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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 OSTI2018

MSiReader v1.0: Evolving Open-Source Mass Spectrometry Imaging Software for Targeted and Untargeted Analyses

A major update to the mass spectrometry imaging (MSI) software MSiReader is presented, offering a multitude of newly added features critical to MSI analyses. MSiReader is a free, open-source, and vendor-neutral software written in the MATLAB platform and is capable of analyzing most common MSI data formats. A standalone version of the software, which does not require a MATLAB license, is also distributed. The newly incorporated data analysis features expand the utility of MSiReader beyond simple visualization of molecular distributions. The MSiQuantification tool allows researchers to calculate absolute concentrations from quantification MSI experiments exclusively through MSiReader software, significantly reducing data analysis time. An image overlay feature allows the incorporation of complementary imaging modalities to be displayed with the MSI data. A polarity filter has also been incorporated into the data loading step, allowing the facile analysis of polarity switching experiments without the need for data parsing prior to loading the data file into MSiReader. A quality assurance feature to generate a mass measurement accuracy (MMA) heatmap for an analyte of interest has also been added to allow for the investigation of MMA across the imaging experiment. Most importantly, as new features have been added performance has not degraded, in fact it has been dramatically improved. These new tools and the improvements to the performance in MSiReader v1.0 enable the MSI community to evaluate their data in greater depth and in less time. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

How robust are estimates of key parameters in standard viral dynamic models?

Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Identification of aggregate building thermal dynamic model and unmeasured internal heat load from data

This paper is on the problem of simultaneously identifying the parameters of an aggregate thermal dynamic model of a multi-zone building and unknown disturbances from input-output data. An aggregate model is a single-zone equivalent of a multi-zone building, and is useful for many purposes, including model based control of large heating, ventilation and air conditioning (HVAC) equipment that delivers thermal energy to the entire building. A key challenge in identification is the presence of unknown disturbance since it is not measurable but non-negligible.We first present a principled method to aggregate a multizone building model into a single zone model. We then provide a method to identify thermal parameters and the unknown disturbance for this aggregate (single-zone) model. Finally, we test our proposed identification algorithm to data generated from a virtual building. A key insight provided by the aggregation method allows us to recognize under what conditions the estimation of the disturbance signal will be necessarily poor and uncertain.

Guo, Zhong↗
DOE OSTI2018

Alpha Air Sample Counting Efficiency Versus Dust Loading: Evaluation of a Large Data Set

Dust loading on air sample filters is known to cause a loss of efficiency for direct counting of alpha activity on the filters, but the amount of dust loading and the correction factor needed to account for attenuated alpha particles is difficult to assess. In this paper, correction factors are developed by statistical analysis of a large database of air sample results for a uranium and plutonium processing facility at the Savannah River Site. As is typically the case, dust-loading data is not directly available, but sample volume is found to be a reasonable proxy measure; the amount of dust loading is inferred by a combination of the derived correction factors and a Monte Carlo model. The technique compares the distribution of activity ratios [beta/(beta + alpha)] by volume and applies a range of correction factors on the raw alpha count rate. The best-fit results with this method are compared with MCNP modeling of activity uniformly deposited in the dust and analytical laboratory results of digested filters. Finally, a linear fit is proposed to evenly-deposited alpha activity collected on filters with dust loading over a range of about 2 mg cm -2 to 1,000 mg cm -2 .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Data-Driven Distribution System Load Modeling for Quasi-Static Time-Series Simulation

This paper presents a data-driven distribution system load modeling methodology targeting quasi-static time-series (QSTS) simulation. The proposed methodology is appropriate for modeling down to the level of the customer transformer, and it has three main features: 1) both load pattern diversity and intra-second load variability are considered, 2) the load profiles can be populated for multiple nodes on a circuit in such a way that the diversity factor of the feeder can be defined and tuned, and 3) the load aggregation method can be used to populate the profiles for different nodes at various load aggregation levels. Furthermore, as the foundation of the modeling methodology, variability and diversity libraries have been established based on high-resolution load data collected on customer transformers from real utility feeders. The proposed modeling methodology has been used to build load data sets for both the IEEE 123-bus feeder model and a realistic utility feeder model. The QSTS simulation results on the two evaluation feeders have demonstrated that the load data sets established from the proposed modeling methodology can effectively capture the load impact on feeder operations. For the realistic utility feeder, the effectiveness of the proposed methodology has also been validated by comparing the voltage characteristics of the feeder with modeled loads and the voltage characteristics of realistic voltage data from the same feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Building Energy Baseline Analysis Package

The package includes several programs that perform calculations involving time-series energy data (also known as "interval load data", including "smart meter" and "green button" data). The software is written in Python and R and requires both of those languages. It also requires Python packages (??), and the RT "optparse" library.

