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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Annual PM2.5 and cardiovascular mortality rate data: Trends modified by county socioeconomic status in 2,132 US counties

This article contains data on county-level socioeconomic status for 2132 US counties and each county's average annual cardiovascular mortality rate (CMR) and fine particulate matter (PM2.5) concentration for 21 years (1990e2010). County CMR, PM2.5, and socioeconomic data were obtained from the US National Center for Health Statistics, US Environmental Protection Agency's Community Multiscale Air Quality modeling system, and the US Census, respectively. Annual socioeconomic indices were created using seven county-level measures from the 1990, 2000, and 2010 US Census using factor analysis. Quintiles of this index were used to generate categories of county socioeconomic status. This national data set contains data for annual PM2.5 and CMR changes over a time-period when there was a significant reduction in US air pollutants (following the enactment of the 1970 Clean Air Act). These data are associated with the article “The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time” [1]. Data are stored in a comma separated value format and can be downloaded from the USEPA ScienceHub data repository (https://doi.org/10.23719/ 1506014).

Socioeconomic status↗
DOE OSTI2022

The uncertain role of rising atmospheric CO 2 on global plant transpiration

As CO 2 concentration in the atmosphere rises, there is a need for improved physical understanding of its impact on global plant transpiration. This knowledge gap poses a major hurdle in robustly projecting changes in the global hydrologic cycle. For this reason, here we review the different processes by which atmospheric CO 2 concentration affects plant transpiration, the several uncertainties related to the complex physiological and radiative processes involved, and the knowledge gaps which need to be filled in order to improve predictions of plant transpiration. Additionally, although there is a high degree of certainty that rising CO 2 will impact plant transpiration, the exact nature of this impact remains unclear due to complex interactions between CO 2 and climate, and key aspects of plant morphology and physiology. The interplay between these factors has substantial consequences not only for future climate and global vegetation, but also for water availability needed for sustaining the productivity of terrestrial ecosystems. Future changes in global plant transpiration in response to enhanced CO 2 are expected to be driven by water availability, atmospheric evaporative demand, plant physiological processes, emergent plant disturbances related to increasing temperatures, and the modification of plant physiology and coverage. Considering the universal sensitivity of natural and agricultural systems to terrestrial water availability we argue that reliable future projections of transpiration is an issue of the highest priority, which can only be achieved by integrating monitoring and modeling efforts to improve the representation of CO 2 effects on plant transpiration in the next generation of earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A scalable framework for quantifying field-level agricultural carbon outcomes

Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N 2 O and CH 4 ) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Environment (E) × Management (M) × Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the “System-of-Systems” solution. Such a “System-of-Systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices (M), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R&D) priorities to achieve different components of the “System-of-Systems” solution, thus accomplishing the Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Metal dissolution from first principles: Potential-dependent kinetics and charge transfer

Metal dissolution and its inverse process are integral to both corrosion and electrodeposition; however, many mechanistic details regarding the dissolution process are challenging to decipher. These include how ion dissolution kinetics and charge transfer are influenced by the competition between metal and solvent interactions under an electrode potential. Here, in this work, we introduce a computational framework based on density functional theory with grand-canonical treatment of electrons to directly predict the potential energy landscape for metal dissolution at a constant potential. Using aluminum as an example, we demonstrate that dissolution kinetics is governed by competing kinetics between two physical processes associated with metal–metal bond breaking and ion-migration within the electrical double layer, respectively. We identify a kinetic transition between regimes dominated by each of these processes and show that this transition depends on the operating electrode potential, among other key factors. It is further found that kinetics and thermodynamics of these processes can be described with a simple, one-parameter Marcus-theory-type model. Beyond offering new understanding of charge transfer during dissolution, our simulation protocol provides a recipe for directly predicting other important quantities in electrochemical reactions from first principles that are difficult to measure, such as the symmetry factor.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Monitoring pipeline integrity of underground gas storage facilities using membrane-based electrochemical sensors

