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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 OSTI2017

A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures

We present that EPA-MOVES (Motor Vehicle Emission Simulator) is often integrated with traffic simulators to assess emission levels of large-scale urban networks with signalized intersections. High variations in speed profiles exist in the context of congested urban networks with signalized intersections. The traditional average-speed-based emission estimation technique with EPA-MOVES provides faster execution while underestimates the emissions in most cases because of ignoring the speed variation at congested networks with signalized intersections. In contrast, the atomic second-by-second speed profile (i.e., the trajectory of each vehicle)-based technique provides accurate emissions at the cost of excessive computational power and time. We addressed this issue by developing a novel method to determine the link-driving-schedules (LDSs) for the EPA-MOVES tool. Our research developed a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the LDS for EPA-MOVES that is capable of producing emission estimates better than the average-speed-based technique with execution time faster than the atomic speed profile approach. We applied the HC-DTW on a sample data from a signalized corridor and found that HC-DTW can significantly reduce computational time without compromising the accuracy. The developed technique in this research can substantially contribute to the EPA-MOVES-based emission estimation process for large-scale urban transportation network by reducing the computational time with reasonably accurate estimates. This method is highly appropriate for transportation networks with higher variation in speed such as signalized intersections. Lastly, experimental results show error difference ranging from 2% to 8% for most pollutants except PM 10 .

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Steam generator model design parameter sensitivity study for small modular reactor system

Here, this study focuses on design parameter sensitivity studies pertaining to several Once-Through Steam Generator (OTSG) model cases both with and without a riser using python and advanced risk assessment and optimization tool, i.e. Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory (INL), to support a Small Modular Reactor (SMR) system. The presented Steam Generator (SG) python-based model is a mathematical representation of a steam-generating unit for a Pressurized Water Reactor (PWR)-type SMR system, including fluid flow and heat transfer equations, models, and correlations. Design studies involve changing the model’s input design parameters (e.g., temperature, pressure, mass flow rate) to observe the resulting effects on the output of the system, such as the Heat Transfer Coefficient (HTC), Reynolds number, Nusselt number, and heat transfer performance. Sensitivity studies analyze the degree to which system output and/or desired parameters (e.g., HTC or heat transfer performance) are sensitive to changes in the input parameters. By using RAVEN, detailed design parametric sensitivity studies. Six input parameters—namely, the pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid) of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (i.e., ±1%, ±5% and ±10 % relative changes) for 600 samples. The analysis results give valuable insights into SG system performance, and provide justification for further research and development such as optimized sensor placement, design verification, validation, and optimization.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Development of message passing-based graph convolutional networks for classifying cancer pathology reports

Abstract Background Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to the task of clinical information extraction from cancer pathology reports, extracting the following six properties: main site, subsite, laterality, histology, behavior, and grade. Results We evaluated the clinical task performance of the FastMPN models in terms of micro- and macro-averaged F1 scores. A comparison was performed with the multi-task convolutional neural network (MT-CNN) model. Results show that the FastMPN model is equivalent to or better than the MT-CNN. Conclusions Our implementation revealed that our FastMPN model, which is based on the PyTorch platform, can train a large corpus (667,290 training samples) with 202,373 unique words in less than 3 minutes per epoch using one NVIDIA V100 hardware accelerator. Our experiments demonstrated that using this implementation, the clinical task performance scores of information extraction related to tumors from cancer pathology reports were highly competitive.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Natural carbonation of portland cement with synthetic zeolite Y as a supplementary cementitious material

Risks associated with carbonation are a key limitation to greater replacement levels of ordinary portland cement (OPC) by supplementary cementitious materials (SCMs). The addition of pozzolanic SCMs in OPC alters the hydrate assemblage by forming phases like calcium-(alumina)-silicate-hydrate (C-(A)-S-H). The objective of the present study was to elucidate how such changes in hydrate assemblage influence the chemical mechanisms of carbonation in a realistic OPC system. Here, in this paper, we show that synthetic zeolite Y (faujasite) is a highly reactive pozzolan in OPC that reduces the calcium content of hydration products via prompt consumption of calcium hydroxide from the evolving phase assemblage prior to CO 2 exposure. Suppression of portlandite at moderate to high zeolite Y content led to a more damaging mechanism of carbonation by disrupting the formation of a passivating carbonate layer. Without this layer, carbonation depth and CO 2 uptake are increased. Binders containing 12–18% zeolite Y by volume consumed all the calcium hydroxide from OPC during hydration and reduced the Ca/(Si+Al) ratio of the amorphous products to near 0.67. In these cases, higher carbonation depths were observed after exposure to ambient air with decalcification of C-(A)-S-H as the main source of CO 2 buffering. Binders with either 0% or 4% zeolite Y contained calcium hydroxide in the hydrated microstructure, had higher Ca/(Si+Al) ratios, and formed a calcite-rich passivation layer that halted deep carbonation. Although the carbonated layer in the samples with 12% and 18% zeolite Y contained 70% and 76% less calcite than the OPC respectively, their higher carbonation depths resulted in total CO 2 uptakes that were 12x greater than the OPC sample. Passivation layer formation in samples with calcium hydroxide explains this finding and was further supported by thermodynamic modeling. High Si/Al zeolite additives to OPC should be balanced with the calcium content for optimal carbonation resistance.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Prediction of functional aerobic capacity without exercise testing

