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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Loss Factors for Small Distributed Wind Turbines Based on Field Data in the United States

While wind energy production loss due to unavailability, environmental impacts, curtailment, and other causes has been studied and characterized at the utility-scale wind farm level, observation-based characterization of project loss is lacking for distributed wind energy, particularly for projects involving small wind turbines. Contemporary tools and research that support pre-construction distributed wind energy characterization present a wide range of default loss factors to convert gross energy estimates to net: 7-18%. We hypothesize that we can use generation observations from operational distributed wind projects to develop more accurate representations of loss. Using a density-based filtering technique on distributed wind power generation timeseries, we determine periods of typical performance and use them with regression algorithms in a measure-correlate-predict fashion to simulate what the generation would have been during periods of atypical or unreported performance. From there, the actual versus predicted generation leads to the establishment of observation-informed loss factors (median = 17%) for small, single turbine installation distributed wind projects.

17 WIND ENERGY↗
DOE OSTI2016

Distributed Generation to Support Development-Focused Climate Action

This paper explores the role of distributed generation, with a high renewable energy contribution, in supporting low emission climate-resilient development. The paper presents potential impacts on development (via energy access), greenhouse gas emission mitigation, and climate resilience directly associated with distributed generation, as well as specific actions that may enhance or increase the likelihood of climate and development benefits. This paper also seeks to provide practical and timely insights to support distributed generation policymaking and planning within the context of common climate and development goals as the distributed generation landscape rapidly evolves globally. Country-specific distributed generation policy and program examples, as well as analytical tools that can inform efforts internationally, are also highlighted throughout the paper.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Challenges in reducing the computational time of QSTS simulations for distribution system analysis.

The rapid increase in penetration of distributed energy resources on the electric power distribution system has created a need for more comprehensive interconnection modelling and impact analysis. Unlike conventional scenario - based studies , quasi - static time - series (QSTS) simulation s can realistically model time - dependent voltage controllers and the diversity of potential impacts that can occur at different times of year . However, to accurately model a distribution system with all its controllable devices, a yearlong simulation at 1 - second resolution is often required , which could take conventional computers a computational time of 10 to 120 hours when an actual unbalanced distribution feeder is modeled . This computational burden is a clear l imitation to the adoption of QSTS simulation s in interconnection studies and for determining optimal control solutions for utility operations . Our ongoing research to improve the speed of QSTS simulation has revealed many unique aspects of distribution system modelling and sequential power flow analysis that make fast QSTS a very difficult problem to solve. In this report , the most relevant challenges in reducing the computational time of QSTS simulations are presented: number of power flows to solve, circuit complexity, time dependence between time steps, multiple valid power flow solutions, controllable element interactions, and extensive accurate simulation analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Robust metal-ceramic coaxial cable sensors for distributed temperature monitoring in fossil energy power systems

