Automotive diesel engine transient operation: modeling, optimization and control

Traditionally, the study of internal combustion engines operation has focused on the steady-state performance. However, the daily driving schedule of automotive engines is inherently related to unsteady conditions. There are various operating conditions experienced by (diesel) engines that can be classified as transient. Besides the variation of the engine operating point, in terms of engine speed and torque, also the warm up phase can be considered as a transient condition. Chapter 2 has to do with this thermal transient condition; more precisely the main issue is the performance of a Selective Catalytic Reduction (SCR) system during cold start and warm up phases of the engine. The proposal of the underlying work is to investigate and identify optimal exhaust line heating strategies, to provide a fast activation of the catalytic reactions on SCR. Chapters 3 and 4 focus the attention on the dynamic behavior of the engine, when considering typical driving conditions. The common approach to dynamic optimization involves the solution of a single optimal-control problem. However, this approach requires the availability of models that are valid throughout the whole engine operating range and actuator ranges. In addition, the result of the optimization is meaningful only if the model is very accurate. Chapter 3 proposes a methodology to circumvent those demanding requirements: an iteration between transient measurements to refine a purpose-built model and a dynamic optimization which is constrained to the model validity region. Moreover all numerical methods required to implement this procedure are presented. Chapter 4 proposes an approach to derive a transient feedforward control system in an automated way. It relies on optimal control theory to solve a dynamic optimization problem for fast transients. From the optimal solutions, the relevant information is extracted and stored in maps spanned by the engine speed and the torque gradient.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 1: data requirements, processing, and analysis

This is the first part of a study investigating a model-based transient calibration process for diesel engines. The motivation is to populate hundreds of parameters (which can be calibrated) in a methodical and optimum manner by using model-based optimization in conjunction with the manual process so that, relative to the manual process used by itself, a significant improvement in transient emissions and fuel consumption and a sizable reduction in calibration time and test cell requirements is achieved. Empirical transient modelling and optimization has been addressed in the second part of this work, while the required data for model training and generalization are the focus of the current work. Transient and steady-state data from a turbocharged multicylinder diesel engine have been examined from a model training perspective. A single-cylinder engine with external air-handling has been used to expand the steady-state data to encompass transient parameter space. Based on comparative model performance and differences in the non-parametric space, primarily driven by a high engine difference between exhaust and intake manifold pressures (ΔP) during transients, it has been recommended that transient emission models should be trained with transient training data. It has been shown that electronic control module (ECM) estimates of transient charge flow and the exhaust gas recirculation (EGR) fraction cannot be accurate at the high engine ΔP frequently encountered during transient operation, and that such estimates do not account for cylinder-to-cylinder variation. The effects of high engine ΔP must therefore be incorporated empirically by using transient data generated from a spectrum of transient calibrations. Specific recommendations on how to choose such calibrations, how many data to acquire, and how to specify transient segments for data acquisition have been made. Methods to process transient data to account for transport delays and sensor lags have been developed. The processed data have then been visualized using statistical means to understand transient emission formation. Two modes of transient opacity formation have been observed and described. The first mode is driven by high engine ΔP and low fresh air flowrates, while the second mode is driven by high engine ΔP and high EGR flowrates. The EGR fraction is inaccurately estimated at both modes, while EGR distribution has been shown to be present but unaccounted for by the ECM. The two modes and associated phenomena are essential to understanding why transient emission models are calibration dependent and furthermore how to choose training data that will result in good model generalization.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 2: modelling and optimization