The purpose of this study was to develop functional aerobic capacity prediction models without using exercise tests (N-Ex) and to compare the accuracy with Astrand single-stage submaximal prediction methods. The data of 2,009 subjects (9.7% female) were randomly divided into validation (N = 1,543) and cross-validation (N = 466) samples. The validation sample was used to develop two N-Ex models to estimate VO2peak. Gender, age, body composition, and self-report activity were used to develop two N-Ex prediction models. One model estimated percent fat from skinfolds (N-Ex %fat) and the other used body mass index (N-Ex BMI) to represent body composition. The multiple correlations for the developed models were R = 0.81 (SE = 5.3 ml.kg-1.min-1) and R = 0.78 (SE = 5.6 ml.kg-1.min-1). This accuracy was confirmed when applied to the cross-validation sample. The N-Ex models were more accurate than what was obtained from VO2peak estimated from the Astrand prediction models. The SEs of the Astrand models ranged from 5.5-9.7 ml.kg-1.min-1. The N-Ex models were cross-validated on 59 men on hypertensive medication and 71 men who were found to have a positive exercise ECG. The SEs of the N-Ex models ranged from 4.6-5.4 ml.kg-1.min-1 with these subjects.(ABSTRACT TRUNCATED AT 250 WORDS).

Oxygen Consumption↗
DOE OSTI2023

Evidence of cryptic methane cycling and non-methanogenic methylamine consumption in the sulfate-reducing zone of sediment in the Santa Barbara Basin, California

The recently discovered cryptic methane cycle in the sulfate-reducing zone of marine and wetland sediment couples methylotrophic methanogenesis to anaerobic oxidation of methane (AOM). Here we present evidence of cryptic methane cycling activity within the upper regions of the sulfate-reducing zone, along a depth transect within the Santa Barbara Basin, off the coast of California, USA. The top 0–20 cm of sediment from each station was subjected to geochemical analyses and radiotracer incubations using 35 S–SO$^{2-}_{4}$, 14 C–mono-methylamine, and 14 C–CH 4 to find evidence of cryptic methane cycling. Methane concentrations were consistently low (3 to 16 µM) across the depth transect, despite AOM rates increasing with decreasing water depth (from max 0.05 nmol cm -3 d -1 at the deepest station to max 1.8 nmol cm -3 d -1 at the shallowest station). Porewater sulfate concentrations remained high (23 to 29 mM), despite the detection of sulfate reduction activity from 35 S–SO$^{2-}_{4}$ incubations with rates up to 134 nmol cm -3 d -1 . Metabolomic analysis showed that substrates for methanogenesis (i.e., acetate, methanol and methylamines) were mostly below the detection limit in the porewater, but some samples from the 1–2 cm depth section showed non-quantifiable evidence of these substrates, indicating their rapid turnover. Estimated methanogenesis from mono-methylamine ranged from 0.2 to 0.5 nmol cm -3 d -1 . Discrepancies between the rate constants (k) of methanogenesis (from 14 C–mono-methylamine) and AOM (from either 14 C–mono-methylamine-derived 14 C–CH 4 or from directly injected 14 C–CH 4 ) suggest the activity of a separate, concurrent metabolic process directly metabolizing mono-methylamine to inorganic carbon. We conclude that the results presented in this work show strong evidence of cryptic methane cycling occurring within the top 20 cm of sediment in the Santa Barbara Basin. The rapid cycling of carbon between methanogenesis and methanotropy likely prevents major build-up of methane in the sulfate-reducing zone. Furthermore, our data suggest that methylamine is utilized by both methanogenic archaea capable of methylotrophic methanogenesis and non-methanogenic microbial groups. We hypothesize that sulfate reduction is responsible for the additional methylamine turnover, but further investigation is needed to elucidate this metabolic activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Controlled synthesis of Ag{sub 3}PO{sub 4}/BiVO{sub 4} composites with enhanced visible-light photocatalytic performance for the degradation of RhB and 2, 4-DCP