The project aims to develop a new type of low cost, robust metal-ceramic coaxial cable (MCCC) Fabry-Pérot interferometer (FPI) sensor and demonstrate the capability of cascading a series of FPI sensing points in a single MCCC for real-time distributed monitoring of temperature up to 1000°C. The technical accomplishments of this project include: (i) development of metal and ceramic materials for fabricating the MCCC suitable for use in high temperature (up to 1000oC) fossil fuel, (ii) design, construction and test of single point MCCC-FPI sensors for high temperature measurement at deviation within ±2.7%, (iv) demonstration of the MCCC-FPI sensors for real-time distributed temperature measurement with desired sensitivity, spatial resolution, stability, and response speed that are important to practical applications, and (v) development of a novel MCCC-FPI sensor platform for measuring high frequency dielectric constants for solids and liquids as function of temperature. Single point MCCC-FPI sensors are fabricated based on the FPI parameters, including the relationship between reflector width and reflectance as well as the inter-reflector distance for a FPI, estimated by model calculations. MCCC-FPI sensors have been made using insulators of packed ceramic powders, ceramic tubes, and air or inert gases; outer conductors of various metal tubes; inner conductors of wires of the same metal materials as the tubes used; and reflectors of dense ceramic discs, casted ceramic discs and air gaps. The insulator, conductor and reflector materials are investigated for their thermal, chemical, and structural stabilities under temperatures up to 1000°C in simulated coal-derived syngas. Based on the experimental findings, alumina tube and discs, and stainless steel tube and wire are selected for reflector, insulator and outer and inner conductors, respectively. Air or inert gases are found to be useful for reflectors (in the form of narrow groves) or insulators (in the form of empty chamber cavity between ceramic discs) in construction of MCCC-FPI sensors. Single point MCCC-FPI sensors have been fabricated using stainless steel as conductors, dense α-alumina tubes as dielectric insulators, and two air gaps in the insulator as reflectors. The MCCC-FPI sensor with air gap reflectors has been demonstrated for measuring temperature between 200 and 500°C using microwave as sensing signal in a frequency range of 2 – 8 GHz. The temperature measurement is achieved by monitoring the frequency shift (Δƒ) of a selected resonant peak of the microwave interferogram reflected from the pair of air gap reflectors. The MCCC-FPI sensor exhibited excellent linear temperature-dependence for Δƒ, good accuracy (deviation ±2.7%), and reasonable response speed (<180 s for stabilization). Thermal drifting of the sensing signal was also rather minimal for the MCCC-FPI sensors after operating for more than one week at around 500oC. MCCC-FPI sensors with each containing 2 or 3 pairs of reflectors (i.e. 2 or 3 FPIs in a single cable) have been fabricated and demonstrated for distributed temperature measurement between 250 and 550°C in a microwave frequency range of 3-6 GHz. The measurement is achieved by monitoring the frequency shift of a selected resonant peak of the reflected microwave interferogram, which is obtained by the join-time-domain signal processing method. The results demonstrate that the FPI sensing points in both the three-point and two-point MCCC sensors can measure distributed high temperature with good reproducibility and consistency in multiple runs of measurement tests. The sensor signal displays excellent linear dependence between temperature and the corresponding frequency shift. The 2-point and 3-point MCCC-FPI sensors also exhibit fast response to environmental temperature changes. A three-point MCCC-FPI sensor has been further examined for temperature measurement up to 1000°C for multiple heating-cooling cycles and the results prove that the sensor is suitable for distributed temperature measurement up to 1000°C. MCCC-FPI sensors with more than 4 FPIs point in a single line are made in two structures: the first uses alumina tube as insulation and air gaps as reflectors; and the second uses air chamber as insulation and complete circular alumina discs (2 mm-thick) as reflectors. MCCC-FPIs in neither of the two structures are able to exceed four FPIs in a cable because the reflections from the FPIs are too strong for the microwave to transmit through >4 FPIs over a long distance. Specially shaped alumina discs made by removing parts of the circular discs are found to decrease the microwave reflectance because of reduced reflection areas. However, the smallest reflectance (in terms of percentage of the incident signal) obtained in this work is as high as 7.5%, which is still too large to construct more than 10 FPIs in a single MCCC. Nevertheless, the feasibility of the proposed multipoint MCCC-FPI has been demonstrated using air insulation and 2 mm-thick low-ε r Teflon discs as reflectors on a 2 m-long cable containing 10 FPIs (with reflectance <5% on each FPI). These results indicate that the contrast of dielectric constant between the insulation and reflector has a more significant impact on the reflectance. Thus, future research efforts should also be directed to developing ceramic materials (dense or porous) with low dielectric constant values which, when used as insulation or reflector, can realize large numbers of FPIs in a single cable over a long distance. To assist the development of high temperature dielectric materials, a novel MCCC-FPI sensor platform is developed and successfully demonstrated for measuring high frequency dielectric constants for fluids, packed dielectric solids, and ceramic tube materials and determining ε r dependencies on temperature and chemical composition, etc..

36 MATERIALS SCIENCE↗
DOE OSTI2017

The Distributed Geothermal Market Demand Model (dGeo): Documentation

The National Renewable Energy Laboratory (NREL) developed the Distributed Geothermal Market Demand Model (dGeo) as a tool to explore the potential role of geothermal distributed energy resources (DERs) in meeting thermal energy demands in the United States. The dGeo model simulates the potential for deployment of geothermal DERs in the residential and commercial sectors of the continental United States for two specific technologies: ground-source heat pumps (GHP) and geothermal direct use (DU) for district heating. To quantify the opportunity space for these technologies, dGeo leverages a highly resolved geospatial database and robust bottom-up, agent-based modeling framework. This design is consistent with others in the family of Distributed Generation Market Demand models (dGen; Sigrin et al. 2016), including the Distributed Solar Market Demand (dSolar) and Distributed Wind Market Demand (dWind) models. dGeo is intended to serve as a long-term scenario-modeling tool. It has the capability to simulate the technical potential, economic potential, market potential, and technology deployment of GHP and DU through the year 2050 under a variety of user-defined input scenarios. Through these capabilities, dGeo can provide substantial analytical value to various stakeholders interested in exploring the effects of various techno-economic, macroeconomic, financial, and policy factors related to the opportunity for GHP and DU in the United States. This report documents the dGeo modeling design, methodology, assumptions, and capabilities.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Distribution System V1G PEV Charging Impacts Report

