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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.

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NASA NTRS1978

Coronal gas in the Galaxy. I - A new survey of interstellar O VI

Ultraviolet spectra of 40 O- and B-type stars have been intensively scanned in the vicinity of the O VI transitions at 1032 and 1038 A with the Copernicus satellite. This survey of the high-temperature (log T about 5.5) phase of interstellar gas is an extension of the earlier observations of interstellar O VI by Jenkins and Meloy (1974), bringing the total coverage up to 72 stars. Except for a few distant stars, the overall quality of the present data surpasses that of the earlier work. Plots of absorption optical depths versus radial velocity are presented for both transitions in each star. Total column densities (or their upper limits), velocity centroids, and velocity widths have been extracted from the profiles and tabulated, with the results of Jenkins and Meloy (1974) included. Qualitatively, the highly variable O VI densities and velocities seem to exhibit no systematic patterns or regional trends; there is no recognizable correlation in the behavior of coronal gas with pronounced features of galactic structure.

Jenkins, E. B.↗
NASA NTRS2019

TESS Data Release Notes: Sectors 1 – 13, Multi-Sector Search, DR20

These Data Release Notes provide information on the processing and export of data from the Transiting Exoplanet Survey Satellite (TESS). This data release is a combined, multi-sector transit search only. The underlying data products from individual observing sectors have been previously released. The data products included in this data release are the Data Validation (DV) reports, time series, and associated xml les for the threshold crossing events (TCEs) found by searching a combined data set including data from multiple observing sectors. These data products were generated by the TESS Science Processing Operations Center (SPOC, Jenkins et al., 2016) at NASA Ames Research Center from data collected by the TESS instrument, which is managed by the TESS Payload Operations Center (POC) at Massachusetts Institute of Technology (MIT). The format and content of these data products are documented in the Science Data Products Description Document (SDPDD)1. The SPOC science algorithms are based heavily on those of the Kepler Mission science pipeline, and are described in the Kepler Data Processing Handbook (Jenkins, 2017)2. The Data Validation algorithms are documented in Twicken et al. (2018) and Li et al. (2019). The TESS Instrument Handbook (Vanderspek et al., 2018)3 contains more information about the TESS instrument design, detector layout, data properties, and mission operations. The TESS Mission is funded by NASA's Science Mission Directorate.

Burke, Christopher J.↗
NASA NTRS2020

TESS Data Release Notes: Sector 21 DR 29

These Data Release Notes provide information on the processing and export of data from the Transiting Exoplanet Survey Satellite (TESS). The data products included in this data release are full frame images (FFIs), target pixel les, light curve les, collateral pixel les, cotrending basis vectors (CBVs), and Data Validation (DV) reports, time series, and associated xml les. These data products were generated by the TESS Science Processing Operations Center (SPOC, Jenkins et al., 2016) at NASA Ames Research Center from data collected by the TESS instrument, which is managed by the TESS Payload Operations Center (POC) at Massachusetts Institute of Technology (MIT). The format and content of these data products are documented in the Science Data Products Description Document (SDPDD)1. The SPOC science algorithms are based heavily on those of the Kepler Mission science pipeline, and are described in the Kepler Data Processing Handbook (Jenkins, 2017).2 The Data Validation algorithms are documented in Twicken et al. (2018) and Li et al. (2019). The TESS Instrument Handbook (Vanderspek et al., 2018) contains more information about the TESS instrument design, detector layout, data properties, and mission operations. The TESS Mission is funded by NASA's Science Mission Directorate.

Michael M. Fausnaugh↗
NASA NTRS2020

TESS Data Release Notes: Sector 24, DR35

These Data Release Notes provide information on the processing and export of data from the Transiting Exoplanet Survey Satellite (TESS). The data products included in this data release are full frame images (FFIs), target pixel fi les, light curve fi les, collateral pixel fi les, cotrending basis vectors (CBVs), and Data Validation (DV) reports, time series, and associated xml fi les. These data products were generated by the TESS Science Processing Operations Center (SPOC, Jenkins et al., 2016) at NASA Ames Research Center from data collected by the TESS instrument, which is managed by the TESS Payload Operations Center (POC) at Massachusetts Institute of Technology (MIT). The format and content of these data products are documented in the Science Data Products Description Document (SDPDD). The SPOC science algorithms are based heavily on those of the Kepler Mission science pipeline, and are described in the Kepler Data Processing Handbook (Jenkins, 2019).2 The Data Validation algorithms are documented in Twicken et al. (2018) and Li et al. (2019). The TESS Instrument Handbook (Vanderspek et al., 2018) contains more information about the TESS instrument design, detector layout, data properties, and mission operations. The TESS Mission is funded by NASA's Science Mission Directorate.

