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

PACE: Pricing And Cost Estimating handbook

The PACE (Pricing and Cost Estimating) system, its purpose, makeup, use, and capabilities are described

PACE 2: Pricing and Cost Estimating Handbook

An automatic data processing system to be used for the preparation of industrial engineering type manhour and material cost estimates has been established. This computer system has evolved into a highly versatile and highly flexible tool which significantly reduces computation time, eliminates computational errors, and reduces typing and reproduction time for estimators and pricers since all mathematical and clerical functions are automatic once basic inputs are derived

Cost analysis of new and retrofit hot-air type solar assisted heating systems

A detailed cost analysis/cost improvement study was performed on two Department of Energy/National Aeronautics and Space Administration operational test sites to determine actual costs and potential cost improvements of new and retrofit hot air type, solar assisted heating and hot water systems for single family sized structures. This analysis concentrated on the first cost of a system which included procurement, installation, and integration of a solar assisted heating and hot water system on a new or retrofit basis; it also provided several cost projections which can be used as inputs to payback analyses, depending upon the degree of optimism or future improvements assumed. Cost definitions were developed for five categories of cost, and preliminary estimates were developed for each. The costing methodology, approach, and results together with several candidate low cost designs are described

Neutron irradiation of Am-241 effectively produces curium

Computer study was made on the production of multicurie amounts of highly alpha-active curium 242 from americium 241 irradiation. The information available includes curium 242 yields, curium composition, irradiation data, and production techniques and safeguards

Digital Signal Processing Education: Technology and Tradition

In this paper we discuss a DSP course presented to both University students and to participants on industrial short courses. The "traditional" DSP course will typically run over one to two semesters and usually cover the fundamental mathematics of z-, Laplace and Fourier transforms, followed by the algorithm and application detail. In the course we will discuss, the use of advanced DSP software and integrated support software allow the presentation time to be greatly shortened and more focussed algorithm and application learning to be introduced. By combining the traditional lecture with the use of advanced DSP software, all harnessed by the web, we report on the objectives, syllabus, and mode of teaching

Running-mass models of inflation, and their observational constraints

If the inflaton sector is described by softly broken supersymmetry, and the inflaton has unsuppressed couplings, the inflaton mass will run strongly with scale. Four types of model are possible. The prediction for the spectral index involves two parameters, while the COBE normalization involves a third, all of them calculable functions of the relevant masses and couplings. A crude estimate is made of the region of parameter space allowed by present observation.Comment: Latex file, 20 pages, 11 figures, uses epsf.sty. Comment on the observation of the spectral index scale dependence added; Fig. 3-6 improve

The Distribution of some large mammals in Kenya

Volume: XXI

Computational fluid dynamics challenges for hybrid air vehicle applications

This paper begins by comparing turbulence models for the prediction of hybrid air vehicle (HAV) flows. A 6 : 1 prolate spheroid is employed for validation of the computational fluid dynamics (CFD) method. An analysis of turbulent quantities is presented and the Shear Stress Transport (SST) k-ω model is compared against a k-ω Explicit Algebraic Stress model (EASM) within the unsteady Reynolds-Averaged Navier-Stokes (RANS) framework. Further comparisons involve Scale Adaptative Simulation models and a local transition transport model. The results show that the flow around the vehicle at low pitch angles is sensitive to transition effects. At high pitch angles, the vortices generated on the suction side provide substantial lift augmentation and are better resolved by EASMs. The validated CFD method is employed for the flow around a shape similar to the Airlander aircraft of Hybrid Air Vehicles Ltd. The sensitivity of the transition location to the Reynolds number is demonstrated and the role of each vehicle£s component is analyzed. It was found that the ¦ns contributed the most to increase the lift and drag