Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “neighbor selection”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Measurements of NH3 absorption coefficients with a C-13/O-16/2 laser

Measurements of NH3 absorption coefficients are presented for several transitions of a C-13(O-16)2 laser for small concentrations of NH3(p less than 1 torr) for absorption lines broadened to 1 atm with N2. NH3 absorption coefficients were determined for laser transitions R(8)(920.2194 wavelengths/cm) to R(28)(933.8808 wavelengths/cm) of the 00 1 - (10 0,02 0)I band. The strongest absorption coefficient K = 36.09 + or - 1.43 per (atm-cm) was measured for the R(18) transition for the NH3 line, aQ(6,6), and is larger than has been found in any previous measurements with a CO2 laser. The dependence of K on total pressure was also obtained for select transitions, and the frequency separation between the R(18) laser transition and the neighboring NH3 line aQ(6,6) was determined to be 550 + or - 50 MHz. These results are significant for long path absorption monitoring of NH3 with CO2 lasers since the path length can be reduced by approximately 40% and for heterodyne detection of NH3 since the relative position of the laser transition to the NH3 absorption line is well within the bandpass of Hg-Cd-Te photomixers.

Allario, F.↗
NASA NTRS2011

Collaborative Clustering for Sensor Networks

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events, as well as faster responses such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if individual nodes can communicate directly with their neighbors. Previously, a method was developed by which machine learning classification algorithms could collaborate to achieve high performance autonomously (without requiring human intervention). This method worked for supervised learning algorithms, in which labeled data is used to train models. The learners collaborated by exchanging labels describing the data. The new advance enables clustering algorithms, which do not use labeled data, to also collaborate. This is achieved by defining a new language for collaboration that uses pair-wise constraints to encode useful information for other learners. These constraints specify that two items must, or cannot, be placed into the same cluster. Previous work has shown that clustering with these constraints (in isolation) already improves performance. In the problem formulation, each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. Each learner clusters its data and then selects a pair of items about which it is uncertain and uses them to query its neighbors. The resulting feedback (a must and cannot constraint from each neighbor) is combined by the learner into a consensus constraint, and it then reclusters its data while incorporating the new constraint. A strategy was also proposed for cleaning the resulting constraint sets, which may contain conflicting constraints; this improves performance significantly. This approach has been applied to collaborative clustering of seismic and infrasonic data collected by the Mount Erebus Volcano Observatory in Antarctica. Previous approaches to distributed clustering cannot readily be applied in a sensor network setting, because they assume that each node has the same view of the data set. A view is the set of features used to represent each object. When a single data set is partitioned across several computational nodes, distributed clustering works; all objects have the same view. But when the data is collected from different locations, using different sensors, a more flexible approach is needed. This approach instead operates in situations where the data collected at each node has a different view (e.g., seismic vs. infrasonic sensors), but they observe the same events. This enables them to exchange information about the likely cluster membership relations between objects, even if they do not use the same features to represent the objects.

Wagstaff. Loro :/↗
DOE OSTI2025

Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost

Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to ab initio molecular dynamics (MD) simulations. However, fitting high-quality MLIPs remains a challenging, time-consuming, and computationally intensive task where numerous trade-offs have to be considered, e.g., How much and what kind of atomic configurations should be included in the training set? Which level of ab initio convergence should be used to generate the training set? Which loss function should be used for fitting the MLIP? Which machine learning architecture should be used to train the MLIP? The answers to these questions significantly impact both the computational cost of MLIP training and the accuracy and computational cost of subsequent MLIP MD simulations. In this study, we use a configurationally diverse beryllium dataset and quadratic spectral neighbor analysis potential. We demonstrate that joint optimization of energy versus force weights, training set selection strategies, and convergence settings of the ab initio reference simulations, as well as model complexity can lead to a significant reduction in the overall computational cost associated with training and evaluating MLIPs. This opens the door to computationally efficient generation of high-quality MLIPs for a range of applications which demand different accuracy versus training and evaluation cost trade-offs.

