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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Precipitation and nitrogen application stimulate soil nitrous oxide emission

Precipitation and nitrogen (N) fertilization are the two most important drivers for soil nitrous oxide (N 2 O) emission. However, the effects of changes in N fertilization and precipitation patterns (i.e., precipitation intensity and frequency) on N 2 O emissions in agricultural fields are still unclear. In this study, we simulated soil N 2 O emission under different precipitation patterns (6 precipitation intensities, and 12 precipitation frequencies by either merging or splitting precipitation events) and N fertilization rates (low, typical, and high N fertilization) in a cornfield using the DeNitrification-DeComposition model. The model was parameterized and validated using meteorological data and N experimental measurements in Nashville, Tennessee, USA. Results showed that soil water filled pore space (WFPS) and simulated soil N 2 O emission increased as precipitation intensity increased. Less frequent but high intensity precipitation treatments reduced the soil WFPS by 25.2% and stimulated soil N 2 O emission by 45.3%, while more frequent but low intensity precipitation treatments increased soil WFPS by 9.0% and reduced soil N 2 O emission by 23.9%. Compared to typical N fertilization, the sensitivity of soil N 2 O emission to precipitation was higher under high N than low N fertilization treatments, and the response ratios were 50.0% and 40.1%, respectively. There was significant interactive effect of precipitation intensity and N fertilization on soil N 2 O emission. Furthermore, these findings improved our understanding of precipitation and N impacts on soil N 2 O emissions and provided useful knowledge for irrigation and N fertilizer management in agriculture to mitigate greenhouse gas emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Estimation of net primary productivity using a process-based model in Gansu Province, Northwest China

The ecological structure in the arid and semi-arid region of Northwest China with forest, grassland, agriculture, Gobi, and desert, is complex, vulnerable, and unstable. It is a challenging and sustaining job to keep the ecological structure and improve its ecological function. Net primary productivity (NPP) modeling can help to improve the understanding of the ecosystem, and therefore, improve ecological efficiency. The boreal ecosystem productivity simulator (BEPS) model provides the possibility of NPP modeling in terrestrial ecosystem, but it has some limitations for application in arid and semi-arid regions. In this paper we improve the BEPS model, in terms of its water cycle by adding the processes of infiltration and surface runoff, to be applicable in arid and semi-arid regions. We model the NPP of forest, grass, and crop in Gansu Province as an experimental area in Northwest China in 2003 using the improved BEPS model, parameterized with moderate resolution remote sensing imageries and meteorological data. The modeled NPP using improved BEPS agrees better with the ground measurements in Qilian Mountain than that with original BEPS, with a higher R2 of 0.746 and lower root mean square error (RMSE) of 46.53 gC/m2 compared to R2 of 0.662 and RMSE of 60.19 gC/m2 from original BEPS. The modeled NPP of three vegetation types using improved BEPS show evident differences compared to that using original BEPS, with the highest difference ratio of 9.21% in forest and the lowest value of 4.29% in crop. The difference ratios between different vegetation types lie on the dependence on natural water sources. The modeled NPP in five geographic zones using improved BEPS are higher than those with original BEPS, with higher difference ratio in dry zones and lower value in wet zones.

Wang, Peijuan↗
DOE OSTI2017

The laboratory environmental algae pond simulator (LEAPS) photobioreactor: Validation using outdoor pond cultures of Chlorella sorokiniana and Nannochloropsis salina

A bench-scale photobioreactor system, the Laboratory Environmental Algae Pond Simulator (LEAPS), was designed and constructed to simulate outdoor pond cultivation for a wide range of geographical locations and seasons. The LEAPS consists of six well-mixed glass column photobioreactors submerged in a temperature controlled waterbath (-2 °C to >60 °C) and illuminated from above by a programmable multicolor LED lighting system (0 to 2,500 µmol/m 2 -sec). Measured incident light intensities and water temperatures deviated from the respective light and temperature setpoints on average only 2.3% and 0.9%, demonstrating accurate simulation of light and temperature conditions measured in outdoor ponds. In order to determine whether microalgae strains cultured in the LEAPS exhibit the same linear phase biomass productivity as in outdoor ponds, Chlorella sorokiniana and Nannochloropsis salina were cultured in the LEAPS bioreactors using light and temperature scripts measured previously in the respective outdoor pond studies. For Chlorella sorokiniana, the summer season biomass productivity in the LEAPS was 6.6% and 11.3% lower than in the respective outdoor ponds in Rimrock, Arizona, and Delhi, California; however, these differences were not statistically significant. For Nannochloropsis salina, the winter season biomass productivity in the LEAPS was 9.2% higher than in the respective outdoor ponds in Tucson, Arizona, a difference shown to be not statistically significant. Potential reasons for the positive or negative divergence in LEAPS performance, relative to outdoor ponds, are discussed. To demonstrate the utility of the LEAPS in predicting productivity, two other strains – Scenedesmus obliquus and Stichococcus minor – were evaluated using the summer season script for Rimrock, Arizona. For both strains, the productivity was around 11.6 g/m 2 -day at 25 cm culture depth. In conclusion, the LEAPS is an accurate pond simulator and thus offers a reliable, fast, and cost-effective way to screen microalgae strains and operating conditions for high biomass productivity and co-product yields.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Impact of post-rainfall evaporation from porous roof tiles on building cooling load in subtropical China

