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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.

This paper proposes an approach to use ABS wheel speed sensor signals together with other vehicle state information from a brake control module to detect an unbalanced tire or tires in real-time. The proposed approach consists of two-stage algorithms that mix a qualitative method using band-pass filtering with a quantitative parameter identification using conditional least squares. This two-stage approach can improve the robustness of tire imbalance or imbalances. The proposed approach is verified through vehicle testing and the test results show the effectiveness of the approach.

This paper proposes a novel algorithm. This algorithm is called Self-Organizing Fuzzy Neural Network (SOFNN). SOFNN revolutionizes how researchers apply control theories, image/signal processing on control systems and other applications. In general, SOFNN is an identification technique that automatically initiates, builds and fine-tunes the required network parameters. SOFNN evaluates required structures without predefined parameters or expressions regarding systems. SOFNN sets out to learn and configure a system's characteristics. Self-constructing and self-tuning features enable SOFNN to handle complex, non-linear, and time-varying systems with higher accuracy, making systems identification easier. SOFNN constructs and fine-tunes the system parameter through two phases. The two phases are the construction and the parameter-tuning phase. The two phases run concurrently allowing SOFNN to identify systems on-line.

The purpose of this paper is to make a preliminary assessment of the potential toxicity of compounds that might be emitted from diesel vehicles using urea/SCR technology. The use of urea as a reductant in the removal of NOx from the exhaust of diesel-powered vehicles has the potential to emit at least seven thermal decomposition products and unreacted urea from the tail-pipe. These compounds include: urea, ammonia, cyanate ion, biuret, cyanuric acid, ammelide, ammeline, and melamine. The toxicity data base for these compounds, in general, is poor. In addition, there have been few, if any, studies examining the inhalation route of exposure - the most likely route of exposure for people from vehicle exhaust. The measurement and identification of these compounds from the exhaust of urea/SCR- equipped vehicles is needed to prioritize the kinds of health effects studies required to understand the toxicity of these compounds.

The headlamp beam pattern development process contains both subjective and objective evaluations. The subjective evaluation, communication between the customer and the engineer, was developed in previous work [1][2]. This paper presents exploratory models used in the identification of objective photometric variables of a beam pattern that relate to the subjective impression of the beam pattern. Additional research will allow use of the photometric variables and their selected ranges for designing and evaluating beam patterns to achieve improved customer pleasing beam pattern driving experiences.

Many foams exhibit significant strain rate dependency in their mechanical responses. To characterize these foams, a strain rate dependent constitutive model is formulated and implemented in an explicit dynamic finite element code developed at FORD. The constitutive model is developed in conjunction with a Lagrangian eight node solid element with twenty four degrees of freedom. The constitutive model has been used to model foams in a number crash analysis problems. Results obtained from the analyses are compared to the experimental data. Evidently, numerical results show excellent agreement with the experimental data.

Modal parameter identification is used to identify those parameters of the model which describe the dynamic properties of a vibration system. Structural dynamic methods and technologies have been used with great success by the automotive industry. Experimental Modal Analysis is the process of determining the modal parameters (modes, natural frequencies, damping factors) of a linear, time-invariant system. One common reason for experimental modal analysis is the verification or correction of the results of the analytical approach. Another reason to extract the modal parameters experimentally is due to its use for future evaluations such as structural modifications. This paper presents the Experimental Modal Analysis of a car in trimmed body configuration which represents a complex system due to the systems attached to it, increasing its damping and measuring noise. Both frequency and time domain system identification methods are studied to obtain the main modes of the structure.

IN THE first part of this paper dealing with the metallurgical aspects of the camshaft-tappet problems met with in the design of the new Ford overhead-valve V8 engines, Messrs. Laird and Stevens describe the events which led to the adoption of the as-cast alloy-iron camshaft, nitro-carburized martensitic tappet combination. The combination cited works well in the engines described, but it is not implied that it will perform satisfactorily elsewhere. In the second portion of the paper, Mr. Iles discusses the test schedule devised in connection with the development of the camshaft-tappet materials in the new engines. It is stated that important findings will occur when such tests involve a large number of parts, making possible the study of results on a frequency basis. Tests have shown that a predominantly martensitic tappet structure results in superior performance in combination with the as-cast alloy-iron camshaft used.

This paper discusses a motor vehicle noise source identification technique designed for use during the SAE J986a or similar drive-by test procedure. It provides, by application of the Fourier Transform, the capability to obtain a narrowband (9.8 Hz) frequency resolution over an extended frequency range (0-10,000 Hz) at the peak vehicle noise level, a particular RPM, or a particular vehicle location in the test zone. Other features include corrections for the Doppler shift, averaging of noise tests, and subtraction of spectra of two separate noise tests from a component disconnect/reconnect procedure. The above analysis, in conjunction with the noise source isolation resulting directly from the disconnect procedure, identifies the major vehicle noise contributors in terms of their respective amplitudes and frequencies.

Structural system identification was applied to small sample problems and to large automotive structures. The technique combines mass and stiffness matrices from a finite element model with experimental mode shapes and natural frequencies to produce improved mass and stiffness matrices. In numerical experiments on small problems the procedure enabled us to identify regions of original finite element models where changes of model parameters were required to bring finite element predictions into better agreement with exact results. Also, node point displacements were calculated for small structures subjected to time-dependent sinusoidal loads, and it was found that predictions of displacements from “improved” models were more accurate than original finite element model predictions when forcing frequencies were within the range of experimental frequencies used with structural system identification calculations.

This paper presents the application of model predictive control (MPC) to DOC temperature control during DPF regeneration. The model predictive control approach is selected for its advantage - using a model to optimize control moves over horizon while handling constraints. Due to the slow thermal dynamics of the DOC and DPF, computational bandwidth is not an issue, allowing for more complex calculations in each control loop. The control problem is formulated such that all the engine control actions, other than far post injection, are performed by the existing production engine controller, whereas far post injection is selected as the MPC manipulated variable and DOC outlet temperature as the controlled variable. The Honeywell OnRAMP Design Suite (model predictive control software) is used for model identification, control design and calibration.

This work presents an application of airborne source path contribution analysis with emphasis on prediction of wideband sounds inside a cabin from measurements made around a stand-alone engine. The heart of the method is a time domain source path receiver technique wherein the engine surface is modeled as a number of source points. Nearfield microphone measurements and transfer functions are used to quantify the source strengths at these points. This acoustic engine model is then used in combination with source-to-receiver transfer functions to calculate sound levels at other positions, such as at the driver's ear position. When combining all the data, the in-cabin engine sound can be synthesized even before the engine is physically installed into the vehicle. The method has been validated using a powertrain structure artificially excited by several shakers playing band-limited noise so as to produce a complicated vibration pattern on the surface.