36 MATERIALS SCIENCE↗
NASA NTRS1983

The spatial correlation function of rich clusters of galaxies

A series of objective statistical estimators is applied to directly study the three-dimensional distribution of rich clusters, using a recently completed redshift sample of 104 Abell clusters out to distance class four and galactic latitude of 30 deg or more. The sample and the relevant positional and redshift selection effects are discussed, and the two-point angular correlation function and nearest-neighbor distribution of clusters in the sample are determined, as well as in the larger D = 5+6 sample and in several subsamples of different richness classes and latitude zones. The redshift correlation function of clusters for various angular separations on the sky is examined as a test for the reality of the observed angular correlations. The two-point spatial correlation function of clusters in the sample is determined, and the consistency among the three observed correlation functions is tested. A model that describes all the observed correlations is presented.

Bahcall, N. A.↗
NASA NTRS2020

Neighbors on the Red Planet: Mars Science Laboratory Relay Coordination Post InSight Arrival

The Mars Science Laboratory (MSL) strategic communications planning toolset selects orbiter relay opportunities that the rover will use for transmitting data back to Earth. The toolset was extensively reworked in preparation for the Interior Exploration using Seismic Investigations, Geodesy and Heat Transport's (InSight) arrival at Mars in November 2018 and the regular use of Mars Atmosphere and Volatile Evolution (MAVEN) and Trace Gas Orbiter (TGO) orbiters as relay assets in addition to Mars Reconnaissance Orbiter (MRO) and 2001 Mars Odyssey (ODY). Overflight selection criteria was automated in the new toolset to take into account overflight deconfliction and down-selection as well as tactical timeline planning impacts and total data return. This was done while remaining flexible and configurable for changing mission priorities. As the Curiosity rover ages, the MSL planning team must overcome issues such as reduced budgets, memory bank anomalies, and reduced power availability. These are some examples of factors that affect the strategic communications planning toolset. In addition to adapting to evolving internal mission needs, the toolset must also be flexible to changes in the relay planning interface with other landers. The concept of shared relay or “split passes” was introduced when InSight began operating on Mars just 600 kilometers away and at the same longitude as MSL. This proximity meant that orbiters could now communicate with two landers during the same relay session. Over time, this new operational use-case became common practice as InSight and MSL settled into relay planning negotiations. Today, many TGO relay sessions are shared between MSL and InSight. As more orbiters pursue integrating this capability into nominal relay operations, and more landers arrive on Mars, the strategic communications process and toolsets increases in complexity. Development of a single tool that schedules relay sessions for all orbiters and landers simultaneously may become necessary as the number of Martian spacecraft increases. Examples of how toolset selection criteria and capabilities have helped or hindered MSL planning will be presented. Ongoing improvements to MSL toolsets and processes, as well as to shared relay tools such as Mars Relay Operations Service (MaROS) and General Telecom Predictor (GTP) are also discussed in this paper. Examples of how toolset selection criteria and capabilities have helped or hindered MSL planning will be presented. Ongoing improvements to MSL toolsets and processes, as well as to shared relay tools such as Mars Relay Operations Service (MaROS) and General Telecom Predictor (GTP) are also discussed in this paper.

