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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Ducted Fuel Injection Provides Consistently Lower Soot Emissions in Sweep to Full-Load Conditions

Earlier studies have proven how ducted fuel injection (DFI) substantially reduces soot for low- and mid-load conditions in heavy-duty engines, without significant adverse effects on other emissions. Nevertheless, no comprehensive DFI study exists showing soot reductions at high- and full-load conditions. This study investigated DFI in a single-cylinder, 1.7-L, optical engine from low- to full-load conditions with a low-net-carbon fuel consisting of 80% renewable diesel and 20% biodiesel. Over the tested load range, DFI reduced engine-out soot by 38.1–63.1% compared to conventional diesel combustion (CDC). This soot reduction occurred without significant detrimental effects on other emission types. Thus, DFI reduced the severity of the soot–NO x tradeoff at all tested conditions. While DFI delivered considerable soot reductions in thepresent study, previous DFI studies at low- and mid-load conditions delivered larger soot reductions (>90%) compared to CDC operation at the same conditions. Therefore, the DFI configuration used here has been deemed nonoptimal (in terms of parameters such as the injector-spray and piston geometries), and several improvements are recommended for future studies with high-load DFI. Further, these improvements include employing better spray-duct alignment, a deeper piston bowl with a smaller injector umbrella angle, and a fuel injector that opens and closes faster. The study also suggests future research to make DFI ready for commercialization, such as metal-engine tests to ensure desirable DFI performance over an engine’s complete speed/load map. Overall, this study supports the continued development and commercialization of DFI to meet upcoming emissions regulations for heavy-duty vehicles. Specifically, multicylinder engine experiments and CFD simulations should be utilized to optimize the performance and clarify the full potential of DFI.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

The role of current and emerging technologies in meeting Japan’s mid- to long-term carbon reduction goals

Using Japan as a proxy for a developed nation, we investigated the role of existing and nascent technologies in curbing carbon emissions. We simulated possible pathways to meeting 2030 and 2050 emission targets within the Japanese electricity supply sector using a single-region model in The Integrated MARKAL-EFOM System (TIMES). Critically, our simulations incorporate novel technologies like hydrogen electrolysers, carbon capture, photochemical water splitting, and emerging photovoltaic cells, assess long-term impacts up to the year 2100, and include life-cycle emissions and learning curves for parameters such as investment cost, efficiency, and emission coefficients. Results indicate that a hybrid approach, using nuclear power and hydrogen from renewable energy-based electrolysis, is cost-effective and provides long-term emission reduction along with energy security. Finally, nuclear, wind, solar, and hydrogen from renewables emerge as key emission reduction technologies, while natural gas with carbon capture plays a minor role in achieving emission reduction targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

A Proof of the Asymptotic Variance of Path Length Estimators for Single-Collision Monte Carlo Source Iteration in the Thick Diffusion Limit

Here, we prove a theorem relating the variance of path length estimators for single-collision Monte Carlo source iteration to a parameter that becomes infinitesimally small in an important physical regime arising in radiative transfer. In our usage, “single-collision Monte Carlo source iteration” refers to Monte Carlo Boltzmann transport methods in which each Monte Carlo particle history includes no more than a single collision, and the physics of multiple scattering is modeled by lagging the scattering source term and iterating until this term converges. Our theorem can be used to construct variance reduction techniques which improve the order of the estimator variance. This enables calculations that would otherwise require impractically large sample sizes to achieve practical estimator uncertainties. We believe this is the first postulation of a theorem relating estimator variance to a limiting case parameter for single-collision Monte Carlo source iteration, and the first proof of such a theorem. We illustrate the theorem’s value with an example in which the authors of a transport method used the theorem to design a variance reduction technique that improved the uncertainty of their solution by a factor of about 500 for a proxy problem from radiative transfer that contains both optically-thick and optically-thin material.

