Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Plugs”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

UQpy Version 4.2: Uncertainty quantification with Python

We introduce a new module for the UQpy software package which extends its capabilities into the field of Scientific Machine Learning. This module builds on PyTorch to create a flexible and robust platform for uncertainty quantification in machine learning. The scientific machine learning module of UQpy introduces custom layers, neural networks, and neural network trainers that are compatible with torch version 2.2.2 and allow for “plug and play” integration into existing torch code.

Neural networks↗
DOE OSTI2020

Refueling infrastructure planning in intercity networks considering route choice and travel time delay for mixed fleet of electric and conventional vehicles

The range anxiety has been a major factor that affects the market acceptance of electric vehicles. Even with the recent development of battery technologies, a lack of charging stations and range anxiety are still significant concerns, specifically for intercity trips. This calls for more investments in building charging stations and advancing battery technologies to increase the market share of electric vehicles and improve sustainability. This study suggests a configuration for plug-in electric vehicle charging infrastructure to support long-distance intercity trips of electric vehicles at the network level. A model is proposed to minimize the total system cost including infrastructure investment (building charging stations/spots) and travel time delays (charging time, waiting time in the queue, and detour time to access charging stations). This study fills existing gaps in the literature by capturing realistic patterns of travel demand and considering flow-dependent charging delays at charging stations. Furthermore, the proposed model, which is formulated as a mixed-integer program with nonlinear constraints, solves the optimization problem at the network level. At the network level, impacts of charging station locations on the traffic assignment problem with a mixed fleet of electric and conventional vehicles need to be considered. To this end, a traffic assignment module is integrated with a simulated annealing algorithm. The numerical experiments show a satisfactory application of the model for a full-scale case study (intercity network in Michigan). The solution quality and efficiency of the proposed solution algorithm are evaluated against those of an enumeration approach for a small case study. The results suggest that even for the current market share and charging stations’ setting, a significant investment is needed to support intercity trips without range anxiety issues and with acceptable delays. Additionally, through sensitivity analyses, the required infrastructure and battery investments to support intercity trips with acceptable delays are established for hypothetical increased market shares and battery size in the future.

42 ENGINEERING↗
DOE OSTI2020

Optimizing workplace charging facility deployment and smart charging strategies

This study introduces a workplace charging (WPC) optimization model that maximizes the total satisfied electric miles of employees’ plug-in electric vehicles, subject to a given annual budget. The model optimizes both planning decisions of charger number and power levels and operation decisions of charging spot assignment and charging schedule for the given temporal distribution of charging demands and varied electricity prices. Results of experiments based on national average travel data indicate that the actual WPC strategy varies by budget level. Through optimization, the strategy could reduce impacts of the varied electricity price by shifting charging schedules to periods when electricity prices are low. Also, the model is expanded to study the trade-off between providing WPC and addressing consequence of degraded charging service by including the per-mile shadow cost of unsatisfied charging demand. Finally, we observe that their relative competitiveness mainly depends on the actual shadow cost of WPC.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Data-driven method for electric vehicle charging demand analysis: Case study in Virginia

Electric vehicle (EV) adoption in the U.S. will be accelerated by the historic $7.5 billion public investments in EV charging infrastructure. Careful analysis of EV charging demands plays a vital role in understanding the energy requirements, power grid impact, and smart charging management opportunities of EVs. To this end, this paper develops a data-driven trip-chaining-based modeling framework including five steps: Trip data acquisition and preprocessing, EV adoption modeling, travel itinerary synthesis, EV charging demand simulation and EV load profile generation. The developed analysis framework was demonstrated using real-world data for one region in Virginia, U.S. The results show that the proposed modeling framework can work effectively. For the study region in 2040, the predicted number of plug-in EVs is 470,114, resulting in a weekly charging demand of 38,078,127 kWh (55% home, 9% work, and 36% public) in September and 45,920,358 kWh (61% home, 9% work, and 30% public) in February.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Low-temperature oxidation of methane and methanol on iridium oxides

