Publications

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The inclusion of computer simulations in the study and design of complex engineering systems has created a need for efficient approaches to simulation-based optimization. For example, in water resources management problems, optimization problems regularly consist of objective functions and constraints that rely on output from a PDE-based simulator. Various assumptions can be made to simplify either the objective function or the physical system so that gradient-based methods apply, however the incorporation of realistic objection functions can be accomplished given the availability of derivative-free optimization methods. A wide variety of derivative-free methods exist and each method has both advantages and disadvantages. Therefore, to address such problems, we propose a hybrid approach, which allows the combining of beneficial elements of multiple methods in order to more efficiently search the design space. Specifically, in this paper, we illustrate the capabilities of two novel algorithms; one which hybridizes pattern search optimization with Gaussian Process emulation and the other which hybridizes pattern search and a genetic algorithm. We describe the hybrid methods and give some numerical results for a hydrological application which illustrate that the hybrids find an optimal solution under conditions for which traditional optimal search methods fail.

Developing quantum chemistry programs on the coming generation of exascale computers will be a difficult task. The programs will need to be fault-tolerant and minimize the use of global operations. This work explores the use a task-based model that uses a data-centric approach to allocate work to different processes as it applies to quantum chemistry. After introducing the key problems that appear when trying to parallelize a complicated quantum chemistry method such as coupled-cluster theory, we discuss the implications of that model as it pertains to the computational kernel of a coupled-cluster program - matrix multiplication. Also, we discuss the extensions that would required to build a full coupled-cluster program using the task-based model. Current programming models for high-performance computing are fault-intolerant and use global operations. Those properties are unsustainable as computers scale to millions of CPUs; instead one must recognize that these systems will be hierarchical in structure, prone to constant faults, and global operations will be infeasible. The FAST-OS HARE project is introducing a scale-free computing model to address these issues. This model is hierarchical and fault-tolerant by design, allows for the clean overlap of computation and communication, reducing the network load, does not require checkpointing, and avoids the complexity of many HPC runtimes. Development of an algorithm within this model requires a change in focus from imperative programming to a data-centric approach. Quantum chemistry (QC) algorithms, in particular electronic structure methods, are an ideal test bed for this computing model. These methods describe the distribution of electrons in a molecule, which determine the properties of the molecule. The computational cost of these methods is high, scaling quartically or higher in the size of the molecule, which is why QC applications are major users of HPC resources. The complexity of these algorithms means that MPI alone is insufficient to achieve parallel scaling; QC developers have been forced to use alternative approaches to achieve scalability and would be receptive to radical shifts in the programming paradigm. Initial work in adapting the simplest QC method, Hartree-Fock, to this the new programming model indicates that the approach is beneficial for QC applications. However, the advantages to being able to scale to exascale computers are greatest for the computationally most expensive algorithms; within QC these are the high-accuracy coupled-cluster (CC) methods. Parallel coupledcluster programs are available, however they are based on the conventional MPI paradigm. Much of the effort is spent handling the complicated data dependencies between the various processors, especially as the size of the problem becomes large. The current paradigm will not survive the move to exascale computers. Here we discuss the initial steps toward designing and implementing a CC method within this model. First, we introduce the general concepts behind a CC method, focusing on the aspects that make these methods difficult to parallelize with conventional techniques. Then we outline what is the computational core of the CC method - a matrix multiply - within the task-based approach that the FAST-OS project is designed to take advantage of. Finally we outline the general setup to implement the simplest CC method in this model, linearized CC doubles (LinCC).

We will present results of the design, operation, and performance of surface ion micro-traps fabricated at Sandia. Recent progress in the testing of the micro-traps will be highlighted, including successful motional control of ions and the validation of simulations with experiments.

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Semiconductor saturable absorbers (SESAs) introduce loss into a solid-state laser cavity until the cavity field bleaches the absorber producing a high-energy pulse. Multiple quantum wells (MQWs) of AlGaInAs grown lattice-matched to InP have characteristics that make them attractive for SESAs. The band gap can be tuned around the target wavelength, 1064 nm, and the large conduction band offset relative to the AlInAs barrier material helps reduces the saturation fluence, and transparent substrate reduces nonsaturable losses. We have characterized the lifetime of the bleaching process, the modulation depth, the nonsaturable losses, and the saturation fluence associated with SESAs. We compare different growth conditions and structure designs. These parameters give insight into the quality of the epitaxy and effect structure design has on SESA performance in a laser cavity. AlGaInAs MQWs were grown by MOVPE using a Veeco D125 machine using methyl-substituted metal-organics and hydride sources at a growth temperature of 660 C at a pressure of 60 Torr. A single period of the basic SESA design consists of approximately 130 to 140 nm of AlInAs barrier followed by two AlGaInAs quantum wells separated by 10 nm AlInAs. This design places the QWs near the nodes of the 1064-nm laser cavity standing wave. Structures consisting of 10-, 20-, and 30-periods were grown and evaluated. The SESAs were measured at 1064 nm using an optical pump-probe technique. The absorbance bleaching lifetime varies from 160 to 300 nsec. The nonsaturable loss was as much as 50% for structures grown on n-type, sulfur-doped InP substrates, but was reduced to 16% when compensated, Fe-doped InP substrates were used. The modulation depth of the SESAs increased linearly from 9% to 30% with the number of periods. We are currently investigating how detuning the QW transition energy impacts the bleaching characteristics. We will discuss how each of these parameters impacts the laser performance.