Plug-in Electric Vehicles (PEVs) are a clean form of transportation that will require a new control paradigm between the energy supply and the transportation sectors as PEV adoption continues to grow. Meeting the projected PEV energy demands will require implementing PEV / grid integration methods beyond charging PEVs at incentivized times. MATLAB was used to simulate the daily vehicle trip demands and charging processes of hundreds of PEVs. The National Household Travel Survey (NHTS) data was used as the source of vehicle travel data, and EV Project provided measured PEV charging data to calibrate the PEV population models. GridLAB-D analyses used a prototypical feeder and PEV population models to evaluate residential PEV V1G (unidirectional) charging in multiple locations, V1G PEV distribution feeder impacts, Time-Of-Use (TOU) rate effects on PEV charging, and charging control methods. The key findings were: optimized charging using California TOU rates could result in savings of over $20 per month for non-TOU program participants; non-TOU participants consume 30-40% of PEV charging energy; about 50% of PEV drivers have their transportation fuel bill reduced by 50 dollars per month; 6.6kW charging will cause distribution transformer power limits to be exceeded on prototypical feeders if the household PEV adoption rate reaches 0.75 PEVs per home; distribution feeder loading is proportional to PEV charging rate; a method was devised to remotely identify when residential transformer overloads occur using changes in PEV charging rate; and the low-cost TOU period is composed of a distribution of PEVs needing small, medium and larger energy needs that can be more effectively shifted as groups to mitigate the TOU peak power and enable a higher PEV adoption rate per household without overloading distribution transformers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Distributed Wind Research Program Workshop Report

The U.S. Department of Energy’s (DOE’s) Wind Energy Technologies Office (WETO) Distributed Wind Research Program seeks to enable wind technology as an affordable, accessible, and compatible distributed energy resource (DER) option for individuals, businesses, and communities. WETO’s Distributed Wind Research Program includes national laboratory projects and partnerships with industry and academia. WETO organized the Distributed Wind Research Program Workshop to bring together laboratory and industry project stakeholders to achieve the following objectives: Educate - Create a shared high-level understanding for the breadth of WETO’s distributed wind research Collaborate - Facilitate coordination and collaboration between funding recipients to leverage resources Innovate - Understand future research challenges and opportunities. This workshop convened over 80 participants from various organizations. Workshop participants were asked for their individual feedback based on their own expertise and experience.

17 WIND ENERGY↗
DOE OSTI2021

Opportunities and Challenges to Capturing Distributed Battery Value via Retail Utility Rates and Programs