Price, Phillip↗
DOE OSTI2017

Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data

Current practice in whole time series clustering of residential meter data focuses on aggregated or subsampled load data at the customer level, which ignores day-to-day differences within customers. This information is critical to determine each customer’s suitability to various demand side management strategies that support intelligent power grids and smart energy management. Clustering daily load shapes provides fine-grained information on customer attributes and sources of variation for subsequent models and customer segmentation. In this paper, we apply 11 clustering methods to daily residential meter data. We evaluate their parameter settings and suitability based on 6 generic performance metrics and post-checking of resulting clusters. Finally, we recommend suitable techniques and parameters based on the goal of discovering diverse daily load patterns among residential customers. To the authors’ knowledge, this paper is the first robust comparative review of clustering techniques applied to daily residential load shape time series in the power systems’ literature.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

Residential electricity load profiles and their diversity have become increasingly important to realize the benefits of Smart or Transactive Energy Networks (TENs). An important element of TENs will be practical, accurate, and implementable residential load forecasting techniques. While there have been many approaches to short-term load forecasting, few have included forecasting for individual households, partly because the high volatility and idiosyncrasies present in individual household load data can pose significant challenges. In this study, we develop a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features (termed a CLSAF model) to improve short-term household electricity load forecasting accuracy by employing three strategies: autoregressive features selection, exogenous features selection, and a “default” state to avoid overfitting at times of high load volatility. We include aggregations of apartments to floor and building level, because utilities may favor transactive approaches that rely on aggregator models, e.g., a cluster of consumers as opposed to an individual. We demonstrate that the CLSAF model, by virtue of its enhanced feature representation and modest computational resources, can accomplish load forecasting in a multi-family residential building across three spatial granularities (individual apartment/household, floor, and building levels), with an accuracy improvement of up to 25% compared to a persistence model. We propose a data screening technique to characterize time-series electricity-load data. This technique is suitable for integration into a TEN ecosystem and allows one to estimate confidence levels of the load forecasts to optimize computational resources and the risks associated with uncertain forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

AGC-4 Experiment Irradiation Monitoring Data Qualification Interim Report

The Graphite Technology Development Program is running a series of six experiments to quantify the effects of irradiation on nuclear grade graphite. The fourth experiment, Advanced Graphite Creep 4 (AGC 4), began with Advanced Test Reactor (ATR) cycle 157D on May 30, 2015, and has been irradiated for two cycles. The capsule was removed from the reactor after ATR cycle 158A, which ended on January 2, 2016, due to interference with another experiment. Irradiation will resume when the interfering experiment is removed from the reactor. This report documents qualification of AGC 4 experiment irradiation monitoring data for use by the Advanced Reactor Technologies (ART) Technology Development Office (TDO) Program for research and development activities required to design and license the first HTR nuclear plant. Qualified data meet the requirements for use as described in the experiment planning and quality assurance documents. Failed data do not meet the requirements and provide no useable information. Trend data may not meet all requirements, but still provide some useable information. Use of Trend data requires assessment of how any deficiencies affect a particular use of the data. All thermocouples (TCs) have functioned throughout the AGC-4 experiment. All temperature data are Qualified for use by the ART TDO Program. Argon, helium, and total gas flow data were within expected ranges and are Qualified for use by the ART TDO Program. Discharge gas line moisture values were consistently low during cycle 157D. At the start of cycle 158A, gas moisture briefly spiked to over 600 ppmv and then declined throughout the cycle. Moisture values are within the measurement range of the instrument and are Qualified for use by the ART TDO Program. Graphite creep specimens were subjected to one of three loads, 393, 491, or 589 lbf. For a brief period during cycle 157D between 12:19 on June 2, 2015 and 08:23 on June 11, 2015 the load cells were wired incorrectly resulting in missing stack load data. Missing stack loads were estimated from measured ram pressures using regression equations developed from the existing data from cycle 157D. Estimated stack loads during this period are considered to be an accurate representation of actual load applied to the stacks. These loads deviate slightly from the planned loads. This deviation does not prevent the data from being Qualified for use, but must be taken into account when analyzing the effect of load on creep. Stack displacement increased consistently throughout the first two cycles with total displacement ranging from 0.4 to 0.8 in. During ATR outages, a set of pneumatic rams raised the stacks of graphite creep specimens to ensure the specimens were not stuck within the test train. This stack raising was performed twice. All stacks were raised successfully each time. The load and displacement data are Qualified for use by the ART TDO Program.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Collective Analysis of Alpha Particle Losses Due to Self-Absorption by Mass Loading on Radioactive Particulate Glass Fiber Filters

