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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mass flow rate effect on a rotating detonation combustor with an axial air injection

Changing the flow rate of reactants being injected into a rotating detonation combustor (RDC) results in interesting behavior of the system. Prior studies have found that an increase in mass flow rate gradually increases the detonation wave speed before splitting the wave into multiple fronts. Here, the focus of this study is in understanding the physics of such behavior through a combination of experiments and numerical simulations. For this purpose, the axial air inlet-based RDC geometry is used. In the experiments, the wave velocity increased similar to prior studies. Corresponding full-scale simulations show that increase in mass flow rate by increasing pressure of the feed plenums shortens the recovery time of the injectors. This causes a more uniform fuel–air mixture to form prior to the arrival of the detonation wave. As a result, a more ideal detonation is observed, which leads to an increased wave velocity. Details of the detonation structure and variation with mass flow rate are analyzed. The presence and variation of the different deflagration zones in such non-premixed RDCs are also discussed.

42 ENGINEERING↗
DOE OSTI2021

Flowback Test Analyses at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) Site

Abstract In 2017 and 2019, injection testing was carried out in three zones in a vertical well in granite at the Frontier Observatory for Research in Geothermal Energy site near Milford, Utah, USA. In several injection cycles, flowback was implemented rather than shut-in. The goal was to explore an alternative to prolonged shut-in periods for inferring closure stress, formation compressibility, and formation permeability (permeability thickness product). The flowback procedures involved a cyclic flowback/shut-in, while pressure decreased. The flowback data are presented, and analyses are shown. The inferred closure stress(es) from flowback analyses are lower than for equivalent injection cycles that were strictly shut-in. Relatively high formation compressibility obtained from the flowback analyses indicates an extensive, fractured system. This study also includes numerical simulation of the flowback events. The numerical model shows that the rebound pressure is not necessarily the lower bound of the minimum principal stress. The signature of stiffness change can be identified as the process when the depletion mainly transitions from hydraulic fracture to natural fractures from numerical analysis. Overall, flowback potentially has advantages over shut-in because of the reduced time to closure.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Sum frequency generation (SFG) microscopy analysis of cellulose microfibrils in Physcomitrium patens gametophore leaf

Physcomitrium patens is a good model system to study the structure and function relationships of cellulose synthase (CESA) proteins since the life cycle of moss is short and genetic modification is relatively easy. Here, we report a microscopic sum frequency generation (SFG) vibrational spectroscopy analysis of P. patens leaves in a never-dried and fully hydrated state. Using imaging-SFG spectroscopy, cellulose microfibril (CMF) organization in the midrib, laminar, and marginal regions of the gametophore leaf were analyzed separately. The azimuth angle dependence of SFG signals showed that CMFs in the thick stereid cell walls in the midrib are highly aligned along the microfibril angle of ~ 30° with respect to the midrib axis. CMFs in the marginal cell walls also are preferentially tilted toward the longitudinal axis of leaf with the characteristic features of secondary cell walls. On the other hand, CMFs in the lamina cell walls have no preferential orientation and their SFG intensity is low, which is consistent with the primary cell wall property. Finally, the thick walls and highly aligned CMFs in the midrib and marginal cells of P. patens leaves are consistent with their support function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Unveiling mechanism of surface-guided platinum nanowire growth

We report surface-guided growth of non-planar nanowires on functional substrates offers the new opportunity to precisely control their diameter, length, density and alignment, which will greatly benefit their direct integration into practical devices for large-scale applications. However, control of noble metal nanowires growth and arrangement remains a great challenge, and the mechanistic understanding is still limited. Herein, we choose Pt as a model material system to study the synthesis conditions required to control the in situ growth of aligned Pt nanowires on flat titanium substrate via a one-step and room-temperature green chemical synthesis process in aqueous solution. Most importantly, it is for the first time discovered that ordered nanoarrays and self-assembled nanoflowers can be directly grown on flat Ti substrate by merely adjusting the reaction times, without use of any soft/hard templates, surfactants and organic solvents. The proposed surface-guided growth mechanisms for nanoarray and nanoflower formation in this study may shed light on understanding of oriented arrangement/assembly of one-dimensional Pt nanowires into desirable morphologies for a variety of practical device applications.