Highlights: • Ag{sub 3}PO{sub 4}/BiVO{sub 4} composites were synthesized by in-situ precipitation method. • Ag{sub 3}PO{sub 4}/BiVO{sub 4} is more efficient than pure Ag{sub 3}PO{sub 4} and BiVO{sub 4} for RhB and 2, 4-DCP degradation. • Ag{sub 3}PO{sub 4}/BiVO{sub 4} can act as a high performance and low cost photocatalyst. - Abstract: Ag{sub 3}PO{sub 4}/BiVO{sub 4} composites were fabricated by in-situ precipitation method. Results showed that Ag{sub 3}PO{sub 4}/BiVO{sub 4} composites exhibited higher photocatalytic activity than that of pure BiVO{sub 4} and Ag{sub 3}PO{sub 4} sample under visible light irradiation. The effect of Ag{sub 3}PO{sub 4}:BiVO{sub 4} ratio on the photocatalytic activity was systemically studied and the optimum molar ratio of Ag{sub 3}PO{sub 4}:BiVO{sub 4} was 1:5. The Ag{sub 3}PO{sub 4}/BiVO{sub 4}-5 exhibited excellent photocatalytic activity for the degradation of Rhodamine B (ca. 92% in 30 min) and 2, 4-dichlorophenol (ca. 84% in 180 min) under visible light irradiation. The enhanced photocatalytic activity could be mainly ascribed to the suitable Ag{sub 3}PO{sub 4}/BiVO{sub 4} heterojunction interface and the smaller particles of Ag{sub 3}PO{sub 4} compared to pure Ag{sub 3}PO{sub 4} particles which could promote the separation of photoinduced electron-hole pairs. The Ag{sub 3}PO{sub 4}/BiVO{sub 4} composite can significantly reduce the silver consumption compared to the pure Ag{sub 3}PO{sub 4}. It is worth noting that Ag{sub 3}PO{sub 4}/BiVO{sub 4} composite can act as a high performance and low cost visible-light-driven photocatalyst for environmental remediation.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Network design for quantifying urban CO<sub>2</sub> emissions: assessing trade-offs between precision and network density

The majority of anthropogenic CO 2 emissions are attributable to urban areas. While the emissions from urban electricity generation often occur in locations remote from consumption, many of the other emissions occur within the city limits. Evaluating the effectiveness of strategies for controlling these emissions depends on our ability to observe urban CO 2 emissions and attribute them to specific activities. Cost-effective strategies for doing so have yet to be described. Here we characterize the ability of a prototype measurement network, modeled after the Berkeley Atmospheric CO 2 Observation Network (BEACO 2 N) in California's Bay Area, in combination with an inverse model based on the coupled Weather Research and Forecasting/Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) to improve our understanding of urban emissions. The pseudo-measurement network includes 34 sites at roughly 2 km spacing covering an area of roughly 400 km 2 . The model uses an hourly 1 × 1 km 2 emission inventory and 1 × 1 km 2 meteorological calculations. We perform an ensemble of Bayesian atmospheric inversions to sample the combined effects of uncertainties of the pseudo-measurements and the model. We vary the estimates of the combined uncertainty of the pseudo-observations and model over a range of 20 to 0.005 ppm and vary the number of sites from 1 to 34. We use these inversions to develop statistical models that estimate the efficacy of the combined model–observing system in reducing uncertainty in CO 2 emissions. We examine uncertainty in estimated CO 2 fluxes on the urban scale, as well as for sources embedded within the city such as a line source (e.g., a highway) or a point source (e.g., emissions from the stacks of small industrial facilities). Using our inversion framework, we find that a dense network with moderate precision is the preferred setup for estimating area, line, and point sources from a combined uncertainty and cost perspective. The dense network considered here (modeled after the BEACO 2 N network with an assumed mismatch error of 1 ppm at an hourly temporal resolution) could estimate weekly CO 2 emissions from an urban region with less than 5 % error, given our characterization of the combined observation and model uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Quantum-Inspired Power System Reliability Assessment

To enable an in-depth study of power system operation and planning, the assessment of standard reliability indices is inevitable. The Monte Carlo Simulation (MCS) approach is a broadly used method in replacing the analytical methods in reliability indices assessment. The accuracy of MCS, however, highly depends on the sampling size, and hence, a complicated system with large number of components requires a large sampling size and daunting computational effort. To address this shortcoming, we, in this paper attempt to take advantage of potentials of the quantum computing (QC) for power system reliability assessment by realizing the following contributions: 1) an innovative quantum model designed for reliability assessment; 2) a quantum circuit that achieves the quadratic speed up compared to the classical MCS method; 3) an efficient quantum amplitude estimation (QAE) algorithm to accurately evaluate the reliability indices. The accuracy and efficacy of the quantum reliability method are extensively verified and demonstrated on both radial and mesh distribution systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2018

The Integrated Impact of Diet on Human Immune Response, the Gut Microbiota, and Nutritional Status During Adaptation to a Spaceflight Analog