In this study, we derived a relationship between filter mass loading and the percent loss during analysis using the mass loading data collected from six previous studies of self-absorption. Components of mass loading include particulate dust, radioactive particulates, and filter material. In a research report published in 1984, Higby calculated a minimum burial depth for an alpha particle to be lost due to absorption (100% loss) of about 3.7 mg/cm 2 based on calculations for the range of 239-Pu alpha particles in glass fiber filters. From there, Higby concluded that a correction factor of 0.85 assumes approximately 15% losses in the count rate of both alpha and beta particles. In 2000, Luetzelschwab et al. recommended assuming a 40% loss at a loading of 3.3 mg/cm 2 and a 28% loss for a loading of 2.3 mg/cm 2 which included the frontal face mass of the filter. More recently, the 100% losses due to absorption were reported to be in the 10 mg/cm 2 range. Presented here is a trinomial relationship method of relating percent loss due to self-absorption to filter mass loading, based on data reported by Higby, Luetzelschwab et al., Huang et al., Barnett et al., Smith et al., and Hogue et al. Under normal operating conditions at the stacks monitored by Effluent Management, the mass loading of sample filters averages 0.09 ± 0.12 (2s) mg/cm 2 (excluding negative values and outliers) and ranges from 0 mg/cm 2 to 0.24 mg/cm 2 . Based on current mass loading results for Effluent Management stack sample filters, the forced-zero trinomial relationship method estimated self-absorption losses of less than 5%. Because American National Standards Institute/Health Physics Society N13.1-2011 guidelines indicate a correction factor should be used when the penetration of radioactive material into the collection media or self-absorption of radiation by the material collected would reduce the count rate by more than 5%, it is possible continued application of a correction factor to the Effluent Management stack samples is no longer necessary. Nevertheless, continuing to assign a correction factor at the 5% threshold (i.e., 0.95) would be a conservative approach.

36 MATERIALS SCIENCE↗
DOE OSTI2018

DWT-Based Aggregated Load Modeling and Evaluation for Quasi-Static Time-Series Simulation on Distribution Feeders

This paper presents an extension of a previously reported discrete wavelet transform (DWT) based load modeling methodology which targets at modeling time-series load profiles to enable the more effective and realistic quasi-static time-series simulation of distribution feeders. The time-series load model is composed of two major parts: 1) The low-resolution field-measured load data which usually have a resolution of 30 or 15 min., 2) The high-resolution load variability model data extracted from the established variability database which can have resolution up to 1 sec. A load aggregation methodology is developed to aggregate the load profiles so that load profiles at different transformer ratings can be effectively modeled. Validation, evaluation, and analysis of the developed load modeling approach has been performed on the IEEE-123 feeder and an actual utility feeder from California. The analysis completed on the two feeders have demonstrated the effectiveness and revealed the value of the developed model for distribution feeder quasi-static time-series simulation at high temporal resolution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

DWT-Based Aggregated Load Modeling and Evaluation for Quasi-Static Time-Series Simulation on Distribution Feeders: Preprint