This is the second part of a study investigating a model-based transient calibration process for diesel engines. The first part addressed the data requirements and data processing required for empirical transient emission and torque models. The current work focuses on modelling and optimization. The unexpected result of this investigation is that when trained on transient data, simple regression models perform better than more powerful methods such as neural networks or localized regression. This result has been attributed to extrapolation over data that have estimated rather than measured transient air-handling parameters. The challenges of detecting and preventing extrapolation using statistical methods that work well with steady-state data have been explained. The concept of constraining the distribution of statistical leverage relative to the distribution of the starting solution to prevent extrapolation during the optimization process has been proposed and demonstrated. Separate from the issue of extrapolation is preventing the search from being quasi-static. Second-order linear dynamic constraint models have been proposed to prevent the search from returning solutions that are feasible if each point were run at steady state, but which are unrealistic in a transient sense. Dynamic constraint models translate commanded parameters to actually achieved parameters that then feed into the transient emission and torque models. Combined model inaccuracies have been used to adjust the optimized solutions. To frame the optimization problem within reasonable dimensionality, the coefficients of commanded surfaces that approximate engine tables are adjusted during search iterations, each of which involves simulating the entire transient cycle. The resulting strategy, different from the corresponding manual calibration strategy and resulting in lower emissions and efficiency, is intended to improve rather than replace the manual calibration process.

Diesel spray mixing limited vaporization with non-ideal and multi-component fuel thermophysical property effects

Internal combustion engines are, and will continue to be, a primary mode of power generation for ground transportation. Challenges exist in meeting fuel consumption regulations and emission standards while upholding performance, as fuel prices rise, and resource depletion and environmental impacts are of increasing concern. Diesel engines are advantageous due to their inherent efficiency advantage over spark ignition engines; however, their NOx and soot emissions can be difficult to control and reduce due to an inherent tradeoff. Diesel combustion is spray and mixing controlled providing an intrinsic link between spray and emissions, motivating detailed, fundamental studies on spray, vaporization, mixing, and combustion characteristics under engine relevant conditions. An optical combustion vessel facility has been developed at Michigan Technological University for these studies, with detailed tests and analysis being conducted. In this combustion vessel facility a preburn procedure for thermodynamic state generation is used, and validated using chemical kinetics modeling both for the MTU vessel, and institutions comprising the Engine Combustion Network international collaborative research initiative. It is shown that minor species produced are representative of modern diesel engines running exhaust gas recirculation and do not impact the autoignition of n-heptane. Diesel spray testing of a high-pressure (2000 bar) multi-hole injector is undertaken including non-vaporizing, vaporizing, and combusting tests, with sprays characterized using Mie back scatter imaging diagnostics. Liquid phase spray parameter trends agree with literature. Fluctuations in liquid length about a quasi-steady value are quantified, along with plume to plume variations. Hypotheses are developed for their causes including fuel pressure fluctuations, nozzle cavitation, internal injector flow and geometry, chamber temperature gradients, and turbulence. These are explored using a mixing limited vaporization model with an equation of state approach for thermopyhysical properties. This model is also applied to single and multi-component surrogates. Results include the development of the combustion research facility and validated thermodynamic state generation procedure. The developed equation of state approach provides application for improving surrogate fuels, both single and multi-component, in terms of diesel spray liquid length, with knowledge of only critical fuel properties. Experimental studies are coupled with modeling incorporating improved thermodynamic non-ideal gas and fuel

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.

Determinación de la distribución de tamaño y número de partículas provenientes de motores diesel operando en trafico real con equipos embarcados