Effective monitoring of internal corrosion risk is crucial to ensuring the safety and longevity of natural gas pipeline infrastructure. While electrochemical sensors are commonly used to assess corrosion rates and corrosion indicators in aqueous fluids, they are rarely used in gas pipelines as these fluids lack the ionic conductivity needed for electrochemical measurements. The inclusion of ion-conductive membranes into electrochemical sensors can extend their functionality into humidified gas streams, providing critical information about emerging corrosion events that are common during withdrawal season in pipeline systems downstream from underground storage facilities. In parallel, new protective films, like those obtained through cold spray coating, are being developed to protect oil and gas pipelines and recover losses in structural integrity due to corrosion damage. Herein, we demonstrate how membrane-based electrochemical sensors (MBES) can be used to monitor fluid corrosivity by examining their response to changes in water content for a wide range of fluid compositions. It was found that MBES readings were highly sensitive to water content changes with membrane conductivity measurements varying from 10 –6 to 10 –1 S cm -1 , and corrosion rate measurements which varied from 10 –7 to 1 mm y -1 . Electron microscopy confirmed that the self-healing characteristics of metal coating films were still active despite their inclusion into an MBES probe. In conclusion, these findings indicate that membrane-based corrosion monitoring can be expanded to monitor coated-pipeline materials and provide early detection of emerging corrosion upsets relevant to underground gas storage facilities.

Electrochemical sensor↗
DOE OSTI2026

Field-based AFDD for refrigerant undercharge in residential HVAC systems: enhancing reliability through false alarm mitigation

This study evaluated rule-based and machine learning (ML) based automated fault detection and diagnostics (AFDD) algorithms for detecting refrigerant undercharge faults in residential heating, ventilation, and air conditioning (HVAC) systems, using actual building data and a minimal set of features. The ML-based algorithms included Decision Tree (DT) and K-Nearest Neighbors (KNN). Both the rule-based and ML-based algorithms demonstrated the capability to detect refrigerant undercharge faults of -30% or more. Both types of algorithms exhibited false alarms before the implementation of a false alarm mitigation algorithm, which motivated the development of such a mitigation strategy. After applying the mitigation, false alarms were substantially reduced, with the rule-based algorithm decreasing to 0.6% and the ML-based algorithms reaching 0%, while maintaining strong detection performance. Although the rule-based algorithm initially showed lower performance compared to the ML-based algorithms, its detection accuracy improved after mitigation to a level comparable to the ML-based algorithms. These results confirm that combining false alarm mitigation with both rule-based and ML-based AFDD algorithms significantly enhances practical reliability while preserving robust fault detection capabilities. Furthermore, the findings demonstrate the potential for field deployment of these algorithms in residential HVAC systems and highlight the importance of minimizing false alarms.

False Alarm↗
DOE OSTI2021

Comprehensive powertrain modeling for heavy-duty applications: A study of plug-in hybrid electric bus

A comprehensive forward-looking powertrain model with an efficiency-based control strategy was developed to achieve real-time optimization of plug-in hybrid electric buses while considering the real vehicle drivability and the practical operation of all powertrain components under real driving conditions. The control strategy is based on a supervisory control algorithm that alternately employs charge-dominant control and discharge-dominant control to manage multiple powertrain sources, and enable an optimal overall efficiency-based powertrain operation state by maximizing the inherent optimal powertrain efficiency through best all transmission gear choice and the blend of motor and/or engine power. In the model, a component energy efficiency database was developed to rapidly enable an smart and optimal operating state determined from a set of efficiency maps characterizing the component and powertrain control states as a function of the instantaneous vehicle operational condition. The results revealed that the energy savings achieved with the innovative powertrain control improved by 10%–30% compared with the baseline hybrid powertrain control strategy. The benefits of eco-driving with respect to energy consumption were also evaluated using the powertrain model. The powertrain control model was implemented into a real hybrid bus. Measured energy savings with the optimized control strategy were similar to the simulated results.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Ultra-clean condensing gas furnace enabled with acidic gas reduction

Natural gas furnaces are the most common space heating equipment in the U.S. residential and commercial building markets. However, current residential natural gas condensing furnaces generate substantial acidic condensate as well as significant emissions of sulfur oxides (SOx), nitrogen oxides (NOx), carbon monoxide (CO), hydrocarbons (HC), and methane (CH 4 ) contributing to environmental degradation of air, water, and soil. This paper describes a novel solution to reduce the environmental impact of natural gas condensing furnaces based on the technology of monolithic acidic gas reduction (AGR) catalyst for SOx trapping, NOx redox to nitrogen, and oxidation of formic acid, CO, HC, and CH 4 . This technology offers a new condensing natural gas furnace with both ultra-clean flue gas and neutral condensate. Here, a prototype of the condensing gas furnace with the AGR component is demonstrated to have condensate with pH = 7, NOx emissions of 1–2 ng/J, and an annual fuel utilization efficiency (AFUE) of 96%. The AGR component and the AGR-enabled furnace were tested for long-term reliability and durability, as well as for SOx storage and regeneration activity. In addition, this paper provides new data on measurements of the specific acidic gas content in natural gas condensing furnaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Enhanced thermal reliability and performance of calcium chloride hexahydrate phase change material using cellulose nanofibril and graphene nanoplatelet

