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The spectral absorptance of the 3.4-[[micro]meter] band of methane, ethane, propane, and butane has been measured with a Fourier transform infrared spectrometer over a range of temperatures from 296 to 900 K. The measurements were made at a 4-[cm.sup.-1] resolution and integrated over the entire band to give the total absorptance. The total absorptance is found to behave in such a way that it can be correlated by a combination of algebraic expressions that depend on the gas temperature and concentration. Average discrepancies between the correlations and the measurements are less than 4%, with maximum differences of no greater than 17%. In addition, the correlations presented here for methane are shown to be in good agreement with established models. Expressions given for the integrated intensity of each gas show an inverse dependence on temperature, reflecting the associated change in density. OCIS codes: 300.0300, 300.1030, 300.6300, 300.6340, 300.6390.

The major factors that impact residential fire losses and casualties are relatively well known both on the national level and for any given state. Interestingly, relatively little has been done in comparing fire loss data between states. If state fire loss data are compared, one should be able to identify contributing factors that influence differences in fire losses between states. As an example, it is known that construction standards, regulatory policy, socio-economic factors, etc. all influence the frequency and severity of fires. In this study, subsets of National Fire Incident Reporting System (NFIRS) data for the two largest US states by population are studied in order to identify how areas of origin, items first ignited, and heat sources contribute to the odds of casualties or fatalities occurring in fires, as well as to examine differences between the two states for these three factors. Data on residential home fires from 2006 to 2010 were gathered from the Texas and California NFIRS databases, Texas Fire Incident Reporting System and California All Incident Reporting System, respectively, for this purpose. Examination of the datasets separately using logistic regression models emphasized that fires started in the living room or den, fires in which the item first ignited was a flammable liquid, piping, or filter, and fires that were initiated from cigarettes, pipes, and cigars, all have significantly high odds of resulting in both casualties and fatalities for both states. Additionally, logistic regression modeling with interactions between state and area of origin, item first ignited, and heat source indicated that for many categories, the odds of a fire resulting in a casualty in Texas is roughly 1.5 times higher than the same fire in California. [ABSTRACT FROM PUBLISHER]

Accurate prediction of the radiative heat transfer is of great importance in simulations of combustion processes. To compute the total radiative transfer, it is necessary to integrate the radiative transfer equation (RTE) over the entire wavelength spectrum, a highly time consuming task that still demands the use of gas models. In this work, a finite volume code is used to simulate a steady-state methane-air diffusion flame. The code solves the equations for the conservation of mass, momentum, individual species and energy. The solution also includes a chemistry model for the formation of soot, which is of major importance in the computation of the radiation heat transfer. The radiation exchanges am computed by the discrete ordinates method, and using two different approaches: the gray gas model and the spectral-line based weighted-sum-of-grays-gases (SLW) model. The results for the temperature distribution, gas species concentration, and soot formation am examined with the two different radiation gas models used. [ABSTRACT FROM AUTHOR]

Stochastic simulations are becoming increasingly important in numerous engineering applications. The solution to the governing equations are complicated due to the high-dimensional spaces and the presence of randomness. In this paper we present (), a software library to solve various types of Stochastic Differential Equations (SDE) as well as estimate statistical distributions from the moments. The library provides a suite of tools to solve various SDEs using the method of moments (MoM) as well as estimate statistical distributions from the moments using moment matching algorithms. For a large class of problems, MoM provide efficient solutions compared with other stochastic simulation techniques such as Monte Carlo (MC). In the physical sciences, the moments of the distribution are usually the primary quantities of interest. The library enables the solution of moment equations derived from a variety of SDEs, with closure using non-standard Gaussian quadrature. In engineering risk assessment and decision making, statistical distributions are required. The library implements tools for fitting the Generalized Lambda Distribution (GLD) with the given moments. The objectives of this paper are (1) to briefly outline the theory behind moment methods for solving SDEs/estimation of statistical distributions; (2) describe the organization of the software and user interfaces; (3) discuss use of standard software engineering tools for regression testing, aid collaboration, distribution and further development. A number of representative examples of the use of in various engineering applications are presented and future areas of research are discussed. [ABSTRACT FROM AUTHOR]

Accurate measurement of surface temperature distribution is of concern in the semiconductor industries, particularly in rapid thermal processing. The International Technology Roadmap for Semiconductors 2004 has established requirements of uncertainties of ± 1.5 °C at a temperature of 1000 °C, with temperature calibration traceable to ITS-90 (International Temperature Scale-1990). Light-pipe radiation thermometers (LPRTs) are becoming increasingly important as an industrial tool for temperature measurement, especially in the semiconductor industry. However, achieving improved accuracy will be difficult without fully understanding several issues associated with LPRTs. In this paper, the "drawdown effect," the "shadow effect," and the "environmental effect" are investigated. The drawdown effect is caused by the physical mass of the light-pipe probe acting as a radiation heat sink for the measured object. The shadow effect is caused by distortion of the wafer radiosity distribution due to the presence of the light-pipe probe. The environmental effect is caused by nonspecular reflection due to surface imperfection or impurity of the light-pipe material. Monte Carlo simulation was conducted to better understand the underlying physics, and the simulation results are compared to the experiment results. The latter two effects were found to be very important in the present study. [ABSTRACT FROM AUTHOR]

This paper presents a model‐based study of the impact of implementing radiant (radiation‐dominated) concentric‐tube recuperators on fuel use in an indirectly‐fired industrial heat treating (austenitization/quench hardening) furnace. The recuperators recover some of the waste heat from the burner exhaust to preheat the inlet air to the burners, thus reducing the amount of fuel required for combustion. The novelty of this work consists of developing a physics‐based model of a recuperator, which captures local gas‐to‐surface convective heat transfer and long‐range surface‐to‐surface radiation interactions, and using the model to analyze and optimize the energy use of the furnace. An iterative algorithm is proposed to solve the poorly‐conditioned energy balance equations. The steady‐state system operation is optimized using surrogate models. Extensive simulation studies demonstrate that heat integration reduces fuel consumption significantly without affecting product quality. © 2018 American Institute of Chemical Engineers AIChE J, 65: e16414 2019 [ABSTRACT FROM AUTHOR]