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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Wastewater reuse benefits for municipal complete retention lagoons: Life cycle assessment and dynamic modeling

Complete retention lagoons with wastewater reuse for agricultural purposes may offer sustainability advantages over alternative systems for small communities in semiarid regions. This study quantifies the environmental life cycle impact of adopting agriculture water reuse systems using case study data to estimate operating and building infrastructure impacts and spatial–temporal modeling to quantify resource trade-offs. Water reuse system benefits are highly dependent on supply–storage–demand dynamics. The relative size of irrigated agricultural land to the lagoon size was the most significant factor influencing site water application rates. The benefits are sensitive to changes in air emissions occurring from the agricultural land and further emphasize the importance of proper fertilizer management when adopting water reuse systems. Wastewater reuse from complete retention lagoons reduce life cycle GHG emissions, primarily through excavation reductions, offset fertilizer use, and especially from increased crop yields from wastewater reuse at previously rainfed sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures

Abstract Part quality manufactured by the laser powder bed fusion process is significantly affected by porosity. Existing works of process–property relationships for porosity prediction require many experiments or computationally expensive simulations without considering environmental variations. While efforts that adopt real-time monitoring sensors can only detect porosity after its occurrence rather than predicting it ahead of time. In this study, a novel porosity detection-prediction framework is proposed based on deep learning that predicts porosity in the next layer based on thermal signatures of the previous layers. The proposed framework is validated in terms of its ability to accurately predict lack of fusion porosity using computerized tomography (CT) scans, which achieves a F1-score of 0.75. The framework presented in this work can be effectively applied to quality control in additive manufacturing. As a function of the predicted porosity positions, laser process parameters in the next layer can be adjusted to avoid more part porosity in the future or the existing porosity could be filled. If the predicted part porosity is not acceptable regardless of laser parameters, the building process can be stopped to minimize the loss.

42 ENGINEERING↗
DOE OSTI2022

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

Abstract There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Attention-based convolutional capsules for evapotranspiration estimation at scale

Evapotranspiration (ET) measures the amount of water lost from the Earth's surface to the atmosphere and is an integral metric for both agricultural and environmental sciences. Understanding and quantifying ET is critical for achieving effective management of freshwater and irrigation systems. However, current ET estimation models suffer from a trade-off between accuracy and spatial coverage. In this study, we introduce our model Quench, a neural network architecture that achieves highly-accurate ET estimates over large continuous spatial extents. Quench uses our novel Attention-Based Convolutional Capsule for its neural network layers to identify areas of focus and efficiently extract ET information from satellite imagery. Benchmarks that profile our model's performance show substantive improvements in accuracy, with up to 128% increase in accuracy compared to traditional convolutional-based and process-based models. Finally, Quench also demonstrates consistent model performance over high geospatial variability and a diverse array of regions, seasons, climates, and vegetations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Microstructure-Sensitive modeling of surface roughness and notch effects on extreme value fatigue response

Fatigue life knockdown due to surface roughness induces local stress concentrations. Physical fatigue experiments are crucial to qualify materials for use but are limited in their capability to examine the mechanisms underlying the fatigue crack formation driving force due to these stress concentrations. Microstructure-sensitive computational models can aid in this regard and are employed in this work to examine the effects of surface roughness in Al 7075-T6. Digital microstructure models are subjected to crystal plasticity finite element method (CPFEM) simulations to examine the combined effects of microstructure and grain scale asperities on the driving force for fatigue crack formation. Following elastic–plastic shakedown, mesoscale volume-averaged fatigue indicator parameters (FIPs) are computed within fatigue damage process zones of grains. An increase in the intensity of realistic surface roughness profiles corresponds accordingly to larger FIPs but it is difficult to precisely interpret the mechanisms and effects of this intensification. We thus parametrically investigate surface asperities that couple with microstructure and lack of constraint on slip at the free surface to describe microstructure-sensitive surface roughness knockdown effects on fatigue resistance. The depth of a single valley (i.e., notch) due to surface roughness is found to be more detrimental to fatigue resistance than the radius (i.e., notch width, not to be confused with notch acuity). Finally, we also find that different crystallographic textures, grain morphologies, and number of sampled grains have a limited effect on the depth of influence of these notches.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Modeling shock-induced void collapse in single-crystal Ta systems at the mesoscales

