Monitoring of cooling tower performance in a nuclear reactor facility is necessary to ensure safe operation; however, instrumentation for measuring performance characteristics can be difficult to install and may malfunction or break down over long duration experiments. This paper describes employing a thermodynamic approach to quantify cooling tower performance, the Merkel model, which requires only five parameters, namely, inlet water temperature, outlet water temperature, liquid mass flowrate, gas mass flowrate, and wet bulb temperature. Using this model, a general method to determine cooling tower operation for a nuclear reactor was developed in situations when neither the outlet water temperature nor gas mass flowrate are available, the former being a critical piece of information to bound the Merkel integral. Furthermore, when multiple cooling tower cells are used in parallel (as would be in the case of large-scale cooling operations), only the average outlet temperature of the cooling system is used as feedback for fan speed control, increasing the difficulty of obtaining the outlet water temperature for each cell. To address these shortcomings, this paper describes a method to obtain individual cell outlet water temperatures for mechanical forced-air cooling towers via parametric analysis and optimization. In this method, the outlet water temperature for an individual cooling tower cell is acquired as a function of the liquid-to-gas ratio (L/G). Leveraging the tight tolerance on the average outlet water temperature, an error function is generated to describe the deviation of the parameterized L/G to the highly controlled average outlet temperature. The method was able to determine the gas flowrate at rated conditions to be within 3.9% from that obtained from the manufacturer’s specification, while the average error for the four individual cooling cell outlet water temperatures were 1.6 °C, -0.5 °C, -1.0 °C, and 0.3 °C.
Bayesian inference is used to calibrate a bottom-up home PLC network model with unknown loads and wires at frequencies up to 30 MHz. A network topology with over 50 parameters is calibrated using global sensitivity analysis and transitional Markov Chain Monte Carlo (TMCMC). The sensitivity-informed Bayesian inference computes Sobol indices for each network parameter and applies TMCMC to calibrate the most sensitive parameters for a given network topology. A greedy random search with TMCMC is used to refine the discrete random variables of the network. This results in a model that can accurately compute the transfer function despite noisy training data and a high dimensional parameter space. The model is able to infer some parameters of the network used to produce the training data, and accurately computes the transfer function under extrapolative scenarios.
Reichardt, Thomas A.; Eaton, Samuel W.; Kulp, Thomas J.; DeJong, Stephanie D.; Ray, Will R.; Karnowski, Tom K.; Wetherington, Randall W.; Willis, Michael W.; Marcillo, Omar M.; Maceira, Monica M.; Chai, Chengping C.; Cardenas, Edna C.; Watson, Scott W.; Chichester, David C.; Gammans, Christine G.; Krebs, John K.; d'Entremont, Brian d.
Remote detection of a surface-bound chemical relies on the recognition of a pattern, or "signature," that is distinct from the background. Such signatures are a function of a chemical's fundamental optical properties, but also depend upon its specific morphology. Importantly, the same chemical can exhibit vastly different signatures depending on the size of particles composing the deposit. We present a parameterized model to account for such morphological effects on surface-deposited chemical signatures. This model leverages computational tools developed within the planetary and atmospheric science communities, beginning with T-matrix and ray-tracing approaches for evaluating the scattering and extinction properties of individual particles based on their size and shape, and the complex refractive index of the material itself. These individual-particle properties then serve as input to the Ambartsumian invariant imbedding solution for the reflectance of a particulate surface composed of these particles. The inputs to the model include parameters associated with a functionalized form of the particle size distribution (PSD) as well as parameters associated with the particle packing density and surface roughness. The model is numerically inverted via Sandia's Dakota package, optimizing agreement between modeled and measured reflectance spectra, which we demonstrate on data acquired on five size-selected silica powders over the 4-16 μm wavelength range. Agreements between modeled and measured reflectance spectra are assessed, while the optimized PSDs resulting from the spectral fitting are then compared to PSD data acquired from independent particle size measurements.
Validating predictive models and quantifying uncertainties inherent in the modeling process is a critical component of the HARD Solids Venture program [1]. Our current research focuses on validating physics-based models predicting the optical properties of solid materials for arbitrary surface morphologies and characterizing the uncertainties in these models. We employ a systematic and hierarchical approach by designing physical experiments and comparing the experimental results with the outputs of computational predictive models. We illustrate this approach through an example comparing a micro-scale forward model to an idealized solid-material system and then propagating the results through a system model to the sensor level. Our efforts should enhance detection reliability of the hyper-spectral imaging technique and the confidence in model utilization and model outputs by users and stakeholders.