Mathematics and Computing↗
DOE OSTI2023

Modeling the First Hydrogen Direct Reduction Pilot Reactor for Ironmaking in the USA Using Finite Element Analysis and Its Validation Using Pilot Plant Trial Data

Direct reduction of hematite pellets with hydrogen (H 2 ) was used to produce directly reduced iron (DRI) in a pilot scale reactor at a pellet feed rate of 21.4 kg/h. At a steady state, operational parameters of the pilot plant (gas recycling rate and inlet temperature) along with key reactor output parameters, the pellet metallization, and the internal temperature profile of the reactor were reported for two scenarios with high recycle and low recycle rate of H 2 . Scenario 1, with a high recycle rate of 400 L/min H 2 along with external heating of 870 °C, gave an average metallization of 91.8%, while Scenario 2, with low recycle rate of 100 L/min H 2 and external heating of 850 °C gave a metallization of 67.8% due to the higher moles of H 2 available for reduction and the external energy required for the endothermic reduction reaction in Scenario 1 as compared with Scenario 2. Finite element analysis was used to build a model of the shaft reactor, which was validated against the metallization and internal temperature profile data. The average metallization values predicted by the model were very close to the metallization values obtained from the pilot plant samples, with 90.9% average metallization for Scenario 1 and 65.6% average metallization for Scenario 2. The internal temperature profiles in the lower region of the reactor obtained from the model were very close to these pilot plant data, with a maximum difference of 52.7 °C and 67.6 °C for Scenarios 1 and 2, respectively. The pilot plant reactor model was used extensively in the commissioning of the pilot plant and to predict the startup outcomes for a given set of operating parameters.

08 HYDROGEN↗
DOE OSTI2022

CaMn 0.9 Ti 0.1 O 3 based redox catalysts for chemical looping – Oxidative dehydrogenation of ethane: Effects of Na 2 MoO 4 promoter and degree of reduction on the reaction kinetics

Reduction kinetics and stability of 20 wt% Na 2 MoO 4 -promoted CaMn 0.9 Ti 0.1 O 3 were investigated for its applications in Chemical Looping – Oxidative Dehydrogenation (CL-ODH) of ethane, a potential alternative for ethylene production with higher efficiency and lower emissions. Here, the present work reports a kinetics model and parameters for a Na 2 MoO 4 -promoted, Ti-doped CaMnO 3 (CaMn 0.9 Ti 0.1 O 3 ) redox catalyst under H 2 and C 2 H 4 . A first-order reaction model provides the best fit for the reduction of Na 2 MoO 4 /CaMn 0.9 Ti 0.1 O 3 under H 2 , while the C 2 H 4 reduction is well described by an Avrami–Erofe’ev model. The activation energy for C 2 H 4 oxidation is approximately three times higher than that for H 2 conversion, showing that the activation of C 2 H 4 is significantly more difficult on the surface of the redox catalyst. The reduction rate of Na 2 MoO 4 /CaMn 0.9 Ti 0.1 O 3 under H 2 at 750 °C is more than two orders of magnitude greater than that under C 2 H 4 , while the reduction rate of unpromoted CaMn 0.9 Ti 0.1 O 3 is comparable under H 2 and C 2 H 4 , showing that the addition of Na 2 MoO 4 effectively suppresses C 2 H 4 combustion relative to H 2 oxidation. The kinetics results for Na 2 MoO 4 /CaMn 0.9 Ti 0.1 O 3 confirm its excellent selectivity towards hydrogen combustion, making it a promising candidate under CL-ODH. Additionally, the stability of the CaMn 0.9 Ti 0.1 O 3 @ Na 2 MoO 4 core-shell structure, which was the underlying reason for the excellent selectivity, was examined under both shallow and deep reductions. It was determined that deep reduction of the redox catalyst, e.g. higher than 80% solid conversion, would lead to loss of sodium and hence to decreased selectivity for hydrogen combustion. In contrast, the core-shell structure was well-maintained, exhibiting excellent performance after 50 redox cycles when deep reduction of the redox catalyst was avoided. This study offers a basis for both the CL-ODH reactor design and redox catalyst optimizations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Machine Learning for Joint Quality Control