Spaceflight impacts human physiology, including well documented immune system dysregulation. Diet, immune function, and the microbiome are interlinked, but diet is the only one of these factors that we have the ability to easily, and significantly, alter on Earth or during flight. As we understand dietary impacts on physiology more thoroughly, we may then improve the spaceflight diet to improve crew health and potentially reduce spaceflight-associated physiological alterations. It is expected that increasing the consumption of fruits and vegetables and bioactive compounds (e.g., omega-3 fatty acids, lycopene, flavonoids) and therefore enhancing overall nutritional intake from the nominal shelf-stable, fully-processed space food system could serve as a countermeasure to improve human immunological profiles, the taxonomic profile of the gut microbiota, and nutritional status, especially where currently dysregulated during spaceflight. This interdisciplinary study will determine the effect of the current shelf-stable spaceflight diet compared to an "enhanced" shelf-stable spaceflight diet (25% more foods rich in omega-3 fatty acids, lycopene, flavonoids, and more fruits, and vegetables in general). The NASA Human Exploration Research Analog (HERA) 2017 missions, consisting of four 45-day missions with closed chamber confinement and realistic mission simulation in a high-fidelity mock space vehicle, will serve as a platform to replicate mission stressors and the effects on crew biochemistry, immunology, and the gut microbiome. Bio sampling of crewmembers is scheduled for selected intervals pre- and in-mission. Data collection also includes dietary intake recording. Outcome measures will include immune markers (e.g., peripheral leukocyte distribution, inflammatory cytokine profiles, T cell function), the taxonomic and metatranscriptomic profile of the gut microbiome, and nutritional status biomarkers and metabolites. Statistical evaluations will determine physiological and biochemical shifts in relation to nutrient intake and study phase. To date, sample collection has been completed for 2 crewmembers from the first mission, aka Campaign 4 Mission 1. Mission 2 was terminated after 22 days due to effects of Hurricane Harvey, and sample collection was not completed. Sample collection will continue for Campaign 4 Mission 3 and 4 prior to comprehensive sample analysis. Beneficial improvements will provide evidence of the impact of diet on crew health and adaptation to this spaceflight analog, and will aid in the design and development of more-efficient targeted dietary interventions for exploration missions.

Douglas, G. L.↗
DOE OSTI2017

Development and Implementation of an Automatic Continuous Online Monitoring and Control Platform for Polymerization Reactions to Sharply Boost Energy and Resource Efficiency in Polymer Manufacturing

The project goal was to create an energy saving paradigm shift in how polymers are manufactured in the 21st century. It used Automatic Continuous Online Monitoring of Polymerization reactions (ACOMP) integrated for the first time with automatic active control to create the innovative ‘ACOMP/Control Interface’, or ‘ACOMP/CI’. ACOMP/CI will begin the transformation from old, inefficient processes into highly evolved, energy and resource efficient ones. The ACOMP platform is broadly applicable to many types of reactions and processes throughout the vast polymer industry. The industry provides materials for sectors such as automotive, aerospace, oil recovery, agriculture, paints, resins, adhesives, pharmaceuticals and therapeutic proteins, optics, electronics, lightweight building materials, and many more. The U.S. chemical industry is one of the last major sectors in which the U.S. has top global stature. It consumes 24% of all U.S. manufacturing energy, produces over $800B of product annually, supports 25% of the U.S. GDP and employs over 6 million people. It is also a major source of GHG emissions. Polymers make up approximately 30% of this sector. It is estimated that annually 60 TBtu of energy could be saved and 3 million tons less of GHG emissions produced by optimizing production in the polyolefin manufacturing sector alone. The project scope included first time design and prototyping of an ACOMP/CI, creation of active reaction controllers, and demonstration of control capabilities on ideal, low concentration polymerization reactions. All these elements of the scope were met, including advances and findings not originally anticipated. Extensions to more complex reactions, beyond the reactor capabilities of the current project ACOMP/CI, such as polyolefins and other high pressure/high temperature reactions, are being proposed in Fall 2017 to CESMII, a DoE based NNMI. The initial proposal was for a three year funded project, but this was reduced to a two year project and budget due to funding constraints. Hence, some of the original plans, such as adaptation of the ACOMP/CI to more relevant industrial processes, such as emulsion and dispersion technologies, could not be carried out. A third year of funding was requested at the end of the project, but DoE did not have resources to grant this. The sub-contractor Fluence Analytics (previously Advanced Polymer Monitoring Technologies, Inc) designed, prototyped, and commissioned a working ACOMP/CI by June 2015. The reaction characteristics to be automatically controlled were i) conversion kinetics, ii) molecular weight, iii) copolymer composition, and iv) simultaneous molecular weight and composition. A two pronged control strategy was used. The Tulane/Fluence group took a basic principles approach that did not rely on kinetic models. The LSU group took a more complex, non-linear model-oriented approach involving complete kinetic descriptions of the reaction system Each of these approaches proved successful in their own way. By April 2016 fully automatic control of conversion and weight average molecular weight, Mw, trajectories was achieved using the Tulane/Fluence (TF) basic principles controller. Similar results were obtained by the LSU non-linear model controller by August 2016. The demonstration system was aqueous free radical polymerization of acrylamide, Am. The control variables were temperature and semi-batch feed to the reactor of Am monomer and initiator. A demonstration of active manual conversion control in an industrial process using high solids in inverse emulsion polymerization of Am was achieved. During Summer 2016 the TF controller was used in conjunction with a chain transfer agent, another control variable, to automatically produce multi-modal molecular weight distributions, MWD, in a single reactor. Industrially, multi-modal MWD are produced by mixing products made in separate reactors, requiring significant extra time, energy, and reactor resources. Recognizing the industrial potential a patent on automatic production of multi-modal polymers was filed, and DoE acknowledged. In Fall 2016 the TF team developed a basic principles controller for copolymer composition and demonstrated it on aqueous free radical copolymerization of comonomers Am and styrene sulfonate, SS. TF then fused the Mw and composition controllers to achieve simultaneous control of both Mw and copolymer composition trajectories. Numerous simultaneous trajectories were demonstrated, including a trimodal composition distribution with constant Mw. Meanwhile, the LSU group developed a Kalman filter to improve the results of their automatic Mw and conversion controller and successful tests were made. During the project the TF team developed a means of computing full MWD during polymer synthesis without need for any chromatographic separation, based on model distributions. This means the polymer product is ‘born characterized’ and this can eliminate post-manufacture analytical laboratory quality control. TF filed a joint patent application on this new approach to chromatography-free determination of MWD with acknowledgment to DoE. The Tulane group obtained a 60MHz NMR during the project and recently completed the first work on separating three comonomers, Am, SS, and Na-acrylate, with a first demonstration of terpolymer composition control with the TF basic principles controller. Widespread dissemination of ACOMP/CI in the polymer manufacturing sector will bolster DoE goals of energy efficiency and reduced GHG emissions: The ability to monitor and actively control polymerization reactions will lead to more efficient use of energy and non-renewable resources, plant and labor time, increase the safety of manufacturing personnel, and will enhance product quality and lead to feasibility of manufacturing of polymers currently too complex for industrial scale production, while leading to less GHG emissions per kilo of product, and allowing for increased U.S. competitiveness in this enormous manufacturing sector. When ACOMP/CI is expanded to the polyolefin industry it is estimated that 60 TeraBTU/year of energy can be saved. Much of this saving is anticipated to come from optimized control of grade changeovers in steady state reactors and maintenance of steady states. Conclusions: ACOMP’s ability to provide continuous realtime data streams of measured polymer and reaction characteristics made it possible, for the first time, to directly and automatically control free radical polymerization reactions. An industrial client of Fluence Analytics has requisitioned the first ACOMP/CI which uses the TF basic principles controller. This sets the stage for FA to add control features to the ACOMP systems it has begun to install on the industrial scale beginning in 2014. Recommendations: This successful project was mainly limited to ideal polymerizations not of an industrial sort. The most energy intensive portion of polymer manufacturing is polyolefins. Adoption of ACOMP/CI to this enormous industrial sector faces the enormous challenges of high temperature, high pressure continuous sampling and high temperature sensor operation to obtain the continuous data needed for direct reaction control. The project team has a strategy for achieving this ambitious goal and will present it in Fall 2017 as a proposal to CESMII/DoE. It is recommended that this upcoming proposal be funded in order to make full use of the achievements of this just ended DoE project as the next step towards making polyolefin ACOMP/CI an energy saving reality. It is projected that ACOMP/CI can have its first polyolefin testbed demonstrations within two years of beginning the proposed project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Reducing Deferrals by Integrating Health Homes with Weatherization