This paper presents an extension of a previously reported discrete wavelet transform (DWT) based load modeling methodology which targets at modeling time-series load profiles to enable the more effective and realistic quasi-static time-series simulation of distribution feeders. The time-series load model is composed of two major parts: 1) The low-resolution field-measured load data which usually have a resolution of 30 or 15 min., 2) The high-resolution load variability model data extracted from the established variability database which can have resolution up to 1 sec. A load aggregation methodology is developed to aggregate the load profiles so that load profiles at different transformer ratings can be effectively modeled. Validation, evaluation, and analysis of the developed load modeling approach has been performed on the IEEE-123 feeder and an actual utility feeder from California. The analysis completed on the two feeders have demonstrated the effectiveness and revealed the value of the developed model for distribution feeder quasi-static time-series simulation at high temporal resolution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

AGC-4 Experiment Irradiation Monitoring Data Qualification Final Report

The Graphite Technology Development Program ran a series of six experiments to quantify the effects of irradiation on nuclear grade graphite. This report focuses on the fourth experiment, Advanced Graphite Creep 4 (AGC 4). The Advanced Reactor Development (ARD) Technology Development Office (TDO) Program for research and development activities require documentation of qualified monitoring data to design and license the first high-temperature reactor nuclear plant. Qualified data meets the requirements for use as described in the experiment planning and quality assurance documents. Failed data do not meet the requirements and provide no useable information. Trend data may not meet all requirements but still provide some useable information. Use of Trend data requires assessment of how any deficiencies affect a particular use of the data. AGC-4 began with Advanced Test Reactor (ATR) Cycle 157D on May 30, 2015. After irradiating the graphite for two cycles, the capsule was removed from the reactor after ATR Cycle 158A, which ended on January 2, 2016, due to interference with another experiment. Irradiation was resumed with Cycle 162A on October 7, 2017 after the interfering experiment was removed from the reactor. AGC-4 irradiation completed by the end of Cycle 166B on January 10, 2020. Between Cycles 162A and 166B, AGC-4 capsule was removed from the reactor core during two Powered Axial Locator Mechanism (PALM) Cycles 163A and 165A to avoid overheating. All thermocouples (TCs) have functioned throughout the AGC-4 experiment. A total of 9,256,184 out of all 11,167,213 temperature records (or 82.9%) are Qualified for use by the ARD Program and 1,911,029 missing values are Failed. Argon, helium, and total gas flow data were within expected ranges, except only eight out-of-range values occurred during outages. A total of 21,240,627 out of all 22,288,5103 gas flow rates (or 92.8%) are Qualified for use by the ARD Program and 1,644,476 are Failed records mostly due to missing values. Discharge gas line moisture values were consistently low during full ATR power, except for 162B, when moisture content was increasing to more than 200 ppmv by the end of this cycle. During outages, moisture content reached as high as 700 ppmv. 1,089,941 out of a total of 1,107,331 moisture values (or 98.4%) are within the measurement range of the instrument and are Qualified for use by the ARD Program and 17,390 missing moisture values are Failed. Graphite creep specimens were subjected to one of three loads: 393, 491, or 589 lbf. For a brief period during Cycle 157D between 12:19 on June 2, 2015, and 08:23 on June 11, 2015, the load cells were wired incorrectly, resulting in missing stack load data. Missing stack loads were estimated from measured ram pressures using regression equations developed from the existing data from Cycle 157D. Estimated stack loads during this period are considered to be an accurate representation of actual load applied to the stacks. These loads deviate slightly from the planned loads. This deviation does not prevent the data from being Qualified for use but must be taken into account when analyzing the effect of load on creep. 6,095,728 out of a total of 6,403,985 moisture values (or 95.2%) are within the measurement range of the instrument and are Qualified for use by the ARD Program and 308,257 missing load values are Failed. Stack displacement increased consistently throughout the eight cycles, with total displacement reached highest value of 2.4 in by the end of irradiation. During ATR outages, a set of pneumatic rams raised the stacks of graphite creep specimens to ensure the specimens were not stuck within the test train. This stack raising was performed seven times throughout irradiation. All stacks were raised successfully each time. 4,744,974 out of a total of 6,094,513 displacement values (or 77.9%) are within the measurement range of the instrument and are Qualified for use by the ARD Program and 1,349,539 displacement values are Failed mostly due to missing values. Analyses were conducted on correlations between TCs to look for trends and step changes that might indicate instrument degradation or failure. Correlation analysis was used to identify instances when TCs form short circuits, referred to as virtual junctions, which result in TCs reporting temperatures from some location in the capsule other than the location where they were intended to read. No evidence of virtual junctions was found. Analyses were also conducted on control charts of temperature differences of between two TCs, which are expected to behave consistently throughout the entire irradiation period. Upward or downward trend over time indicates at least one TC in the pair was drifted. Examining control charts for all 66 possible pairs out of twelve TCs installed in the AGC-4 capsules reveal no clear drift failures occurred, except unstable behavior of two TCs in Zone 3, TC-7 and TC-8, over irradiation time. In conclus