Iridium oxides (IrO 2 ) are of significant interest for low-temperature oxidation of small molecules such as CH 4 and CH 3 OH, although the physical origin of their high activity remains under debate. Here, we demonstrate that the enhanced activity of IrO 2 arises from the formation of coordinatively unsaturated (CUS) oxygen species. By combining ambient-pressure X-ray spectroscopy and density functional theory calculations, we present evidence for the formation of CUS oxygen during CH 4 and CH 3 OH oxidation. Such surface speciation correlates with the conversion of methane to carbon dioxide and methanol to methyl formate on rutile IrO 2 and hydrous IrO 2 powder catalysts in a plug-flow reactor at room temperature. These findings extend the understanding of the physical origin of the higher activity of iridium oxide thin-film catalysts to powder catalysts and provide insights into the tuneability of iridium-oxide-containing catalysts for low-temperature C–H and O–H bond activation.

AP-XPS↗
DOE OSTI2026

Time Matters: A Survival Analysis of Public Electric Vehicle Charging Infrastructure Utilization

The rapid adoption of plug-in electric vehicles (PEVs) places significant demands on public charging infrastructure, making it critical to understand and optimize charger utilization. This study provides one of the most comprehensive analyses of charging behavior to date by applying a survival analysis to a dataset of nearly 16 million level 2 (L2) and direct current (DC) fast charger sessions across the United States from 2017 to 2022. Using Kaplan-Meier curves and log rank tests, our analysis reveals statistically significant and distinct duration patterns influenced by charger type, time of day, and day of the week. We find that L2 charging sessions exhibit high variability tied to venue type, whereas DC sessions are more uniform, typically lasting 30-45 min. This study introduces the operational efficiency score (OES), a metric for standardizing the performance evaluation of charging stations. Our findings offer actionable insights for optimizing charger deployment, developing dynamic pricing strategies to reduce vehicle dwell time, and improving load management for grid operators, ultimately enhancing the efficiency and availability of public charging infrastructure.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

HMGA2 contributes to vascular development and sprouting angiogenesis by promoting IGFBP2 production

Highlights: • Our findings demonstrates that HMGA2 promotes central arteries formation in zebrafish. • HMGA2 induces vascular development through modulation of IGFBP2 whatever in vivo or in vitro. • The mRNA expressions of HMGA2 and IGFBP2 increased significantly in peripheral blood of Hemangioma patients. • HMGA2-IGFBP2 are promising candidates towards therapeutical vascularization in vascular diseases like Hemangioma. Angiogenesis is the process by which new blood vessels form from preexisting vessels and regulates the processes of embryonic development, wound healing and tumorigenesis. HMGA2 is involved in the occurrence of several cancers, but its biological role and the exact downstream genes involved in vascular development and sprouting angiogenesis remain largely unknown. Here, we first found that HMGA2 knockdown in zebrafish embryos resulted in defects of central artery formation. RNA sequencing revealed that IGFBP2 was significantly downregulated by interference with HMGA2, and IGFBP2 overexpression reversed the inhibition of brain vascular development caused by HMGA2 deficiency. In vitro, we further found that HMGA2 knockdown blocked the migration, tube formation and branching of HUVECs. Similarly, IGFBP2 protein overexpression attenuated the impairments induced by HMGA2 deficiency. Moreover, the promotion of angiogenesis by HMGA2 overexpression was verified in a Matrigel plug assay. We next found that HMGA2 bound directly to a region in the IGFBP2 promoter and positively regulated IGFBP2 expression. Interestingly, the mRNA expression levels of HMGA2 and IGFBP2 were increased significantly in the peripheral blood of hemangioma patients, indicating that overexpression of HMGA2 and IGFBP2 results in vessel formation, consistent with the results of the in vivo and in vitro experiments. In summary, our findings demonstrate that HMGA2 promotes central artery formation by modulating angiogenesis via IGFBP2 induction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Metabolic flux optimization of iterative pathways through orthogonal gene expression control: Application to the β-oxidation reversal