Distributed battery deployment is increasing with advanced metering, control, and communication technologies, leaving electric utilities with an under-utilized, flexible grid resource in aggregate. Rates can reflect locational and temporal prices while utility incentive-based programs allow DERs to provide direct grid services. However, utilities must balance accurately reflecting dynamic grid conditions versus simple and feasible design that encourages customer participation. Currently, most rates and incentive-based programs are simple, but as penetration of DER and advanced controls increase, dynamic designs could become prevalent. Utilities could encourage providing multiple services to optimize distributed battery dispatch and value streams, however, challenges persist when stacking services across distribution and bulk systems. A DER committed to multiple discrete services concurrently necessitates coordination between operators and a clear hierarchy of commitments. One way to address this is to separate commitments by time or capacity. For services that follow cyclic, predictable patterns, or those that are peak driven with predictability, an operator could ensure sufficient state of charge for participation, leaving time where a distributed battery could otherwise provide different services by segmenting participation temporally. To provide continuous or unexpected services, a battery operator may use state of charge management to reserve some percentage of the battery and segment participation by capacity. Macroeconomic trends, load patterns, generation profiles, and grid configurations drive variation in value and the subsequent implications for utility offerings and how a customer might participate. As distributed battery adoption increases, both regulators and utilities will need to ensure no adverse grid impacts and encourage provision of societal value beyond the customer domain.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from the from the Petra Nova Parish Holdings CCUS project in the West Ranch Field, Texas and the Chester-16 reef CO2 injection site in Northern Michigan which is part of the DOE Midwestern Carbon Sequestration Project. The research team is led by Texas A&M University and includes Battelle as a subcontractor with support from Shell, Anadarko, Chevron and JX Nippon. A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world’s largest post-combustion CO2 capture and utilization initiative. With a fluvial formation geology and large contrasts in permeability, this is a challenging and novel application of CO2 enhanced oil recovery (EOR). We build a predictive dynamic model of the subsurface that incorporates the multiphase and compositional data acquired during the pilot operation. The calibrated model is used for the carbon dioxide plume imaging. The study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. In the second step, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. In this study, we analyze the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. The study also shows that nonlinearity of the onset time method can be -smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. We extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warm-back analysis of the temperature data. In addition to employing simulation models and inverse methods for CO2 plume imaging, we also initialized a data-driven technology for detecting inter-well connectivity based on production and pressure data. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. Texas A&M University, the lead organization in the project, was primarily responsible for the development of tomographic approaches for CO2 plume mapping in conjunction with distributed pressure, temperature and seismic onset time data. Battelle, as a subcontractor, was primarily responsible for the development of analytical and empirical methods for analyzing transient injection rate and pressure data from point/line sources such as injection and monitoring wells. An additional area of emphasis for Battelle was the use of machine learning for such tasks as inferring reservoir connectivity information from injection-production data, and identifying variable importance for machine learning-based proxy models developed from full-physics simulations. The two organizations also collaborated on the application of the tomographic inversion methodology for a field data set.

02 PETROLEUM↗
DOE OSTI2025

DistOPF: Advanced Solutions for Distribution Optimal Power Flow Analysis - DistOPF v0.2 Documentation

To achieve an affordable and reliable energy system, research on power distribution system is often focused on integration of distributed generators, energy storage solution, EV charging, smart meters, and other advanced assets that may benefit from or require more advanced control and optimization techniques. Despite this focus on advanced distribution system topics, early researchers and grid scientists often start from scratch when developing optimization programs for power distribution systems. This report introduces DistOPF, a Python package that consolidates years of research into a versatile and modular tool. DistOPF provides researchers with essential capabilities to solve distribution system Optimal Power Flow (OPF) problems using standard network models. Additionally, it offers a platform to benchmark both new and existing algorithms against established test systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Distribution System State Estimation in the Presence of High Solar Penetration

In recent years, the low-to-medium voltage distribution networks are experiencing rising levels of distributed energy resources, including renewable generation, along with improved sensing, communication and automation infrastructure. As such, state estimation methods for distribution systems are becoming increasingly relevant as a means to enable better control strategies that can both leverage the benefits and mitigate the risks associated with high penetration of variable and uncertain distributed generation resources. Primary challenges of this problem include modeling complexities (nonlinear non-convex power-flow equations), limited availability of sensor measurements, and high penetration of uncertain renewable generation. This paper formulates the distribution system state estimation as a nonlinear weighted least squares problem, based on sensor measurements as well as forecasts data (both load and generation). We investigate the sensitivity of state estimator accuracy to (load/generation) forecast uncertainties, sensor accuracy, and sensor coverage levels.

Distribution system state estimation, nonlinear op↗
DOE OSTI2023

A Modeling Toolkit for Comparing AC and DC Electrical Distribution Efficiency in Buildings

Recently, there has been considerable research interest in the potential for DC distribution systems in buildings instead of the traditional AC distribution systems. Due to the need for performing power conversions between DC and AC electricity, DC distribution may provide electrical efficiency advantages in some systems. To support comparative evaluations of AC-only, DC-only, and hybrid AC/DC distribution systems in buildings, a new modeling toolkit called the Building Electrical Efficiency Analysis Model (BEEAM) was developed and is described in this paper. To account for harmonics in currents or voltages arising from nonlinear devices, the toolkit implements harmonic power flow, along with nonlinear device behavioral descriptions derived from empirical measurements. This paper describes the framework, network equations, device representations, and an implementation of the toolkit in an open source software package, including a component library and graphical interface for creating circuits. Simulations of electrical behavior and device and system efficiencies using the toolkit are compared with experimental measurements of a small office environment in a variety of operating and load configurations. A detailed analysis of uncertainty estimation is also provided. Key findings were that a comparison of predicted versus measured efficiencies and power losses in the validation testbed using the initial toolkit implementation predicted device- and system-level efficiencies with reasonably good accuracy under both balanced and unbalanced AC scenarios. An uncertainty analysis also revealed that the maximum estimated error for system efficiency across all scenarios was 3%, and measured and modeled system efficiency agreed within the experimental uncertainty in approximately half of the scenarios. Based on the correspondence between simulation and measurement, the toolkit is proposed by the authors as a potentially useful tool for comparing efficiency in AC, DC, and hybrid AC/DC distribution systems in buildings.

