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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. Here, in this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

In planta production of the nylon precursor beta-ketoadipate

Beta-ketoadipate (βKA) is an intermediate of the βKA pathway involved in the degradation of aromatic compounds in several bacteria and fungi. Beta-ketoadipate also represents a promising chemical for the manufacturing of performance-advantaged nylons. We established a strategy for the in planta synthesis of βKA via manipulation of the shikimate pathway and the expression of bacterial enzymes from the βKA pathway. Using Nicotiana benthamiana as a transient expression system, we demonstrated the efficient conversion of protocatechuate (PCA) to βKA when plastid-targeted bacterial-derived PCA 3,4-dioxygenase (PcaHG) and 3-carboxy-cis,cis-muconate cycloisomerase (PcaB) were co-expressed with 3-deoxy-D-arabinoheptulosonate 7-phosphate synthase (AroG) and 3-dehydroshikimate dehydratase (QsuB). This metabolic pathway was reconstituted in Arabidopsis by introducing a construct (pAtβKA) with stacked pcaG, pcaH, and pcaB genes into a PCA-overproducing genetic background that expresses AroG and QsuB (referred as QsuB-2). The resulting QsuB-2 x pAtβKA stable lines displayed βKA titers as high as 0.25 % on a dry weight basis in stems, along with a drastic reduction in lignin content and improvement of biomass saccharification efficiency compared to wild-type controls, and without any significant reduction in biomass yields. Using biomass sorghum as a potential crop for large-scale βKA production, techno-economic analysis indicated that βKA accumulated at titers of 0.25 % and 4 % on a dry weight basis could be competitively priced in the range of $2.04-34.49/kg and $0.47-2.12/kg, respectively, depending on the selling price of the residual biomass recovered after βKA extraction. This study lays the foundation for a more environmentally-friendly synthesis of βKA using plants as production hosts.

Kazaz, Sami↗
DOE OSTI2022

The economic value of photovoltaic performance loss mitigation in electricity spot markets

Photovoltaic (PV) performance is affected by reversible and irreversible losses. These can typically be mitigated through responsive and proactive operations and maintenance (O&M) activities. However, to generate profit, the cost of O&M must be lower than the value of the recovered electricity. This value depends both on the amount of recovered energy and on the electricity prices, which can vary significantly over time in spot markets. The present work investigates the impact of the electricity price variability on the PV profitability and on the related O&M activities in Italy, Portugal, and Spain. Here, it is found that the PV revenues varied by 1.6 × to 1.8 × within the investigated countries in the last 5 years. Moreover, forecasts predict higher average prices in the current decade compared to the previous one. These will increase the future PV revenues by up to 60% by 2030 compared to their 2015–2020 mean values. These higher revenues will make more funds available for better maintenance and for higher quality components, potentially leading to even higher energy yield and profits. Linearly growing or constant price assumptions cannot fully reproduce these expected price trends. Furthermore, significant price fluctuations can lead to unexpected scenarios and alter the predictions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

How to Minimize Faradaic Efficiency Error in Electrochemical CO 2 Reduction for Gas Products

Faradaic efficiency (FE) is an important metric for evaluating electrochemical processes, such as carbon dioxide reduction, that is often used for performance comparisons. Although the equation to calculate FE is well-known, details needed to yield accurate values are often overlooked, potentially leading to errors exceeding 100%. Avoiding any errors is crucial for drawing correct conclusions, especially during a catalysis optimization process. Factors with high potential for FE errors include incorrect mass flow rates and temperature values, imprecise gas chromatography calibrations, and uncorrected gas viscosities, while factors with less potential are also identified. In conclusion, this manuscript presents FE calculation guidelines intended for both newcomers and experts in the field of electrochemical fuel production.

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

Outer shell symmetry for double shell capsules with aluminum ablators

Double shell targets are a promising potential avenue to obtain robust neutron yield at current laser facilities. Similar to single shell designs, double shells require the symmetric implosion of an ablator in order to uniformly compress and heat a fuel volume, with the goal of achieving thermonuclear burn. Significant differences between double and single shells include the usage of an aluminum ablator as well as a reverse ramp laser pulse. In addition, double shells require a different convergence than single shells for fuel ignition. Numerical implosion studies at various energies with comparisons to experimental outcomes are required to gain confidence that simulations can capture the ablator shape from subscale to full scale. The current work builds on previous implosion experiments conducted at 1-MJ laser energy to confirm achieved ablator symmetry at 1.25 and 1.5 MJ. Average ablator P2 and P4 shapes measured in these experiments are within 5% of the simulated shape, which merits the platforms for further experimental studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2024