The U.S. Department of Energy’s Weatherization Assistance Program (WAP) improves energy efficiency and household health for low-income families, yet a substantial number of otherwise eligible homes are deferred from due to health and safety concerns in their homes, including mold, moisture damage, or structural deficiencies. These deferred homes are often occupied by marginalized or otherwise vulnerable residents who may be exposed to elevated indoor air pollutant concentration. Integrating Healthy Homes (HH) interventions with weatherization has been proposed as a strategy to reduce deferrals, address environmental justice concerns, and improve health outcomes. However, limited data on the health and economic implications of such integration exist. This study aimed to assess the potential health benefits and savings-to-investment ratio (SIR) associated with integrating Healthy Homes interventions into weatherization programs for homes typically deferred from WAP services. We collected indoor air pollutant data from homes undergoing health home renovations, including fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO), and formaldehyde, with sampling times up to one month. Except for fomaldehyde, which was measured using passive UMEx badges, the other metrics were measured using the airQ Pro indoor air quality monitor.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2001

Laser Materials Processing for NASA's Aerospace Structural Materials

Lasers are useful for performing operations such as joining, machining, built-up freeform fabrication, and surface treatment. Due to the multifunctional nature of a single tool and the variety of materials that can be processed, these attributes are attractive in order to support long-term missions in space. However, current laser technology also has drawbacks for space-based applications. Specifically, size, power efficiency, lack of robustness, and problems processing highly reflective materials are all concerns. With the advent of recent breakthroughs in solidstate laser (e.g., diode-pumped lasers) and fiber optic technologies, the potential to perform multiple processing techniques in space has increased significantly. A review of the historical development of lasers from their infancy to the present will be used to show how these issues may be addressed. The review will also indicate where further development is necessary to realize a laser-based materials processing capability in space. The broad utility of laser beams in synthesizing various classes of engineering materials will be illustrated using state-of-the art processing maps for select lightweight alloys typically found on spacecraft. Both short- and long-term space missions will benefit from the development of a universal laser-based tool with low power consumption, improved process flexibility, compactness (e.g., miniaturization), robustness, and automation for maximum utility with a minimum of human interaction. The potential advantages of using lasers with suitable wavelength and beam properties for future space missions to the moon, Mars and beyond will be discussed. The laser processing experiments in the present report were performed using a diode pumped, pulsed/continuous wave Nd:YAG laser (50 W max average laser power), with a 1064 nm wavelength. The processed materials included Ti-6AI-4V, Al-2219 and Al-2090. For Phase I of this project, the laser process conditions were varied and optimized to see the effects on melt-quenching, cladding/alloying (using the pre-placed powder technique), and cutting. Key parameters such laser power, pulse repetition frequency, process speed, and shield gas flow and the observed process characteristics such as plasma formation during laser/material interaction, have been reported for all experimental runs. Preliminary materials characterization of select samples was carried out using various microscopy, diffraction, spectroscopy and microhardness test methods, and reported. Select nitridation results of Ti-6AI-4V using nitrogen assist gas indicated the successful formation of hard titanium nitrides with much higher hardness (2180 kg/sq mm). A cost-effective and simple powder delivery system has been successfully fabricated for the further experimentation in Phase H.

Nagarathnam, Karthik↗
DOE OSTI2020

Subtask 1.3 – Integrated Carbon Capture and Storage for North Dakota Ethanol Production

The Energy & Environmental Research Center (EERC), in partnership with Red Trail Energy, LLC (RTE), a North Dakota ethanol producer; the North Dakota Industrial Commission (NDIC) Renewable Energy Program (REP); and the U.S. Department of Energy (DOE), conducted a feasibility and implementation study for a commercial carbon capture and storage (CCS) effort. This subtask provided technical support and developed recommended practices of how small-scale industrial CO2 emitters (<1,000,000 tonnes of CO2 emitted annually) may economically deploy CCS. The 64-million-gallon dry mill RTE ethanol facility, emitting an average 180,000 tonnes of CO2 annually, was the subject of the case study. The positive outcome of this research, which shows technical and economic potential for ethanol–CCS in North Dakota, has resulted in RTE acquiring an approved Permit to Drill on December 2, 2019, for a stratigraphic test well in early 2020, a necessary step toward a North Dakota CO2 Storage Facility Permit (SFP) for the RTE CCS effort. Outcomes include 1) validating the Broom Creek Formation as a regional target for CCS, 2) determining the full carbon life cycle of an industrial fuel production facility with CCS, 3) developing a field implementation plan (FIP) for small-scale CCS, and 4) determining the validity and pathway for using CCS to meet low-carbon fuel (LCF) standards. The technical team included the EERC, RTE, Trimeric Corporation, Schlumberger Carbon Services, and Computer