Model reduction techniques in flexible multibody dynamics with application to engine cranktrain simulation

The development of a multibody model of a motorbike engine cranktrain is presented in this work, with an emphasis on flexible component model reduction. A modelling methodology based upon the adoption of non-ideal joints at interface locations, and the inclusion of component flexibility, is developed: both are necessary tasks if one wants to capture dynamic effects which arise in lightweight, high-speed applications. With regard to the first topic, both a ball bearing model and a journal bearing model are implemented, in order to properly capture the dynamic effects of the main connections in the system: angular contact ball bearings are modelled according to a five-DOF nonlinear scheme in order to grasp the crankshaft main bearings behaviour, while an impedance-based hydrodynamic bearing model is implemented providing an enhanced operation prediction at the conrod big end locations. Concerning the second matter, flexible models of the crankshaft and the connecting rod are produced. The well-established Craig-Bampton reduction technique is adopted as a general framework to obtain reduced model representations which are suitable for the subsequent multibody analyses. A particular component mode selection procedure is implemented, based on the concept of Effective Interface Mass, allowing an assessment of the accuracy of the reduced models prior to the nonlinear simulation phase. In addition, a procedure to alleviate the effects of modal truncation, based on the Modal Truncation Augmentation approach, is developed. In order to assess the performances of the proposed modal reduction schemes, numerical tests are performed onto the crankshaft and the conrod models in both frequency and modal domains. A multibody model of the cranktrain is eventually assembled and simulated using a commercial software. Numerical results are presented, demonstrating the effectiveness of the implemented flexible model reduction techniques. The advantages over the conventional frequency-based truncation approach are discussed.

Mutual Coupling Reduction Techniques in Electronic Steering Antennas in X Band

This work provides the development of an antenna for satellite communications onboard systems based on the recommendations ITU-R S.580-6 [1] and ITU-R S.465-5 [2]. The antenna consists of printed elements grouped in an array, working in a frequency band from 7.25 up to 8.4 GHz (15% of bandwidth). In this working band, transmission and reception are included simultaneously. The antenna reaches a gain about 31 dBi, has a radiation pattern with a beam width smaller than 10oand dual circular polarization. It has the capability to steer in elevation through a Butler matrix to 45

Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics

In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectrograms from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple classifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.

Robust model-order reduction of complex biological processes

This paper addresses robust model-order reduction of a high dimensional nonlinear partial differential equation (PDE) model of a complex biological process. Based on a nonlinear, distributed parameter model of the same process which was validated against experimental data of an existing, pilot-scale BNR activated sludge plant, we developed a state-space model with 154 state variables in this work. A general algorithm for robustly reducing the nonlinear PDE model is presented and based on an investigation of five state-of-the-art model-order reduction techniques, we are able to reduce the original model to a model with only 30 states without incurring pronounced modelling errors. The Singular perturbation approximation balanced truncating technique is found to give the lowest modelling errors in low frequency ranges and hence is deemed most suitable for controller design and other real-time applications. (C) 2002 Elsevier Science Ltd. All rights reserved.

Evaluation of Work Zone Speed Reduction Measures, 2000

The Iowa Department of Transportation (DOT) has made improving work zone (WZ) safety a high priority. Managing vehicle speeds through work zones is perceived to be an important factor in achieving this goal. A number of speed reduction techniques are currently used by transportation agencies throughout the country to control speeds and reduce speed variation at work zones. The purpose of this project is to study these and other applicable work zone speed reduction strategies. Furthermore, this research explores transportation agencies' policies regarding managing speeds in long-term, short-term, and moving work zones. This report consists of three chapters. The first chapter, a literature review, examines the current speed reduction practices at work zones and provides a review of the relevant literature. The speed control strategies reviewed in this chapter range from posting regulatory and advisory speed limit signs to using the latest radar technologies to reduce speeds at work zones. The second chapter includes a short write-up for each identified speed control technique. The write-up includes a description, the results of any field tests, the benefits and the costs of the technology or technique. To learn more about other state policies regarding work zone speed reduction and management, the Center for Transportation Research and Education conducted a survey. The survey consists of six multipart questions. The third chapter provides summaries of the response to each question.

