Search Results

Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.

Developing a new technology requires decision-makers to understand the technology's implications on an organization's objectives, which depend on user needs targeted by the technology. If these needs are common between two organizations, collaboration could result in more efficient technology development. For hybrid truck design, both commercial manufacturers and the military have similar performance needs. As the new technology penetrates the truck market, the commercial enterprise must quantify how the hybrid's superior fuel efficiency will impact consumer purchasing and, thus, future enterprise profits. The Army is also interested in hybrid technology as it continues its transformation to a more fuel-efficient force. Despite having different objectives, maximizing profit and battlefield performance, respectively, the commercial enterprise and Army can take advantage of their mutual needs.

The pulleys employed in automotive accessory drive systems very often consist of a two piece assembly; a multitude of fastening techniques are used in completing the assembly. There are numerous assembly methods and a variety of distinct pulley configurations dictated by the various automobile manufacturers in accordance with individual accessory drive needs. An expert system is being developed to evaluate the merit of multiple assembly alternatives for a specific pulley application. The expert system provides a consistent evaluation tool for assembly alternatives, balancing the influence of product cost, strength and quality considerations. The knowledge-based system is implemented in an expert system shell called AGNESS (A Generalized Network-based Expert System Shell). The expert system judges the acceptability of various pulley assembly techniques, assigning a high “merit value” to the better designs and proportionately lower values to less desirable designs.

The reach capability of drivers is currently represented in vehicle design practice in two ways. The SAE Recommended Practice J287 presents maximum reach capability surfaces for selected percentiles of a generic driving population. Driver reach is also simulated using digital human figure models. In typical applications, a family of figure models that span a large range of the target driver population with respect to body dimensions is positioned within a digital mockup of the driver's workstation. The articulated segments of the figure model are exercised to simulate reaching motions and driver capabilities are calculated from the constraints of the kinematic model. Both of these current methods for representing driver reach are substantially limited. The J287 surfaces are not configurable for population characteristics, do not provide the user with the ability to adjust accommodation percentiles, and do not provide any guidance on the difficulty of reaches that are attainable.

We propose a new metamodeling method to characterize the output (response) random process of a dynamic system with random parameters, excited by input random processes. The metamodel can be then used to efficiently estimate the time-dependent reliability of a dynamic system using analytical or simulation-based methods. The metamodel is constructed by decomposing the input random processes using principal components or wavelets and then using a few simulations to estimate the distributions of the decomposition coefficients. A similar decomposition is also performed on the output random process. A kriging model is then established between the input and output decomposition coefficients and subsequently used to quantify the output random process corresponding to a realization of the input random parameters and random processes. What distinguishes our approach from others in metamodeling is that the system input is not deterministic but random.

The effect of skip-firing (which is often applied in optical engine work) on the available in-cylinder oxygen concentration was investigated with a laser-induced fluorescence imaging setup that combines the measurement of fluorescence signals from toluene and 3-pentanone to quantitatively determine the distribution of molecular oxygen. We describe in detail the image processing procedure for this measurement. The reduction of in-cylinder oxygen when switching from skip-fired to continuous-fired engine operation is measured and compared to traditional exhaust measurements.

Conclusions based on the airborne experiments with laser radars are summarized in this paper. Details of the equipment and the flight procedures will be displayed during the oral presentation at the conference.

A new approach to driver seat-position modeling is presented. The equations of the Seating Accommodation Model (SAM) separately predict parameters of the distributions of male and female fore/aft seat position in a given vehicle. These distributions are used together to predict specific percentiles of the combined male-and-female seat-position distribution. The effects of vehicle parameters-seat height, steering-wheel-to-accelerator pedal distance, seat-cushion angle, and transmission type-are reflected in the prediction of mean seat position. The mean and standard deviation of driver population stature are included in the prediction for the mean and standard deviation of the seat-position distribution, respectively. SAM represents a new, more flexible approach to predicting fore/aft seat-position distributions for any driver population in passenger vehicles. Model performance is good, even at percentiles in the tails of the distribution.

Industrial tasks performed by standing workers are among those most commonly simulated using digital human models. Workers often walk, turn, and take acyclic steps as they perform these tasks. Current h uman modeling tools lack the capability to simulate these whole body motions accurately. Most models simulate walking by replaying joint angle trajectories corresponding to a general gait pattern. Turning is simulated poorly if at all, and violations of kinematic constraints between the feet and ground are common. Moreover, current models do not accurately predict foot placement with respect to loads and other hand targets, diminishing the utility of the associated ergonomic analyses. A new approach to simulating stepping and walking in task-oriented activities is proposed. Foot placements and motions are predicted from operator and task characteristics using empirical models derived from laboratory data and validated using field data from an auto assembly plant.

Thermal distortions of friction disks caused by frictional heating modify pressure distribution on friction surfaces. Pressure distribution, in turn, determines distribution of generated frictional heat. These interdependencies create a complex thermoelastic system that, under some conditions, may become unstable and may lead to severe pressure concentrations with very high local temperature and stress. The phenomenon is responsible for many common thermal failure modes of friction elements and is known as frictionally excited thermoelastic instability (TEI). In the paper, one of the cases of TEI is investigated theoretically and experimentally. The study involves a two-disk structure with one fiction disk and one matching steel disk that have one friction interface. An unsteady heat conduction problem and an elastic contact problem are modeled as axisymmetric ones and are solved using the finite element method.