Vandermey, Nancy↗
DOE OSTI2022

Manipulating the oxygen reduction reaction pathway on Pt-coordinated motifs

Electrochemical oxygen reduction could proceed via either 4e - -pathway toward maximum chemical-to-electric energy conversion or 2e - -pathway toward onsite H 2 O 2 production. Bulk Pt catalysts are known as the best monometallic materials catalyzing O 2 -to-H 2 O conversion, however, controversies on the reduction product selectivity are noted for atomic dispersed Pt catalysts. Here, we prepare a series of carbon supported Pt single atom catalyst with varied neighboring dopants and Pt site densities to investigate the local coordination environment effect on branching oxygen reduction pathway. Manipulation of 2e - or 4e - reduction pathways is demonstrated through modification of the Pt coordination environment from Pt-C to Pt-N-C and Pt-S-C, giving rise to a controlled H 2 O 2 selectivity from 23.3% to 81.4% and a turnover frequency ratio of H 2 O 2 /H 2 O from 0.30 to 2.67 at 0.4 V versus reversible hydrogen electrode. Energetic analysis suggests both 2e - and 4e - pathways share a common intermediate of *OOH, Pt-C motif favors its dissociative reduction while Pt-S and Pt-N motifs prefer its direct protonation into H 2 O 2 . By taking the Pt-N-C catalyst as a stereotype, we further demonstrate that the maximum H 2 O 2 selectivity can be manipulated from 70 to 20% with increasing Pt site density, providing hints for regulating the stepwise oxygen reduction in different application scenarios.

36 MATERIALS SCIENCE↗
DOE OSTI2026

CO (2) Electroreduction on Borated Copper Surfaces: Boron Active Sites, Not Copper

Boron-doped copper has recently emerged as an active and stable catalyst for the electrochemical reduction of CO 2 to value-added C 2 products. Here, in this work, we develop a realistic model of CO electroreduction on surface borides of copper under operational conditions, taking into account the effects of electrode potential, electrolyte environment, and pH. We study the possible reconstruction of the electrocatalyst surface using grand canonical DFT and global optimization to obtain a potential-dependent grand canonical ensemble description of metastable, hydrogen-covered catalyst surfaces. Two key surface configurations, low H coverage (LC, −0.6 V SHE ) and high H coverage (HC, −0.8 V SHE ), dominate this ensemble, with the former being kinetically persistent and C 2 selective under strongly reducing conditions. Nonmetallic boron sites on the surface copper boride are found to bind CO more strongly than copper sites, and mechanistic investigation of CO electroreduction pathways presents a surprisingly unconventional case of boron-centered reactivity in contrast to typical copper-centered reactivity. Neighboring boron sites present along boron chains on the surface copper boride are found to facilitate C−C coupling, thereby driving the high C 2 selectivity of this electrocatalyst.

boron-doped copper↗
DOE OSTI2024

Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Investigating the large-scale environment of wide-angle tailed radio galaxies in the local Universe

We present a statistical analysis of the large-scale (up to two Mpc) environment of a homogeneous and complete sample, both in radio and optical selection, of wide-angle tailed radio galaxies (WATs) in the local Universe (i.e., with redshifts z ≲ 0.15). The analysis is carried out using the parameters obtained from cosmological neighbors within two Mpc of the target source. Results regarding the large-scale environments of the WATs are then compared with those of Fanaroff–Riley type I (FR Is) and type II (FR IIs) radio galaxies listed in two other homogeneous and complete catalogs and selected with the same criterion adopted for the WATs catalog. We obtain indication that at low redshift, WATs inhabit environments with a larger number of galaxies than those of FR Is and FR IIs. In the explored redshift range, the physical size of the galaxy group or cluster in which the WATs reside appears to be almost constant with respect to FR Is and FR IIs, being around one Mpc. From the distribution of the concentration parameter, defined as the ratio between the number of cosmological neighbors lying within 500 kpc and within one Mpc, we conclude that WATs tend to inhabit the central region of the group or cluster in which they reside, in agreement with the general paradigm that WATs are the brightest cluster or group galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Unveiling Highly Sensitive Active Site in Atomically Dispersed Gold Catalysts for Enhanced Ethanol Dehydrogenation