Nitrogen Oxides Reduction Possibilities at Pulp Mill and Power Plant

Tutkimuksen tarkoituksena oli selvittää typenoksidien vähennysmahdollisuudet Stora Enson Varkauden tehtaiden sellutehtaalla ja voimalaitoksella. Tutkimuksessa käsiteltiin tehdasalueen suurimpia typenoksidien päästölähteitä: soodakattilaa, meesauunia, kuorikattilaa, öljykattilaa ja muovi-alumiinijakeen kaasutuslaitosta. Tutkimuksessa selvitettiin typenoksidipäästöjen syntymekanismit ja erilaisiin polttotekniikoihin soveltuvat typenoksidien vähennystekniikat. Varkauden tehtaiden typenoksidien vuosipäästö vuonna 2001 oli 836 tonnia. Kansallinen lainsäädäntö, kansainväliset sopimukset sekä paras käytettävissä oleva tekniikka (BAT) huomioiden selvitettiin kuhunkin kohteeseen parhaiten soveltuvat ratkaisut. Tutkimuksen perusteella laadittiin toimenpideohjelma, joka määrittelee suositeltavan toteutusjärjestyksen typenoksidien vähennystoimenpiteille. Toimenpideohjelman tärkeimpinä kriteereinä pidettiin vuonna 2004 tulevan uuden ympäristöluvan arvioituja luparajoja sekä toimenpiteiden kustannustehokkuutta. Toteutusjärjestyksessä ensimmäiseksi valittiin koeajojakson järjestäminen ajon optimoimiseksi kiertopetikattilalla ja toiseksi meesauunin ajon optimointi jatkuvatoimisen NOx-analysaattorin avulla. Seuraaviksi toimenpiteiksi ehdotettiin vertikaali-ilmajärjestelmän käyttöönottoa soodakattilalla sekä SNCR-järjestelmän asennusta kuorikattilalle. Saavutettava NOx-vähennys tulisi olemaan 10 – 45 % ja hinta 30 – 3573 EUR vähennettyä NOx-tonnia kohti. Tutkimuksen osana Ilmatieteen laitoksella teetetyn typenoksidien leviämisselvityksen mukaan Stora Enson tehtaiden NOx-päästöjen vaikutus Varkauden ilmanlaatuun on hyvin pieni. Suurin osa NOx-päästöistä aiheutuu liikenteestä.

Applications of Fractal Based Structures in Radar Cross Section Reduction

This thesis presents the Radar Cross Section measurements of different geometric structures such as flat plate,cylinder, corner reflector and circular cone loaded with fractal based metallo dielectric structures.Use of different fractal geometris,metallizations of different shapes as well as the frequency tanability is investigated for TE and TM polarization of the incident electromagnetic field.Application of fractal based metallo-dielectric structures results in RCS reduction over a wide range of frequency bands.RCS enhancement of dihedral corner is observed at certain acute and obtuse corner angles.The experimental results are validated using electromagnetic simulation softwares.

State estimation using model order reduction for unstable systems

The problem of state estimation occurs in many applications of fluid flow. For example, to produce a reliable weather forecast it is essential to find the best possible estimate of the true state of the atmosphere. To find this best estimate a nonlinear least squares problem has to be solved subject to dynamical system constraints. Usually this is solved iteratively by an approximate Gauss–Newton method where the underlying discrete linear system is in general unstable. In this paper we propose a new method for deriving low order approximations to the problem based on a recently developed model reduction method for unstable systems. To illustrate the theoretical results, numerical experiments are performed using a two-dimensional Eady model – a simple model of baroclinic instability, which is the dominant mechanism for the growth of storms at mid-latitudes. It is a suitable test model to show the benefit that may be obtained by using model reduction techniques to approximate unstable systems within the state estimation problem.

A comparative review of dimension reduction methods in approximate Bayesian computation

Approximate Bayesian computation (ABC) methods make use of comparisons between simulated and observed summary statistics to overcome the problem of computationally intractable likelihood functions. As the practical implementation of ABC requires computations based on vectors of summary statistics, rather than full data sets, a central question is how to derive low-dimensional summary statistics from the observed data with minimal loss of information. In this article we provide a comprehensive review and comparison of the performance of the principal methods of dimension reduction proposed in the ABC literature. The methods are split into three nonmutually exclusive classes consisting of best subset selection methods, projection techniques and regularization. In addition, we introduce two new methods of dimension reduction. The first is a best subset selection method based on Akaike and Bayesian information criteria, and the second uses ridge regression as a regularization procedure. We illustrate the performance of these dimension reduction techniques through the analysis of three challenging models and data sets.

