Improved decision support for engine-in-the-loop experimental design optimization

Experimental optimization with hardware in the loop is a common procedure in engineering and has been the subject of intense development, particularly when it is applied to relatively complex combinatorial systems that are not completely understood, or where accurate modelling is not possible owing to the dimensions of the search space. A common source of difficulty arises because of the level of noise associated with experimental measurements, a combination of limited instrument precision, and extraneous factors. When a series of experiments is conducted to search for a combination of input parameters that results in a minimum or maximum response, under the imposition of noise, the underlying shape of the function being optimized can become very difficult to discern or even lost. A common methodology to support experimental search for optimal or suboptimal values is to use one of the many gradient descent methods. However, even sophisticated and proven methodologies, such as simulated annealing, can be significantly challenged in the presence of noise, since approximating the gradient at any point becomes highly unreliable. Often, experiments are accepted as a result of random noise which should be rejected, and vice versa. This is also true for other sampling techniques, including tabu and evolutionary algorithms. After the general introduction, this paper is divided into two main sections (sections 2 and 3), which are followed by the conclusion. Section 2 introduces a decision support methodology based upon response surfaces, which supplements experimental management based on a variable neighbourhood search and is shown to be highly effective in directing experiments in the presence of a significant signal-to-noise ratio and complex combinatorial functions. The methodology is developed on a three-dimensional surface with multiple local minima, a large basin of attraction, and a high signal-to-noise ratio. In section 2, the methodology is applied to an automotive combinatorial search in the laboratory, on a real-time engine-in-the-loop application. In this application, it is desired to find the maximum power output of an experimental single-cylinder spark ignition engine operating under a quasi-constant-volume operating regime. Under this regime, the piston is slowed at top dead centre to achieve combustion in close to constant volume conditions. As part of the further development of the engine to incorporate a linear generator to investigate free-piston operation, it is necessary to perform a series of experiments with combinatorial parameters. The objective is to identify the maximum power point in the least number of experiments in order to minimize costs. This test programme provides peak power data in order to achieve optimal electrical machine design. The decision support methodology is combined with standard optimization and search methods – namely gradient descent and simulated annealing – in order to study the reductions possible in experimental iterations. It is shown that the decision support methodology significantly reduces the number of experiments necessary to find the maximum power solution and thus offers a potentially significant cost saving to hardware-in-the-loop experi- mentation

Inflation on Moduli Space and Cosmic Perturbations

We show that a moduli space of the form predicted by string theory, lifted by supersymmetry breaking, gives rise to successful inflation for large regions of parameter space without any modification or fine tuning. This natural realization of inflation relies crucially on the complex nature of the moduli fields and the multiple points of enhanced symmetry, which are generic features of moduli space but not usually considered in inflationary model building. Our scenario predicts cosmic perturbations with an almost exactly flat spectrum for a wide range of scales with running on smaller, possibly observable, scales. The running takes the form of either an increasingly steep drop off of the spectrum, or a rise to a bump in the spectrum before an increasingly steep drop off.Comment: 23 pages, 4 figures; Added Fig. 1 and re-emphasis on dynamical selection of desirable initial angles for inflaton modulus. To be published in JHE

A method for the extraction and quantitation of phycoerythrin from algae

A summary of a new technique for the extraction and quantitation of phycoerythrin (PHE) from algal samples is described. Results of analysis of four extracts representing three PHE types from algae including cryptomonad and cyanophyte types are presented. The method of extraction and an equation for quantitation are given. A graph showing the relationship of concentration and fluorescence units that may be used with samples fluorescing around 575-580 nm (probably dominated by cryptophytes in estuarine waters) and 560 nm (dominated by cyanophytes characteristics of the open ocean) is provided

A General Analytic Formula for the Spectral Index of the Density Perturbations produced during Inflation

The standard calculation of the spectrum of density perturbations produced during inflation assumes that there is only one real dynamical degree of freedom during inflation. However, there is no reason to believe that this is actually the case. In this paper we derive general analytic formulae for the spectrum and spectral index of the density perturbations produced during inflation.Comment: 10 pages, more explanation and references added, version to be published in Progress of Theoretical Physic

Inflation model building in moduli space

A self-consistent modular cosmology scenario and its testability in view of future CMB experiments are discussed. Particular attention is drawn to the enhanced symmetric points in moduli space which play crucial roles in our scenario. The running and moreover the running of running for the cosmic perturbation spectrum are also analyzed.Comment: 5 pages, to appear in PASCOS04 proceeding