Understanding the role of microstructural heterogeneities on the shock wave propagation and defect evolution behavior is essential to predicting the dynamic response of metals. Heterogeneities, such as voids, provide challenges to understanding the wave propagation behavior as the shock-void interaction can collapse the void and result in large plastic strains and significant heating (hot spot formation) in the metal. Accurate modeling of this phenomenon requires predicting the void collapse mechanisms and the related heat generation and dissipation mechanisms (hotspot formation) that determine the microstructure evolution. While molecular dynamics (MD) simulations can model the void collapse behavior, the time/length scale capabilities pose a challenge to connect with continuum models or the experimental scales. Here, this study presents the capability of the newly developed quasi-coarse-grained dynamics (QCGD) method that extends the MD simulations to larger system sizes and longer times to model this phenomenon. This study uses QCGD simulations to investigate the mechanisms of shock wave interactions with pre-existing voids in single-crystal Ta microstructures for variations in shock pressures, void size, and loading orientations. For a given orientation, the plasticity contributions and rates of void collapse are observed to vary with shock pressures and void size. The larger void sizes and higher pressures result in increased temperatures (hot spots) and faster void collapse rates and unravel the variations in the plasticity contributions. In addition, QCGD simulations investigate the post-collapse microstructure evolution as a release wave travels through the hot spot region. The simulations reveal that the reduction in temperatures due to heat dissipation initiates the dynamic recrystallization behavior in the hotspot regions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Assessing the life-cycle environmental impacts of the wood pallet sector in the United States

Wood pallets play a critical role in the movement and storage of goods worldwide. They are an important component in the complex global supply chain and used by almost every industry. It is therefore important to assess the environmental implications of the wood pallet supply chain and identify optimization strategies that can be implemented. In this paper, primary 2018 annual production data collected from U.S. pallet manufacturers were used to develop the first industry-average life-cycle inventory (LCI). A new functional unit was proposed to perform a more refined and accurate environmental life-cycle assessment of the wood pallet supply chain. Using the LCI data developed, a cradle-to-grave industry-average life-cycle impact assessment was performed. This novel approach quantifies environmental impacts of a generic multi-use pallet, including repair and remanufacturing. The total global warming impact was 10.4 kg CO 2 e per 45.4 t of pallet loads of product delivered using wood pallets. The manufacturing stage contributed the most, about 35%, followed by the raw material supply stage. About 41% of total primary energy consumption was from renewable sources, with most sourced from biomass. Fossil fuels comprised about 52% of the total (225 MJ per functional unit) primary energy consumption. Total environmental impact was significantly affected by two main parameters: reference service life and load-bearing capacity. Pallet repair was also found to be an important component of the wood pallet supply chain, which has a low environmental footprint compared with the overall impact of a pallet and enables mitigation of overall impact by extending the reference service life. Finally, at end-of-life, common industry practices demonstrated substantial potential environmental benefits that can minimize overall environmental impact.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Local Lagrangian reduced-order modeling for the Rayleigh-Taylor instability by solution manifold decomposition

The Rayleigh-Taylor instability is a classical hydrodynamic instability of great interest in various disciplines of science and engineering, including astrophysics, atmospheric sciences and climate, geophysics, and fusion energy. Analytical methods cannot be applied to explain the long-time behavior of the Rayleigh-Taylor instability, and therefore, numerical simulation of the full problem is required. However, in order to capture the growth of amplitude of perturbations accurately, both the spatial and temporal discretizations need to be extremely fine for traditional numerical methods, and long-time simulation may become prohibitively expensive. In this paper, we propose efficient reduced order model techniques to accelerate the simulation of the Rayleigh-Taylor instability in compressible gas dynamics. Here, we introduce a general framework for decomposing the solution manifold to construct the temporal domain partition and temporally-local reduced order model construction with varying Atwood number. We propose two practical approaches in this framework, namely decomposition by physical time and by penetration distance. Numerical results are presented to examine the performance of the proposed approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

High strain-rate strength response of single crystal tantalum through in-situ hole closure imaging experiments

The properties of crystalline materials often depend on directionality and operating conditions. Specifically, the strength of materials can depend anisotropically on crystal direction and the loading condition. To probe these effects, a preliminary series of high strain-rate (> 105/s) strength plate-impact hole closure experiments were performed on high purity single crystal Tantalum cubes. The orientation of the single crystals with respect to impact/loading were varied to provide data to inform crystal plasticity modeling efforts. The experiments consist of in-situ high-resolution X-ray radiographic imaging of the hole collapse under dynamic compression conditions to infer the material strength via its resistance to closure at increasing levels of plastic strain. The experiments are compared against hydrocode simulation predictions. Here, a comparison with simple elastic perfectly plastic strength model predictions is presented to elucidate the response of the different crystal orientations at high strain-rate and large plastic strains.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A geospatial environmental and techno-economic framework for sustainable phosphorus management at livestock facilities