The use of lightweight material combinations has been highly demanded in manufacturing automotive structures. However, making robust dissimilar material joints of such lightweight materials is still challenging. A significant barrier to achieving high-quality and repeatable joint performance is a deficient understanding of the relationship between the welding process, joint attributes, and joint performance. In this context, welding factors refer to material, equipment, environment, and process parameters, while joint features comprise specific microstructural attributes of the weld such as nugget size, heat affected zone (HAZ) topology, intermetallic layer thickness, and sheet thickness reduction. Joint performance is quantified in terms of strength (e.g., tensile shear, coach peel, cross-tension), weld size, and hardness, among other factors. While there have been many attempts to establish this process-structure-property relationship by developing a model derived from the associated physics and first principles, the complexity of the joining processes compounded by the complex interactions with different materials in an automotive assembly line environment, has hindered the usefulness of such attempts. The complexity is further exacerbated using different stacking materials, especially comprising dissimilar material combinations. In practice, the common approach has been the laborious process of creating welds, characterizing them, and then physically testing them through experimentation. With the emergence of artificial intelligence (AI) methods, an alternative pathway to eliciting the desired process-structure-property relationship at an accelerated pace is to use a data-driven approach by employing machine-learning (ML) techniques. This approach is benefitted by the availability of large streams of data, generated through years of research and testing by original equipment manufacturers, in the form of material, process, environmental, equipment, microstructural, and bulk-scale performance information from multimodal, multiscale sensors making measurements from laboratory-scale to production-scale processes. During Phase I efforts, which ended in fiscal year (FY) 2021, the Oak Ridge National Laboratory and Pacific Northwest National Laboratory (ORNL/PNNL) team demonstrated the effectiveness of different ML/AI frameworks in modeling complex relationships between resistance spot welding (RSW) process parameters, weld attributes, and joint properties using a subset of data from General Motors (GM). In FY 2022, the project team further refined and expanded their respective ML models to analyze additional welds with new weld stack-ups and materials to enhance the ML model predictive capability. ORNL extended its unified deep neural networks (DNN) ML training and prediction framework with new data streams of process parameters, and PNNL extended its model describing RSW process parameters’ associations with weld attributes. In FY 2023, the project team completed the development of the AI/ML architecture for analyzing aluminum/steel joints manufactured by GM via RSW and transitioned into the inline welding quality monitoring task for steel/steel RSW joints provided by GM.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Geochemistry and Microbiology Predict Environmental Niches with Conditions Favoring Potential Microbial Activity in the Bakken Shale

The Bakken Shale and underlying Three Forks Formation is an important oil and gas reservoir in the United States. The hydrocarbon resources in this region are accessible using unconventional oil and gas extraction methods, including horizontal drilling and hydraulic fracturing. However, the geochemistry and microbiology of this region are not well understood, although they are known to have major implications for productivity and water management. In this study, we analyzed the produced water from 14 unconventional wells in the Bakken Shale using geochemical measurements, quantitative PCR (qPCR), and 16S rRNA gene sequencing with the overall goal of understanding the complex dynamics present in hydraulically fractured wells. Bakken Shale produced waters from this study exhibit high measurements of total dissolved solids (TDS). These conditions inhibit microbial growth, such that all samples had low microbial loads except for one sample (well 11), which had higher 16S rRNA gene copies. Our produced water samples had elevated chloride concentrations typical of other Bakken waters. However, they also contained a sulfate concentration trend that suggested higher occurrence of sulfate reduction, especially in wells 11 and 18. The unique geochemistry and microbial loads recorded for wells 11 and 18 suggest that the heterogeneous nature of the producing formation can provide environmental niches with conditions conducive for microbial growth. This was supported by strong correlations between the produced water microbial community and the associated geochemical parameters including sodium, chloride, and sulfate concentrations. The produced water microbial community was dominated by 19 bacterial families, all of which have previously been associated with hydrocarbon-reservoirs. These families include Halanaerobiaceae, Pseudomonadaceae, and Desulfohalobiaceae which are often associated with thiosulfate reduction, biofilm production, and sulfate reduction, respectively. Notably, well 11 was dominated by sulfate reducers. Our findings expand the current understanding of microbial life in oil and gas related environments, provide new insights into how the unique produced water conditions shape microbial communities, and demonstrate thatproduced water chemistry is tightly linked with microbiota in the Bakken Shale. Finally, our study shows that additional research efforts are necessary to understand this ecosystem and highlights the need for work in other unconventional reservoirs.