Rainfall occurs frequently in subtropical regions of China, with the subsequent water evaporation from building roofs impacting the thermal performance and the energy consumption of buildings. In this paper, we proposed a novel simulation method using actual meteorological data to evaluate this impact. New features were developed in EnergyPlus to enable the simulation: (1) an evaporation latent heat flux source term was added to the heat balance equation of the external surface and (2) algorithms for the evaporative cooling module (ECM) were developed and implemented into EnergyPlus. The ECM experimental results showed good agreement with the simulated results. The ECM was used to assess the impact of evaporation from porous roof tiles on the cooling load of a one-floor building in subtropical China. The results show that the evaporation process decreased the maximal values of the external and internal roof surface temperatures by up to 6.4 °C and 3.2 °C, respectively, while the lower internal surface temperature decreased the room accumulated cooling load by up to 14.8% during the hot summer period. Lastly, the enhanced EnergyPlus capability can be used to evaluate the evaporative cooling performance of roofs with water-storage mediums, as well as to quantify their impact on building cooling loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Uncertainties of isoprene emissions in the MEGAN model estimated for a coniferous and broad-leaved mixed forest in Southern China

Using local observed emission factor, meteorological data, vegetation 5 information and dynamic MODIS LAI, MEGANv2.1 was constrained to predict the isoprene emission from Dinghushan forest in the Pearl River Delta region during a field campaign in November 2008, and the uncertainties in isoprene emission estimates were quantified by the Monte Carlo approach. The results indicate that MEGAN can predict the isoprene emission reasonably during the campaign, and the mean value of isoprene emission is 2.35 mg m-2 h-1 in daytime. There are high uncertainties associated with the MEGAN inputs and calculated parameters, and the relative error can be as high as -89 to 111% for a 95% confidence interval. The emission factor of broadleaf trees and the activity factor accounting for light and temperature dependence are the most important contributors to the uncertainties in isoprene emission estimated for the Dinghushan forest during the campaign. The results also emphasize the importance of accurate observed PAR and temperature to reduce the uncertainties in isoprene emission estimated by model, because the MEGAN model activity factor accounting for light and temperature dependence is highly sensitive to PAR and temperature.

Situ, S.↗
DOE OSTI2024

Air quality impacts from the development of unconventional oil and gas well pads: Air toxics and other volatile organic compounds

Unconventional oil and natural gas development (UOGD) has expanded rapidly across the United States in recent decades and raised concerns about associated air quality impacts. While significant effort has been made to quantify methane emissions, relatively few observations have been made of Volatile Organic Compounds (VOCs), especially during drilling and completion of new wells. Extensive air monitoring during development of several large, multi-well pads in Broomfield, Colorado, in the Denver-Julesburg Basin, provides a novel opportunity to examine changes in local air toxics and other VOC concentrations during well drilling and completions and production. These operations offer an especially useful case to study as several management practices were implemented to reduce emissions (e.g., electrified, grid-powered drill rigs and closed loop fluid handling systems to reduce truck traffic and limit fluid handling on the pad). With simultaneous measurements of methane and 50 VOCs from October 2018 to December 2022 at as many as 19 sites near well pads, in adjacent neighborhoods, and at a more distant reference location, we identify impacts from each phase of well development and production. Use of weekly, time-integrated canisters, a Proton Transfer Reaction Mass Spectrometer (PTR-MS), continuous photoionization detectors (PID) to trigger canister collection upon detection of VOC-rich plumes, and an instrumented vehicle, provided a powerful suite of measurements to characterize both transient plumes and longer-term changes in air quality. Prior to the start of well development, VOC gradients were small across Broomfield. Once drilling commenced, concentrations of oil and gas (O&G) related VOCs, including alkanes and aromatics, increased around active well pads. Concentration increases were clearly apparent during certain operations, including drilling, coil tubing/millout operations, and production tubing installation. Emissions of C 8 –C 10 n-alkanes during drilling operations highlighted the importance of VOC emissions from synthetic drilling mud chosen to reduce odor impacts. More than 90 samples were collected of transient plumes. Using composition measurements, meteorological data, and information about well pad activities, these plumes were connected with specific UOGD operations including drilling, flowback, and production equipment maintenance. The chemical signatures of these plumes differed by operation type (e.g., C 8 –C 10 n-alkanes constituted a larger fraction of measured VOCs in drilling-related plumes). Concentrations of individual, oil and gas-related VOCs in these plumes were often several orders of magnitude higher than in background air, with maximum ethane and benzene concentrations of 79,600 and 819 ppbv, respectively. Because these plumes typically impact a monitoring site for just several minutes, they are easily missed by slower-responding instruments. Study measurements highlight future emission mitigation opportunities during UOGD operations, including better control of emissions from shakers that separate drill cuttings from drilling mud, production separator maintenance operations, and periodic emptying of sand cans during flowback operations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Learning-based CO 2 concentration prediction: Application to indoor air quality control using demand-controlled ventilation