Balancing relative expression of pathway genes to minimize flux bottlenecks and metabolic burden is one of the key challenges in metabolic engineering. This is especially relevant for iterative pathways, such as reverse β-oxidation (rBOX) pathway, which require control of flux partition at multiple nodes to achieve efficient synthesis of target products. Here, we develop a plasmid-based inducible system for orthogonal control of gene expression (referred to as the TriO system) and demonstrate its utility in the rBOX pathway. Leveraging effortless construction of TriO vectors in a plug-and-play manner, we simultaneously explored the solution space for enzyme choice and relative expression levels. Remarkably, varying individual expression levels led to substantial change in product specificity ranging from no production to optimal performance of about 90% of the theoretical yield of the desired products. We obtained titers of 6.3 g/L butyrate, 2.2 g/L butanol and 4.0 g/L hexanoate from glycerol in E. coli, which exceed the best titers previously reported using equivalent enzyme combinations. Since a similar system behavior was observed with alternative termination routes and higher-order iterations, we envision our approach to be broadly applicable to other iterative pathways besides the rBOX. Here, considering that high throughput, automated strain construction using combinatorial promoter and RBS libraries remain out of reach for many researchers, especially in academia, tools like the TriO system could democratize the testing and evaluation of pathway designs by reducing cost, time and infrastructure requirements.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Biodiesel Ethers: Fatty Acid-Derived Alkyl Ether Fuels as Improved Bioblendstocks for Mixing-Controlled Compression Ignition Engines

In the last 20 years, biodiesel consumption in the United States has swiftly increased to ~2 billion gallons per year as a renewable supplement to fossil fuel. However, further expansion of biodiesel use is currently limited in part by poor cold weather performance, which prevents year-round blending and necessitates blend walls ≤5% v/v. In order to provide a diesel fuel blendstock with improved cold weather performance (cloud point, pour point, and cold filter plug point), while at the same time maintaining other required fuel performance specifications, several biodiesel redox analogues were synthesized and tested. The best performing candidate fuels from this class showed improvement in the derived cetane number (29.3% shorter ignition delay), lower heating value (+4.7 MJ/kg), relative sooting tendency (–7.4 YSI/MJ), and cloud point (15 °C lower) when compared to a B100 biodiesel composed of an identical fatty acid profile. It was observed as a general trend that the reduced form of biodiesel, fatty alkyl ethers (FAEs), shows performance improvements in all fuel property metrics. The suite of improved properties provided by FAEs gives biodiesel producers the opportunity to diversify their portfolio of products derived from lipid and alcohol feedstocks to include long-chain alkyl ethers, a biodiesel alternative with particular applicability for winter weather conditions across the US.

09 BIOMASS FUELS↗
DOE OSTI2022

Coprocessing Biomass Fast Pyrolysis and Catalytic Fast Pyrolysis Oils with Vacuum Gas Oil in Refinery Hydroprocessing

