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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 577 records · Page 32

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NASA NTRS1981

Sensitivity analysis of the add-on price estimate for the silicon web growth process

The web growth process, a silicon-sheet technology option, developed for the flat plate solar array (FSA) project, was examined. Base case data for the technical and cost parameters for the technical and commercial readiness phase of the FSA project are projected. The process add on price, using the base case data for cost parameters such as equipment, space, direct labor, materials and utilities, and the production parameters such as growth rate and run length, using a computer program developed specifically to do the sensitivity analysis with improved price estimation are analyzed. Silicon price, sheet thickness and cell efficiency are also discussed.

Mokashi, A. R.↗

DOE OSTI2021

Microchannel Reactor for Ethanol to Butene: CRADA 503 [Abstract only]

A key challenge facing most bioprocessing operations is that multiple unit operations are required, thereby resulting in complex, energy-intensive, and expensive processes. Further, biomass transportation costs drive the need for smaller, distributed processing plants. To incorporate the smaller scales desirable for biomass, novel processes must be developed with reduced capital costs. With over 20 years of experience in the development and commercialization of microchannel reactor technology, Oregon State University will partner with Pacific Northwest National Laboratory to demonstrate a microchannel reactor with lower capital costs for an alcohol-to-jet (ATJ) process technology that is currently being commercialized by LanzaTech. Ethanol can be produced from biomass feedstocks such as LanzaTech’s proprietary biochemical process using carbon from a number of possible feedstocks; syngas generated from biomass resources (e.g., MSW, organic industrial waste, agriculture waste) or reformed biogas, or from other biomass feedstocks such as corn kernel fiber. Ethanol then undergoes catalytic dehydration to form ethylene followed by a two-step oligomerization, hydrogenation, and fractionation to control the hydrocarbon product slate to the jet-range. Successful process development aided by a market pull for low carbon aviation fuel has spurred scale-up and commercial demonstration. However, Sustainable Aviation Fuel is a very price sensitive market and improved economics through process intensification will make the current ATJ process even more attractive. Recent efforts at PNNL have culminated in the development of a new catalyst technology for the conversion of ethanol to n-butene-rich olefins. A greater than 90% conversion, total olefin selectivity of 80-90% (n-butene selectivity ~60%), and good stability over a 100 hour test duration has been demonstrated at the bench scale. Producing butene-rich olefins directly from ethanol with high yield is new and impactful because the higher olefins can be selectively oligomerized to distillate-range hydrocarbons, thus eliminating one process step from the current ATJ process. Further, coupling the severely endothermic ethanol dehydration with exothermic C-C bond formation results in more energy efficient processing. Additional intensification and energy savings will stem from incorporating this new ethanol to n-butene catalyst technology within the ATJ process implemented using a microchannel reactor platform. Due to recent advances in microchannel manufacturing methods and associated cost reductions we believe the time is right to adapt this technology toward new commercial bioconversion applications.

02 PETROLEUM↗

DOE OSTI2024

Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene

Frontal polymerization (FP) is a self-sustaining curing process that enables rapid and energy-efficient manufacturing of thermoset polymers and composites. Computational methods conventionally used to simulate the FP process are time-consuming, and repeating simulations are required for sensitivity analysis, uncertainty quantification, or optimization of the manufacturing process. Here, in this work, we develop an adaptive surrogate deep-learning model for FP of dicyclopentadiene (DCPD), which predicts the evolution of temperature and degree of cure orders of magnitude faster than the finite-element method (FEM). The adaptive algorithm provides a strategy to select training samples efficiently and save computational costs by reducing the redundancy of FEM-based training samples. The adaptive algorithm calculates the residual error of the FP governing equations using automatic differentiation of the deep neural network. A probability density function expressed in terms of the residual error is used to select training samples from the Sobol sequence space. The temperature and degree of cure evolution of each training sample are obtained by a 2D FEM simulation. The adaptive method is more efficient and has a better prediction accuracy than the random sampling method. With the well-trained surrogate neural network, the FP characteristics (front speed, shape, and temperature) can be extracted quickly from the predicted temperature and degree-of-cure fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

Unsteady Pressure Sensitive Paint Camera Calibration Improvements

New challenges have arisen in processing a significantly increased volume of data collected during recent large-scale demonstrations of unsteady pressure-sensitive paint. Techniques designed for several thousands of images collected in a lab do not necessarily scale to tens of millions collected in a large-scale test. New techniques are needed to meet the tighter requirements on robustness and accuracy that accompany larger wind tunnel models, higher camera resolution, and more run conditions per test. This paper outlines several such techniques and improvements in regard to the camera calibration process.