There have been increasing concerns over the air quality inside buildings as high levels of bio-effluents can cause nausea, dizziness, headaches, and fatigue to the people working in those spaces. First published in 2004 as Standard 62.1, ASHRAE Standard 62.2-2019 requires highly occupied spaces to implement heating, ventilation, and air conditioning (HVAC) that can dilute contaminants produced by occupants. In this regard, occupant-centric ventilation control has been regarded as an effective practice to maintain a satisfactory indoor air quality (IAQ) when dealing with highly variable occupancy environments. However, few established models in current literature and practice consider dynamic occupancy behavior and adaptive IAQ control. To address this gap, a dynamic indoor CO2 model is constructed using machine learning algorithms to forecast CO2concentrations across a range of forecasting horizons. Herein, we tuned and compared six state-of-the-algorithms—including Support Vector Machine, Ada Boost, Random Forest, Gradient Boosting, Logistic Regression, and Multilayer Perceptron. The algorithms’ performances are validated using CO 2 and historical meteorological data collected from a campus classroom with a variable occupancy rate. Simulation results showed that Multilayer Perceptron can strongly predict the volatile CO 2 behavior and also outperforms other algorithms in terms of accuracy. Furthermore, a control strategy capable of modeling and detecting dynamic patterns of CO 2 level is utilized to modulate the ventilation rate in real-time and also reduce the energy consumption. The proposed controller reduced the HVAC fan’s energy consumption by 51.4% and provide ventilation as needed per the ASHRAE standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Inductive predictions of hydrologic events using a Long Short-Term Memory network and the Soil and Water Assessment Tool

We present machine learning methods to predict hydrologic features such as streamflow and soil moisture from spatially and temporally varying hydrological and meteorological data. Here, we used a temporal reduction technique to reduce computation and memory requirements and trained a Long Short-Term Memory (LSTM) network to predict soil moisture and streamflow over multiple watersheds. We show LSTM networks can be trained in a fraction of the time required by complex process-based and attention-based models such as Soil and Water Assessment Tool (SWAT) and GeoMAN without sacrificing accuracy. We also demonstrate that outside data - sourced from a watershed other than the target - can be used to train LSTM to comparable or even superior prediction accuracy. The success of LSTM in such spatially-inductive settings shows hydrologic features can be predicted with minimal prior knowledge of the watershed in question. Finally, we make all methodologies of this work publicly available as an end-to-end software pipeline that facilitates rapid prototyping of hydrologic learners.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Estimation of the relationship between remotely sensed anthropogenic heat discharge and building energy use

This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. Anthropogenic heat discharge was estimated based on a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. Building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/ Energy Information Administration survey data, Assessor's parcel data, GIS floor areas data, and remote sensing-derived building height data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Single-Blind Quantification of Natural Gas Leaks from 1 km Distance Using Frequency Combs

A new method is tested in a single-blind study for detection, attribution, and quantification of methane emissions from the natural gas supply chain, which contribute substantially to annual U.S. emissions. The monitoring approach couples atmospheric methane concentration measurements from an open-path dual frequency comb laser spectrometer with meteorological data in an inversion to characterize emissions. During single-blind testing, the spectrometer is placed >1 km from decommissioned natural gas equipment configured with intentional leaks of control- lable rate. Single, steady emissions ranging from 0 to 10.7 g min -1 (0-34.7 scfh) are detected, located, and quantified at three gas pads of varying size and complexity. The system detects 100% of leaks, including leaks as small as 0.96 g min -1 (3.1 scfh). It attributes leaks to the correct pad or equipment group (tank battery, separator battery, wellhead battery) 100% of the time and to the correct equipment (specific separator, tank, or wellhead) 67% of the time. All leaks are quantified to within 3.7 g min -1 (12 scfh); 94% are quantified to within 2.8 g min -1 (9 scfh). Finally, these tests are an important initial demonstration of the methodology’s viability for continuous monitoring of large regions, with extension to other trace gases and industries.