In recent years, thermal energy storage (TES) has gained attention for its role in enhancing renewable energy solutions and sustainable energy consumption. The usage of strontium chloride hexahydrate (SCH), graphene nanoplatelet (GNP), and cellulose nanofibril (CNF) additives were investigated to enhance the performance of calcium chloride hexahydrate (CCH) based on the melting/solidification behavior for TES applications. In this work, we develop a promising phase-change-material (PCM) formulation by introducing these additives that reduce supercooling, improve the thermal conductivity and stabilizing the energy storage capacity of CCH. Rheological characterizations demonstrated that the addition of 1 wt% of CNF into CCH produced the required improvement in viscosity and boosted solid-like rheological behavior. Structural characterizations show a physical mixing of the materials within the PCM composites. Our observations show that the amphiphilicity of CNF enables the surface attachment to GNP via hydrophobic interactions providing effective dispersion of GNP throughout the PCM composite. The addition of a nucleating agent, SCH decreased the degree of supercooling of ~20 g of CCH from >20 °C to 3 °C at a cooling rate of 5 °C/min. Thermal characterization showed the resulting PCM composite has a latent heat of melting of 186 Jg -1 , phase change temperature of 32 °C, and stable thermal properties after being subjected to 70 melt-freeze cycles. Adding CNF and GNP to pure CCH increased its thermal conductivity by 76 %. The high thermal conductivity of GNP and its effective dispersion by CNF is responsible for this enhancement. The study highlights the use of biodegradable nanocellulose for the preparation of sustainable PCM composites with improved performance. In conclusion, these PCM composites are scalable, they have potential to increase energy efficiency and revolutionalize the heating/cooling applications in buildings and other TES systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Laboratory evaluation of cyclic underground hydrogen storage in the Temblor sandstone of the San Joaquin Basin, California

Underground Hydrogen Storage (UHS) in depleted oil and gas reservoirs could provide a cost-effective solution to balance seasonal fluctuations in renewable energy generation. However, data and knowledge on UHS at subsurface conditions are limited so it is difficult to estimate how effective this type of storage could be. In this study, we perform high pressure experiment to measure the effectiveness of cyclic hydrogen (H 2 ) storage in a specimen of Temblor sandstone retrieved from the San Joaquin Basin of California. Our experiment mimics reservoir pressure conditions to measure H 2 -brine relative permeability and fluid-rock interactions over the course of ten charging and discharging cycles. Initial gas breakthrough occurred at 15 % to 25 % H2 saturation in the specimen with 3 % NaCl brine as the resident fluid. Continuing injecting to 4 pore volumes (PV) of H 2 yielded an asymptotic H 2 saturation of 38 % to 41 %, a level often referred to as the irreducible gas saturation based on two-phase flow. The boundary condition in this study mimics the near wellbore region, which experiences bi-directional H 2 flow. This bi-directional flow led to evaporative drying of the specimen resulting in 94 % H 2 saturation at the end of 10th cycle. This indicates that cyclic flow and evaporative drying can lead to more efficient reservoir storage where a larger fraction of the reservoir porosity is usable to store H 2 . The produced gas stream consisted of H 2 mixed with 8 % to 22 % H 2 O, indicating formation dry-out by evaporation. Meanwhile, produced water chemistry indicated calcite and silicate dissolution, with calcite sourced from fossil fragments. This led to a loss of cementation and weakened the rock sample. Combined, our results indicate dry-out, compaction, increased H 2 saturation, rock weakening, and permeability loss during cyclic UHS. Overall, we anticipate that the combined effects should lead to higher than anticipated UHS storage efficiency per volume of sandstone reservoir rock.

08 HYDROGEN↗