TESS↗
NASA NTRS2021

TESS Data Release Notes: Sectors 1 – 36, Multi-sector Search, DR53

These Data Release Notes provide information on the processing and export of data from the Transiting Exoplanet Survey Satellite (TESS). This data release is a combined, multi-sector transit search only. The underlying data products from individual observing sectors have been previously released. The data products included in this data release are the Data Validation (DV) reports, time series, and associated xml files for the threshold crossing events (TCEs) found by searching a combined data set including data from multiple observing sectors. These data products were generated by the TESS Science Processing Operations Center (SPOC, Jenkins et al., 2016) at NASA Ames Research Center from data collected by the TESS instrument, which is managed by the TESS Payload Operations Center (POC) at Massachusetts Institute of Technology (MIT). The format and content of these data products are documented in the Science Data Products Description Document (SDPDD)1. The SPOC science algorithms are based heavily on those of the Kepler Mission science pipeline, and are described in the Kepler Data Processing Handbook (Jenkins, 2020)2. The Data Validation algorithms are documented in Twicken et al. (2018) and Li et al. (2019). The TESS Instrument Handbook (Vanderspek et al., 2018) contains more information about the TESS instrument design, detector layout, data properties, and mission operations. The TESS Mission is funded by NASA's Science Mission Directorate.

TESS↗
DOE OSTI2025

Thoroughly testing and integrating hundreds of Pull Requests per month: ROOT’s new Cost-efficient and Feature Rich GitHub-based CI

ROOT is an open source framework, freely available on GitHub, at the heart of data acquisition, processing and analysis of HE(N)P experiments, and beyond. It is developed collaboratively: contributions are not authored only by ROOT team members, but also by the user community at large: developers and scientists from universities, labs as well as the private sector. More than 1500 GitHub Pull Requests are merged on average per year. It is in this context that code integration acquires a primary role. The review of code contributions isn’t enough: not only they need to be thoroughly reviewed, they also need to be thoroughly tested through a powerful CI infrastructure on several different platforms to comply with the high code quality standards of the project. Since the end of 2023, ROOT moved its continuous integration system from Jenkins to GitHub Actions. In this contribution, we characterise the transition to the GitHub CI, focussing on our strategy, its implementation and the lessons learned, as well as the advantages the new system offers with respect to the previous one. Particular emphasis will be given to the evaluation of the cost-benefit ratio for Jenkins and GitHub Actions for the ROOT project. We also describe how we manage to run in less than one hour thousands of unit, integration, functional and end-to-end tests on different flavours of Windows, four versions of macOS, as well as about ten of the most used Linux distributions, taking advantage of the CERN computing infrastructure.

Piparo, Danilo [CERN]↗
NASA NTRS2017

Kepler Commissioning Data for Measurement of the Pixel Response Function and Focal Plane Geometry

This document describes the Kepler PRF/FPG data release. This data was taken on April 27-29, 2009, during Kepler's commissioning phase in order to measure the pixel response function (PRF) (Bryson et al., 2010a) and focal plane geometry (FPG) (Tenenbaum and Jenkins, 2010). 33,424 stellar targets were observed for 243 long cadences, each with a duration of 14.7 minutes (half the duration of a normal Kepler long cadence). During these 243 cadences the Kepler photometer was moved, pointing in a dither pattern to facilitate PRF measurement. Motion occurred during the even cadences (second, fourth, etc.), with the telescope in stable fine point at each pointing in the dither pattern during the odd cadences (first, third, etc.). The first and last cadences were at the center of the dither pattern. Motion cadences are included in this release, but they do not contain any data. For details on how this data was used to derive the Kepler PRF and FPG models, see Bryson et al. (2010a) and Tenenbaum and Jenkins (2010). Descriptions of the PRF and FPG models are found in Thompson et al. (2016), x2.3.5.17 and x2.3.5.16 respectively. The data in this release can be used to recompute the Kepler PRF and FPG. Such a reconstruction, however, would not reflect measured seasonal changes in the PRF described in Van Cleve et al. (2016b), x5.2.The dither pattern is shown in Figure 1. The crosses show the commanded pointings and the circles show the measured pointings. Measured pointings are different from the commanded pointings due to the early state of calibration of the fine guidance sensors during commissioning (Van Cleve et al., 2016a). The measured offsets from the center of the pattern are given in RADEC offsets and pixel offsets in Table 1. The order of the offsets was randomized during data collection to avoid time-dependent systematics.