Secondary electron emission measurements from imidazolium-based ionic liquids

The electron-induced secondary electron emission (SEE) yields of imidazolium-based ionic liquids are presented for primary electron beam energies between 30 and 1000 eV. These results are important for understanding plasma synthesis of nanoparticles in plasma discharges with an ionic liquid electrode. Due to their low vapor pressure and high conductivity, ionic liquids can produce metal nanoparticles in low-pressure plasmas through reduction of dissolved metal salts. In this work, the low vapor pressure of ionic liquids is exploited to directly measure SEE yields by bombarding the liquid with electrons and measuring the resulting currents. The ionic liquids studied are [BMIM][Ac], [EMIM][Ac], and [BMIM][BF 4 ]. The SEE yields vary significantly over the energy range, with maximum yields of around 2 at 200 eV for [BMIM][Ac] and [EMIM][Ac], and 1.8 at 250 eV for [BMIM][BF 4 ]. Molecular orbital calculations indicate that the acetate anion is the likely electron donor for [BMIM][Ac] and [EMIM][Ac], while in [BMIM][BF 4 ], the electrons likely originate from the [BMIM] + cation. The differences in SEE yields are attributed to varying ionization potentials and molecular structures of the ionic liquids. These findings are essential for accurate modeling of plasma discharges and understanding SEE mechanisms in ionic liquids.

ionic liquids↗
DOE OSTI2020

Sparse thermal data for cellular automata modeling of grain structure in additive manufacturing

Grain growth in the wake of the melt pool formed during alloy-based additive manufacturing (AM) is complex and multifaceted, depending on parameters governing heat transport, fluid flow, and solidification itself. Cellular automata (CA) models have proven effective in providing computationally efficient and physically sound predictions of grain structure for several AM problems, but their efficiency is tied to the performance of heat transport models. CA models use only a small portion of the problem's temperature data (near the moving melt pool boundary), and much of the CA calculations do not affect the final result due to re-melting of material. Coupling of and communication between heat transport and solidification models, and eliminating operations irrelevant towards final grain structure prediction, will be necessary for using these methods for efficient simulation of large parts. In this work, we introduce a procedure of decoupling the CA from temperature field simulation, using files of relevant temperature data written by the heat transport model. This approach is validated against the standard coupling approach using data obtained through the computational fluid dynamics software OpenFOAM. Negligible differences are seen in grain size, volume, and texture distributions for multilayer simulation of test problems, while the quantity of temperature data for these test problems was reduced by four orders of magnitude (from 100s of GB to 10s of MB) and the code performance sped up by a factor of around 50. Furthermore, variability in microstructure as a function of cell size, substrate, time step, and nucleation parameters is studied, and it is found that cell sizes less than or equal to 1.67 μm and sufficiently small time steps yield Αstatistically equivalent microstructures. Finally, a potential use case for this CA approach—the layer-wise convergence in grain structure starting from extremes in initial grain size—is examined. This approach's ability to simulate expected trends in nucleation and epitaxial grain growth for large regions of microstructure, simulated independently of heat transport models themselves, should prove useful for investigation of various microstructure uncertainties and prediction of part-scale experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Entanglement masquerading in the CMB

The simplest single-field inflation models capture all the relevant contributions to the patterns in the Cosmic Microwave Background (CMB) observed today. A key assumption in these models is that the quantum inflationary fluctuations that source such patterns are generated by a particular quantum state — the Bunch-Davies (BD) state. While this is a well-motivated choice from a theoretical perspective, the question arises of whether current data can rule out other, also well motivated, choices of states. In particular, as we previously demonstrated in [1], entanglement is naturally and inevitably dynamically generated during inflation given the presence of a "rolling" spectator scalar field — and the resulting entangled state will yield a primordial power spectrum with potentially measurable deviations compared to the canonical BD result. For this work we developed a perturbative framework to allow a systematic exploration of constraints on (or detection of) entangled states with Planck CMB data using Monte Carlo techniques. We have found that most entangled states accessible with our framework are consistent with the data. One would have to expand the framework to allow a greater variety of entangled states in order to saturate the Planck constraints and more systematically explore any preferences the data may have among the different possibilities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Harvest and nitrogen effects on bioenergy feedstock quality of grass-legume mixtures on Conservation Reserve Program grasslands