03 NATURAL GAS↗
DOE OSTI2012

Solar influence on winter severity in central Europe

The last two winters in central Europe were unusually cold in comparison to the years before. Meteorological data, mainly from the last 50 years, and modelling studies have suggested that both solar activity and El Niño strength may influence such central European winter coldness. To investigate the mechanisms behind this in a statistically robust way and to test which of the two factors was more important during the last 230 years back into the Little Ice Age, we use historical reports of freezing of the river Rhine. The historical data show that 10 of the 14 freeze years occurred close to sunspot minima and only one during a year of moderate El Niño. This solar influence is underpinned by corresponding atmospheric circulation anomalies in reanalysis data covering the period 1871 to 2008. Furthermore, weak solar activity is empirically related to extremely cold winter conditions in Europe also on such long time scales. This relationship still holds today, however the average winter temperatures have been rising during the last decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Surface-based inversions above central Greenland: SBI ABOVE CENTRAL GREENLAND

[1] Surface-based temperature inversions (SBIs) are studied at Summit Station in central Greenland during the period spanning July 2010 to May 2012. The frequency and intensity of SBI are examined using microwave radiometer (MWR) temperature retrievals, radiosonde profiles, and near-surface meteorological data. Using the MWRs' high temporal resolution, the diurnal, monthly, and annual cycles are investigated. Monthly mean values in SBI occurrence and intensity show that surface-based inversions are prevalent in the winter with decreasing values in the summer months. A case study on 20 February 2011 suggests that factors other than solar elevation angle influence the intensity of surface-based inversions. An increase in liquid water path corresponds to a decrease in SBI intensity, suggesting that liquid-bearing clouds, especially within the lowest 1 km, are associated with weaker surface-based inversions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluating Enhanced Reservoir Evaporation Losses From CMIP6‐Based Future Projections in the Contiguous United States

Abstract Enhanced reservoir evaporation has become an emerging concern regarding water loss, especially when compounded with the ever‐increasing water demand. In this study, we evaluated the evaporation rates and losses for 678 major reservoirs (representing nearly 90% of total storage capacity) in the Contiguous United States over historical baseline (1980–2019), near‐term (2020–2039), and mid‐term (2040–2059) future periods. The evaporation rate was estimated using the Lake Evaporation Model (LEM), an advanced lake evaporation model that addresses both heat storage and fetch effects, driven by multi‐ensemble downscaled Coupled Model Intercomparison Projects 6 (CMIP6) projections under the SSP585 emission scenario. The results project that the evaporation loss may increase by 2.5 × 10 7 m 3 /yr through the research period (1980–2059). Among all regions, the Rio Grande is projected to have the largest increasing rate in the near‐term and mid‐term future, with values of 7.11% of 10.25%, respectively. At the seasonal scale, the most significant increase in the evaporation rate is projected during the fall. The evaporation is projected to increase faster than the streamflow over many of the regions in the southwestern US during the summer/fall, suggesting that the shortage of water will be further exacerbated. The climate models contribute the most to the variance, as compared to the other components related to the projection of evaporation losses (e.g., hydrological model, downscaling method, and historical meteorological data set). These findings demonstrate the need to consider accelerated water loss through open water evaporation in long‐term water resources planning across various spatiotemporal scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Observed Effects of Cold Pools on Convection Triggering and Organization during DYNAMO/AMIE

Cold pools that form from existing convection can generate new clouds nearby, a process that has been suggested to contribute to cloud organization. However, some disagreement exists on the role of cold pools in cloud organization over tropical oceans, and much remains to be understood on how this role depends on the state of large-scale disturbances such as the Madden-Julian Oscillation (MJO). Here, this study addresses this question by examining the intraseasonal variability in the properties of cold pools and their effects on convection triggering using observations. The unique set of surface meteorology data and ground-based radars deployed during the DYNAMO/AMIE field campaign are used to identify cold pools and the associated spatiotemporal evolution of rainfall. The results suggest that cold pools enhance rainfall at their expected speed of 3–8 m s –1 , which includes the Doppler-shifting effect by the background wind. The cold pools also enhance rainfall mainly within a 20 km radius, especially in the direction of boundary-layer vertical wind shear. However, gravity waves appear to contribute predominantly to the propagation of convection over a wider range of distances due to their faster propagation speeds. The interface of convection triggering and propagation at different speeds of cold pools and gravity waves seems to contribute to cloud organization. The effectiveness of cold pools and gravity waves on cloud organization also depends highly on the MJO due to the combined effects of changes in environmental humidity and boundary to lower-tropospheric wind.

54 ENVIRONMENTAL SCIENCES↗