Kepler↗
NASA NTRS2017

Kepler Planet Detection Metrics: Window and One-Sigma Depth Functions for Data Release 25

This document describes the window and one-sigma depth functions relevant to the Transiting Planet Search (TPS) algorithm in the Kepler pipeline (Jenkins 2002; Jenkins et al. 2017). The window function specifies the fraction of unique orbital ephemeris epochs over which three transits are observable as a function of orbital period. In this context, the epoch and orbital period, together, comprise the ephemeris of an orbiting companion, and ephemerides with the same period are considered equivalent if their epochs differ by an integer multiple of the period. The one-sigma depth function specifies the depth of a signal (in ppm) for a given light curve that results in a one-sigma detection of a transit signature as a function of orbital period when averaged over all unique orbital ephemerides. These planet detection metrics quantify the ability of TPS to detect a transiting planet signature on a star-by-star basis. They are uniquely applicable to a specific Kepler data release, since they are dependent on the details of the light curves searched and the functionality of the TPS algorithm used to perform the search. This document describes the window and one-sigma depth functions relevant to Kepler Data Release 25 (DR25), where the data were processed (Thompson et al. 2016) and searched (Twicken et al. 2016) with the SOC 9.3 pipeline. In Section 4, we describe significant differences from those reported in Kepler Data Release 24 (Burke Seader 2016) and document our verification method.

Planet Detection Metrics↗
NASA NTRS2017

Kepler Planet Detection Metrics: Pixel-Level Transit Injection Tests of Pipeline Detection Efficiency for Data Release 25

This document describes the results of the fourth pixel-level transit injection experiment, which was designed to measure the detection efficiency of both the Kepler pipeline (Jenkins 2002, 2010; Jenkins et al. 2017) and the Robovetter (Coughlin 2017). Previous transit injection experiments are described in Christiansen et al. (2013, 2015a,b, 2016).In order to calculate planet occurrence rates using a given Kepler planet catalogue, produced with a given version of the Kepler pipeline, we need to know the detection efficiency of that pipeline. This can be empirically determined by injecting a suite of simulated transit signals into the Kepler data, processing the data through the pipeline, and examining the distribution of successfully recovered transits. This document describes the results for the pixel-level transit injection experiment performed to accompany the final Q1-Q17 Data Release 25 (DR25) catalogue (Thompson et al. 2017)of the Kepler Objects of Interest. The catalogue was generated using the SOC pipeline version 9.3 and the DR25 Robovetter acting on the uniformly processed Q1-Q17 DR25 light curves (Thompson et al. 2016a) and assuming the Q1-Q17 DR25 Kepler stellar properties (Mathur et al. 2017).

Pixel-Level Transit Injection↗
NASA NTRS1993

A white paper: NASA virtual environment research, applications, and technology

Research support for Virtual Environment technology development has been a part of NASA's human factors research program since 1985. Under the auspices of the Office of Aeronautics and Space Technology (OAST), initial funding was provided to the Aerospace Human Factors Research Division, Ames Research Center, which resulted in the origination of this technology. Since 1985, other Centers have begun using and developing this technology. At each research and space flight center, NASA missions have been major drivers of the technology. This White Paper was the joint effort of all the Centers which have been involved in the development of technology and its applications to their unique missions. Appendix A is the list of those who have worked to prepare the document, directed by Dr. Cynthia H. Null, Ames Research Center, and Dr. James P. Jenkins, NASA Headquarters. This White Paper describes the technology and its applications in NASA Centers (Chapters 1, 2 and 3), the potential roles it can take in NASA (Chapters 4 and 5), and a roadmap of the next 5 years (FY 1994-1998). The audience for this White Paper consists of managers, engineers, scientists and the general public with an interest in Virtual Environment technology. Those who read the paper will determine whether this roadmap, or others, are to be followed.