Developing a desirable ethanol dehydrogenation process necessitates a highly efficient and selective catalyst with low cost. Herein, we show that the “complex active site” consisting of atomically dispersed Au atoms with the neighboring oxygen vacancies (Vo) and undercoordinated cation on oxide supports can be prepared and display unique catalytic properties for ethanol dehydrogenation. The “complex active site” Au-Vo-Zr 3+ on Au 1 /ZrO 2 exhibits the highest H 2 production rate, with above 37,964 mol H 2 per mol Au per hour (385 g H 2 $g^{-1}_{Au}$ h -1 ) at 350 °C, which is 3.32, 2.94 and 15.0 times higher than Au 1 /CeO 2 , Au 1 /TiO 2 , and Au 1 /Al 2 O 3 , respectively. Combining experimental and theoretical studies, we demonstrate the structural sensitivity of these complex sites by assessing their selectivity and activity in ethanol dehydrogenation. Our study sheds new light on the design and development of cost-effective and highly efficient catalysts for ethanol dehydrogenation. Fundamentally, atomic-level catalyst design by colocalizing catalytically active metal atoms forming a structure-sensitive “complex site”, is a crucial way to advance from heterogeneous catalysis to molecular catalysis. In conclusion, our study advanced the understanding of the structure sensitivity of the active site in atomically dispersed catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unveiling Highly Sensitive Active Site in Atomically Dispersed Gold Catalysts for Enhanced Ethanol Dehydrogenation

Developing a desirable ethanol dehydrogenation process necessitates a highly efficient and selective catalyst with low cost. Herein, we show that the “complex active site” consisting of atomically dispersed Au atoms with the neighboring oxygen vacancies (Vo) and undercoordinated cation on oxide supports can be prepared and display unique catalytic properties for ethanol dehydrogenation. The “complex active site” Au–Vo–Zr 3+ on Au 1 /ZrO 2 exhibits the highest H 2 production rate, with above 37,964 mol H 2 per mol Au per hour (385 g H 2 $\text{g}^{-1}_{\text{Au}} \text{h}^{–1}$) at 350 °C, which is 3.32, 2.94 and 15.0 times higher than Au 1 /CeO 2 , Au 1 /TiO 2 , and Au 1 /Al 2 O 3 , respectively. Combining experimental and theoretical studies, we demonstrate the structural sensitivity of these complex sites by assessing their selectivity and activity in ethanol dehydrogenation. Our study sheds new light on the design and development of cost-effective and highly efficient catalysts for ethanol dehydrogenation. Fundamentally, atomic-level catalyst design by colocalizing catalytically active metal atoms forming a structure-sensitive “complex site”, is a crucial way to advance from heterogeneous catalysis to molecular catalysis. Finally, our study advanced the understanding of the structure sensitivity of the active site in atomically dispersed catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Electric Field Control of Magnetic Skyrmion Helicity in a Centrosymmetric 2D van der Waals Magnet

Two-dimensional van der Waals magnets hosting topological magnetic textures, such as skyrmions, show promise for spintronics and quantum computing. Electrical control of these topological spin textures is crucial for enhancing operational performance and functionality. Here, using electron microscopy combined with in situ electric and magnetic biasing, we show that the skyrmion helicity in insulating Cr 2 Ge 2 Te 6 can be controlled by the direction of the external electric field applied during the field cooling process. The electric field-tuned helicity remains stable at low temperature, even amid variations in magnetic and electric fields. Our theoretical investigation reveals that nonzero Dzyaloshinskii-Moriya interactions between nearest neighbors, induced by the electric field, change their sign upon reversing the electric field direction, thereby facilitating helicity selection. The electrical control of magnetic helicity demonstrated in this study can be extended to other centrosymmetric skyrmion-hosting magnets, paving the way for future device designs in topological spintronics and quantum computing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Insights into the Mechanism of Methanol Steam Reforming Tandem Reaction over CeO 2 Supported Single-Site Catalysts