Dose Reduction in Standard Head CT: First Results from a New Scanner Using Iterative Reconstruction and a New Detector Type in Comparison with Two Previous Generations of Multi-slice CT

PURPOSE Computed tomography (CT) accounts for more than half of the total radiation exposure from medical procedures, which makes dose reduction in CT an effective means of reducing radiation exposure. We analysed the dose reduction that can be achieved with a new CT scanner [Somatom Edge (E)] that incorporates new developments in hardware (detector) and software (iterative reconstruction). METHODS We compared weighted volume CT dose index (CTDIvol) and dose length product (DLP) values of 25 consecutive patients studied with non-enhanced standard brain CT with the new scanner and with two previous models each, a 64-slice 64-row multi-detector CT (MDCT) scanner with 64 rows (S64) and a 16-slice 16-row MDCT scanner with 16 rows (S16). We analysed signal-to-noise and contrast-to-noise ratios in images from the three scanners and performed a quality rating by three neuroradiologists to analyse whether dose reduction techniques still yield sufficient diagnostic quality. RESULTS CTDIVol of scanner E was 41.5 and 36.4 % less than the values of scanners S16 and S64, respectively; the DLP values were 40 and 38.3 % less. All differences were statistically significant (p < 0.0001). Signal-to-noise and contrast-to-noise ratios were best in S64; these differences also reached statistical significance. Image analysis, however, showed "non-inferiority" of scanner E regarding image quality. CONCLUSIONS The first experience with the new scanner shows that new dose reduction techniques allow for up to 40 % dose reduction while still maintaining image quality at a diagnostically usable level.

Incorporation of Hysteretic Effects in Model-Order Reduction Analysis of Magnetic Devices

The ability to predict the properties of magnetic materials in a device is essential to ensuring the correct operation and optimization of the design as well as the device behavior over a wide range of input frequencies. Typically, development and simulation of wide-bandwidth models requires detailed, physics-based simulations that utilize significant computational resources. Balancing the trade-offs between model computational overhead and accuracy can be cumbersome, especially when the nonlinear effects of saturation and hysteresis are included in the model. This study focuses on the development of a system for analyzing magnetic devices in cases where model accuracy and computational intensity must be carefully and easily balanced by the engineer. A method for adjusting model complexity and corresponding level of detail while incorporating the nonlinear effects of hysteresis is presented that builds upon recent work in loss analysis and magnetic equivalent circuit (MEC) modeling. The approach utilizes MEC models in conjunction with linearization and model-order reduction techniques to process magnetic devices based on geometry and core type. The validity of steady-state permeability approximations is also discussed.

Limiting Internal Supply Voltage Spikes in DC-DC Converters

Implementing monolithic DC-DC converters for low power portable applications with a standard low voltage CMOS technology leads to lower production costs and higher reliability. Moreover, it allows miniaturization by the integration of two units in the same die: the power management unit that regulates the supply voltage for the second unit, a dedicated signal processor, that performs the functions required. This paper presents original techniques that limit spikes in the internal supply voltage on a monolithic DC-DC converter, extending the use of the same technology for both units. These spikes are mainly caused by fast current variations in the path connecting the external power supply to the internal pads of the converter power block. This path includes two parasitic inductances inbuilt in bond wires and in package pins. Although these parasitic inductances present relative low values when compared with the typical external inductances of DC-DC converters, their effects can not be neglected when switching high currents at high switching frequency. The associated overvoltage frequently causes destruction, reliability problems and/or control malfunction. Different spike reduction techniques are presented and compared. The proposed techniques were used in the design of the gate driver of a DC-DC converter included in a power management unit implemented in a standard 0.35 mu m CMOS technology.

Estimation of rotational frequency response functions

As it is widely known, in structural dynamic applications, ranging from structural coupling to model updating, the incompatibility between measured and simulated data is inevitable, due to the problem of coordinate incompleteness. Usually, the experimental data from conventional vibration testing is collected at a few translational degrees of freedom (DOF) due to applied forces, using hammer or shaker exciters, over a limited frequency range. Hence, one can only measure a portion of the receptance matrix, few columns, related to the forced DOFs, and rows, related to the measured DOFs. In contrast, by finite element modeling, one can obtain a full data set, both in terms of DOFs and identified modes. Over the years, several model reduction techniques have been proposed, as well as data expansion ones. However, the latter are significantly fewer and the demand for efficient techniques is still an issue. In this work, one proposes a technique for expanding measured frequency response functions (FRF) over the entire set of DOFs. This technique is based upon a modified Kidder's method and the principle of reciprocity, and it avoids the need for modal identification, as it uses the measured FRFs directly. In order to illustrate the performance of the proposed technique, a set of simulated experimental translational FRFs is taken as reference to estimate rotational FRFs, including those that are due to applied moments.