Null, Cynthia H.↗
NASA NTRS2004

Characterization of Unsteady Flow Structures Near Leading-Edge Slat: PIV Measurements - Part 1

A comprehensive computational and experimental study has been performed at the NASA Langley Research Center as part of the Quiet Aircraft Technology (QAT) Program to investigate the unsteady flow near a leading-edge slat of a two-dimensional, high-lift system. This paper focuses on the experimental effort conducted in the NASA Langley Basic Aerodynamics Research Tunnel (BART) where Particle Image Velocimetry (PIV) data was acquired in the slat cove and at the slat trailing edge of a three-element, high-lift model at 4, 6, and 8 degrees angle of attack and a freestream Mach Number of 0.17. Instantaneous velocities obtained from PIV images are used to obtain mean and fluctuating components of velocity and vorticity. The data show the recirculation in the cove, reattachment of the shear layer on the slat lower surface, and discrete vortical structures within the shear layer emanating from the slat cusp and slat trailing edge. Detailed measurements are used to examine the shear layer formation at the slat cusp, vortex shedding at the slat trailing edge, and convection of vortical structures through the slat gap. Selected results are discussed and compared with unsteady, Reynolds-Averaged Navier-Stokes (URANS) computations for the same configuration in a companion paper by Khorrami, Choudhari, and Jenkins (2004). The experimental dataset provides essential flow-field information for the validation of near-field inputs to noise prediction tools.

Jenkins, Luther N.↗
NASA NTRS2005

Characterization of Unsteady Flow Structures Around Tandem Cylinders for Component Interaction Studies in Airframe Noise

A joint computational and experimental study has been performed at NASA Langley Research Center to investigate the unsteady flow generated by the components of an aircraft landing gear system. Because the flow field surrounding a full landing gear is so complex, the study was conducted on a simplified geometry consisting of two cylinders in tandem arrangement to isolate and characterize the pertinent flow phenomena. This paper focuses on the experimental effort where surface pressures, 2-D Particle Image Velocimetry, and hot-wire anemometry were used to document the flow interaction around the two cylinders at a Reynolds Number of 1.66 x 10(exp 5), based on cylinder diameter, and cylinder spacing-todiameter ratios, L/D, of 1.435 and 3.70. Transition strips were applied to the forward cylinder to produce a turbulent boundary layer upstream of the flow separation. For these flow conditions and L/D ratios, surface pressures on both the forward and rear cylinders show the effects of L/D on flow symmetry, base pressure, and the location of flow separation and attachment. Mean velocities and instantaneous vorticity obtained from the PIV data are used to examine the flow structure between and aft of the cylinders. Shedding frequencies and spectra obtained using hot-wire anemometry are presented. These results are compared with unsteady, Reynolds-Averaged Navier-Stokes (URANS) computations for the same configuration in a companion paper by Khorrami, Choudhari, Jenkins, and McGinley (2005). The experimental dataset produced in this study provides information to better understand the mechanisms associated with component interaction noise, develop and validate time-accurate computer methods used to calculate the unsteady flow field, and assist in modeling of the radiated noise from landing gears.

Jenkins, Luther N.↗
NASA NTRS2005

Unsteady Flowfield Around Tandem Cylinders as Prototype for Component Interaction in Airframe Noise