We demonstrated how the special synergy between a noble metal single site and neighboring oxygen vacancies provides an "ensemble reaction pool" for high hydrogen generation efficiency and carbon dioxide (CO 2 ) selectivity of a tandem reaction: methanol steam reforming. Specifically, the hydrogen generation rate over single site Ru 1 /CeO 2 catalyst is up to 9360 mol H 2 per mol Ru per hour (579 mL H2 g Ru -1 s -1 ) with 99.5% CO 2 selectivity. Reaction mechanism study showed that the integration of metal single site and O vacancies facilitated the tandem reaction, which consisted of methanol dehydrogenation, water dissociation, and the subsequent water gas shift (WGS) reaction. In addition, the strength of CO adsorption and the reaction activation energy difference between methanol dehydrogenation and WGS reaction play an important role in determining the activity and CO 2 selectivity. Our study paves the way for the further rational design of single site catalysts at the atomic scale. Furthermore, the development of such highly efficient and selective hydrogen evolution systems promises to deliver highly desirable economic and ecological benefits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2005

Assessing Potential Propulsion Breakthroughs

The term, propulsion breakthrough, refers to concepts like propellantless space drives and faster-than-light travel, the kind of breakthroughs that would make interstellar exploration practical. Although no such breakthroughs appear imminent, a variety of investigations into these goals have begun. From 1996 to 2002, NASA supported the Breakthrough Propulsion Physics Project to examine physics in the context of breakthrough spaceflight. Three facets of these assessments are now reported: (1) predicting benefits, (2) selecting research, and (3) recent technical progress. Predicting benefits is challenging since the breakthroughs are still only notional concepts, but kinetic energy can serve as a basis for comparison. In terms of kinetic energy, a hypothetical space drive could require many orders of magnitude less energy than a rocket for journeys to our nearest neighboring star. Assessing research options is challenging when the goals are beyond known physics and when the implications of success are profound. To mitigate the challenges, a selection process is described where: (a) research tasks are constrained to only address the immediate unknowns, curious effects or critical issues, (b) reliability of assertions is more important than their implications, and (c) reviewers judge credibility rather than feasibility. The recent findings of a number of tasks, some selected using this process, are discussed. Of the 14 tasks included, six reached null conclusions, four remain unresolved, and four have opportunities for sequels. A dominant theme with the sequels is research about the properties of space, inertial frames, and the quantum vacuum.

Millis, Marc G.↗
DOE OSTI2021

Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data

High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1987

A microstructural lattice model for strain oriented problems: A combined Monte Carlo finite element technique

A specialized, microstructural lattice model, termed MCFET for combined Monte Carlo Finite Element Technique, was developed which simulates microstructural evolution in material systems where modulated phases occur and the directionality of the modulation is influenced by internal and external stresses. In this approach, the microstructure is discretized onto a fine lattice. Each element in the lattice is labelled in accordance with its microstructural identity. Diffusion of material at elevated temperatures is simulated by allowing exchanges of neighboring elements if the exchange lowers the total energy of the system. A Monte Carlo approach is used to select the exchange site while the change in energy associated with stress fields is computed using a finite element technique. The MCFET analysis was validated by comparing this approach with a closed form, analytical method for stress assisted, shape changes of a single particle in an infinite matrix. Sample MCFET analytical for multiparticle problems were also run and in general the resulting microstructural changes associated with the application of an external stress are similar to that observed in Ni-Al-Cr alloys at elevated temperature.

Gayda, J.↗
NASA NTRS2003

Understanding of the Dynamics of the Stirling Convertor Advanced by Structural Testing