DC distribution↗
DOE OSTI2016

A statistical study of the average iron charge state distributions inside magnetic clouds for solar cycle 23

Magnetic clouds (MCs) are the interplanetary counterparts of coronal magnetic flux ropes. They can provide valuable information regarding flux rope characteristics at their eruption stage in the corona, which is unable to be explored in situ at present. In this paper, we make a comprehensive survey of the average iron charge-state (〈Q〉Fe) distributions inside 96 MCs for solar cycle 23 using Advanced Composition Explorer (ACE) data. Since the 〈Q〉Fe in the solar wind are typically around 9+ to 11+, the Fe charge state is defined as being high when the 〈Q〉Fe is larger than 12+, which implies the existence of a considerable amount of Fe ions with high charge states (e.g., ≥16+). The statistical results show that the 〈Q〉Fe distributions of 92 (∼96%) MCs can be classified into four groups with different characteristics. In group A (11 MCs), the 〈Q〉Fe shows a bi-modal distribution with both peaks being higher than 12+. Group B (4 MCs) presents a unimodal distribution of 〈Q〉Fe, with its peak being higher than 12+. In groups C (29 MCs) and D (48 MCs), the 〈Q〉Fe remains higher and lower than 12+ throughout ACE’s passage through the MC, respectively. Possible explanations of these distributions are discussed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

OSSOS. VIII. The Transition between Two Size Distribution Slopes in the Scattering Disk

The scattering trans-Neptunian Objects (TNOs) can be measured to smaller sizes than any other distant small-body population. We use the largest sample yet obtained, 68 discoveries, primarily by the Outer Solar System Origins Survey (OSSOS), to constrain the slope of its luminosity distribution, with sensitivity to much fainter absolute H-magnitudes than previous work. Using the analysis technique in Shankman et al., we confirm that a single slope for the H-distribution is not an accurate representation of the scattering TNOs and Centaurs, and that a break in the distribution is required, in support of previous conclusions. A bright-end slope of α {sub b} = 0.9 transitioning to a faint-end slope α {sub f} of 0.4–0.5 with a differential number contrast c from 1 (a knee) to 10 (a divot) provides an acceptable match to our data. We find that break magnitudes H {sub b} of 7.7 and 8.3, values both previously suggested for dynamically hot Kuiper Belt populations, are equally non-rejectable for a range of α {sub f} and c in our statistical analysis. Our preferred divot H-distribution transitions to α {sub f} = 0.5 with a divot of contrast c = 3 at H {sub b} = 8.3, while our preferred knee H-distribution transitions to α {sub f} = 0.4 at H {sub b} = 7.7. The intrinsic population of scattering TNOs required to match the OSSOS detections is 3 × 10{sup 6} for H {sub r} < 12, and 9 × 10{sup 4} for H {sub r} < 8.66 (D ≳ 100 km), with Centaurs having an intrinsic population two orders of magnitude smaller.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

New Fitting Formula for Cosmic Nonlinear Density Distribution

We have measured the probability distribution function (PDF) of a cosmic matter density field from a suite of N-body simulations. We propose the generalized normal distribution of version 2 (N{sub v2}) as an alternative fitting formula to the well-known log-normal distribution. We find that N{sub v2} provides a significantly better fit than that of the log-normal distribution for all smoothing radii (2, 5, 10, 25 [Mpc h {sup −1}]) that we studied. The improvement is substantial in the underdense regions. The development of non-Gaussianities in the cosmic matter density field is captured by continuous evolution of the skewness and shift parameters of the N{sub v2} distribution. We present the redshift evolution of these parameters for aforementioned smoothing radii and various background cosmology models. All the PDFs measured from large and high-resolution N-body simulations that we use in this study can be obtained from the web site https://astro.kias.re.kr/jhshin.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