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

EDXplorer: A Utility for APA Analysis

The automated particle analysis (APA) method of scanning electron microscopy (SEM) energy dispersive X-ray spectroscopy (EDS/EDX) is a useful tool for analyzing the elemental and morphological data of particulate samples. Often, such datasets have many thousands of particles, and it can be difficult to sift through the data to find meaningful trends. EDXplorer is a software utility for processing APA data. They enable the user to easily load data and determine the important components and aspects of the datasets using a powerful and versatile library of data plotting functions, mainly centered around scatter plots and histograms. These programs are intended to fill a void in the data processing of data from certain instruments, where often the user must rely on their own code or other software that is not user-friendly. EDXplorer is intended as a general plotting utility for browsing through data and discovering data trends.

Moseley, Duncan [ORNL] (ORCID:0000000343518347)↗
DOE OSTI2013

IDEA Clean Energy Application Center

The DOE Clean Energy Application Centers were launched with a goal of focusing on important aspects of our nation’s energy supply including Efficiency, Reliability and Resiliency. Clean Energy solutions based on Combined Heat & Power (CHP), District Energy and Waste Heat Recovery are at the core of ensuring a reliable and efficient energy infrastructure for campuses, communities, and industry and public enterprises across the country. IDEA members which include colleges and universities, hospitals, airports, downtown utilities as well as manufacturers, suppliers and service providers have long-standing expertise in the planning, design, construction and operations of Clean Energy systems. They represent an established base of successful projects and systems at scale and serve important and critical energy loads. They also offer experience, lessons learned and best practices which are of immense value to the sustained growth of the Clean Energy sector. IDEA has been able to leverage the funds from the project award to raise the visibility, improve the understanding and increase deployment CHP, District Energy and Waste Heat Recovery solutions across the regions of our nation, in collaboration with the regional CEAC’s. On August 30, 2012, President Obama signed an Executive Order to accelerate investments in industrial energy efficiency (EE), including CHP and set a national goal of 40 GW of new CHP installation over the next decade IDEA is pleased to have been able to support this Executive Order in a variety of ways including raising awareness of the goal through educational workshops and Conferences and recognizing the installation of large scale CHP and district energy systems. A supporting key area of collaboration has involved IDEA providing technical assistance on District Energy/CHP project screenings and feasibility to the CEAC’s for multi building, multi-use projects. The award was instrumental in the development of a first-order screening/feasibility tool for these types of community energy projects. The Excel based tool incorporates hourly climate based building loads data to arrive at the composite energy demand for the district and compares the Net Present Value (NPV) of the costs of CHP/DE alternatives. This tool has been used to provide assistance to several projects in the Northeast, Mid-Atlantic, Intermountain and Pacific Regions. The tool was disseminated to the CEACs and supplemented by a Training Webinar and a How to Guide IDEA produced a US Community Energy Development Guide to support mayors, planners, community leaders, real estate developers and economic development officials who are interested in planning more sustainable urban energy infrastructure, creating community energy master plans and implementing CHP/ District Energy systems in cities, communities and towns. IDEA has collected industry data and provided a comprehensive data set containing information on District Energy installations in the US. District energy systems are present in 49 states and theDistrict of Columbia. Of the 597 systems 55% were DE alone while the remainder was some combination of CHP, district heating, and district cooling. District energy systems that do not currently involve electric generation are strong near-term candidates for the adoption of CHP due to the magnitude of their aggregated thermal load. This data has helped inform specific and targeted initiatives including technical assistance provided by the CEAC’s for EPA’s Boiler MACT Compliance by large District Heating System boilers. These outcomes have been greatly enabled by the close coordination and collaboration with DOE CEAC leadership and with the eight regional US DOE Clean Energy Application Centers and the award’s incremental funding has allowed IDEA to leverage our resources to be an effective champion for Clean Energy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