Perennial grass mixtures established on Conservation Reserve Program (CRP) lands can be an important source of feedstock for bioenergy production. This study aimed to evaluate management practices for optimizing the quality of bioenergy feedstock and stand persistence of grass-legume mixtures under diverse environments. A 5-year field study (2008–2012) was conducted to assess the effects of two harvest timings (at anthesis vs after complete senescence) and three nitrogen (N) rates (0, 56, 112 kg N ha -1 ) on biomass chemical compositions (i.e., cell wall components, ash, volatiles, total carbon, and N contents) and the feedstock energy potential, examined by the theoretical ethanol yield (TEY) and the total TEY (i.e., the product of biomass yield and TEY, L ha -1 ), of cool-season mixtures in Georgia and Missouri and a warm-season mixture in Kansas. The canonical correlation analysis (CCA) was used to investigate the effect of vegetative species transitions on feedstock quality. Although environmental variations (mainly precipitation) greatly influenced the management effect on chemical compositions, the delayed harvest after senescence generally improved feedstock quality. In particular, the overall cell wall concentrations and TEY of the warm-season mixtures increased by approximately 7%. Additional N supplies improved the total TEY of both mixtures by ~1.6–4.2 L ha -1 per 1.0 kg N ha -1 input but likely lowered the feedstock quality, particularly for the cool-season mixture. The cell wall concentrations of cool-season mixture reduced by approximately 3%–6%. The CCA results indicated that the increased legume compositions (under low N input) likely enhanced lignin but reduced ash concentrations. This field research demonstrated that with proper management, grass-legume mixtures on CRP lands can provide high-quality feedstock for bioenergy productions.

09 BIOMASS FUELS↗
DOE OSTI2022

Purple acid phosphatase2 stimulates a futile cycle of lipid synthesis and degradation, and mitigates the negative growth effects of triacylglycerol accumulation in vegetative tissues

Storage lipids (mostly triacylglycerols, TAGs) serve as an important energy and carbon reserve in plants and hyperaccumulation of TAG in vegetative tissues can cause negative effects on plant growth. Purple acid phosphatase2 (PAP2) was shown previously to affect carbon metabolism and boost plant growth. However, the effects of PAP2 on lipid metabolism remain unknown. Here, we demonstrated PAP2 as a factor that can stimulate a futile cycle of fatty acid (FA) synthesis and degradation and mitigate negative growth effects associated with TAG accumulation in vegetative tissues. Constitutive expression of PAP2 in Arabidopsis thaliana enhanced both lipid synthesis and degradation in leaves and led to substantial increase in seed oil yield. Suppressing lipid degradation in a PAP2-overexpressing line by disrupting sugar-dependent1 (SDP1), a predominant TAG lipase, significantly elevated vegetative TAG content and improved plant growth. Diverting FAs from membrane lipids to TAGs in PAP2-overexpressing plants by constitutively expressing phospholipid:diacylglycerol acyltransferase1 (PDAT1) increased TAG contents to up to 6% of dry weight in vegetative tissues without compromising biomass yield. Overall, these results highlight the potential of combining PAP2 with TAG-promoting factors to enhance carbon assimilation, FA synthesis and allocation to TAGs for optimized plant growth and storage lipid accumulation in vegetative tissues.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Microbiome differences in sugarcane and metabolically engineered oilcane accessions and their implications for bioenergy production

Abstract Oilcane is a metabolically engineered sugarcane ( Saccharum spp. hybrid) that hyper-accumulates lipids in its vegetable biomass to provide an advanced feedstock for biodiesel production. The potential impact of hyper-accumulation of lipids in vegetable biomass on microbiomes and the consequences of altered microbiomes on plant growth and lipid accumulation have not been explored so far. Here, we explore differences in the microbiome structure of different oilcane accessions and non-modified sugarcane. 16S SSU rRNA and ITS rRNA amplicon sequencing were performed to compare the characteristics of the microbiome structure from different plant compartments (leaf, stem, root, rhizosphere, and bulk soil) of four greenhouse-grown oilcane accessions and non-modified sugarcane. Significant differences were only observed in the bacterial microbiomes. In leaf and stem microbiomes, more than 90% of the entire microbiome of non-modified sugarcane and oilcane was dominated by similar core taxa. Taxa associated with Proteobacteria led to differences in the non-modified sugarcane and oilcane microbiome structure. While differences were observed between multiple accessions, accession 1566 was notable in that it was consistently observed to differ in its microbial membership than other accessions and had the lowest abundance of taxa associated with plant-growth-promoting bacteria. Accession 1566 is also unique among oilcane accessions in that it has the highest constitutive expression of the WRI 1 transgene. The WRI 1 transcription factor is known to contribute to significant changes in the global gene expression profile, impacting plant fatty acid biosynthesis and photomorphogenesis. This study reveals for the first time that genetically modified oilcanes associate with distinct microbiomes. Our findings suggest potential relationships between core taxa, biomass yield, and TAG in oilcane accessions and support further research on the relationship between plant genotypes and their microbiomes.