Camera calibration↗

NASA NTRSDate not supplied

Unsteady Pressure Sensitive Paint Camera Calibration Improvements

Camera calibration↗

NASA NTRS1972

Trace elements profiles, notably Hg, from a preliminary study of the Apollo 15 deep-drill core.

The possible thermal gradient near the surface during a lunation is considered together with the heat flow from the interior, the physical process of Hg migration, the results from core and trench samples from previous missions, and other temperature sensitive phenomena that may help understand the processes. U, Os, and Ru concentrations in the deep drill core samples are of potential interest and are summarized in a table. The Os tends to parallel the Hg profile with depth.

Jovanovic, S.↗

DOE OSTI2023

Aerosol Hygroscopic Growth, Mixing State, and Cloud Condensation Nuclei Activity during TRACER (Field Campaign Report)

Convective clouds play a critical role in the Earth’s climate system. Recent research has shown that a realistic representation of convective processes is critical to constraining climate sensitivity in global climate models. Theoretical and modeling studies showed that aerosols could have strong dynamic feedback to convection in warm and humid environments through enhancing ice-related processes and condensational growth. A few observation-based studies also suggested the influence of aerosols on convective cloud and precipitation properties. However, robust observational quantification of an aerosol effect on convective clouds isolated from other factors remains elusive. Understanding the impact of aerosol on convective clouds requires knowledge of the cloud condensation nuclei (CCN) spectrum, which represents the number of particles that uptake water and form cloud droplets as a function of supersaturation. The water uptake by aerosol is also of critical importance for the direct interaction of aerosol with radiation (i.e., aerosol direct effect) due to light scattering and absorption by aerosol. While both droplet activation under supersaturated conditions (i.e., relative humidity RH > 100%) and the hygroscopic growth under sub-saturated conditions (i.e., RH < 100%) are strongly influenced by particle hygroscopicity, the thermodynamic regimes and measurement methods are quite different. Aerosol particles, especially organic particles, can exhibit higher hygroscopicity for droplet activation than that for hygroscopic growth. In the subsaturated regime, the hygroscopicity of organic particles can also vary strongly with RH. However, global climate models usually treat organic species in aerosols with a constant hygroscopicity, potentially introducing substantial uncertainties in the quantification of aerosol radiative effects.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1989

Orbital maneuvering vehicle teleoperation and video data compression

The Orbital Maneuvering Vehicle (OMV) and concepts of teleoperation and video data compression as applied to OMV design and operation are described. The OMV provides spacecraft delivery, retrieval, reboost, deboost and viewing services, with ground-control or Space Station operation, through autonomous navigation and pilot controlled maneuvers. Communications systems are comprised of S-band RF command, telemetry, and compressed video data links through the TDRSS and GSTDN networks. The control console video monitors display a monochrome image at an update rate of five frames per second. Depending upon the mode of operation selected by the pilot, the video resolution is either 255 x 244 pixels, or 510 x 244 pixels. Since practically all video image redundancy is removed by the compression process, the video reconstruction is particularly sensitive to data transmission bit errors. Concatenated Reed-Solomon and convolution coding are used with helical data interleaving for error detection and correction, and an error-containment process minimizes the propagation of error effects throughout the video image. Video sub-frame replacement is used, in the case of a non-correctable error or error burst, to minimize the visual impact to the pilot.

Jones, Steve↗

DOE OSTI2020

BioSTEAMDevelopmentGroup/biosteam

BioSTEAM is a fast and flexible package for the design, simulation, and techno-economic analysis of biorefineries under uncertainty. BioSTEAM’s framework is built to streamline and automate early-stage technology evaluations and to enable rigorous sensitivity and uncertainty analyses. Complete biorefinery configurations are available at the Bioindustrial-Park GitHub repository, BioSTEAM’s premier repository for biorefinery models and results. The long-term growth and maintenance of BioSTEAM is supported through both community-led development and the research institutions invested in BioSTEAM. Through the open-source and community-lead platform, BioSTEAM aims to foster communication and transparency within the biorefinery research community for an integrated effort to expedite the evaluation of candidate biofuels and bioproducts. Additionally, an agile life cycle assessment (LCA) platform has been designed to interface with BioSTEAM, BioSTEAM-LCA. This open-source, installable package allows users to perform streamlined LCAs of biorefineries. The focus of BioSTEAM-LCA is to streamline and automate early-stage environmental impact analyses of processes and technologies, and to enable rigorous sensitivity and uncertainty analyses linking process design, performance, economics, and environmental impacts.