Pt nanoflower↗
DOE OSTI2021

Local ordering in disordered Nd x Zr 1-x O 2-0.5x pyrochlore as observed using neutron total scattering

Pyrochlore complex oxides (A 2 B 2 O 7 ) are an important class of fluorite-derivative ceramics with exceptional chemical and structural versatility which make them ideal model systems for studying disordering mechanisms over a range of spatial scales. Here, neutron total scattering methods were used to analyze the structural behavior in the non-stoichiometric series Nd x Zr 1-x O 2-0.5x (0.5 ≤ x ≤ 0.23) as a function of Nd content, x. Characterization of the structure functions using Rietveld refinement and the pair distribution functions with small-box refinement reveal complex disordering pathways; the average, long-range phase changes over a very narrow compositional range from pyrochlore ($Fd\bar{3}m$) to defect fluorite ($Fm\bar{3}m$) through full randomization of the cation and anion sublattices at x ~ 0.31, while the local, short-range structure continuously adopts a weberite-type atomic arrangement (C222 1 ). Comparison to a previously studied Ho 2 Ti 2-x Zr x O 7 solid solution series reveals how changes in chemical composition and stoichiometry modify defect formation and determine how disordering progresses across different length scales in pyrochlore oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MX precipitate behavior in an irradiated advanced Fe-9Cr steel: Helium effects on phase stability

As part of an ongoing series aimed at optimizing Fe-9Cr reduced activation ferritic/martensitic (RAFM) alloys for fusion energy systems, this study explores MX precipitate behavior under dual-ion irradiations, specifically examining correlations between helium transmutation and irradiation-induced damage. Utilizing single and dual-beam ion irradiation, the research explores the combined effects of helium (10–25 appm He/dpa), temperature (400–600 °C), and damage levels (15–100 dpa) on the microstructural evolution of CNA9 steel, a variant of Castable Nanostructured Alloys (CNAs). The study demonstrates that helium co-implantation hinders radiation-enhanced coarsening of MX-TiC precipitates at 500 and 600 °C, maintaining MX-TiC precipitate stability at moderate damage levels (15 dpa) but failing to prevent complete precipitate dissolution at higher damage levels (≥50 dpa) when irradiated at 500 °C. Here, a generalized precipitate stability model suggests that helium-induced suppression of diffusion alters the balance between recoil resolution and back diffusion for MX-TiC precipitates, enhancing the current understanding of precipitate behavior under damage and transmutation simulated dual-ion irradiation conditions.

Characterization↗
DOE OSTI2020

Ultrastructure of the green alga Dunaliella salina strain CCAP19/18 (Chlorophyta) as investigated by quick-freeze deep-etch electron microscopy

The single-celled green alga Dunaliella salina is a model system for studies on stress biology, in particular regarding secondary carotenoid accumulation. Under non-stress conditions the cells are green, but under abiotic stress the cells turn orange, because they switch their metabolism and accumulate β-Carotene in globules in the chloroplast. For the first time, Quick-freeze deep-etch electron microscopy was used to visualize cellular structures in green and orange cells of D. salina strain CCAP19/18. This allowed us to present an in-depth analysis of the cellular ultrastructure describing and comparing the features of the two cell types. Our images illustrate the presence of a pericellular matrix for this strain of D. salina. The pericellular matrix was spongy and strands of unknown material anchored it into the plasma membrane. The cytoplasm contained a variety of vesicles, vacuoles, and acidocalcisomes. We could show for strain CCAP19/18 that cytoplasmic lipid bodies were often in close proximity to and sometimes in contact with the outer chloroplast envelope membrane and with the endoplasmic reticulum. Major visible differences between green and orange cells were in the chloroplast: the orange cells have greatly reduced amounts of thylakoid membranes and greatly increased numbers of β-Carotene globules. We showed that the β-Carotene globules often made point contacts with thylakoid membranes, and frequently laid side-by-side along the thylakoid membrane surface, providing support to studies that indicated exchange of molecules between β-Carotene globules and thylakoid membranes. A unique finding was the β-Carotene globule duplets, suggesting intermediate stages in β-Carotene globule morphogenesis. Overall, the β-Carotene globules appear to be similar to plant plastoglobuli. We provide this description of the cellular ultrastructure features as a resource in the context of the recent publication of the genome of D. salina strain CCAP19/18 to expand on the knowledge regarding this novel reference strain.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Techno-economic analysis of advanced small modular nuclear reactors