Fast pyrolysis and catalytic fast pyrolysis (CFP) have been considered to be promising approaches for converting lignocellulosic biomass into liquid bio-oils followed by upgrading to produce fuel-range hydrocarbon products. Co-processing fast pyrolysis and CFP bio-oils with petroleum feedstocks leverages the existing petroleum refining infrastructure, which reduces Capex for the overall conversion technologies for biomass to fuel and enables fast adoption of the technologies and biofuels. Here, we reported the co-processing of different woody fast pyrolysis and CFP bio-oils with petroleum vacuum gas oil (VGO) at 5-25% bio-oil blending levels over a NiMo sulfide catalyst for mild hydrocracking. The catalyst activities over ~300 hours time on stream, the product yield and properties, and the biogenic carbon content in products are provided. Co-processing of the raw fast pyrolysis bio-oil in our configuration was not successful because the instability of the bio-oil resulted in reactor plugging and bio-oil stabilization by hydrogenation enabled their stable co-processing with VGO, whereas the CFP bio-oil can be co-processed without pretreatment. Simultaneous hydrodesulfurization, hydrodeoxygenation, and hydrocracking reactions occurred during co-processing and no obvious decrease in hydrodesulfurization and hydrocracking conversion of VGO was observed, suggesting the minimal impact of co-processed bio-oils on the reaction of VGO and also the simultaneous conversion of bio-oil and VGO to produce fuel products with much-reduced S and O content. Biogenic carbon content in co-processed products calculated by yield mass balance, together with results from isotopic measurements, indicates high biogenic carbon incorporation into liquid hydrocarbon products. In conclusion, higher biogenic carbon incorporation into fuel products was observed when co-processing CFP bio-oils than the fast pyrolysis bio-oils and over 90% of carbon in CFP bio-oil was incorporated into liquid hydrocarbon products.

09 BIOMASS FUELS↗
DOE OSTI2020

Real Time 3D Observations of Portland Cement Carbonation at CO2 Storage Conditions

Depleted oil reservoirs are considered a viable solution to the global challenge of CO 2 storage. A key concern is whether the wells can be suitably sealed with cement to hinder the escape of CO 2 . Under reservoir conditions, CO 2 is in its supercritical state, and the high pressures and temperatures involved make real-time microscopic observations of cement degradation experimentally challenging. Here, we present an in situ 3D dynamic X-ray micro computed tomography (μ-CT) study of well cement carbonation at realistic reservoir stress, pore-pressure, and temperature conditions. The high-resolution time-lapse 3D images allow monitoring the progress of reaction fronts in Portland cement, including density changes, sample deformation, and mineral precipitation and dissolution. By switching between flow and nonflow conditions of CO 2 -saturated water through cement, we were able to delineate regimes dominated by calcium carbonate precipitation and dissolution. For the first time, we demonstrate experimentally the impact of the flow history on CO 2 leakage risk for cement plugging. In-situ μ-CT experiments combined with geochemical modeling provide unique insight into the interactions between CO 2 and cement, potentially helping in assessing the risks of CO 2 storage in geological reservoirs.

58 GEOSCIENCES↗
DOE OSTI2026

Locating Undocumented Wells Using Historical Oil and Gas Exploration Maps: A Case Study in Osage County, Oklahoma

Undocumented oil and gas wells lack reliable information about their locations and characteristics, making them difficult to identify. These wells can result in unanticipated delays and costs in the development of nearby surface and subsurface resources, and, if improperly plugged, can cause contamination. This study leverages historical petroleum exploration maps to locate such wells, focusing on Osage County, Oklahoma. Two sets of early 20th century oil and gas exploration maps by the United States Geological Survey were georeferenced and analyzed using a computer vision model to detect well symbols. The locations of detected wells were compared to the location of known wells in the database from the Bureau of Indian Affairs Osage Agency to identify potential undocumented wells. The analysis yielded over 500 potential undocumented wells, with dry holes constituting the largest fraction. Field verification confirmed the presence of some undocumented wells. Comparison with prior work revealed limited overlap, underscoring the complementary value of historical oil and gas maps for locating undocumented wells. This approach demonstrates the utility of integrating historical cartographic resources with modern geospatial and machine learning techniques to improve the identification and management of undocumented wells.

