Waste heat recovery from adiabatic diesel engines by exhaust-driven Brayton cycles

An evaluation of Bryton Bottoming Systems (BBS) as waste heat recovery devices for future adiabatic diesel engines in heavy duty trucks is presented. Parametric studies were performed to evaluate the influence of external and internal design parameters on BBS performance. Conceptual design and trade-off studies were undertaken to estimate the optimum configuration, size, and cost of major hardware components. The potential annual fuel savings of long-haul trucks equipped with BBS were estimated. The addition of a BBS to a turbocharged, nonaftercooled adiabatic engine would improve fuel economy by as much as 12%. In comparison with an aftercooled, turbocompound engine, the BBS-equipped turbocharged engine would offer a 4.4% fuel economy advantage. If installed in tandem with an aftercooled turbocompound engine, the BBS could effect a 7.2% fuel economy improvement. The cost of a mass-produced 38 Bhp BBS is estimated at about $6460 or 170/Bhp. Technical and economic barriers that hinder the commercial introduction of bottoming systems were identified. Related studies in the area of waste heat recovery from adiabatic diesel engines and NASA-CR-168255 (Steam Rankine) and CR-168256 (Organic Rankine)

Capital Ownership And Its Impact On International Trade And Economic Growth: The Tunisian Experience

Despite the widespread belief that a privatized economy performs better than a centrally planned one, there is no empirical evidence on whether changing the structure of capital ownership affects trade and growth in developing countries. This paper addresses this issue by analyzing and comparing the distinctive effects of privately and publicly owned capital on international trade and economic growth. Based on a modified version of the neo-classical one-sector aggregate production technology, we investigate the intertemporal interactions among the growth rate of real output, private capital, public capital, international trade and labor. The results of applying our methodology to data from Tunisia suggest that private capital performs better than public capital in promoting economic growth and international trade.  Despite the widespread belief that a privatized economy performs better than a centrally planned one, there is no empirical evidence on whether changing the structure of capital ownership affects trade and growth in developing countries. This paper addresses this issue by analyzing and comparing the distinctive effects of privately and publicly owned capital on international trade and economic growth. Based on a modified version of the neo-classical one-sector aggregate production technology, we investigate the intertemporal interactions among the growth rate of real output, private capital, public capital, international trade and labor. The results of applying our methodology to data from Tunisia suggest that private capital performs better than public capital in promoting economic growth and international trade. &nbsp

Interactive Two-Stage Stochastic fuzzy Rough Programming for Water Resources Management

This paper deals with a fuzzy programming approach for treating an interactive two-stage stochastic rough-interval water resource management. The approach has been developed by incorporating an interactive fuzzy resolution method within a rough two-stage stochastic programming framework. The approach can not only tackle dual rough intervals presented as an inexact boundary intervals that exist in the objective function and the left- and right-hand sides of the constraints that are associated with different levels of economic penalties when the promised policy targets are violated. The results indicate that a set of solutions under different feasibility degrees has been generated for planning the water resources allocation. They can help the decision makers to conduct in depth analysis of tradeoffs between economic efficiency and constraint-violation risk, as well as enable them to identify, in an interactive way, a desired compromise between satisfaction degree of the goal and feasibility of the constraints. A management example in terms of rough-intervals water resources allocation has been treated for the sake of applicability of the proposed approach

Solutions Globales Régulières pour Quelques Équations Lineaires D'évolution du Type Pseudo-Différentiel Singulier

2000 Mathematics Subject Classification: 35C15, 35D05, 35D10, 35S10, 35S99.We give here examples of equations of type (1) ∂tt2 y -p(t, Dx) y = 0, where p is a singular pseudo-differential operator with regular global solutions when the Cauchy data are regular, t ∈ R, x ∈ R5

Permeability Prediction and Diagenesis in Tight Carbonates Using Machine Learning Techniques

Machine learning techniques have found their way into many problems in geoscience but have not been used significantly in the analysis of tight rocks. We present a case study testing the effectiveness of artificial neural networks and genetic algorithms for the prediction of permeability in tight carbonate rocks. The dataset consists of 130 core plugs from the Portland Formation in southern England, all of which have measurements of Klinkenberg-corrected permeability, helium porosity, characteristic pore throat diameter, and formation resistivity. Permeability has been predicted using genetic algorithms and artificial neural networks, as well as seven conventional ‘benchmark’ models with which the machine learning techniques have been compared. The genetic algorithm technique has provided a new empirical equation that fits the measured permeability better than any of the seven conventional benchmark models. However, the artificial neural network technique provided the best overall prediction method, quantified by the lowest root-mean-square error (RMSE) and highest coefficient of determination value (R2). The lowest RMSE from the conventional permeability equations was from the RGPZ equation, which predicted the test dataset with an RMSE of 0.458, while the highest RMSE came from the Berg equation, with an RMSE of 2.368. By comparison, the RMSE for the genetic algorithm and artificial neural network methods were 0.433 and 0.38, respectively. We attribute the better performance of machine learning techniques over conventional approaches to their enhanced capability to model the connectivity of pore microstructures caused by codependent and competing diagenetic processes. We also provide a qualitative model for the poroperm characteristics of tight carbonate rocks modified by each of eight diagenetic processes. We conclude that, for tight carbonate reservoirs, both machine learning techniques predict permeability more reliably and more accurately than conventional models and may be capable of distinguishing quantitatively between pore microstructures caused by different diagenetic processes