Modelling Group (CMG). Findings from activities conducted November 2016 – May 2020 are summarized as follows. Several key steps have been accomplished toward validating the Broom Creek Formation as a CO2 injection and storage target at the RTE CCS site, including verification of the presence and structure of sandstone layers that may comprise the potential CO2 storage reservoir and the several thousand feet of overlying confining zone. Work included site characterization using existing data, interpretation of seismic data within the study area, and geologic modeling simulation of CO2 injection. Interpreted results estimate 3000 feet of confining zone between the Broom Creek Formation (storage target) and the lowermost underground source of drinking water (e.g., the Fox Hills Formation). The thickness of the Broom Creek injection target varies 230–420 ft within the survey area. Results were used to inform the location of a stratigraphic test well and associated characterization test program. No impediments were identified within the targeted CO2 storage complex that would prevent the project from moving forward. A stratigraphic test well is the next step to validate these results and to acquire remaining data necessary to develop a North Dakota CO2 SFP application. The EERC generated full carbon life cycle estimates for CCS integration with the RTE ethanol facility. Results indicated that an average 40% reduction in CO2 emissions is possible through CCS implementation. Approximately half of the carbon in the overall life cycle for a dry mill ethanol plant is generated through the fermentation process and emitted to the atmosphere; this is the CO2 stream targeted for CCS. The remaining carbon is attributed to corn feedstock farming (diesel, fertilizer), energy for fuel processing (natural gas, electricity), and transportation (diesel) of the fuel product. Life cycle carbon estimates are also affected by the anticipated energy consumption of a potential capture facility, which depend on the type of CO2 product generated. For example, a 30%–40% net CO2 emission reduction is estimated if a liquefied CO2 facility were incorporated compared to a 40%–50% net CO2 reduction if a supercritical “injection-grade” CO2 product is generated; i.e., more energy is required to further refine the CO2 stream, affecting the full carbon life cycle estimates. A CCS FIP was developed and initiated at the RTE CCS site, resulting in the development of several guidance documents: a CO2 Capture Process Design Package, a North Dakota CO2 geologic Storage Permits Template, and a Public Outreach Package for CCS in North Dakota. General FIP components include CO2 capture system, pipeline and well designs; monitoring, verification, and accounting (MVA) plans; geologic characterization and testing programs; and permitting and outreach plans. Vendor bids were also acquired for the CO2 liquefaction facility. Near-surface characterization (groundwater and soil gas sampling) and geologic characterization (seismic survey) were initiated to inform development of a UIC Class VI-compliant MVA plan compliant with a North Dakota CO2 SFP. Designs (well and geologic testing) were completed for a stratigraphic test well compliant with a North Dakota CO2 SFP. In addition, the outreach plan was executed, including community open houses, meetings with city/county/state officials, and development of public materials. Although other entities continued to mature incentive programs in 2019–2020, California and the Internal Revenue Service (IRS) currently provide the most advanced economic opportunities for CCS integrated with fuel production. The California Low-Carbon Fuel Standard (LCFS) adopted a CCS Protocol in January 2019, allowing submittal of a design-based pathway (DBP) application for an approved temporary (not certified) carbon intensity value for a fully engineered facility. An ethanol–CCS DBP application to the California LCFS Program (officially approved February 28, 2020) was developed to show that the RTE CCS effort meets LCFS requirements. The approved DBP provides confidence to advance the project and supports potential investments. Other entities continue to mature incentive programs. The IRS issued guidance in February 2020 that addresses the definition of beginning of construction and revenue procedure on partnerships for the Enhancement of Carbon Dioxide Sequestration Credit (a.k.a. Section 45Q) CCS tax credit program; the IRS anticipates issuing further guidance on issues such as secure geologic storage, utilization qualifications, and recapture of claimed credits. Maturing incentive programs coupled with workable permitting regulations provide confidence to advance CCS projects in North Dakota and support financial investment to proceed with designing, constructing, and implementing CCS projects at small-scale fuel production facilities. The largest hurdles for CCS implementation at small-scale industrial systems are often business/economic-related (i.e., not technical). Market uncertainty already exists for agriculture-based alternative fuels such as corn ethanol, for which production has increased by ~33%, and prices have correspondingly lowered since 2015. Passing the Section 45Q tax credit program improves economic feasibility for CCS but may require external investors for a small business to achieve maximum benefits. Public–private partnerships with NDIC and DOE have resulted in foundational technical and regulatory knowledge, growing stakeholder confidence, and a pathway to implementation that enables similar industrial CCS projects in the region to advance. This subtask was funded through the EERC–DOE Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by NDIC and RTE. The authors would also like to thank CMG, ESRI, IHS, Neuralog, and Schlumberger for allowing the use of their software packages in support of this work.