09 BIOMASS FUELS↗
DOE OSTI2024

Outer shell symmetry for double shell capsules with aluminum ablators

Double shell targets are a promising potential avenue to obtain robust neutron yield at current laser facilities. Similar to single shell designs, double shells require the symmetric implosion of an ablator in order to uniformly compress and heat a fuel volume, with the goal of achieving thermonuclear burn. Significant differences between double and single shells include the usage of an aluminum ablator as well as a reverse ramp laser pulse. In addition, double shells require a different convergence than single shells for fuel ignition. Numerical implosion studies at various energies with comparisons to experimental outcomes are required to gain confidence that simulations can capture the ablator shape from subscale to full scale. The current work builds on previous implosion experiments conducted at 1-MJ laser energy to con firm achieved ablator symmetry at 1.25 and 1.5 MJ. Average ablator P2 and P4 shapes measured in these experiments are within 5% of the simulated shape, which merits the platforms for further experimental studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

From Plastic Waste to Fuel: Pyrolysis and Gasification of Polyethylene for Hydrogen Production

Thermochemical conversion processes offer promising solutions to address the plastic pollution crisis by transforming plastic waste into valuable products, notably hydrogen. In this study, thermal pyrolysis and steam gasification of polyethylene (PE), the most abundantly produced plastic waste, are investigated in a drop tube reactor system. Various process parameters, namely temperature, residence time, and feedstock composition, are evaluated to establish their correlations with reaction performance. Coal refuse, obtained from discarded thickener underflow in coal processing, is introduced as a co-feedstock for gasification to enhance PE handling and examine synergistic effects on product distribution, particularly H2 yield and syngas quality. Furthermore, the potential of low-cost, environmentally friendly catalysts (i.e., iron oxides, coal ash) for tar reforming is explored.

Natesakhawat, Sittichai↗
DOE OSTI2024

Optimizing the combustion synthesis of FeAlxOy catalysts for microwave-assisted thermocatalytic dehydrogenation of fossil fuels

The growing demand for hydrogen requires the development of clean and energy-efficient technologies for its synthesis. Microwave-assisted thermocatalytic dehydrogenation of fossil fuels has demonstrated the potential to produce H2 with high yield and selectivity, and simultaneously generate valuable nanostructured carbon byproducts. In prior work, iron-based alumina (FeAlxOy) catalysts for this process were made via solution combustion synthesis (SCS). However, the effect of SCS parameters on the dehydrogenation performance is not well understood. The present study investigates this by varying the SCS fuel, Fe:Al molar ratio, and heating mode. The results show subtle changes of these parameters can result in significant differences in the phase composition, specific surface area, and microwave absorbing properties of FeAlxOy, which all affect microwave-assisted dehydrogenation. Notably, H2 selectivity can be increased from 30% to 74%. Statistical testing determined that the SCS fuel used was the most significant SCS parameter affecting dehydrogenation performance.

Chanoi, Zachary A.↗
DOE OSTI2021

Nontargeted vs. Targeted vs. Smart Load Shifting Using Heat Pump Water Heaters

Deployment of CTA-2045–enabled devices is increasing in the U.S. market. These devices allow utilities or third-party aggregators to control appliance energy use in homes, and could also be applied to end uses in small commercial buildings. This study focuses on a field study using CTA-2045–enabled water heaters to shift electric load off the peak and toward periods when renewable resources are more prevalent (e.g., near noon for solar resources and near midnight for wind resources). The following load shifting strategies were compared to understand effects on the aggregate load-shifting capabilities of Heat Pump Water Heaters (HPWHs) and on consumer hot water supply: non-targeted (traditional), targeted (grouped, with different shifting schedules) and “smart” (adaptive control commands). The results of this study show that targeted and smart control strategies yield significantly more load-shifting potential from a population of water heaters than the non-targeted approach without sacrificing hot water supply to occupants. However, as control commands become more aggressive, aggregators may face challenges in meeting consumer hot water demand. Furthermore, the findings and lessons learned can benefit electric utilities and inform updates to manufacturer controls and communications standards. The data collected may also be useful for developing and validating HPWH models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Supplementary spatial data for Available Land for Cellulosic Biofuel Production: A Supply Chain Centered Comparison

Collection of the underlying spatially explicit data for Available Land for Cellulosic Biofuel Production: A Supply Chain Centered Comparison. Includes raw biomass yield data and soil carbon sequestration potential data for three types of marginal land for the USA midwest at the field level including field areas. Collection also includes raw land rasters for the three types of marginal land, model parameters for the MILP model used in the study, and results used to generate the figures in the paper.

bioenergy landscape design↗