Energy - Petroleum↗
DOE OSTI2024

Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data

The paper presents the development of algorithms for mass and energy constrained neural network models that can exactly conserve the overall mass and energy of distributed chemical process systems, even though the noisy transient data used for optimal model training violate the same. In contrast to approximately satisfying mass and energy balance constraints of a system by soft penalization of objective function, algorithms have been developed for solving equality-constrained nonlinear optimization problems, thus providing the guarantee of exactly satisfying the system mass and energy conservation laws. For developing dynamic mass-energy constrained network models for distributed systems, hybrid series and parallel dynamic-static neural networks have been leveraged. The developed algorithms for solving both the training and forward problems are validated using both steady-state and dynamic data in the presence of various noise characteristics. The developed data-driven algorithms are flexible to exactly satisfy mass and energy balance constraints for dynamic chemical processes if the system holdup information is available. The proposed network structures and algorithms are applied to the development of data-driven lumped and distributed models of an adiabatic superheater/reheater system, a nonisothermal continuous stirred tank reactor, as well as an electrically heated plug-flow reactor system where one form of energy gets transformed to another. It has been observed that the mass-energy constrained neural networks yield a root mean squared error of <1% with respect to the system truth for the case studies evaluated in this work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

CO 2 Capture Using Electrochemically Mediated Amine Regeneration

We describe the current status of our research on, and the development of, the Electrochemically Mediated Amine Regeneration (EMAR) process for the capture of CO2 from flue gases. The absorption step in the EMAR process is the same as in the widely used amine process, but the desorption step is accomplished electrochemically as opposed to by a thermal swing used in the amine process. Our laboratory unit has operated continuously for over 200 h, spanning 130 absorption/desorption cycles. We have established an understanding of the thermodynamics of the EMAR process and provided engineering estimates of key features (energetics and sizing) of this technology. The EMAR system does not rely on steam integration, making it a truly plug-and-play unit that can be easily deployed for a range of applications. The EMAR system can also be scaled-down for distributed, modular, and small-scale operations (e.g., stainless steel mini-mills, etc.). Here, results to date indicate that the EMAR process has the potential to be a viable option for post-combustion CO2 capture.

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

PDFDataExtractor: A Tool for Reading Scientific Text and Interpreting Metadata from the Typeset Literature in the Portable Document Format

The layout of portable document format (PDF) files is constant to any screen, and the metadata therein are latent, compared to mark-up languages such as HTML and XML. No semantic tags are usually provided, and a PDF file is not designed to be edited or its data interpreted by software. However, data held in PDF files need to be extracted in order to comply with opensource data requirements that are now government-regulated. In the chemical domain, related chemical and property data also need to be found, and their correlations need to be exploited to enable data science in areas such as data-driven materials discovery. Such relationships may be realized using text-mining software such as the “chemistry-aware” natural-language-processing tool, ChemDataExtractor; however, this tool has limited data-extraction capabilities from PDF files. This study presents the PDFDataExtractor tool, which can act as a plug-in to ChemDataExtractor. It outperforms other PDF-extraction tools for the chemical literature by coupling its functionalities to the chemical-named entityrecognition capabilities of ChemDataExtractor. The intrinsic PDF-reading abilities of ChemDataExtractor are much improved. The system features a template-based architecture. This enables semantic information to be extracted from the PDF files of scientific articles in order to reconstruct the logical structure of articles. While other existing PDF-extracting tools focus on quantity mining, this template-based system is more focused on quality mining on different layouts. PDFDataExtractor outputs information in JSON and plain text, including the metadata of a PDF file, such as paper title, authors, affiliation, email, abstract, keywords, journal, year, document object identifier (DOI), reference, and issue number. With a self-created evaluation article set, PDFDataExtractor achieved promising precision for all key assessed metadata areas of the document text.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Interactions of an Imine Polymer with Nanoporous Silica and Carbon in Hybrid Adsorbents for Carbon Capture

