Three-dimensional dynamic model for off-road vehicles using discrete body dynamics

This paper presents a simple, reliable dynamics model of off-road vehicle operation in real-time (RT) on terrain with obstacles. The numerical model was formulated by a new method – DBD (Discrete Body Dynamics). The new method is based on a discrete-element method, where the equations of motion are linear and simple to solve.In this new method, the suspension systems are composed of soft and stiff springs and dampers (instead of suspension arms and joints constrains), to present the kinematics and dynamics of real suspension. Reduction of the number of bodies and avoidance of constraints significantly improves model efficiency and simplicity.The tires–soil interaction was modeled using Brixius prediction. Specific soil properties were obtained from the classification system for each tire–soil interaction, size, and geometric area. The tire–ground contact was determined by topographic surface and adjustment of the forces and direction acting on the tires.The proposed method allows quick and simple definition of a vehicle. The model is written as an independent software infrastructure, enabling easy integration with any other software component.Simulation results were compared with Siemens' VL commercial multibody dynamics program. The performance of the proposed method was very similar to the commercial program (R² > 0.9), with the significant advantage of much higher RT performance.     

Generalized error-dependent prediction uncertainty in multivariate calibration

Most of the current expressions used to calculate figures of merit in multivariate calibration have been derived assuming independent and identically distributed (iid) measurement errors. However, it is well known that this condition is not always valid for real data sets, where the existence of many external factors can lead to correlated and/or heteroscedastic noise structures. In this report, the influence of the deviations from the classical iid paradigm is analyzed in the context of error propagation theory. New expressions have been derived to calculate sample dependent prediction standard errors under different scenarios. These expressions allow for a quantitative study of the influence of the different sources of instrumental error affecting the system under analysis. Significant differences are observed when the prediction error is estimated in each of the studied scenarios using the most popular first-order multivariate algorithms, under both simulated and experimental conditions.     

Prediction of retention of O-ethyl,O-aryl and N-isopropyl phosphoroamidothioates in RP-HPLC from molecular structure parameters

Summary Multi-variable regression analysis between lnk_w, c (in retention equation lnk=lnk_w+cC_b) and molecular structure parameters, including hydrophobicity, electric effect, field effect and position-specific effect constant, was carried out with O-ethyl, O-aryl and N-isopropyl phosphoroamidothioates as test solutes. With these quantitative relationships, the retention behaviour of these solutes for different mobile phase compositions was predicted. The results showed that there are only 26.7% of total, experimentally measured, capacity factors with relative deviations larger than 5% and only 2.2% with deviations larger than 10%, which means that it is possible to apply the method reported to predict retention values for qualitative purposes for different mobile phase compositions.     

Temperature influence on mixing properties of {ethyl tert-butyl ether (ETBE) + gasoline additives}

The densities and ultrasonic velocity of {ethyl tert-butyl ether (ETBE) + (benzene, toluene, ethylbenzene, isooctane, tert-butyl alcohol, and ethanol)} over the temperature range (288.15 to 323.15) K and atmospheric pressure, have been measured over the whole concentration range. The experimental excess volumes and deviation of isentropic compressibilities data have been analysed in terms of different theoretical models. The gathered data improve open literature related to gasoline additives, and help to understand the ETBE volumetric and acoustic trend into different chemical environment.     

Basic oxygen furnace steelmaking end-point prediction based on computer vision and general regression neural network

End-point prediction is one of the most difficult problems in basic oxygen furnace (BOF) steelmaking process. To address this problem, some researchers have proposed some methods based on flame image processing and pattern classification. Because of the dynamically changing flame and real-time needs during the blowing process, there are still some issues that need to be solved. We propose a novel method based on accurate and fast multi flame features extraction and general regression neural network (GRNN). Firstly, flame images were acquired, and then the background of each image was removed via color similarity determination algorithm; secondly, color, texture, and boundary features were extracted; the fast and robust boundary and texture features were extracted by using the proposed methods, and these features were tested for their validity to the end-point prediction via comparing them with some other similar methods; finally, the prediction model was built using multi-features and GRNN. The experimental results demonstrated that it is accurate and fast to use the proposed method to the BOF end-point predict.     

On a notion of partially conditionally identically distributed sequences

A notion of conditionally identically distributed (c.i.d.) sequences has been studied as a form of stochastic dependence weaker than exchangeability, but equivalent to it in the presence of stationarity. We extend such notion to families of sequences. Paralleling the extension from exchangeability to partial exchangeability in the sense of de Finetti, we propose a notion of partially c.i.d. dependence, which is shown to be equivalent to partial exchangeability for stationary processes. Partially c.i.d. families of sequences preserve attractive limit properties of partial exchangeability, and are asymptotically partially exchangeable. Moreover, we provide strong laws of large numbers and two central limit theorems. Our focus is on the asymptotic agreement of predictions and empirical means, which lies at the foundations of Bayesian statistics. Natural examples of partially c.i.d. constructions are interacting randomly reinforced processes satisfying certain conditions on the reinforcement.     

“Blind Test 3” calculations of the performance and wake development behind two in-line and offset model wind turbines

This is a report on the comparison between measurement data and predictions presented at the “Blind Test 3” Workshop organized jointly by Nowitech and Norcowe in Bergen, 10 and 11 December, 2013. A number of researchers were invited to predict the performances and the wake development behind two model wind turbines that have been extensively tested at the Department of Energy and Process Engineering, NTNU. The turbines were arranged in-line, but slightly offset so that the wake of the upstream turbine only interacted with roughly half the area swept by the second rotor. This is a common event in most wind parks and produces flow fields that are both complicated and harmful for the downstream turbine. As expected it turned out to be a difficult flow to predict.Contributions were received from five different groups using a range of methods, from fully resolved Reynolds averaged Computational Fluid Dynamics (CFD) models to Large Eddy Simulations (LES). The range of results was large but the overall trend is that the current methods predict the power generation as well as the thrust force reasonably well. But there is a large uncertainty in the prediction of the turbulence field in the wake. Hence, the LES method consistently performed better than the others.     

Prediction of Fractional Brownian Motion-Type Processes

Abstract

We introduce a class of continuous-time Gaussian processes with stationary increments via moving-average representation with good MA coefficient. The class includes fractional Brownian motion with Hurst index less than 1/2 as a typical example. It also includes processes which have different indices corresponding to the local and long-time properties, repsectively. We derive some basic properties of the processes, and, using the results, we establish a prediction formula for them. The prediction kernel in the formula is given explicitly in terms of MA and AR coefficients.     

A Class of Sparse Invertible Matrices and Their Use for Nonlinear Prediction of Nearly Periodic Time Series with Fixed Period

We introduce a class of sparse matrices U _m (A _{p 1} ) of order m by m, where m is a composite natural number, p ₁ is a divisor of m, and A _{p 1} is a set of nonzero real numbers of length p ₁. The construction of U _m (A _{p 1} ) is achieved by iteration, involving repetitive dilation operations and block-matrix operations. We prove that the matrices U _m (A _{p 1} ) are invertible and we compute the inverse matrix (U _m (A _{p 1} ))^?1 explicitly. We prove that each row of the inverse matrix (U _m (A _{p 1} ))^?1 has only two nonzero entries with alternative signs, located at specific positions, related to the divisors of m. We use the structural properties of the matrix (U _m (A _{p 1} ))^?1 in order to build a nonlinear estimator for prediction of nearly periodic time series of length m with fixed period.