Cortes-Peña, Yoel↗

DOE OSTI2020

BioSTEAMDevelopmentGroup/thermosteam

Cortes-Peña, Yoel↗

DOE OSTI2020

Collinear drop

We introduce collinear drop jet substructure observables, which are unaffected by contributions from collinear radiation, and systematically probe soft radiation within jets. These observables can be designed to be either sensitive or insensitive to process-dependent soft radiation originating from outside the jet. Such collinear drop observables can be exploited as variables to distinguish quark, gluon, and color neutral initiated jets, for testing predictions for perturbative soft radiation in Monte Carlo simulations, for assessing models and universality for hadronization corrections, for examining the efficiency of pileup subtraction methods, and for any other application that leaves an imprint on soft radiation. We discuss examples of collinear drop observables that are based both on clustering and on jet shapes. Using the soft-collinear effective theory we derive factorization expressions for collinear drop observables from QCD jets, and carry out a resummation of logarithmically enhanced contributions at next-to-leading-logarithmic order. We also identify an infinite class of collinear drop observables for which the leading double logarithms are absent.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Capsule network-based semantic segmentation model for thermal anomaly identification on building envelopes

Thermography technology is widely used to inspect thermal anomalies in building façade systems. Computer vision-based techniques provide opportunities to autonomously detect such heat anomalies to significantly improve the efficiency of decision-making for building envelope retrofitting and maintenance. Here, in this work, we propose a novel Capsule Network-based deep learning model – CapsLab – that detects and identifies thermal anomalies by semantic segmentation. CapsLab is built based on our proposed prediction-tuning capsule (PT-Capsule) layer. Different from a traditional capsule layer, which consists of part-whole transformation and capsule-routing process, the proposed layer is composed of a prediction and tuning process, which helps decreasing the number of model parameters significantly. While the applicability of traditional Capsule Networks (CapsNets) has been limited to simpler tasks and smaller datasets due to their scalability issue, we can leverage the lightweight of the proposed PT-Capsule layer, and apply it to the semantic segmentation task. In this work, we also employ our previously presented performance metric, referred to as the Anomaly Identification Metric (AIM) (Kakillioglua et al. 2021), to evaluate the segmentation outputs. Traditional performance metrics do not accurately reflect the true performance of the segmentation models in thermal anomaly identification due to the high subjectivity in the annotation process and higher overlap ratio sensitivity of the standard metrics. AIM, on the other hand, is robust to these drawbacks. Experimental results show, both qualitatively and quantitatively, that our proposed segmentation method can effectively segment the thermal anomalies. Specifically, our model provides 9.38% and 13.53% improvements over the baseline model – DeepLabV3+ – based on traditional mIoU score and the AIM score, respectively, while requiring less model parameters and less computation at the same time. In addition, the scores that the AIM metric generates better align with the scores provided by building performance experts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

A consolidated bioprocess design to produce multiple high-value platform chemicals from lignocellulosic biomass and its technoeconomic feasibility

5-Hydroxymethyl furfural (HMF) and furfurals are DOE-listed platform chemicals that can be derived from the renewable carbon in the lignocellulosic biomasses and have the potential to replace petroleum-derived alter- natives. High substrate cost and use of expensive solvents limit the economic feasibility of bio-based HMF production on an industrially relevant scale. The study presents an experimental optimized condition that maximizes the chemical-free production of HMF and furfurals without lowering the yield of total fermentable sugars from Saccharum bagasse. Hydrothermal pretreatment at 210 °C for 15 min yielded approximately 10%, 12%, and 46% of HMF, furfurals, and fermentable sugars per gram of dry biomass, respectively. Additionally, the study proposes a consolidated bioprocess model to produce and recover four high-value bioproducts i.e., HMF, furfurals, ethanol, and acetic acid based on the experimental results and evaluates its technoeconomic feasibility considering HMF as the main product. The minimum selling price (MSP) of HMF was estimated to be 930.6 USD/ t which is competitive with its petroleum-derived precursor alternative p-xylene (1,113 USD/t). The sensitivity analysis performed for the process parameters suggests that pretreatment cost and revenues from coproducts immensely influence the MSP of HMF. The preliminary technoeconomic analysis performed on the consolidated bioprocess design indicates that additional revenue streams from diversified coproducts in biorefineries aid in lowering the MSP of high-value bioproducts.

09 BIOMASS FUELS↗

DOE OSTI2025

First measurement of differential cross sections for muon neutrino charged current interactions on argon with a two-proton final state using the MicroBooNE detector

We present the first measurement of differential cross sections for charged-current muon neutrino interactions on argon with one muon, two protons, and no pions in the final state. These final states are dominated by two-nucleon knockout interactions, which are complicated to model and for which there is currently limited information about the characteristics of these interactions in existing neutrino-nucleus scattering data. Detailed investigations of two-nucleon knockout are vital to support upcoming experiments exploring the nature of the neutrino. Among the different kinematic quantities measured, the opening angle between the two protons, the angle between the total proton momentum and the muon, and the total transverse momentum of the final state system are most sensitive to the underlying physics processes as embodied in various theoretical models.