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Fluids with higher thermal conductivities are sought for fluidic cooling systems in applications including microprocessors and high-power lasers. By adding high thermal conductivity nanoscale metal and metal oxide particles to a fluid the thermal conductivity of the fluid is enhanced. While particle aggregates play a central role in recent models for the thermal conductivity of nanofluids, the effect of particle diffusion in a temperature field on the aggregation and transport has yet to be studied in depth. The present work separates the effects of particle aggregation and diffusion using parallel plate experiments, infrared microscopy, light scattering, Monte Carlo simulations, and rate equations for particle and heat transport in a well dispersed nanofluid. Experimental data show non-uniform temporal increases in thermal conductivity above effective medium theory and can be well described through simulation of the combination of particle aggregation and diffusion. The simulation shows large concentration distributions due to thermal diffusion causing variations in aggregation, thermal conductivity and viscosity. Static light scattering shows aggregates form more quickly at higher concentrations and temperatures, which explains the increased enhancement with temperature reported by other research groups. The permanent aggregates in the nanofluid are found to have a fractal dimension of 2.4 and the aggregate formations that grow over time are found to have a fractal dimension of 1.8, which is consistent with diffusion limited aggregation. Calculations show as aggregates grow the viscosity increases at a faster rate than thermal conductivity making the highly aggregated nanofluids unfavorable, especially at the low fractal dimension of 1.8. An optimum nanoparticle diameter for these particular fluid properties is calculated to be 130 nm to optimize the fluid stability by reducing settling, thermal diffusion and aggregation.

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Long chain alcohols possess major advantages over the currently used ethanol as bio-components for gasoline, including higher energy content, better engine compatibility, and less water solubility. The rapid developments in biofuel technology have made it possible to produce C{sub 4}-C{sub 5} alcohols cost effectively. These higher alcohols could significantly expand the biofuel content and potentially substitute ethanol in future gasoline mixtures. This study characterizes some fundamental properties of a C{sub 5} alcohol, isopentanol, as a fuel for HCCI engines. Wide ranges of engine speed, intake temperature, intake pressure, and equivalence ratio are investigated. Results are presented in comparison with gasoline or ethanol data previously reported. For a given combustion phasing, isopentanol requires lower intake temperatures than gasoline or ethanol at all tested speeds, indicating a higher HCCI reactivity. Similar to ethanol but unlike gasoline, isopentanol does not show two-stage ignition even at very low engine speed (350 rpm) or with considerable intake pressure boost (200 kPa abs.). However, isopentanol does show considerable intermediate temperature heat release (ITHR) that is comparable to gasoline. Our previous work has found that ITHR is critical for maintaining combustion stability at the retarded combustion phasings required to achieve high loads without knock. The stronger ITHR causes the combustion phasing of isopentanol to be less sensitive to intake temperature variations than ethanol. With the capability to retard combustion phasing, a maximum IMEP{sub g} of 5.4 and 11.6 bar was achieved with isopentanol at 100 and 200 kPa intake pressure, respectively. These loads are even slightly higher than those achieved with gasoline. The ITHR of isopentanol depends on operating conditions and is enhanced by simultaneously increasing pressures and reducing temperatures. However, increasing the temperature seems to have little effect on ITHR at atmospheric pressure, but it does promote hot ignition. Finally, the dependence of ignition timing on equivalence ratio, here called {phi}-sensitivity, is measured at atmospheric intake pressure, showing that the ignition of isopentanol is nearly insensitive to equivalence ratio when thermal effects are removed. This suggests that partial fuel stratification, which has been found effective to control the HRR with two-stage ignition fuels, may not work well with isopentanol at these conditions. Overall, these results indicate that isopentanol has a good potential as a HCCI fuel, either in neat form or in blend with gasoline.

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Metal-organic frameworks (MOFs) represent a diverse and rapidly expanding class of materials comprising metal ions bridged by organic linker molecules. These robust crystalline structures have been found to exhibit exceptionally large surface areas, paving the way for diverse applications ranging from gas storage and separations to catalysis, drug delivery, and sensing. Less well understood are the intrinsic luminescence properties of MOFs, which arise from the electronic transitions within the hybrid metal-organic structure. Recently, we reported the observation of scintillation in stilbene-based MOFs, representing the discovery of the first completely new class of radiation detection materials since the advent of plastic scintillators in 1950. Photoluminescence and ion-induced luminescence spectroscopy of these materials show that both the luminescence spectrum and its timing can be varied by altering the local environment of the chromophore, establishing critical insight towards the rational design of materials for specific radiation detection applications. In this work, we describe the luminescence and scintillating properties of a series of isoreticular MOFs (IRMOFs), emphasizing the structural and electronic effects associated with systematic modification of the chromophore. Among these structures are IRMOFs based on naphthyl, biphenyl, terphenyl, and stilbene dicarboxylate linkers, for which unique structural changes and optical properties are observed. In addition to chemical changes in the structure, framework interpenetration may also be synthetically controlled, resulting in pairs of catenated and non-catenated IRMOFs based upon the same organic linker. The distinct interchromophore distances and solvate structure in these pairs lead to unique luminescence spectra that are interpreted in terms of energy transfer interactions. These spectral changes provide insight into the mechanism for radiation-induced luminescence, which for MOFs may differ significantly from the photoluminescence spectrum.