42 ENGINEERING↗
NASA NTRS2019

TPSAS-NF1676L-32493-DND

The Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has supported the Open Data Cube (ODC) initiative to provide a data architecture solution that has value to its global users and increases the impact of EO satellite data. ODC is an open-source platform for processing satellite data. We have developed software products and tools around the core ODC that would help users perform machine learning on EO satellite data. The recent United Nations (UN) Sustainable Development Agenda provides a shared blueprint for peace and prosperity for people and for the planet, considering our current situation and helping to create a plan. The core of this agenda is a set of seventeen Sustainable Development Goals (SDGs), which represent an urgent call for action by all countries - both developed and developing - in a global partnership. The CEOS SEO team has recently developed and released a set of innovative Jupyter notebooks addressing UN SDGs 6.6.1 (spatial extents of water-related ecosystems), 11.3.1 (ratio of land consumption rate to population growth rate), and 15.3.1 (proportion of land that is degraded over total land area). These notebooks empower users by providing features that will assist with streamlining analysis ready data retrieval, processing, and visualization. We have recently incorporated several machine learning techniques in these notebooks. In this paper, we present the lessons learned from our experience on classifying land using supervised and unsupervised machine learning techniques using ODC framework for UN SDGs. We identify the current limitations of ODC to seamlessly support machine learning techniques. We propose features that would help machine learning, specifically within the ODC framework. We propose a thematic indexing/loading of data for both unsupervised learning as well as data annotation/labeling pipeline. Currently, ODC supports machine learning by separating data-management from the analysis process. It works as a mechanism to load cubes of data. ODC does not natively support features that are vital in machine learning such as validation splits, fair/balanced sampling, establishing load size constraints, etc. We believe that our proposed features will empower users by providing features that bring machine learning techniques closed to ODC. Enhancements to ODC to better accommodate machine learning techniques can assist in fulfilling UN SDGs such as 6.3.2, 6.4.2, 6.6.1, 11.3.1, 14.1.1, 15.1.1, 15.3.1, and 15.4.2.

