Towards in-cylinder flow informed engine control strategies using linear stochastic estimation

Many modern I.C. engines rely on some form of active control of injection, timing and/or ignition to help combat tailpipe out emissions, increase the fuel economy and engine drivability. However, development of these strategies is often optimised to suit the average cycle at each condition; an assumption that can lead to sub-optimal performance, especially an increase in particulate (PN) emissions as I.C. engine operation, and in-particular it’s charge motion is subject to cycle-to-cycle variation (CCV). Literature shows that the locations of otherwise repeatable large-scale flow structures may vary by as much 25% of the bore dimension; this could have an impact on fuel break-up and distribution and therefore subsequent combustion performance and emissions. In the presented work, a method is presented that allows full-field flow velocity information to be estimated in real-time from only a limited number of point velocity measurements using linear stochastic estimation (LSE). Three sensor arrangements – single bisecting ‘line-of-sight’, a central cluster and a circumferential ring - which are deemed applicable to implementation in an I.C. engine are compared over all test flow conditions; with all providing useful estimations of the flow field. It is shown how with even a modest number of point measurements it is possible to achieve at least 85% correlation between estimates and original data allowing cycle characterisation to be achieved. Information gathered from this technique could provide inputs to engine control strategies to account for the CCV of the in-cylinder flow

Spurious PIV vector correction using Linear Stochastic Estimation

Techniques for the experimental determination of velocity fields such as particle image velocimetry (PIV) can often be hampered by spurious vectors or sparse regions of measurement which may occur due to a number of reasons. Commonly used methods for detecting and replacing erroneous values are often based on statistical measures of the surrounding vectors and may be influenced by further poor data quality in the region. A new method is presented in this paper using Linear Stochastic Estimation for vector replacement (LSEVR) which allows for increased flexibility in situations with regions of spurious vectors. LSEVR is applied to PIV dataset to demonstrate and assess its performance relative to commonly used bilinear and bicubic interpolation methods. For replacement of a single vector, all methods performed well, with LSEVR having an average error of 11% in comparison to 14% and 18% for bilinear and bicubic interpolation respectively. A more significant difference was found in replacement of clusters of vectors which showed average vector angle errors of 10°, 9° and 6° for bilinear, bicubic and LSEVR respectively. Error in magnitude was 3% for both interpolation techniques and 1% for LSEVR showing a clear benefit to using LSEVR for conditions that require multiple clustered vectors to be replaced

Comparative analysis of models and performance indicators for optimal service facility location

This study investigates the optimal process for locating generic service facilities by applying and comparing several well-known basic models from the literature. At a strategic level, we emphasize that selecting the right location model to use could result in a problematic and possibly misleading task if not supported by appropriate quantitative analysis. For this reason, we propose a general methodological framework to analyze and compare the solutions provided by several models to obtain a comprehensive evaluation of the location decisions from several different perspectives. Therefore, a battery of key performance indicators (KPIs) has been developed and calculated for the different models’ solutions. Additional insights into the decision process have been obtained through a comparative analysis. The indicators involve topological, coverage, equity, robustness, dispersion, and accessibility aspects. Moreover, a specific part of the analysis is devoted to progressive location interventions over time and identifying core location decisions. Results on randomly generated instances, which simulate areas characterized by realistic geographical or demographic features, are reported to analyze the models’ behavior in different settings and demonstrate the methodology’s general applicability. Our experimental campaign shows that the p-median model behaves very well against the proposed KPIs. In contrast, the maximal covering problem and some proposed back-up coverage models return very robust solutions when the location plan is implemented through several progressive interventions over time

Optimal sensor location for distributed-sensor systems using multivariate regression

The performance of monitoring and control techniques for distributed-sensor systems is affected by the choice of the measurement sensor location. In this paper a methodology is suggested to solve the optimal sensor location problem. The suggested algorithm does not require any explicit knowledge of the plant model, and is based upon a sequential procedure selecting at each iteration the most informative measurement input, and updating the input and output spaces by subtracting the information explained by the computed regressor. The effectiveness of the proposed algorithm is assessed by means of two simulated case studies concerning the location of sensors in tubular reactors where product composition need to be estimated from temperature measurements

Optimal sensor location for distributed-sensor systems using multivariate regression

The performance of monitoring and control techniques for distributed-sensor systems is affected by the choice of the measurement sensor location. In this paper a methodology is suggested to solve the optimal sensor location problem. The suggested algorithm does not require any explicit knowledge of the plant model, and is based upon a sequential procedure selecting at each iteration the most informative measurement input, and updating the input and output spaces by subtracting the information explained by the computed regressor. The effectiveness of the proposed algorithm is assessed by means of two simulated case studies concerning the location of sensors in tubular reactors where product composition need to be estimated from temperature measurements

H2-Optimal Sensor Location

Optimal sensor placement is an important problem with many applications; placing thermostats in rooms, installing pressure sensors in chemical columns or attaching vibration detection devices to structures are just a few of the examples. Frequently, this placement problem is encountered while noise is present. The H_2-optimal control is a strategy designed for systems that have exogenous disturbing inputs. Therefore, one approach for the optimal sensor location problem is to combine it with the H_2-optimal control. In this work the H_2-optimal control is explained and combined with the sensor placement problem to create the H_2-optimal sensor location problem. The problem is examined for the one-dimensional beam equation and the two-dimensional diffusion equation in an L-shaped region. The optimal sensor location is calculated numerically for both models and multiple scenarios are considered where the location of the disturbance and the actuator are varied. The effect of different model parameters such as the weight of the state and the disturbance are investigated. The results show that the optimal sensor location tends to be close to the disturbance location