Here, small modular nuclear reactors (SMRs) represent a robust opportunity to develop low-carbon and reliable power with the potential to meet cost parity with conventional power systems. This study presents a detailed, bottom-up economic evaluation of a 12 × 77 MW e (924 MW e total) light-water SMR (LW-SMR) plant, a 4 × 262 MW e (1,048 MW e ) gas-cooled SMR (GC-SMR) plant, and a 5 × 200 MW e (1,000 MW e total) molten salt SMR (MS-SMR) plant. Cost estimates are derived from equipment costs, labor hours, material inputs, and process-engineering models. The advanced SMRs are compared to natural gas combined cycle plants with and without post-combustion carbon capture and a conventional large nuclear reactor. Overnight capital cost (OCC) and levelized cost of energy (LCOE) estimates are developed. The OCC of the LW-SMR, GC-SMR, and MS-SMR are found to be $\$4,844$/kW, $\$4,355$/kW, and $\$3,985$/kW respectively. The LCOE of the LW-SMR, GC-SMR, and MS-SMR are found to be $\$89.6$/MWh, $\$81.5$/MWh, and $\$80.6$/MWh respectively. A Monte Carlo analysis is performed, for which the OCC and construction time of the LW-SMR is found to have a lower mean and standard deviation than a conventional large reactor. The LW-SMR OCC is found to have a mean of $\$5,233$/kW with a standard deviation of $\$658$/kW and a 90 % probability of remaining between $\$4,254$/kW and $\$6,399$/kW, while the construction duration is found to have a mean of 4.5 years with a standard deviation of 0.8 years and a 90 % probability of remaining between 3.4 and 6.0 years. The economic impact of economies of scale, simplification, modularization, and construction time for SMRs are discussed. Additionally, policy implications for direct SMR capital subsidies and the impact of a carbon tax on natural gas emissions are explored.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Smart detection of indoor occupant thermal state via infrared thermography, computer vision, and machine learning

The ability to measure occupants’ thermal state in real time will enable major advances in the control of air conditioning systems. This study proposes predicting occupant thermal state by a combination of infrared thermography, computer vision, and machine learning. The approach (1) uses cheek, nose, and hand temperatures because they are least subject to blockage by hair, glasses, and clothing; (2) measures the distribution of skin temperatures within geometrically defined sub-areas of the face and hand; and (3) uses temperature differences within and between these areas to eliminate the effects of calibration drift that are unavoidable in thermal infrared (TIR) cameras. Two series of tests were conducted, respectively in an outdoor carport and an indoor environmental chamber, collecting a total of 48,422 sets of cheek, nose, and hand skin temperatures using a TIR camera and computer-vision technology, coupled with 715 subjective responses of thermal sensations. To predict occupant thermal state, Random Forest classification models were built using either absolute skin temperatures (the maximum and median temperatures of cheek and hand segments, and the temperature of the central spot on the nose), or intra- and inter-segment temperature differences of cheeks, hands, and nose. These measurements were found to accurately predict occupant thermal state. Using the maximum and median temperatures for cheek and nose, or for cheek and hand, predicts thermal state with an accuracy of 92–96%. In conclusion, using only the intra- and inter-segment temperature differences from cheek and nose is 83% accurate; adding the hand temperature differences increases the accuracy to 96%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Thermodynamics and its prediction and CALPHAD modeling: Review, state of the art, and perspectives

Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable, when interacting with its surroundings. The combined law of thermodynamics derived by Gibbs about 150 years ago laid the foundation of thermodynamics. In Gibbs combined law, the entropy production due to internal processes was not included, and the 2nd law was thus practically removed from the Gibbs combined law, so it is only applicable to systems under equilibrium, thus commonly termed as equilibrium or Gibbs thermodynamics. Gibbs further derived the classical statistical thermodynamics in terms of the probability of configurations in a system in the later 1800's and early 1900's. With the quantum mechanics (QM) developed in 1920's, the QM-based statistical thermodynamics was established and connected to classical statistical thermodynamics at the classical limit as shown by Landau in the 1940's. In 1960's the development of density functional theory (DFT) by Kohn and co-workers enabled the QM prediction of properties of the ground state of a system. On the other hand, the entropy production due to internal processes in non-equilibrium systems was studied separately by Onsager in 1930's and Prigogine and co-workers in the 1950's. In 1960's to 1970's the digitization of thermodynamics was developed by Kaufman in the framework of the CALculation of PHAse Diagrams (CALPHAD) modeling of individual phases with internal degrees of freedom. CALPHAD modeling of thermodynamics and atomic transport properties has enabled computational design of complex materials in the last 50 years. Our recently termed zentropy theory integrates DFT and statistical mechanics through the replacement of the internal energy of each individual configuration by its DFT-predicted free energy. The zentropy theory is capable of accurately predicting the free energy of individual phases, transition temperatures and properties of magnetic and ferroelectric materials with free energies of individual configurations solely from DFT-based calculations and without fitting parameters, and is being tested for other phenomena including superconductivity, quantum criticality, and black holes. Those predictions include the singularity at critical points with divergence of physical properties, negative thermal expansion, and the strongly correlated physics. Furthermore, those individual configurations may thus be considered as the genomic building blocks of individual phases in the spirit of the materials genome®. This has the potential to shift the paradigm of CALPHAD modeling from being heavily dependent on experimental inputs to becoming fully predictive with inputs solely from DFT-based calculations and machine learning models built on those calculations and existing experimental data through newly developed and future open-source tools. Furthermore, through the combined law of thermodynamics including the internal entropy production, it is shown that the kinetic coefficient matrix of independent internal processes is diagonal with respect to the conjugate potentials in the combined law, and the cross phenomena that the phenomenological Onsager flux and reciprocal relationships are due to the dependence of the conjugate potential of a molar quantity on nonconjugate molar quantities and other potentials, which can be predicted by the zentropy theory and CALPHAD modeling.

