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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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At least 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

How to see hidden patterns in metamaterials with interpretable machine learning

Machine learning models can assist with metamaterials design by approximating computationally expensive simulators or solving inverse design problems. However, past work has usually relied on black box deep neural networks, whose reasoning processes are opaque and require enormous datasets that are expensive to obtain. Here, in this work, we develop two novel machine learning approaches to metamaterials discovery that have neither of these disadvantages. These approaches, called shape-frequency features and unit-cell templates, can discover 2D metamaterials with user-specified frequency band gaps. Our approaches provide logical rule-based conditions on metamaterial unit-cells that allow for interpretable reasoning processes, and generalize well across design spaces of different resolutions. The templates also provide design flexibility where users can almost freely design the fine resolution features of a unit-cell without affecting the user’s desired band gap.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Revealing Occupancy Patterns in an Office Building through the Use of Occupancy Sensor Data

Energy simulation programs like DOE-2 and EnergyPlus are tools that have been proven to aid with energy calculations to predict energy use in buildings. Some inputs to energy simulation models are relatively easy to find, including building size, orientation, construction materials, and HVAC system size and type. Others vary with time (e.g. weather and occupancy) and some can be a challenge to estimate in order to create an accurate simulation. In this paper, the analysis of occupancy sensor data for a large commercial, multi-tenant office building is presented. It details occupancy diversity factors for private offices and summarizes the same for open offices, hallways, conference rooms, break rooms, and restrooms in order to better inform energy simulation parameters. Long-term data were collected allowing results to be presented to show variations of occupancy diversity factors in private offices for time of day, day of the week, holidays, and month of the year. The diversity factors presented differ as much as 46% from those currently published in ASHRAE 90.1 2004 energy cost method guidelines, a document referenced by energy modelers regarding occupancy diversity factors for simulations. This may result in misleading simulation results and may introduce inefficiencies in the final equipment and systems design.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Species- and element-specific patterns of metal flux from contaminated wetlands versus metals shed with exuviae in emerging dragonflies

Dragonfly adults and their aquatic immature stages are important parts of food webs and provide a link between aquatic and terrestrial components. During emergence, contaminants can be exported into terrestrial food webs as immature adults fly away or be shed with their exuviae and remain in the wetland. Our previous work established metals accumulating in dragonfly nymphs throughout a contaminated constructed wetland designed to regulate pH and sequester trace metals from an industrial effluent line. In this paper we evaluated the concentration and mass of metals leaving the wetland in flying emergents versus remaining in the wetland with the shed exuviae in 10 species of dragonflies belonging to 8 genera. Nine elements (Cu, Zn, Cd, Mn, V, Mg, Fe, Al, Pb) were evaluated that include essential and nonessential elements as well as trace and major metals. Metal concentrations in the emergent body and exuviae can differ by orders of magnitude. Aluminum, Fe, Mn, and Pb were largely shed in the exuviae. Vanadium and Cd were more variable among species but also tended to be shed with the exuviae. In contrast, Cu, Zn, and Mg showed a higher tendency to leave the wetland with an emerging dragonfly. Metals shed in dragonfly exuviae can moderate the transport of metals from contaminated wetlands. Taxonomic- and metal-specific variability in daily metal flux from the wetland depended upon concentration accumulated, individual body mass, and number of individuals emerging, with each factor's relative importance often differing among species. This illustrates the importance of evaluating the mass of metals in an individual and not only concentrations. Furthermore, differences in numbers of each species emerging will magnify differences in individual metal flux when calculating community metal flux. A better understanding of the variability of metal accumulation in nymphs/larvae and metal shedding during metamorphosis among both metals and species is needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Adverse pregnancy outcomes in women with changing patterns of exposure to the emissions of a municipal waste incinerator