SGP Merged Aerosol Size Distribution (CPC+SMPS+APS)

Five years (2009-2013) of data from three instrument at the SGP site were merged in order to create a new aerosol size distribution dataset. Data from the scanning mobility particle sizer (SMPS), part of the TDMA system, were combined with size distribution data from the aerodynamic particle sizer (APS). The merged size distribution from those two instruments spanned the diameter (mobility) size range between ~12 nm and ~14 um with 215 bins (Collins 2010). Total aerosol number concentrations from a TSI 3010 condensation particle counter (CPC) were then used to adjust and constrain the SMPS+APS size distributions and to extend the size distribution down to ~7 nm. Collins, D: Tandem Differential Mobility Analyzer/Aerodynamic Particular Sizer (APS) Handbook. DOE ARM Climate Research Facility. DOE/SC-ARM-TR-090, 2010.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

Neurofilament protein is differentially distributed in subpopulations of corticocortical projection neurons in the macaque monkey visual pathways

Previous studies of the primate cerebral cortex have shown that neurofilament protein is present in pyramidal neuron subpopulations displaying specific regional and laminar distribution patterns. In order to characterize further the neurochemical phenotype of the neurons furnishing feedforward and feedback pathways in the visual cortex of the macaque monkey, we performed an analysis of the distribution of neurofilament protein in corticocortical projection neurons in areas V1, V2, V3, V3A, V4, and MT. Injections of the retrogradely transported dyes Fast Blue and Diamidino Yellow were placed within areas V4 and MT, or in areas V1 and V2, in 14 adult rhesus monkeys, and the brains of these animals were processed for immunohistochemistry with an antibody to nonphosphorylated epitopes of the medium and heavy molecular weight subunits of the neurofilament protein. Overall, there was a higher proportion of neurons projecting from areas V1, V2, V3, and V3A to area MT that were neurofilament protein-immunoreactive (57-100%), than to area V4 (25-36%). In contrast, feedback projections from areas MT, V4, and V3 exhibited a more consistent proportion of neurofilament protein-containing neurons (70-80%), regardless of their target areas (V1 or V2). In addition, the vast majority of feedback neurons projecting to areas V1 and V2 were located in layers V and VI in areas V4 and MT, while they were observed in both supragranular and infragranular layers in area V3. The laminar distribution of feedforward projecting neurons was heterogeneous. In area V1, Meynert and layer IVB cells were found to project to area MT, while neurons projecting to area V4 were particularly dense in layer III within the foveal representation. In area V2, almost all neurons projecting to areas MT or V4 were located in layer III, whereas they were found in both layers II-III and V-VI in areas V3 and V3A. These results suggest that neurofilament protein identifies particular subpopulations of corticocortically projecting neurons with distinct regional and laminar distribution in the monkey visual system. It is possible that the preferential distribution of neurofilament protein within feedforward connections to area MT and all feedback projections is related to other distinctive properties of these corticocortical projection neurons.

Non-NASA Center↗
NASA NTRS2007

NavP: Structured and Multithreaded Distributed Parallel Programming

We present Navigational Programming (NavP) -- a distributed parallel programming methodology based on the principles of migrating computations and multithreading. The four major steps of NavP are: (1) Distribute the data using the data communication pattern in a given algorithm; (2) Insert navigational commands for the computation to migrate and follow large-sized distributed data; (3) Cut the sequential migrating thread and construct a mobile pipeline; and (4) Loop back for refinement. NavP is significantly different from the current prevailing Message Passing (MP) approach. The advantages of NavP include: (1) NavP is structured distributed programming and it does not change the code structure of an original algorithm. This is in sharp contrast to MP as MP implementations in general do not resemble the original sequential code; (2) NavP implementations are always competitive with the best MPI implementations in terms of performance. Approaches such as DSM or HPF have failed to deliver satisfying performance as of today in contrast, even if they are relatively easy to use compared to MP; (3) NavP provides incremental parallelization, which is beyond the reach of MP; and (4) NavP is a unifying approach that allows us to exploit both fine- (multithreading on shared memory) and coarse- (pipelined tasks on distributed memory) grained parallelism. This is in contrast to the currently popular hybrid use of MP+OpenMP, which is known to be complex to use. We present experimental results that demonstrate the effectiveness of NavP.

navigational programming (NavP)↗