42 ENGINEERING↗
DOE OSTI2026

Exploring stages of resilience maturity as communities confront climate risks

Coastal communities are facing increased risks due to climate change, as evidenced by heightened shocks such as hurricanes and wildfires, and stressors including sea-level rise and drought. To confront these risks, many coastal communities are planning and designing their built environments in new ways, with the overall goal of increasing their resilience. However, resilience practices have been widely guided by an event-based timeframe, which may not be sufficient to confront the multiple and overlapping threats expected over the coming decades. To address this concern, this study investigated a long-term perspective on resilience progression, termed resilience maturity. To better understand resilience maturity in practice, this study drew directly on community perspectives, thereby identifying common challenges. Furthermore, variation in resilience maturity across communities provided an opportunity to identify existing successful strategies. Inductive qualitative analysis was used to examine perspectives from 15 interviews with local practitioners across 12 coastal communities. The analysis revealed that communities in early stages of resilience maturity often struggle with local stakeholder alignment and minimal dedicated resilience resources. In contrast, those further along the maturity path often face technology barriers and challenges in aligning regional stakeholders. Few examples of a fully mature resilience culture were found within the communities studied, underscoring systemic barriers to achieving advanced resilience maturity. Overall, these findings supported development of a practical framework for classifying community resilience efforts, identifying common obstacles, and informing localized resilience advancement.

Resilience maturity↗
DOE OSTI2025

Generative learning of densities on manifolds

A generative modeling framework is proposed that combines diffusion models and manifold learning to efficiently sample data densities on manifolds. The approach utilizes Diffusion Maps to uncover possible low-dimensional underlying (latent) spaces in the high-dimensional data (ambient) space. Two approaches for sampling from the latent data density are described. The first is a score-based diffusion model, which is trained to map a standard normal distribution to the latent data distribution using a neural network. The second one involves solving an Itô stochastic differential equation in the latent space. Additional realizations of the data are generated by lifting the samples back to the ambient space using Double Diffusion Maps , a recently introduced technique typically employed in studying dynamical system reduction; here the focus lies in sampling densities rather than system dynamics. The proposed approaches enable sampling high dimensional data densities restricted to low-dimensional, a priori unknown manifolds. The efficacy of the proposed framework is demonstrated through a benchmark problem and a material with multiscale structure.

Double diffusion maps↗
DOE OSTI2021

Fundamental insights on ignition and combustion of natural gas in an active fueled pre-chamber spark-ignition system

Pre-chamber spark-ignition (PCSI), either fueled or non-fueled, is a leading concept with the potential to enable diesel-like efficiency in medium-duty (MD) and heavy-duty (HD) natural gas (NG) engines. However, the inadequate scientific base and simulation tools to describe/predict the underlying processes governing PCSI systems is one of the key barriers to market penetration of PCSI for MD/HD NG engines. To this end, experiments were performed in a heavy-duty, optical, single-cylinder engine fitted with an active fueled PCSI module. Furthermore, the spatial and temporal progress of ignition and subsequent combustion of lean-burn natural gas using PCSI system were studied using optical diagnostic imaging and heat release analysis based on main-chamber and pre-chamber pressure measurements.