Tinker, Kara↗
DOE OSTI2024

Statistical properties of filaments in the cosmic web

ABSTRACT In the context of the cosmological and constrained Exploring the Local Universe with the reConstructed Initial Density field (ELUCID) simulation, this study explores the statistical characteristics of filaments within the cosmic web, focussing on aspects such as the distribution of filament lengths and their radial density profiles. Using the classification of the cosmic web environment through the Hessian matrix of the density field, our primary focus is on how cosmic structures react to the two variables $R_{\rm s}$ and $\lambda _{\rm th}$. The findings show that the volume fractions of knots, filaments, sheets, and voids are highly influenced by the threshold parameter $\lambda _{\rm th}$, with only a slight influence from the smoothing length $R_{\rm s}$. The central axis of the cylindrical filament is pinpointed using the medial-axis thinning algorithm of the COsmic Web Skeleton (COWS) method. It is observed that median filament lengths tend to increase as the smoothing lengths increase. Analysis of filament length functions at different values of $R_{\rm s}$ indicates a reduction in shorter filaments and an increase in longer filaments as $R_{\rm s}$ increases, peaking around $2.5R_{\rm s}$. The study also shows that the radial density profiles of filaments are markedly affected by the parameters $R_{\rm s}$ and $\lambda _{\rm th}$, showing a valley at approximately $2R_{\rm s}$, with increases in the threshold leading to higher amplitudes of the density profile. Moreover, shorter filaments tend to have denser profiles than their longer counterparts.

Zhang, Youcai (ORCID:0000000319674091)↗
DOE OSTI2020

Local atomic structure and Ni nanophase segregation in Zn 1-x Ni x S thin films

Here, the local atomic structure of Zn 1-x Ni x S thin films was investigated using X-ray absorption spectroscopy. The films were grown using RF-sputtering at atomic concentrations x = 0.00, x = 0.04, x = 0.08 and x = 0.14. X-ray diffraction shows that the lattice parameter contracts with increasing Ni concentration, consistent with the smaller atomic radii of Ni compared to that one of Zn. Optical absorption reveals a reduction of the bandgap as Ni concentration increases, except for sample x = 0.14 where a complex behavior is observed. Results from X-ray absorption near edge structure (XANES) spectroscopy indicate a shift in the valence state of Ni for the x = 0.14 sample. The analysis of the extended X-ray absorption fine structure (EXAFS) spectra indicate a contraction of ~0.7 Å in the Ni–S bond distance when compared to the Zn–S bond length, which generates local lattice distortions and an increment of the static disorder as the Ni concentration increases. The EXAFS results for sample x = 0.14 show the presence of a Zn 1-x Ni x S phase and a nanoscopic metallic Ni phase with domain sizes below the diffraction limit. These results reveal that the local atomic structure differs in a significant manner from the average crystalline structure, implying its importance for the determination of the electronic properties of this material.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Reduced salen in a monomeric Fe complex

We report the first monomeric formally M I transition metal salen complex without ion pairing. A combination of spectroscopy, crystallography, and computations indicates that reduction of an Fe II salen complex gives substantial reduced ligand character. We also present a computational method for accurately predicting 57 Fe Mössbauer parameters of Fe salen complexes.

Feldman, Dana M. [Yale University, New Haven, CT (↗
DOE OSTI2021

Plasma Guns for Magnetized Fuel Targets for PJMIF

In plasma jet driven magneto-inertial fusion (PJMIF) an array of discrete supersonic plasma jets is used to form a spherically imploding plasma liner, which then compresses a magnetized plasma target to fusion conditions. With funding from ARPA-E’s ALPHA program and from Strong Atomics LLC, HyperJet Fusion Corp and HyperV Technologies Corp. previously developed the plasma guns required for an experimental demonstration of the plasma liner formation part of the concept. A 36-gun demonstration of an imploding spherical plasma liner is currently underway on the PLX facility at Los Alamos National Laboratory. This present project addresses the next step required for a complete PJMIF concept, i.e. developing the magnetized plasma target. We proposed to form the target by stagnating a number of magnetized plasma jets in the center of the target chamber. This is accomplished by adding a bias field coil to the plasma liner gun to form a magnetized plasma jet. This experimental development took place at HyperJet in a geometry replicating the bias field environment that will be seen on a PLX port so the results are directly transferrable to the PLX experiment. The objective of this effort was the technical development and characterization of a new magnetized plasma jet using a high-performance, high momentum flux, contoured-gap coaxial plasma gun appropriate for use on the next stage of the PLX experiment. Electromagnetic modeling of the coil indicated the coil was best placed around the aluminum tubes of the gun transmission line, rather than around the chamber port as originally proposed. This maximized field strength in the breech and yielded much better flux linkage between gun electrodes. A 30-turn coil was ultimately implemented, allowing long pulses that could diffuse through the metal walls on the timescale of interest. \machtwo modeling predicted that less capacitance in the main PFN could potentially improve plasma jet velocities due to better matching of the current to the smaller plasma mass and the existing electrode contour designed to suppress blowby. This proved true, as testing showed markedly improved performance when the original 600uF bank was reduced to 400uF. A number of diagnostics were built and/or upgraded in order to characterize the plasma jets, inluding laser interferomtery for density, photodiodes for velocity, Bdot probes for magnetic field measurements, a Triple probe for temperature measurements, and spectroscopy for impurity content. A plasma gun with the 30-turn magnet coil installed, a 70% reduction in gas valve plenum volume, and a 33% reduction in main PFN capacitance produced a dense, high velocity, well magnetized plasma jet. We met or exceeded virtually all of the plasma jet parameter goals. Peak velocity of 135 km/s exceeded the 100 km/s goal by 35%, while the peak density of over 1.0x10 15 cm -3 was 3.3 times the goal of >3.0x10 14 cm -3 . Shot-to-shot repeatability is excellent, with a jitter of less than 300ns observed on the arrival fronts of the photodiode signals from one shot to the next. Average plasma jet lengths of 33cm (at 120km/s) were slightly longer than the 20cm goal, but jets as short as 11.8cm were observed at 135km/s. Mass is much higher than the targeted goal, averaging 106ug per shot compared to 20ug. The magnetic field is still a bit lower than desired, with a maximum to date of ~811G, compared to the goal of 1000G. Average values, though, were typically in the 300-450G range, when measured further downstream after some expected in flight decay. Temperature measurements are still a work in progress. Increasing the B field and completing temperature measurements will be continued on into the ongoing BETHE project.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