Syed R Rizvi↗
DOE OSTI2018

Suppression of NLRP3 inflammasome attenuates stress-induced depression-like behavior in NLGN3-deficient mice

Highlights: • NLGN3 deletion attenuates stress-induced depression-like phenotype. • NLGN3 deficiency ameliorates neuron injury and glial cell activation. • NLGN3 knockout reduces NLRP3 inflammasome and NF-κB activity in cortex, hippocampus and amygdala. • NLGN3 knockdown alleviates inflammation in LPS-stimulated AST through NLRP3 inflammasome. Depression, regulated by central nervous system (CNS), is a significant inflammatory disorder. Neuroligin3 (NLGN3) has been implicated in brain functions. In the study, a chronic unpredictable mild stress (CUMS) model in wild type (WT) or NLGN3-knockout (KO) mice was established to explore the role of NLGN3 in regulating depression and to reveal the underlying molecular mechanism. The results indicated that NLGN3-knockout markedly reversed the loss of body weight, the reduction of sucrose consumption, the decrease of immobile time in the forced swimming tests (FST) and tail suspension tests (TST) induced by CUMS paradigm. CUMS up-regulated corticosterone (CORT) in serum, and down-regulated serotonin (5-HT), norepinephrine (NE) and brain-derived neurotrophic factor (BDNF) in hippocampus of mice, which were significantly reversed by NLGN3 deficiency. The results further demonstrated that NLGN3-knockout improved the degenerative neurons in cortex and hippocampus of CUMS-treated mice, accompanied with a significant decrease of ionized calciumbinding adapter molecule 1 (Iba-1) and glial fibrillary acidic protein (GFAP) expressions. Additionally, NLGN3-KO mice challenged with CUMS showed a significant reduction of pro-inflammatory cytokines and chemokine, including tumor necrosis factor-alpha (TNF-α), interleukin-18 (IL-18), interleukin-1 beta (IL-1β), interleukin-4 (IL-4), CC-chemokine ligand-1 (CCL-1) and CXC-chemokine ligand-1 (CXCL-1), in cortex, hippocampus and amygdala tissue samples. Western blot analysis suggested that NLGN3-knockout inhibited the activation of nod-like receptor protein 3 (NLRP3) inflammasome and its adaptor of apoptosis-associated speck like protein (ASC), and reduced the expression of Caspase-1, along with the inactivation of nuclear factor-κB (NF-κB) in CUMS-challenged mice. The role of NLGN3 in regulating depression in mice was confirmed in vitro using astrocytes stimulated by LPS that NLGN3 knockdown reduced LPS-induced inflammation. Importantly, the suppressive effects of NLGN3-knockdown on inflammatory response were reversed by NLRP3 or ASC over-expression in AST exposed to LPS. In sum, our findings indicated that suppressing NLGN3 played a potential antidepressant role in CUMS animal model by inactivating NLRP3 inflammasome, providing a new therapeutic avenue for depression.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2011

Assessing the Urban Heat Island Effect Across Biomes in the Continental USA Using Landsat and MODIS

Impervious surface area (ISA) from the Landsat TM and land surface temperature (LST) from MODIS averaged over three annual cycles (2003-2005) are used in a spatial analysis to assess the urban heat island (UHI) skin temperature amplitude and its relationship to development intensity, size, and ecological setting for 38 of the most populous cities in the continental United States. Development intensity zones based on %ISA are defined across urban gradients and used to stratify sampling of LST and NDVI. We find that ecological context significantly influences the amplitude of summer daytime UHI (urban - rural temperature difference) with the largest 8 C (average) for cities built in mixed forest biomes. For all cities ISA is the primary driver for increase in temperature explaining 70% of the total variance. Annually, urban areas are warmer than the non-urban fringe by 2.9 C, except in biomes with arid and semiarid climates. The average amplitude of the UHI is asymmetric with a 4.3 C difference in summer and 1.3 C in winter. In desert environments, UHI's point to a possible heat sink effect. Results show that the urban heat island amplitude increases with city size and is seasonally asymmetric for a large number of cities across most biomes. The implications are that for urban areas developed within forested ecosystems the summertime UHI can be quite high relative to the wintertime UHI suggesting that the residential energy consumption required for summer cooling is likely to increase with urban growth within those biomes.

Imhoff, Marc L.↗
DOE OSTI2023

Decarbonizing or illusion? How carbon emissions of commercial building operations change worldwide

To lead the low-carbon transition in global buildings, this study is the first to use the generalized Divisia index method (GDIM) to identify the factors driving carbon emissions and assess the decarbonization performance in commercial building operations (CBOs) of sixteen countries during 2000–2019. Results show that (1) while the global carbon emissions from CBOs have increased at a modest rate of 0.9%/yr, this trend runs counter to the declining emissions in the U.S. (-1.1%/yr) and the significant growth in China (14.4%/yr), which can be attributed to the impact of economy-related factors. (2) The U.S. and China, as the largest emitters, contributed 66.8% of the samples’ decarbonization of CBOs (919.1 million tons of carbon dioxide). (3) Most countries’ CBOs had a decarbonization efficiency level of less than 10%, except for Spain (27.8%). Spain excelled in both areas of per capita and per floor area with an average efficiency of nearly 30%. Moreover, ridge regression successfully confirms the stability of GDIM results and it should be noted GDIM has limitations in characterizing the end-use activity. Overall, this study tracks the historical decarbonization of global CBOs and offers benchmarks for different emitters to forecast the dynamic of building emissions along with economic booms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