El presente trabajo se propone determinar la distribución de tamaño y número de partículas nanométricas provenientes de motores diésel con equipos embarcados en tráfico extraurbano. Para ello, se utilizaron equipos de medición de última generación en condiciones promedio de conducción en tráfico extraurbano por más de 800 km a lo largo del trayecto Madrid-Badajoz-Madrid mediante un vehículo característico del parque automotor español y se implementaron métodos novedosos y pioneros en el registro de este tipo de emisiones. Todo ello abre el camino para líneas de investigación y desarrollo que contribuirán a entender, dimensionar y cualificar el comportamiento de las partículas, así como su impacto en la calidad de vida de la población. El estudio hace dos grandes aportes al campo. Primero, permite registrar las emisiones en condiciones transitorias propias del tráfico real. Segundo, permite mantener controladas las condiciones de medición y evita la formación aleatoria de partículas provenientes de material volátil, gracias al sistema de adecuación de la muestra de gases de escape incorporado. Como resultado, se obtuvo una muestra abundante y confiable que permitió construir modelos matemáticos para calcular la emisión de partículas nanométricas, ultrafinas, finas y totales sobre las bases volumétrica, espacial y temporal en función de la pendiente del perfil orográfico de la carretera, siempre y cuando esté dentro del intervalo ±5.0%. Estos modelos de cálculo de emisiones reducen tanto los costos de experimentación como la complejidad de los equipos necesarios, y fundamentaron el desarrollo de la primera versión de una aplicación informática que calcula las partículas emitidas por un motor diésel en condiciones de tráfico extraurbano ("Partículas Emitidas por Motores Diésel, PEMDI). ABSTRACT The purpose of this research is to determine the distribution of size and number of nanometric particles that come from diesel engines by means of on-board equipment in extra-urban traffic. In order to do this, cutting-edge measuring equipment was used under average driving conditions in extra-urban traffic for more than 800 km along the Madrid-Badajoz-Madrid route using a typical vehicle from Spain's automotive population and innovative, groundbreaking registering methods for this type of emissions were used. All this paves the way for lines of research and development which should help understand, measure and characterize the behavior of such particles, as well as their impact in the quality of life of the general population. The study makes two important contributions to the field. First, it makes it possible to register emissions under transient conditions, which are characteristic to real traffic. Secondly, it provides a means to keep the measuring conditions under control and prevents the random formation of particles of volatile origin through the built-in adjustment system of the exhaust gas sample. As a result, an abundant and reliable sample was gathered, which enabled the building of mathematical models to estimate the emission of nanometric, ultrafine, fine and total particles on volumetric, spatial and temporal bases as a function of the orographic outline of the road within a ±5.0% range. These emission estimating models lower both the experimentation costs and the required equipment's complexity, and they provided the basis for the development of a first software application version that estimates the particles emitted from diesel engines under extra-urban traffic conditions (Partículas Emitidas por Motores Diésel, PEMDI).

Alternate engines - state of the art study. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Truck noise XI. Evaluation and reduction of heavy-duty truck noise. Final report.

Transportation Department, Office of Noise Abatement, Washington, D.C.

An initial assessment of the literature on the measurement, control, transport, transformation and health effects of unregulated diesel engine emissions. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Toxic gases in heavy duty diesel truck cabs. Final report.

Federal Highway Administration, Washington, D.C.

Intelligent Sensor Systems for Structural Health Monitoring Applications

The convergence between the recent developments in sensing technologies, data science, signal processing and advanced modelling has fostered a new paradigm to the Structural Health Monitoring (SHM) of engineered structures, which is the one based on intelligent sensors, i.e., embedded devices capable of stream processing data and/or performing structural inference in a self-contained and near-sensor manner. To efficiently exploit these intelligent sensor units for full-scale structural assessment, a joint effort is required to deal with instrumental aspects related to signal acquisition, conditioning and digitalization, and those pertaining to data management, data analytics and information sharing. In this framework, the main goal of this Thesis is to tackle the multi-faceted nature of the monitoring process, via a full-scale optimization of the hardware and software resources involved by the {SHM} system. The pursuit of this objective has required the investigation of both: i) transversal aspects common to multiple application domains at different abstraction levels (such as knowledge distillation, networking solutions, microsystem {HW} architectures), and ii) the specificities of the monitoring methodologies (vibrations, guided waves, acoustic emission monitoring). The key tools adopted in the proposed monitoring frameworks belong to the embedded signal processing field: namely, graph signal processing, compressed sensing, ARMA System Identification, digital data communication and TinyML.