This paper describes measurements being made on a series of material systems for the purpose of developing a radiative-transfer model that describes the reflectance of light by granular solids. It is well recognized that the reflectance spectra of granular materials depend on their intrinsic (n(λ) and k(λ)) and extrinsic (morphological) properties. There is, however, a lack of robust and proven models to relate spectra to these parameters. The described work is being conducted in parallel with a modeling effort1 to address this need. Each follows a common developmental spiral in which material properties are varied and the ability of the model to calculate the effects of the changes are tested. The parameters being varied include particle size/shape, packing density, material birefringence, optical thickness, and spectral contribution of a substrate. It is expected that the outcome of this work will be useful in interpreting reflectance data for hyperspectral imaging (HSI), and for a variety of other areas that rely on it.
We assess the measurement of hyperspectral reflectance for the outdoor monitoring of green algae and cyanobacteria cultures with a multi-channel, fiber-coupled spectroradiometer. Reflectance data acquired over a four-week period are interpreted via numerical inversion of a reflectance model, in which the above-water reflectance is expressed as a quadratic function of the single backscattering albedo, dependent on the absorption and backscatter coefficients. The absorption coefficient is treated as the sum of component spectra consisting of the cultured species (green algae or cyanobacteria), dissolved organic matter, and water (including the temperature dependence of the water absorption spectrum). The backscatter coefficient is approximated as the scaled Hilbert transform of the culture absorption spectrum with a wavelength-independent vertical offset. Additional terms in the reflectance model account for the pigment fluorescence features and the water surface reflection of sunlight and skylight. For both the green algae and cyanobacteria, the wavelength-independent vertical offset of the backscatter coefficient is found to scale linearly with daily dry weight measurements, providing the capability for a non-sampling measurement of biomass in outdoor ponds. Other fitting parameters in the reflectance model are compared to auxiliary measurements and physics-based calculations. The magnitudes of the sunlight and skylight water-surface contributions derived from the reflectance model compare favorably with Fresnel reflectance calculations, while the reflectance-derived quantum efficiency of Chl-a fluorescence is found to be in agreement with literature values. To conlclude, the water temperature derived from the reflectance model exhibits excellent agreement with thermocouple measurements during the morning hours and highlights significantly elevated temperatures in the afternoon hours.
Ultraviolet (UV) Raman scattering with a 244-nm laser is evaluated for standoff detection of explosive compounds. The measured Raman scattering albedo is incorporated into a performance model that focused on standoff detection of trace levels of explosives. This model shows that detection at {approx}100 m would likely require tens of seconds, discouraging application at such ranges, and prohibiting search-mode detection, while leaving open the possibility of short-range point-and-stare detection. UV Raman spectra are also acquired for a number of anticipated background surfaces: tile, concrete, aluminum, cloth, and two different car paints (black and silver). While these spectra contained features in the same spectral range as those for TNT, we do not observe any spectra similar to that of TNT.
This project demonstrated the feasibility of a 'pump-probe' optical detection method for standoff sensing of chemicals on surfaces. Such a measurement uses two optical pulses - one to remove the analyte (or a fragment of it) from the surface and the second to sense the removed material. As a particular example, this project targeted photofragmentation laser-induced fluorescence (PF-LIF) to detect of surface deposits of low-volatility chemical warfare agents (LVAs). Feasibility was demonstrated for four agent surrogates on eight realistic surfaces. Its sensitivity was established for measurements on concrete and aluminum. Extrapolations were made to demonstrate relevance to the needs of outside users. Several aspects of the surface PF-LIF physical mechanism were investigated and compared to that of vapor-phase measurements. The use of PF-LIF as a rapid screening tool to 'cue' more specific sensors was recommended. Its sensitivity was compared to that of Raman spectroscopy, which is both a potential 'confirmer' of PF-LIF 'hits' and is also a competing screening technology.