BUQEYE guide to projection-based emulators in nuclear physics

The BUQEYE collaboration (Bayesian Uncertainty Quantification: Errors in Your effective field theory) presents a pedagogical introduction to projection-based, reduced-order emulators for applications in low-energy nuclear physics. The term emulator refers here to a fast surrogate model capable of reliably approximating high-fidelity models. As the general tools employed by these emulators are not yet well-known in the nuclear physics community, we discuss variational and Galerkin projection methods, emphasize the benefits of offline-online decompositions, and explore how these concepts lead to emulators for bound and scattering systems that enable fast and accurate calculations using many different model parameter sets. We also point to future extensions and applications of these emulators for nuclear physics, guided by the mature field of model (order) reduction. All examples discussed here and more are available as interactive, open-source Python code so that practitioners can readily adapt projection-based emulators for their own work.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. Furthermore, we demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

Data-driven model reduction↗
DOE OSTI2020

Beam Hardening Artifact Reduction in X-Ray CT Reconstruction of 3D Printed Metal Parts Leveraging Deep Learning and CAD Models

Nondestructive evaluation (NDE) of additively manufactured (AM) parts is important for understanding the impacts of various process parameters and qualifying the built part. X-ray computed tomography (XCT) has played a critical role in rapid NDE and characterization of AM parts. However, XCT of metal AM parts can be challenging because of artifacts produced by standard reconstruction algorithms as a result of a confounding effect called “beam hardening.” Beam hardening artifacts complicate the analysis of XCT images and adversely impact the process of detecting defects, such as pores and cracks, which is key to ensuring the quality of the parts being printed. In this work, we propose a novel framework based on using available computer-aided design (CAD) models for parts to be manufactured, accurate XCT simulations, and a deep-neural network to produce high-quality XCT reconstructions from data that are affected by noise and beam hardening. Using extensive experiments with simulated data sets, we demonstrate that our method can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art. We also present promising preliminary results of applying the deep networks trained using CAD models to experimental data obtained from XCT of an AM jet-engine turbine blade.

Ziabari, Amir↗
DOE OSTI2025

Visual Instance-aware Prompt Tuning

Visual Prompt Tuning (VPT) has emerged as a parameter-efficient fine-tuning paradigm for vision transformers, with conventional approaches utilizing dataset-level prompts that remain the same across all input instances. We observe that this strategy results in sub-optimal performance due to high variance in downstream datasets. To address this challenge, we propose Visual Instance-aware Prompt Tuning (ViaPT), which generates instance-aware prompts based on each individual input and fuses them with dataset-level prompts, leveraging Principal Component Analysis (PCA) to retain important prompting information. Moreover, we reveal that VPT-Deep and VPT-Shallow represent two corner cases based on a conceptual understanding, in which they fail to effectively capture instance-specific information, while random dimension reduction on prompts only yields performance between the two extremes. Instead, ViaPT overcomes these limitations by balancing dataset-level and instance-level knowledge, while reducing the amount of learnable parameters compared to VPT-Deep. Extensive experiments across 34 diverse datasets demonstrate that our method consistently outperforms state-of-the-art baselines, establishing a new paradigm for analyzing and optimizing visual prompts for vision transformers.