Synergistic application of experiments and numerical simulations is crucial to understanding the underlying physics of airframe noise sources. The current effort is aimed at characterizing the details of the flow interaction between two cylinders in a tandem configuration. This setup is viewed to be representative of several component-level flow interactions that occur when air flows over the main landing gear of large civil transports. Interactions of this type are likely to have a significant impact on the noise radiation associated with the aircraft undercarriage. The paper is focused on two-dimensional, time-accurate flow simulations for the tandem cylinder configuration. Results of the unsteady Reynolds Averaged Navier-Stokes (URANS) computations with a two-equation turbulence model, at a Reynolds number of 0.166 million and a Mach number of 0.166, are presented. The experimental measurements of the same flow field are discussed in a separate paper by Jenkins, Khorrami, Choudhari, and McGinley (2005). Two distinct flow regimes of interest, associated with short and intermediate separation distances between the two cylinders, are considered. Emphasis is placed on understanding both time averaged and unsteady flow features between the two cylinders and in the wake of the rear cylinder. Predicted mean flow quantities and vortex shedding frequencies show reasonable agreement with the measured data for both cylinder spacings. Computations for short separation distance indicate decay of flow unsteadiness with time, which is not unphysical; however, the predicted sensitivity of mean lift coefficient to small angles of attack explains the asymmetric flowfield observed during the experiments.

Khorrami, Meldi R.↗
NASA NTRS2019

Passing the Torch: Kepler's Amazing Discoveries Propel NASA's TESS Mission into Orbit to Search for Earth's Closest Cousins

Our understanding of the universe has changed drastically over the past 30 years. With the launch of NASA Ames' Kepler spacecraft in 2009, remarkable progress has been made in discovering planets orbiting other stars. Recent innovations in astronomy enable us to pursue one of humanity's greatest questions; Are we alone in the Universe? From the Kepler Mission to NASA's Transiting Exoplanet Survey Satellite (TESS), vast data collection with new telescopes will revolutionize the fields of asteroseismology and exoplanetary science. Dr. Jon Jenkins showcases the accomplishments of the Kepler Mission, the new discoveries being made by the TESS, and describes the future of exoplanet research.

transit photometry↗
NASA NTRS2020

A NASA Space Scientist/Astronomer Searching for Habitable Worlds

Dr. Jon M. Jenkins presents a career day talk at Saratoga High School discussing the skill sets needed to pursue a career at NASA as a research scientist/manager. He reviews his path to his career at NASA, discusses NASA's Kepler and TESS Missions and NASA's future missions to study and characterize exoplanets.

Jenkins, Jon M.↗
DOE OSTI2025

dCache CI/CD migration to Kubernetes

For over two decades, the dCache project has provided open-source to satisfy ever-more demanding storage requirements. More than 80 sites worldwide rely on dCache to provide services for LHC experiments, Belle-II, Eu- XFEL, and others. This can be achieved only with a well-established process from a whiteboard, where ideas are created through development, packaging, and testing. The project’s build and test infrastructure is based on Jenkins CI and a set of virtual machines. This infrastructure is maintained by dCache developers. With the introduction of the DESY-central Gitlab server, the developers have started migrating from VM-based testing to container-based deployments in the onsite Kubernetes cluster. As a result, we have packaged dCache containers and Helm charts that can be used by other sites to reproduce our test and build steps quickly or to evaluate new releases on their pre-production systems and, eventually, become a standard model of dCache deployment at the sites. This paper describes the challenges we have faced, the techniques we used to solve them, and the issues that still need to be addressed.

Mkrtchyan, Tigran [DESY]↗
NASA NTRS1975

An algorithm and computer program to locate real zeros of real polynomials

A method for reliably extracting real zeros of real polynomials using an expanded two-point secant and bisection method is formed into an algorithm for a digital computer, and a computer program based on this algorithm is presented. The results obtained with the program show that the proposed method compares favorably with the Laguerre, Newton-Raphson, and Jenkins-Traub methods when the polynomial has all real zeros, and is more efficient when the polynomial has complex zeros.

Hedgley, D. R., Jr.↗
NASA NTRS1980

Statistical Analysis of daily mean temperatures

Data of daily mean temperatures recorded at the Kennedy Center during the period of 1957-1977 were analyzed to forecast daily mean temperatures and their thirty-day moving averages for a period of ten to fifteen days in a given month. Since it is found that the standard deviation is linear in the mean, a logarithmic transformation of the data is used for finding an integrated moving average process IMA by the Box-Jenkins aproach. The first differences of the transformed data seem to fit a moving average model with parameter value 2, MA(2). The consideration of seasonality factor makes the fit worse.

Ross, D. C.↗