A considerable amount research is being conducted on microalgae, since microalgae are becoming a promising source of renewable energy. Most of this research is centered on lipid production in microalgae because microalgae produce triacylglycerol which is ideal for biodiesel fuels. Although we are interested in research to increase lipid production in algae, we are also interested in research to sustain healthy algal cultures in large scale biomass production farms or facilities. The early detection of fluctuations in algal health, productivity, and invasive predators must be developed to ensure that algae are an efficient and cost-effective source of biofuel. Therefore we are developing technologies to monitor the health of algae using spectroscopic measurements in the field. To do this, we have proposed to spectroscopically monitor large algal cultivations using LIDAR (Light Detection And Ranging) remote sensing technology. Before we can deploy this type of technology, we must first characterize the spectral bio-signatures that are related to algal health. Recently, we have adapted our confocal hyperspectral imaging microscope at Sandia to have two-photon excitation capabilities using a chameleon tunable laser. We are using this microscope to understand the spectroscopic signatures necessary to characterize microalgae at the cellular level prior to using these signatures to classify the health of bulk samples, with the eventual goal of using of LIDAR to monitor large scale ponds and raceways. By imaging algal cultures using a tunable laser to excite at several different wavelengths we will be able to select the optimal excitation/emission wavelengths needed to characterize algal cultures. To analyze the hyperspectral images generated from this two-photon microscope, we are using Multivariate Curve Resolution (MCR) algorithms to extract the spectral signatures and their associated relative intensities from the data. For this presentation, I will show our two-photon hyperspectral imaging results on a variety of microalgae species and show how these results can be used to characterize algal ponds and raceways.
Laser-induced fluorescence measurements of cuvette-contained laser dye mixtures are made for evaluation of multivariate analysis techniques to optically thick environments. Nine mixtures of Coumarin 500 and Rhodamine 610 are analyzed, as well as the pure dyes. For each sample, the cuvette is positioned on a two-axis translation stage to allow the interrogation at different spatial locations, allowing the examination of both primary (absorption of the laser light) and secondary (absorption of the fluorescence) inner filter effects. In addition to these expected inner filter effects, we find evidence that a portion of the absorbed fluorescence is re-emitted. A total of 688 spectra are acquired for the evaluation of multivariate analysis approaches to account for nonlinear effects.
The search is on for new renewable energy and algal-derived biofuel is a critical piece in the multi-faceted renewable energy puzzle. It has 30x more oil than any terrestrial oilseed crop, ideal composition for biodiesel, no competition with food crops, can be grown in waste water, and is cleaner than petroleum based fuels. This project discusses these three goals: (1) Conduct fundamental research into the effects that dynamic biotic and abiotic stressors have on algal growth and lipid production - Genomics/Transcriptomics, Bioanalytical spectroscopy/Chemical imaging; (2) Discover spectral signatures for algal health at the benchtop and greenhouse scale - Remote sensing, Bioanalytical spectroscopy; and (3) Develop computational model for algal growth and productivity at the raceway scale - Computational modeling.
Progress in algal biofuels has been limited by significant knowledge gaps in algal biology, particularly as they relate to scale-up. To address this we are investigating how culture composition dynamics (light as well as biotic and abiotic stressors) describe key biochemical indicators of algal health: growth rate, photosynthetic electron transport, and lipid production. Our approach combines traditional algal physiology with genomics, bioanalytical spectroscopy, chemical imaging, remote sensing, and computational modeling to provide an improved fundamental understanding of algal cell biology across multiple cultures scales. This work spans investigations from the single-cell level to ensemble measurements of algal cell cultures at the laboratory benchtop to large greenhouse scale (175 gal). We will discuss the advantages of this novel, multidisciplinary strategy and emphasize the importance of developing an integrated toolkit to provide sensitive, selective methods for detecting early fluctuations in algal health, productivity, and population diversity. Progress in several areas will be summarized including identification of spectroscopic signatures for algal culture composition, stress level, and lipid production enabled by non-invasive spectroscopic monitoring of the photosynthetic and photoprotective pigments at the single-cell and bulk-culture scales. Early experiments compare and contrast the well-studied green algae chlamydomonas with two potential production strains of microalgae, nannochloropsis and dunnaliella, under optimal and stressed conditions. This integrated approach has the potential for broad impact on algal biofuels and bioenergy and several of these opportunities will be discussed.
As part of the U.S. Department of Homeland Security Detect-to-Protect program, a multilab [Sandia National Laboratories (SNL), Lawrence Livermore National Laboratories (LLNL), Pacific Northwest National Laboratory (PNNL), Oak Ridge National Laboratory (ORNL), and Los Alamos National Laboratory (LANL)] effort is addressing the need for useable detect-to-warn bioaerosol sensors for public facility protection. Towards this end, the SNL team is employing rapid fluorogenic staining to infer the protein content of bioaerosols. This is being implemented in a flow cytometry platform wherein each particle detected generates coincident signals of forward scatter, side scatter, and fluorescence. Several thousand such coincident signal sets are typically collected to generate a probability distribution over the scattering and fluorescence values. A linear unmixing analysis is performed to differentiate components in the mixture. After forming a library of pure component distributions from measured pure material samples, the distribution of an unknown mixture of particles is treated as a linear combination of the pure component distributions. The scattering/fluorescence probability distribution data vector a is considered the product of two vectors, the fractional profile f and the scattering/ fluorescence distributions from pure components P. A least squares procedure minimizes the magnitude of the residual vector e in the expression a = fP T + e. The profile f designates a weighting fraction for each particle type included in the set of pure components, providing the composition of the unknown mixture. We discuss testing of this analysis approach and steps we have taken to evaluate the effect of interferents, both known and unknown.