Exploring the emission intensities of ICP OES aided by chemometrics in the geographical discrimination of mineral waters

A rapid method for classification of mineral waters is proposed. The discrimination power was evaluated by a novel combination of chemometric data analysis and qualitative multi-elemental fingerprints of mineral water samples acquired from different regions of the Brazilian territory. The classification of mineral waters was assessed using only the wavelength emission intensities obtained by inductively coupled plasma optical emission spectrometry (ICP OES), monitoring different lines of Al, B, Ba, Ca, Cl, Cu, Co, Cr, Fe, K, Mg, Mn, Na, Ni, P, Pb, S, Sb, Si, Sr, Ti, V, and Zn, and Be, Dy, Gd, In, La, Sc and Y as internal standards. Data acquisition was done under robust (RC) and non-robust (NRC) conditions. Also, the combination of signal intensities of two or more emission lines for each element were evaluated instead of the individual lines. The performance of two classification-k-nearest neighbor (kNN) and soft independent modeling of class analogy (SIMCA)-and preprocessing algorithms, autoscaling and Pareto scaling, were evaluated for the ability to differentiate between the various samples in each approach tested (combination of robust or non-robust conditions with use of individual lines or sum of the intensities of emission lines). It was shown that qualitative ICP OES fingerprinting in combination with multivariate analysis is a promising analytical tool that has potential to become a recognized procedure for rapid authenticity and adulteration testing of mineral water samples or other material whose physicochemical properties (or origin) are directly related to mineral content.

Modeling of Effect of Plastic Deformation on Barkhausen Noise and Magnetoacoustic Emission in Iron With 2% Silicon

Before one models the effect of plastic deformation on magnetoacoustic emission (MAE), one must first treat non-180 degrees domain wall motion. In this paper, we take the Alessandro-Beatrice-Bertotti-Montorsi (ABBM) model and modify it to treat non-180 degrees wall motion. We then insert a modified stress-dependent Jiles-Atherton model, which treats plastic deformation, into the modified ABBM model to treat MAE and magnetic Barkhausen noise (HBN). In fitting the dependence of these quantities on plastic deformation, we apply a model for when deformation gets into the stage where dislocation tangles are formed, noting two chief effects, one due to increased density of emission centers owing to increased dislocation density, and the other due to a more gentle increase in the residual stress in the vicinity of the dislocation tangles as deformation is increased.

Lack of magnetoacoustic emission in iron with 6.5% silicon

The phenomenon of magnetoacoustic emission (MAE) has been ascribed usually to one of two origins: either (1) motion of non-180 degrees domain walls or (2) creation or annihilation of domains. In this paper, we present strong evidence for the argument that the only origin for MAE is motion of non-180 degrees domain walls. The proof is evident as a result of measurements of zero MAE for a wide range of stress in the isotropic zero magnetostrictive polycrystalline alloy of iron with 6.5% silicon. We also explain why it was that the alternative origin was proposed and how the data in that same experiment can be reinterpreted to be consistent with the non-180 degrees wall motion origin. (C) 2008 Elsevier B.V. All rights reserved.

INVESTIGATION OF ACOUSTIC SIGNALS AS A TOOL FOR CHARACTERIZING SPOUTED BED DYNAMICS

The feasibility of characterizing the dynamics of a spouted bed based on acoustic emission (AE) signals is evaluated. Acoustic emission signals were measured in a semi-cylindrical Plexiglas column of diameter 150 mm and height 1000 mm with a conical base of internal angle 60 degrees and 25 mm inlet orifice diameter. Data were obtained for U/U(ms), from 0.3 to 2.0, static bed height from 250 to 500 mm, and glass beads of diameter 1.2 and 2.4 mm. AE signals reflected the effects of particle size and U/U(ms), but in general were insensitive to bed depth, even when there were drastic changes in spouting flow patterns. The results indicate that the AE signals were insensitive to the spouted bed hydrodynamics for the conditions studied. Overall, it appears that the AE analysis is unlikely to be a suitable technique for discriminating spouted bed flow regimes, at least for the range of frequencies and operating conditions investigated.