It is of considerable interest to developers of military vehicles, in early phases of the concept design process as well as in Analysis of Alternatives (AoA) phase, to quickly predict occupant injury risk due to under-body blast loading. The most common occupant injuries in these extremely short duration events arise out of the very high vertical acceleration of vehicle due to its close proximity to hot high pressure gases from the blast. In a prior study [16], an extensive parametric study was conducted in a systematic manner so as to create look-up tables or automated software tools that decision-makers can use to quickly estimate the different injury responses for both stroking and non-stroking seat systems in terms of a suitable blast load parameter. The primary objective of this paper is to quantitatively evaluate the accuracy of using such a tool in lieu of building a detailed model for simulation and occupant injury assessment.

Cost reduction, quality improvement, and regulatory compliance are well-recognized competitive issues. Companies must excel along each of these fronts while operating in an environment of rapid and multi-faceted change, limited financial and human capital, and increasing product development time pressure. In addition, consumers are demanding automobiles that provide greater performance, function, and comfort while emitting lower emissions, consuming fewer gallons of gasoline, injuring fewer humans, and requiring fewer dollars to build and purchase. A solution to these seemingly conflicting objectives is to take a systems view of the product and industry. This paper explores the material decision process so that manufacturers, component suppliers, and material providers may better understand the interlocking web of compromises that shape the pursuit of value-added alternatives and avoidance of unprofitable compromises.

The paper presents a brief summary of results from a Delphi forecast focused on North American Auto industry philosophies, practices, and tools for various phases of the product- development process, and their impact on cost, quality, and design lead time. The forecasting technique is a systematic, iterative method of forecasting based upon the judgement of a panel composed of knowledgeable experts. The study provides a snapshot of current expectations for the product development process, including the use of computer aided design tools, design methodologies, strategies, tools, and design education/training. The paper highlights issues pertaining to product cycle time, organizational barriers, supplier's role and globalization challenges.

New high-temperature Mg alloys are being considered to replace 380 Al in transmission cases, wherein bolt-load retention, and creep, is of prime concern. One of these alloys is die cast AE42, which has much better creep properties than does AZ91D but is still not as creep resistant as 380 Al. It is thus important to investigate bolt-load retention and creep of AE42 as an initial step in assessing its suitability as a material for transmission housings. To that end, the bolt-load retention behavior of die-cast AE42, AZ91D and 380 Al have been examined using standard M10 bolts specially instrumented with stable high-temperature strain gages. The bolt-load retention test pieces were die cast in geometries approximating the flange and boss regions in typical bolted joints. Bolt-load retention properties were examined as a function of time (at least 100 hours), temperature (150 and 175 °C) and initial bolt preload (14 to 34 kN).

Reliable, accurate data on vehicle occupant characteristics could be used to personalize the occupant experience, potentially improving both satisfaction and safety. Recent improvements in 3D camera technology and increased use of cameras in vehicles offer the capability to effectively capture data on vehicle occupant characteristics, including size, shape, posture, and position. In previous work, the body dimensions of standing individuals were reliably estimated by fitting a statistical body shape model (SBSM) to data from a consumer-grade depth camera (Microsoft Kinect). In the current study, the methodology was extended to consider seated vehicle occupants. The SBSM used in this work was developed using laser scan data gathered from 147 children with stature ranging from 100 to 160 cm and BMI from 12 to 27 kg/m2 in various sitting postures.

Little information is available on passenger preferences for posture and support in highly reclined seat configurations. To address this gap, a laboratory study was conducted with 24 adult passengers at seat back angles from 23 to 53 degrees. Passenger preferences for head and neck posture with and without head support were recorded. This paper presents the characteristics of the passengers’ preferred head support with respect to thorax, head, and neck posture.

Over half of the greenhouse gas (GHG) emissions in the United States come from the transportation and electricity generation sectors. To analyze the potential impact of cross-sector cooperation in reducing these emissions, we formulate a bi-level optimization model where the transportation sector can purchase renewable energy certificates (REC) from the electricity generation sector. These RECs are used to offset emissions from transportation in lieu of deploying high-cost fuel efficient technologies. The electricity generation sector creates RECs by producing additional energy from renewable sources. This additional renewable capacity is financed by the transportation sector and it does not impose additional cost on the electricity generation sector. Our results show that such a REC purchasing regime significantly reduces the cost to society of reducing GHG emissions. Additionally, our results indicate that a REC purchasing policy can create electricity beyond actual demand.

Open-Architecture Control Systems allow easy integration of control system that their elements supplied by multiple vendors. The driver behind open architecture is obtaining enhanced system performance at affordable cost. The University of Michigan started a project on open-architecture in 1988. This paper offers a short description of the project, and summarizes the impact of this new technology on the equipment supplier industry (control vendors and machine builders) and the end users of this technology.

The formation of NO was investigated in a spray-guided spark-ignition direct-injection gasoline engine. The influence of variations in intake air temperature and simulated exhaust gas recirculation was examined in an optical single-cylinder engine, fueled with iso-octane. Cycle-resolved simultaneous measurements of OH-chemiluminescence, NO laser induced fluorescence, and fast NO exhaust gas sampling allowed a detailed view of the formation process of NO in this engine. Overall, it was found that cycle-resolved information is needed to explain the differences found between operating conditions, since the initial high stratification of fuel leads to large spatial gradients in the NO concentration. Averaged in-cylinder NO distributions do not adequately reflect the formation process rather than show a smoothed distribution that may even be counter-intuitive based on averaged chemiluminescence data.

DKAD is an automated analysis tool for evaluating dynamic characteristics of accessory drives. Rotation response analysis predicts natural frequencies and effects of crankshaft excitation. Lateral response of each belt span shows the effect of pulley run-out and parametric excitation. DKAD systematically allows a user to define a design and its operating conditions and then performs a sequence of analysis to visualize the rotational and lateral responses. It also allows a user to quickly explore and assess alternative designs. Belt layout and associated parameters can be saved in templates for future reference.