Publications

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Photovoltaic systems are often priced in $/W{sub p}, where Wp refers to the DC power rating of the modules at Standard Test Conditions (1000 W/m{sup 2}, 25 C cell temperature) and $ refers to the installed cost of the system. However, the true value of the system is in the energy it will produce in kWhs, not the power rating. System energy production is a function of the system design and location, the mounting configuration, the power conversion system, and the module technology, as well as the solar resource. Even if all other variables are held constant, the annual energy yield (kWh/kW{sup p}) will vary among module technologies because of differences in response to low-light levels and temperature. Understanding energy yield is a key part of understanding system value. System performance models are used during project development to estimate the expected output of PV systems for a given design and location. Performance modeling is normally done by the system designer/system integrator. Often, an independent engineer will also model system output during a due diligence review of a project. A variety of system performance models are available. The most commonly used modeling tool for project development and due diligence in the United States is probably PVsyst, while those seeking a quick answer to expected energy production may use PVWatts. In this paper, we examine the variation in predicted energy output among modeling tools and users and compare that to measured output.

A conceptual structure for performance assessments (PAs) for radioactive waste disposal facilities and other complex engineered facilities based on the following three basic conceptual entities is described: EN1, a probability space that characterizes aleatory uncertainty; EN2, a function that predicts consequences for individual elements of the sample space for aleatory uncertainty; and EN3, a probability space that characterizes epistemic uncertainty. The implementation of this structure is illustrated with results from PAs for the Waste Isolation Pilot Plant and the proposed Yucca Mountain repository for high-level radioactive waste.

We propose and examine several statistical criteria for characterizing time series of solar irradiance. Time series of irradiance are used in analyses that seek to quantify the performance of photovoltaic (PV) power systems over time. Time series of irradiance are either measured or are simulated using models. Simulations of irradiance are often calibrated to or generated from statistics for observed irradiance and simulations are validated by comparing the simulation output to the observed irradiance. Criteria used in this comparison should derive from the context of the analyses in which the simulated irradiance is to be used. We examine three statistics that characterize time series and their use as criteria for comparing time series. We demonstrate these statistics using observed irradiance data recorded in August 2007 in Las Vegas, Nevada, and in June 2009 in Albuquerque, New Mexico.

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The Health and Safety Executive (HSE) has requested Sandia National Laboratories (SNL) to review the aircraft crash methodology for nuclear facilities that are being used in the United Kingdom (UK). The scope of the work included a review of one method utilized in the UK for assessing the potential for accidental airplane crashes into nuclear facilities (Task 1) and a comparison of the UK methodology against similar International Atomic Energy Agency (IAEA), United States (US) Department of Energy (DOE), and the US Nuclear Regulatory Commission (NRC) methods (Task 2). Based on the conclusions from Tasks 1 and 2, an additional Task 3 would provide an assessment of a site-specific crash frequency for the Dungeness B facility using one of the other methodologies. This report documents the results of Task 2. The comparison of the different methods was performed for the three primary contributors to aircraft crash risk at the Dungeness B site: airfield related crashes, crashes below airways, and background crashes. The methods and data specified in each methodology were compared for each of these risk contributors, differences in the methodologies were identified, and the importance of these differences was qualitatively and quantitatively assessed. The bases for each of the methods and the data used were considered in this assessment process. A comparison of the treatment of the consequences of the aircraft crashes was not included in this assessment because the frequency of crashes into critical structures is currently low based on the existing Dungeness B assessment. Although the comparison found substantial differences between the UK and the three alternative methodologies (IAEA, NRC, and DOE) this assessment concludes that use of any of these alternative methodologies would not change the conclusions reached for the Dungeness B site. Performance of Task 3 is thus not recommended.

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Rates of reactions can be expressed as dn/dt = kcf(n) where n is moles of reaction, k is a rate constant, c is a proportionality constant, and f(n) is a function of the properties of the sample. When the instrument time constant, ?, and k are sufficiently comparable that measured rates are significantly affected by instrument response, correction for instrument response must be done to obtain accurate reaction kinetics. Correction for instrument response has previously been done by truncating early data or by use of the Tian equation. Both methods can lead to significant errors. We describe a method for simultaneous determination of ?, k, and c by fitting equations describing the combined instrument response and rate law to rates observed as a function of time. The method was tested with data on the heat rate from acid-catalyzed hydrolysis of sucrose.

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