Xiao, Xi [ORNL] (ORCID:0009000009316982)↗
DOE OSTI2021

Sequentially prepared Mo-V-Based SCR catalyst for simultaneous Hg 0 oxidation and NO reduction

Molybdenum (Mo)-vanadium (V)-based selective catalytic reduction (SCR) catalyst synthesized by the sequential impregnation of Mo and W followed by V was investigated for simultaneous elemental mercury (Hg 0 ) oxidation and nitrogen oxide (NO) reduction in an existing SCR unit with respect to different TiO 2 phases, calcination temperatures, flue gas constituents, gas velocities, and reaction temperatures. Anatase phase TiO 2 and the calcination temperatures of 400 and 500 degrees C resulted in similar to 69% Hg 0 oxidation at 10 ppmv HCl and 350 degrees C. The high calcination temperature of 700 degrees C resulted in TiO 2 phase transformation from anatase to rutile and agglomeration. The modified SCR catalyst prepared with the impregnation sequence of Mo and W followed by V using anatase TiO 2 and calcination temperature 500 degrees C showed similar to 99% Hg 0 oxidation and 87% NO reduction conversions at an NH 3 /NO molar ratio of 0.9 under 350 degrees C and 5,000 hr -1 space velocity in typical sub-bituminous and lignite coal simulated flue gases. Finally, the effects of these parameters and conditions were further investigated using various characterization techniques including BET, TEM, XRD, NH 3 TPD and XAFS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Building energy efficiency and load flexibility optimization using phase change materials under futuristic grid scenario

This work investigates the energy savings and load flexibility capacity of phase change material (PCM) integrated into building envelope under a future energy generation scenario, where 80% of the energy load comes from renewable sources. Using community-scale modeling, we first determine the net thermal storage required to fully manage the demand variability at a utility grid, and then using whole-building simulations we optimize operating parameters - such as PCM thickness, latent heat, interior setpoint profile, PCM distribution, and heat transfer coefficient - to determine the optimal conditions required for maximum energy reduction and load flexibility without compromising occupants' thermal comfort. The optimal PCM-integrated envelope proposed in this study can provide annual load flexibility up to 33.6% and annual energy savings up to 10.8% in a lightweight residential building located in Baltimore, MD. This study is relevant given the increasing contributions of renewable energy in the total energy generation mix, leading to a significant time-imbalance between peak energy demand and peak energy production. While thermal energy storage using PCM is a recognized technique, there is no known prior study dedicated to examining the thermal performance of PCM-integrated envelope under future energy generation scenarios. This study bridges the research gap by investigating a practical way to implement PCMs in the buildings and maximize the energy efficiency as well as load flexibility related benefits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Activity-based, genome-resolved metagenomics uncovers key populations and pathways involved in subsurface conversions of coal to methane

Microbial metabolisms and interactions that facilitate subsurface conversions of recalcitrant carbon to methane are poorly understood. We deployed an in situ enrichment device in a subsurface coal seam in the Powder River Basin (PRB), USA, and used BONCAT-FACS-Metagenomics to identify translationally active populations involved in methane generation from a variety of coal-derived aromatic hydrocarbons. From the active fraction, high-quality metagenome-assembled genomes (MAGs) were recovered for the acetoclastic methanogen, Methanothrix paradoxum, and a novel member of the Chlorobi with the potential to generate acetate via the Pta-Ack pathway. Members of the Bacteroides and Geobacter also encoded Pta-Ack and together, all four populations had the putative ability to degrade ethylbenzene, phenylphosphate, phenylethanol, toluene, xylene, and phenol. Metabolic reconstructions, gene analyses, and environmental parameters also indicated that redox fluctuations likely promote facultative energy metabolisms in the coal seam. The active "Chlorobi PRB" MAG encoded enzymes for fermentation, nitrate reduction, and multiple oxygenases with varying binding affinities for oxygen. "M. paradoxum PRB" encoded an extradiol dioxygenase for aerobic phenylacetate degradation, which was also present in previously published Methanothrix genomes. Finaly, these observations outline underlying processes for bio-methane from subbituminous coal by translationally active populations and demonstrate activity-based metagenomics as a powerful strategy in next generation physiology to understand ecologically relevant microbial populations.

59 BASIC BIOLOGICAL SCIENCES↗