABSTRACT Situation: There are many advantages during development of a design that come from doing Design Failure Mode Effects Analysis (DFMEA). These advantages include more reliable, safer, self-diagnosing, designs with higher Availability. Strictly from a Design for Reliability (DFR) viewpoint, DFMEA is the key tool to; a. identify and prioritize most critical potential Failure Modes (FMs) of the design, before design development, b. Document critical FM effects and root causes, and c. facilitate corrective actions and DVP&R planning, and d. form a reliability model which can be used to track reliability over the life of the design. Problem: Since even small and simple designs often have a few hundred potential failure modes, preparing a good DFMEA is always a problem of Effectiveness vs., Efficiency. Traditionally it has been very hard to achieve Effectiveness when limited time, money and resources are available and the push for Efficiency, speed or deadlines, causes critical FMs to
ABSTRACT Researchers at Caterpillar have been using Finite Element Analysis or Method (FEA or FEM), Mesh Free Models (MFM) and Discrete Element Models (DEM) extensively to model different earthmoving operations. Multi-body dynamics models using both flexible and rigid body have been used to model the machine dynamics. The proper soil and machine models along with the operator model can be coupled to numerically model an earthmoving operation. The soil – machine interaction phenomenon has been a challenging matter for many researchers. Different approaches, such as FEA, MFM and DEM are available nowadays to model the dynamic soil behavior; each of these approaches has its own limitations and applications. To apply FEA, MFM or DEM for analyzing earthmoving operations the model must reproduce the mechanical behavior of the granular material. In practice this macro level mechanical behavior is not achieved by modeling the exact physics of the microfabric structure but rather by
ABSTRACT Vehicle prognostics are used to estimate the remaining useful life of components or subsystems, based on measured vehicle parameters. This paper presents an overview of a vehicle prognostic system, including the critical tasks associated with configuring such a system. The end user of a vehicle prognostic system focuses on the reports generated by the system that provide indications of vehicle readiness, condition and remaining useful life. These reports are based on measurements recorded from sensors on the vehicle and analyzed either on the vehicle or remotely by a “back office” information management system; the latter also provides usage severity trends. To implement such a system, an engineer must first define the vehicle components of interest and determine “damage correlates”: the relationship between damage occurring on key component(s) and key vehicle parameters that can be obtained from vehicle “bus data”. These “damage correlates” and the associated analysis methods
ABSTRACT Camber Corporation, under contract with the TACOM Life Cycle Management Command Integrated Logistics Support Center, has developed an innovative process of data mining and analysis to extract information from Army logistics databases, identify top cost and demand drivers, understand trends, and isolate environmental issues. These analysis techniques were initially used to assess TACOM-managed equipment in extended operations in Southwest Asia (SWA). In 2009, at the request of TACOM and the Tank Automotive Research, Development and Engineering Center (TARDEC), these data mining processes were applied to four tactical vehicle platforms in support of Condition Based Maintenance (CBM) initiatives. This paper describes an enhanced data mining and analysis methodology used to identify and rank components as candidates for CBM sensors, assess total cost of repair/replacement and determine potential return on investment in applying CBM technology. Also discussed in this paper is the
ABSTRACT In this paper, we discuss a neuroimaging experiment that employed a mission-based scenario (MBS) design, a new approach for designing experiments in simulated environments for human subjects [1]. This approach aims to enhance the realism of the Soldier-task-environment interaction by eliminating many of the tightly-scripted elements of a typical laboratory experiment; however, the absence of these elements introduces several challenges for both the experimental design and statistical analysis of the experimental data. Here, we describe an MBS experiment using a simulated, closed-hatch crewstation environment. For each experimental session, two Soldiers participated as a Commander-Driver team to perform six simulated low-threat security patrol missions. We discuss challenges faced while designing and implementing the experiment before addressing analysis approaches appropriate for this type of experimentation. We conclude by highlighting three example transition pathways from
ABSTRACT In light of the cancellation of MIL-STD 1629A on 4 August 1998 with no superseding document, this paper outlines the tailoring of an effective industry tool for risk identification and prioritization that will lead to more reliable weapon systems for the warfighter, with reduced total ownership costs. The canceled MIL-STD 1629A used Failure Mode Effects and Criticality Analysis (FMECA) which is similar in method to FMEA but with an added factor called Criticality for prioritization. In FMEA approach, criticality is addressed by the Risk Priority Number (RPN) and other ways to prioritize risk beyond those single criteria. Tank Automotive Research Development and Engineering Center (TARDEC), Systems Engineering Group (SEG) has tailored the FMEA’s Severity, Occurrence, and Detection ranking tables to suit DOD Systems by developing an additional scale (1 – 5) for severity and occurrence parameters for the existing industry scale (1 – 10). This will facilitate transitioning risks
ABSTRACT Probabilistic Principal Component Analysis (PPCA) is a promising tool for validating tests and computational models by means of comparing the multivariate time histories they generate to available field data. Following PPCA by interval-based Bayesian hypothesis testing enables acceptance or rejection of the tests and models given the available field data. In this work, we investigate the robustness of this methodology and present sensitivity studies of validating hybrid powertrain models of a military vehicle simulated over different proving ground courses
The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has significantly changed various industries. This study demonstrates the application of a Convolutional Neural Network (CNN) model in Computational Fluid Dynamics (CFD) to predict the drag coefficient of a complete vehicle profile. We have developed a design advisor that uses a custom 3D CNN with a U-net architecture in the DEP MeshWorks environment to predict drag coefficients (Cd) based on car shapes. This model understands the relationship between car shapes and air drag coefficients calculated using computational fluid dynamics (CFD). The AI/ML-based design advisor feature has the potential to significantly decrease the time required for predicting drag coefficients by conducting CFD calculations. During the initial development phase, it will serve as an efficient tool for analyzing the correlation between multiple design proposals and aerodynamic drag forces within a short time frame
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