Efficient carbon capture from stationary point sources can be achieved using hybrid adsorbents comprising nanoporous substrates coated with imine polymers. The physical properties of the CO 2 -adsorbing, nanodispersed polymers are altered by their interactions with the substrate, which in turn may impact their capture capacity. We study silica and carbon nanoporous substrates with different pore morphologies that were impregnated with polymer imine with the goal of characterizing the polymer dispersions in the pores. For silica and carbon samples, the mean densities of confined poly(ethylene imine) (PEI) were measured as functions of polymer loading and temperature using small-angle neutron scattering. Strong densification is found for imine polymers imbibed in mesoporous carbon. PEI in nanoporous silica does not experience this strong densification. At high loadings, plugs form, preferably at the pore throats, and can reduce accessible porosity. CO 2 capture measurements show that PEI interactions with the substrate play an important role. In conclusion, PEI in carbon shows the highest capture capacity at low temperatures and the lowest CO 2 adsorption at high temperatures, making it well-suited for temperature swing adsorption applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Bifunctional Janus Particles as Multivalent Synthetic Nanoparticle Antibodies (SNAbs) for Selective Depletion of Target Cells

Monoclonal antibodies (mAb) have had a transformative impact on treating cancers and immune disorders. SNAbs However, their use is limited by high development time and monetary cost, manufacturing complexities, suboptimal pharmacokinetics, and availability of disease-specific targets. To address some of these challenges, we developed an entirely synthetic, multivalent, Janus nanotherapeutic platform, called Synthetic Nanoparticle Antibodies (SNAbs). SNAbs, with phage-display-identified cell-targeting ligands on one "face" and Fc-mimicking ligands on the opposite "face", were synthesized using a custom, multistep, solid-phase chemistry method. SNAbs efficiently targeted and depleted myeloid-derived immune-suppressor cells (MDSCs) from mouse-tumor and rat-trauma models, ex vivo. Systemic injection of MDSC-targeting SNAbs efficiently depleted circulating MDSCs in a mouse triple-negative breast cancer model, enabling enhanced T cell and Natural Killer cell infiltration into tumors. Finally, our results demonstrate that SNAbs are a versatile and effective functional alternative to mAbs, with advantages of a plug- and-play, cell-free manufacturing process, and high-throughput screening (HTS)-enabled library of potential targeting ligands.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

In Situ Catalytic Fast Pyrolysis using Red Mud Catalyst: Impact of catalytic fast pyrolysis temperature and biomass feedstocks

Catalytic fast pyrolysis (CFP) has been considered as a very promising approach for converting lignocellulosic biomass into higher-quality bio-oils followed by hydrotreating to produce fuelrange products. A reactive, robust, and low-cost catalyst is required to drive the CFP process. Red mud, a side-product produced during the refining of bauxite to alumina, appears to be an effective catalyst for in situ CFP of biomass. In this paper, we report the impact of CFP reaction temperature on the conversion of a pinyon juniper feedstock to bio-oils using red mud as the catalyst and then to fuel-range hydrocarbons by hydrotreating of the produced bio-oil. The yield and quality of the CFP bio-oil produced and the yield and quality of hydrotreated final products were determined. When the CFP process temperature was lowered from 450 to 400ºC, the bio-oil yield increased with minimal differences in the oxygen content, hydrogen-to-carbon ratio, water content, etc. In addition, CFP bio-oils at both temperatures were processed in a single-stage continuous hydrotreater without reactor plugging during the testing period (i.e., ~100 hours on stream). The yield of CFP bio-oil produced at 400ºC was lower than the yield at 450ºC. However, the overall yield, from biomass to hydrocarbon fuel, was still higher for CFP processing at 400ºC than for processing at 450ºC. This indicates the potential for enhancing overall carbon efficiency, including CFP processing and hydrotreating, by employing red mud catalysts for biomass pyrolysis and tuning the CFP parameters. Detailed analysis of bio-oil and hydrotreated products showed a lower content of lignin-derived species in both samples. Furthermore, CFP processing of three different biomass feedstocks corroborated red mud catalyst development for producing improved quality bio-oil and, when combined with hydrotreating, for the production of fuel range hydrocarbons.

Santosa, Daniel M.↗