The NASA Glenn Research Center, the U.S. Department of Energy, and the Stirling Technology Company (STC) are developing a highly efficient, long-life, free-piston Stirling convertor for use as an advanced spacecraft power system for future NASA missions, including deep-space and Mars surface applications. As part of this development, four structural dynamic test programs were recently performed on Stirling Technology Demonstration Convertors (TDC's) that were designed and built by STC under contract to the Department of Energy. This testing was performed in Glenn's Structural Dynamics Laboratory and Microgravity Emissions Laboratory. The first test program, in November and December 1999, demonstrated that the Stirling TDC could withstand the harsh random vibration experienced during a typical spacecraft launch and survive with no structural damage or functional power performance degradation. This was a critical step in enabling the use of Stirling convertors for future spacecraft power systems. The most severe test was a 12.3grms random vibration test, with test durations of 3 min per axis. The random vibration test levels were chosen to simulate, with margin, the maximum anticipated launch vibration conditions. The Microgravity Emissions Laboratory is typically used to measure the dynamics produced by operating space experiments and the resulting impact to the International Space Station's microgravity environment. For the second Stirling dynamic test program, performed in January 2001, the Microgravity Emissions Laboratory was used to characterize the structure-borne disturbances produced by the normal operation of a pair of Stirling convertors. The forces and moments produced by the normal operation of a Stirling system must be recognized and controlled, if necessary, so that other nearby spacecraft components, such as cameras, are not adversely affected. The Stirling convertor pair emitted relatively benign tonal forces at its operational frequency and associated harmonics. Therefore, Stirling power systems will not disturb spacecraft science experiments if minimal appropriate mounting efforts are made. The third test program, performed in February and May 2001, resulted in a modal characterization of a Stirling convertor. Since the deflection of the TDC piston rod, under vibration excitation, was of particular interest, the outer pressure shell was removed to allow access to the rod. Through this testing, the Stirling TDC's natural frequencies and modes were identified. This knowledge advanced our understanding of the successful 1999 vibration test and may be utilized to optimize the output power of future Stirling designs. The fourth test program, in April 2001, was conducted to characterize the structural response of a pair of Stirling convertors, as a function of their mounting interface stiffness. The test results provide guidance for the Stirling power package interface design. Properly designed, the interface may lead to increased structural capability and power performance beyond what was demonstrated in the successful 1999 vibration test. Dynamic testing performed to date at Glenn has shown that the Stirling convertors can withstand liftoff random vibration environments and meet "good neighbor" vibratory emission requirements. Furthermore, the future utilization of the information obtained during the tests will allow the corporation selected to be the Stirling system integrator to optimize their convertor and system interfaces designs. Glenn's Thermo-Mechanical Systems Branch provides Stirling technology expertise under a Space Act Agreement with the Department of Energy. Additional vibration testing by Glenn's Structural Systems Dynamics Branch is planned to continue to demonstrate the Stirling power system's vibration capability as its technology and flight system designs progress.

Hughes, William O.↗
NASA NTRS2014

Herschel/PACS Photometry of Transiting-Planet Host Stars with Candidate Warm Debris Disks

Dust in debris disks is produced by colliding or evaporating planetesimals, which are remnants of the planet formation process. Warm dust disks, known by their emission at less than or equal to 24 micrometer, are rare (4% of FGK main sequence stars) and especially interesting because they trace material in the region likely to host terrestrial planets, where the dust has a very short dynamical lifetime. Statistical analyses of the source counts of excesses as found with the mid-IR Wide Field Infrared Survey Explorer (WISE) suggest that warm-dust candidates found for the Kepler transiting-planet host-star candidates can be explained by extragalactic or galactic background emission aligned by chance with the target stars. These statistical analyses do not exclude the possibility that a given WISE excess could be due to a transient dust population associated with the target. Here we report Herschel/PACS 100 and 160 micron follow-up observations of a sample of Kepler and non-Kepler transiting-planet candidates' host stars, with candidate WISE warm debris disks, aimed at detecting a possible cold debris disk in any one of them. No clear detections were found in any one of the objects at either wavelength. Our upper limits confirm that most objects in the sample do not have a massive debris disk like that in beta Pic. We also show that the planet-hosting star WASP-33 does not have a debris disk comparable to the one around eta Crv. Although the data cannot be used to rule out rare warm disks around the Kepler planet-hosting candidates, the lack of detections and the characteristics of neighboring emission found at far-IR wavelengths support an earlier result suggesting that most of the WISE-selected IR excesses around Kepler candidate host stars are likely due to either chance alignment with background IR-bright galaxies and/or to interstellar emission.

Wide Field Infrared Survey Explorer (WISE)↗