Nutrient pollution of waterbodies is a major worldwide water quality problem. Excessive use and discharge of nutrients can lead to eutrophication and algal blooms in fresh and marine waters, resulting in environmental problems associated with hypoxia, public health issues related to the release of toxins and freshwater scarcity. A promising option to address this problem is the recovery of nutrient releases prior to being discharged into the environment. Driven by the sustainable materials management concept, the COW2NUTRIENT (Cattle Organic Waste to NUTRIent and ENergy Technologies) framework is developed for the techno-economic evaluation and selection of nutrient recovery systems at livestock facilities. Furthermore, environmental vulnerability to nutrient pollution determined through a geographic information system (GIS)-based model and techno-economic information of different state-of-the-art nutrient management technologies are combined in a multi-criteria decision analysis (MCDA) model, resulting in the selection and economic analysis of the most suitable process for each studied livestock facility. This framework has been employed for studying the implementation of sustainable phosphorus management systems at 2,217 livestock facilities in the Great Lakes area, resulting in capital expenses of 2.5 billion USD if only phosphorus recovery technologies are installed, and up to 5.2 billion USD if nutrient management is combined with biogas and power production. However, considering potential economic incentives for the recovery of phosphorus, net revenues up to 230 million USD per year can be achieved. Therefore, the framework presented reveals the potential of implementing nutrient management systems at regional scale for the abatement of phosphorus releases from livestock facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Mercury Removal from Contaminated Water by Wood-Based Biochar Depends on Natural Organic Matter and Ionic Composition

Biochars can remove potentially toxic elements, such as inorganic mercury [Hg(II)] from contaminated waters. However, their performance in complex water matrices is rarely investigated, and the combined roles of natural organic matter (NOM) and ionic composition in the removal of Hg(II) by biochar remain unclear. Here, we investigate the influence of NOM and major ions such as chloride (Cl – ), nitrate (NO 3 – ), calcium (Ca 2+ ), and sodium (Na + ) on Hg(II) removal by a wood-based biochar (SWP700). Multiple sorption sites containing sulfur (S) were located within the porous SWP700. In the absence of NOM, Hg(II) removal was driven by these sites. Ca 2+ bridging was important in enhancing removal of negatively charged Hg(II)-chloro complexes. In the presence of NOM, formation of soluble Hg-NOM complexes (as seen from speciation calculations), which have limited access to biochar pores, suppressed Hg(II) removal, but Cl – and Ca 2+ could still facilitate it. The ability of Ca 2+ to aggregate NOM, including Hg-NOM complexes, promoted Hg(II) removal from the dissolved fraction (<0.45 μm). Hg(II) removal in the presence of Cl – followed a stepwise mechanism. Weakly bound oxygen functional groups in NOM were outcompeted by Cl – , forming smaller-sized Hg(II)-chloro complexes, which could access additional intraparticle sorption sites. Therein, Cl – was outcompeted by S, which finally immobilized Hg(II) in SWP700 as confirmed by extended X-ray absorption fine structure spectroscopy. We conclude that in NOM containing oxic waters, with relatively high molar ratios of Cl – : NOM and Ca 2+ : NOM, Hg(II) removal can still be effective with SWP700.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Mining Nontraditional Water Sources for a Distributed Hydrogen Economy

Securing decarbonized economies for energy and commodities will require abundant and widely available green H 2 . Ubiquitous wastewaters and nontraditional water sources could potentially feed water electrolyzers to produce this green hydrogen without competing with drinking water sources. Herein, we show that the energy and costs of treating nontraditional water sources such as municipal wastewater, industrial and resource extraction wastewater, and seawater are negligible with respect to those for water electrolysis. We also illustrate that the potential hydrogen energy that could be mined from these sources is vast. Based on these findings, we evaluate the implications of small-scale, distributed water electrolysis using disperse nontraditional water sources. Techno-economic analysis and life cycle analysis reveal that the significant contribution of H 2 transportation to costs and CO 2 emissions results in an optimal levelized cost of hydrogen at small- to moderate-scale water electrolyzer size. The implications of utilizing nontraditional water sources and decentralized or stranded renewable energy for distributed water electrolysis are highlighted for several hydrogen energy storage and chemical feedstock applications. Finally, we discuss challenges and opportunities for mining H 2 from nontraditional water sources to achieve resilient and sustainable economies for water and energy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Gas and Propane Combustion from Stoves Emits Benzene and Increases Indoor Air Pollution

Exposure pathways to the carcinogen benzene are well-established from tobacco smoke, oil and gas development, refining, gasoline pumping, and gasoline and diesel combustion. Combustion has also been linked to the formation of nitrogen dioxide, carbon monoxide, and formaldehyde indoors from gas stoves. To our knowledge, however, no research has quantified the formation of benzene indoors from gas combustion by stoves. Across 87 homes in California and Colorado, natural gas and propane combustion emitted detectable and repeatable levels of benzene that in some homes raised indoor benzene concentrations above well-established health benchmarks. Mean benzene emissions from gas and propane burners on high and ovens set to 350 °F ranged from 2.8 to 6.5 μg min –1 , 10 to 25 times higher than emissions from electric coil and radiant alternatives; neither induction stoves nor the food being cooked emitted detectable benzene. Benzene produced by gas and propane stoves also migrated throughout homes, in some cases elevating bedroom benzene concentrations above chronic health benchmarks for hours after the stove was turned off. Combustion of gas and propane from stoves may be a substantial benzene exposure pathway and can reduce indoor air quality.

54 ENVIRONMENTAL SCIENCES↗