42 ENGINEERING↗
DOE OSTI2022

Using lifetime of point defects for dislocation bias in bcc Fe

The interaction between dislocations and point defects is key to deformation processes and microstructural evolution of structural materials. In this work, we compute the lifetime of point defects to describe their interaction with dislocations. This approach can accurately account for the effects of the dislocation core and anisotropic defect dynamics to accumulatively determine the capture efficiency, sink strength, and dislocation bias at different temperatures and dislocation densities. Particularly, the absorption of point defects by straight screw and edge dislocations in a model bcc iron system is studied. The maximum swelling rates based on the obtained bias factors are in close agreement with a variety of experimental measurements, including both neutron and ion-irradiation data, especially when considering the survival fraction for point defects from displacement cascades. Further, this approach applies to many other processes and sinks, such as dislocation loops and interfaces, providing a powerful means to develop fundamental insights critical for improving radiation resistance and mechanical properties of structural materials through controlling defect interaction and evolution.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The deep-DRT: A deep neural network approach to deconvolve the distribution of relaxation times from multidimensional electrochemical impedance spectroscopy data

Electrochemical impedance spectroscopy (EIS) is an experimental technique ubiquitously used to study electrochemical systems. However, conventional EIS data interpretation through physical and equivalent circuit models is challenging because physical models are problem-specific, and equivalent circuits are often just lumped-element analogs lacking physical meaning. The distribution of relaxation times (DRT) has emerged as a complementary approach to resolve these issues. One drawback of conventional DRT deconvolution is that the EIS data is understood to be (only) a function of frequency (i.e. 1D data) and deconvolved accordingly. This work proposes a novel deconvolution method based on deep neural networks (DNNs), allowing the analysis of multidimensional EIS spectra to bridge data dependency on both frequencies and experimental conditions. Two particularly appealing traits of the deep-DRT method developed in this article are that neither regularization nor specific spacing on the state variables defining the experiment are required. Finally, leveraging DNN to examine complex EIS spectra and their dependence on experimental conditions, this work opens a new research direction in the area of EIS analysis and DRT deconvolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

How reliable is distribution of relaxation times (DRT) analysis? A dual regression-classification perspective on DRT estimation, interpretation, and accuracy

The distribution of relaxation times (DRT) has gained increasing attention and adoption in recent years as a versatile method for analyzing electrochemical impedance spectroscopy (EIS) data obtained from complex devices like fuel cells, electrolyzers, and batteries. The DRT deconvolutes the impedance without a priori specification of a generative model, which is especially useful for interpretation and model selection when the governing principles of the system under study are not fully understood. However, DRT estimation is an ill-posed inversion problem that must be addressed with a subjective choice of regularization and tuning, which leaves substantial risk of misleading interpretations of EIS data. In this work, we suggest a new classification view of the DRT inversion to clarify DRT estimation and interpretation. We introduce a dual regression-classification framework that unifies the classification and regression views of the DRT inversion with wide-reaching implications for DRT analysis. The dual framework is employed to demonstrate a new kind of DRT inversion algorithm and develop novel evaluation metrics that capture previously ignored aspects of DRT accuracy. These approaches are applied to both synthetic data and experimental spectra collected from a protonic ceramic fuel cell and a lithium-ion battery to illustrate their broad utility. The dual inversion algorithm shows promising performance for accurate DRT estimation and autonomous model identification, while the dual evaluation approach produces metrics that meaningfully assess the strengths and risks of DRT algorithms. Here this work provides valuable insight for both practical application of the DRT to experimental data and further development of EIS analysis methods.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-performance windows improve thermal survivability of occupants during cold snaps

Exposure to low indoor air temperature is a major contributor to temperature-related mortality during extreme cold events, especially when power outages disrupt operation of space heating systems. This study explores the impact of high-performance windows on the thermal resilience of residential buildings during extreme cold weather and grid power outages, as well as their long-term benefits through energy efficiency and reduced risk of property damage. Building performance simulations were conducted for reference residential buildings in three construction vintages and two major U.S. cities located in cold climate zones, considering two types of extreme cold events: short and severe, and long and milder. Our research found that even houses compliant with current energy codes struggle to maintain safe indoor temperatures for more than a few hours during power outages, necessitating rapid evacuations. High-performance windows can extend the thermal survivability time by up to 3.8 days within a 7-day cold snap and significantly reduce risk of bursting frozen water pipes, depending on the building’s insulation and infiltration level, cold event severity, and occupant vulnerability. This extended thermal safety time is crucial in scenarios where reduced mobility complicates emergency responses in senior housing. In addition to boosting thermal resilience, upgrading older homes with high-performance windows can reduce heating energy consumption by over 18% and cooling energy by 15%. Our findings highlight the need to incorporate thermal resilience assessments into new designs or major retrofits, including the use of typical and extreme weather scenarios and advanced technologies like high-performance windows.

Krelling, Amanda F↗