2-particle 2-hole states↗

DOE OSTI2025

Towards low-carbon low-energy concrete alternatives: Life cycle assessment of carbonated cementitious material-based precast panels

Cement is responsible for 22 % of all global CO 2 emissions from industrial processes. Technological innovation for developing and deploying of alternative materials will be required to decarbonize the cement industry. Carbonated cementitious materials (CCMs) are building materials that rely on carbon mineralization for their strength. A process-based cradle-to-gate life cycle assessment (LCA) was conducted to evaluate the global warming potential (GWP), cumulative energy demand, and water consumption of a lab-scale CCM-based precast panel compared to a conventional precast concrete panel. Since the CCM process is currently a lab-scale early-stage process, the CCM panel showed higher environmental impacts compared to the conventional panel. However, scenario analyses include mature production process scenarios. In conclusion, a sensitivity analysis revealed that the GWP of CCM can be lowered to below that of the conventional panel using polymers, fillers, low-carbon electricity sources, and optimized carbonation parameters.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Earth-Abundant Manganese Nitride Catalysts for Mild-Condition Ammonia Synthesis

Developing advanced catalytic materials for mild-condition ammonia (NH 3 ) synthesis is essential for improving the energy efficiency of the industrial Haber-Bosch process. Here, in this study, we report a ζ-phase manganese nitride (MnN 0.43 ) catalyst for low-temperature NH 3 synthesis. The as-synthesized MnN 0.43 catalyst is protected by a carbon shell, allowing for the storage and processing of the air-sensitive metal nitride under ambient conditions. After activation in situ, the MnN 0.43 catalyst exhibits high activity for NH 3 synthesis at 250–350 °C, surpassing the conventional noble metal based Ru/MgO catalyst. A combination of kinetic, chemisorption, isotope labeling and computational studies indicate that a nitrogen vacancy-mediated associative mechanism accounts for the catalytic enhancements. Our work highlights the great potential of earth-abundant transition metal nitrides for catalyzing mild-condition NH 3 synthesis.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Evidence for Secondary Ice Production in Southern Ocean Maritime Boundary Layer Clouds

Abstract Maritime boundary‐layer clouds over the Southern Ocean (SO) have a large shortwave radiative effect. Yet, climate models have difficulties in representing these clouds and, especially, their phase in this observationally sparse region. This study aims to increase the knowledge of SO cloud phase by presenting in‐situ cloud microphysical observations from the Southern Ocean Clouds, Radiation, Aerosol, Transport Experimental Study (SOCRATES). We investigate the occurrence of ice in summertime marine stratocumulus and cumulus clouds in the temperature range between 6 and −25°C. Our observations show that in ice‐containing clouds, maximum ice number concentrations of up to several hundreds per liter were found. The observed ice crystal concentrations were on average one to two orders of magnitude higher than the simultaneously measured ice nucleating particle (INP) concentrations in the temperature range below −10°C and up to five orders of magnitude higher than estimated INP concentrations in the temperature range above −10°C. These results highlight the importance of secondary ice production (SIP) in SO summertime marine boundary‐layer clouds. Evidence for rime splintering was found in the Hallett‐Mossop (HM) temperature range but the exact SIP mechanism active at lower temperatures remains unclear. Finally, instrument simulators were used to assess simulated co‐located cloud ice concentrations and the role of modeled HM rime‐splintering. We found that CAM6 is deficient in simulating number concentrations across the HM temperature range with little sensitivity to the model HM process, which is inconsistent with the aforementioned observational evidence of highly active SIP processes in SO low‐level clouds.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Strong parametric dispersive shifts in a statically decoupled two-qubit cavity QED system

Qubits in cavity quantum electrodynamic (QED) architectures are often operated in the dispersive regime, in which the operating frequency of the cavity depends on the energy state of the qubit, and vice versa. The ability to tune these dispersive shifts provides additional options for performing either quantum measurements or logical manipulations. Here we couple two transmon qubits to a lumped-element cavity through a shared superconducting quantum interference device (SQUID). In this work, our design balances the mutual capacitive and inductive circuit components so that both qubits are statically decoupled from the cavity with low flux sensitivity, offering protection from decoherence processes. Parametric driving of the SQUID flux enables independent, dynamical tuning of each qubit’s interaction with the cavity. As a practical demonstration, we perform pulsed parametric dispersive readout of both qubits. The dispersive frequency shifts of the cavity mode follow the theoretically expected magnitude and sign. This parametric approach creates an extensible, tunable cavity QED framework with various future applications, such as entanglement and error correction via multi-qubit parity readout, state and entanglement stabilization, and parametric logical gates.

77 NANOSCIENCE AND NANOTECHNOLOGY↗