As part of the U.S. Department of Homeland Security Detect-to-Protect (DTP) program, a multilab [Sandia National Laboratories (SNL), Lawrence Livermore National Laboratories (LLNL), Pacific Northwest National Laboratory (PNNL), Oak Ridge National Laboratory (ORNL), and Los Alamos National Laboratory (LANL)] effort is addressing the need for useable detect-to-warn bioaerosol sensors for public facility protection. Towards this end, the SNL team is investigating the use of rapid fluorogenic staining to infer the protein content of bioaerosols. This is being implemented in a flow cytometer wherein each particle detected generates coincident signals of correlated forward scatter, side scatter, and fluorescence. Several thousand such coincident signal sets are typically collected to generate a distribution describing the probability of observing a particle with certain scattering and fluorescence values. These data are collected for sample particles in both a stained and unstained state. A linear unmixing analysis is performed to differentiate components in the mixture. In this paper, we discuss the implementation of the staining process and the cytometric measurement, the results of their application to the analysis of known and blind samples, and a potential instrumental implementations that would use staining.
Recent EPA regulations targeting mercury (Hg) emissions from utility coal boilers have prompted increased activity in the development of reliable chemical sensors for monitoring Hg emissions with high sensitivity, high specificity, and fast time response. We are developing a portable, laser-based instrument for real-time, stand-off detection of Hg emissions that involves exciting the Hg (6 3P1 ← 6 1S0) transition at 253.7 nm and detecting the resulting resonant emission from Hg (6 3P1). The laser for this approach must be tunable over the Hg absorption line at 253.7 nm, while system performance modeling has indicated a desired output pulse energy ≥0.1 μJ and linewidth ≤5 GHz (full width at half-maximum, FWHM). In addition, the laser must have the requisite physical characteristics for use in coal-fired power plants. To meet these criteria, we are pursing a multistage frequency-conversion scheme involving an optical parametric amplifier (OPA). The OPA is pumped by the frequency-doubled output of a passively Q-switched, monolithic Nd:YAG micro-laser operating at 10-Hz repetition rate and is seeded by a 761-nm, cw distributed-feedback diode laser. The resultant pulse-amplified seed beam is frequency tripled in two nonlinear frequency-conversion steps to generate 253.7-nm light. The laser system is mounted on a 45.7 cm × 30.5 cm breadboard and can be further condensed using custom optical mounts. Based on simulations of the nonlinear frequency-conversion processes and current results, we expect this laser architecture to exceed the desired pulse energy. Moreover, this approach provides a compact, all-solid-state source of tunable, narrow-linewidth visible and ultraviolet radiation, which is required for many chemical sensing applications.
A discussion on an active gas imager that can potentially improve system performance and reliability in Smart Leak Detection and Repair covers conventional single-wavelength imaging; differential imaging; methane detection; modification for detecting fugitive emissions relevant to refineries and chemical plants; and system description. This is an abstract of a paper presented at the AWMA's 99th Annual Conference and Exhibition (New Orleans, LA 6/20-23/2006).
Because many solid objects, both stationary and mobile, will be present in an indoor environment, the design of an indoor aerosol cloud finding lidar (light detection and ranging) instrument presents a number of challenges. The cloud finder must be able to discriminate between these solid objects and aerosol clouds as small as 1-meter in depth in order to probe suspect clouds. While a near IR ({approx}1.5-{micro}m) laser is desirable for eye-safety, aerosol scattering cross sections are significantly lower in the near-IR than at visible or W wavelengths. The receiver must deal with a large dynamic range since the backscatter from solid object will be orders of magnitude larger than for aerosol clouds. Fast electronics with significant noise contributions will be required to obtain the necessary temporal resolution. We have developed a laboratory instrument to detect aerosol clouds in the presence of solid objects. In parallel, we have developed a lidar performance model for performing trade studies. Careful attention was paid to component details so that results obtained in this study could be applied towards the development of a practical instrument. The amplitude and temporal shape of the signal return are analyzed for discrimination of aerosol clouds in an indoor environment. We have assessed the feasibility and performance of candidate approaches for a fieldable instrument. With the near-IR PMT and a 1.5-{micro}m laser source providing 20-{micro}J pulses, we estimate a bio-aerosol detection limit of 3000 particles/l.