Highlights: • Reproductive health outcomes are suitable for risk assessment of a waste incinerator. • Emissions from a municipal solid waste incinerator did not increase miscarriage rate. • Emissions from a municipal solid waste incinerator did not increase birth defects. Municipal solid waste incinerators emissions contain pollutants that, despite their low concentration, might adversely affect reproductive health. In the present study, we examined rates of miscarriage and birth defects among women who resided or were employed in the vicinity of a municipal solid waste incinerator plant from 2003 to 2013. In 2009, a progressive shutdown of the old incineration lines and operation of a new line caused considerably higher atmospheric release of polycyclic aromatic hydrocarbons, particularly of dioxins, due to these irregular operating conditions, technological renovation, and increased capacity. We used dioxin emission levels, based on a dispersion model, to define exposure status of the residing population to air pollutants emitted by the waste incinerator. In women who resided in areas characterized by higher emission exposures compared with a referent area, the relative risk (RR) of miscarriage was 1.04 (95% confidence interval (CI) 0.80–1.32) based on 62 cases overall, with little evidence of a dose-response relation. RRs were similarly null for both 2003–2008 and 2010–2013 periods (RR 1.12 (95% CI 0.80–1.53) and 0.98 (95% CI 0.63–1.48), respectively). Concerning birth defects in the offspring of women residing in the exposed area, no evidence of increased risk emerged, since the prevalence ratio at birth was 0.64 (95% CI 0.29–1.26), with comparable results in the 2003–2008 and 2010–2013 period. Corresponding analyses carried out in municipal residents who worked in the exposed area confirmed these findings. We also did not detect abnormally high rates of miscarriage and birth defects in the exposed cohorts in the single year 2009. Overall, these results do not suggest an effect of exposure to the emissions of the municipal solid waste incinerator we investigated on two indicators of reproductive health. However, the limited statistical stability of the estimates and the absence of individual-based information on some potential confounders suggest caution in the interpretation of study findings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Google search patterns monitoring the daily health impact of heatwaves in England: How do the findings compare to established syndromic surveillance systems from 2013 to 2017?

Highlights: • Heat health-related Google search term frequency increased during heatwave events. • Google data correlated well with validated syndromic heat health indicators. • A similar timeliness was seen between daily Google and syndromic data in England. • Google search data could be of benefit in monitoring heat-related morbidity. One of the implications of climate change is a predicted increase in frequent and severe heatwaves. The impact of heatwaves on the health of the population is captured through real-time syndromic healthcare surveillance systems monitored daily in England during the summer months. Internet search data could potentially provide improved timeliness and help to assess the wider population health impact of heat by capturing a population sub-group who are symptomatic but do not seek healthcare. A retrospective observational study was carried out from June 2013 to September 2017 in England to compare daily trends in validated syndromic surveillance heat-related morbidity indicators against symptom-based heatwave related Google search terms. The degree of correlation was determined with Spearman correlation coefficients and lag assessment was carried out to determine timeliness. Daily increases in frequency in Google search terms during heatwave events correlated well with validated syndromic indicators. Correlation coefficients between search term frequency and syndromic indicators from 2013 to 2017 were highest with the telehealth service NHS 111 (range of 0.684–0.900 by search term). Lag analysis revealed a similar timeliness between the data sources, suggesting Google data did not provide a delayed or earlier signal in the context of England's syndromic surveillance systems. This work highlights the potential benefits for countries which lack established public health surveillance systems to monitor heat-related morbidity and the use of internet search data to assess the wider population health impact of exposure to heat.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States

Demand-response programs can help utilities manage rapidly evolving electric grids, but these programs are subject to the complexities of human behavior. This paper explores a novel method for uncovering heterogeneity in households. In this work, we use a machine-learning method known as a Conditional Inference Tree (c-tree) algorithm to categorize households based on their energy behavior characteristics collected via smart meters, and explore how this translates through into heterogeneity in their real-world response to a DR program. Using data from randomized controlled trial, we generate estimates of the changes in energy use caused by the program within each household group. Our results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods. Thus, the c-tree analysis enables the most tailored targeting of major potential energy savers and could provide the greatest increase in cost-effectiveness of household recruitment into DR programs. Our results also offer fresh insights into the relationships between household energy behavior characteristics – such as peak energy use and “structural winningness” (the ability to save money under a DR program without changing energy-use behaviors) – and household decisions about enrolling in DR programs and reducing energy use. Our research also demonstrates the potential of smart meter data, combined with machine learning and econometric methods, to provide significant value to utilities, program implementers, researchers, and other stakeholders.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Detecting technological maturity from bibliometric patterns

We report the capability to identify emergent technologies based upon easily accessed open-source indicators, such as publications, is important for decision-makers in industry and government. The scientific contribution of this work is the proposition of a machine learning approach to the detection of the maturity of emerging technologies based on publication counts. Time-series of publication counts have universal features that distinguish emerging and growing technologies. We train an artificial neural network classifier, a supervised machine learning algorithm, upon these features to predict the maturity (emergent vs. growth) of an arbitrary technology. With a training set comprised of 22 technologies we obtain a classification accuracy ranging from 58.3% to 100% with an average accuracy of 84.6% for six test technologies. To enhance classifier performance, we augmented the training corpus with synthetic time-series technology life cycle curves, formed by calculating weighted averages of curves in the original training set. Training the classifier on the synthetic data set resulted in improved accuracy, ranging from 83.3% to 100% with an average accuracy of 90.4% for the test technologies. The performance of our classifier exceeds that of competing machine learning approaches in the literature, which report an average classification accuracy of only 85.7% at maximum. Moreover, in contrast to current methods our approach does not require subject matter expertise to generate training labels, and it can be automated and scaled.

97 MATHEMATICS AND COMPUTING↗