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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Modeling and experimental validation of a W f /W-fabrication by chemical vapor deposition and infiltration

Tungsten (W) has a unique combination of excellent thermal properties, low sputter yield, low hydrogen retention, and acceptable activation. Therefore, W is presently the main candidate for the first wall material in future fusion devices. However, its intrinsic brittleness and its further embrittlement during operation bears the risk of a sudden and catastrophic component failure. As a countermeasure, tungsten fiber-reinforced tungsten (W f /W) with extrinsic toughening is being developed. A possible synthesis route is chemical vapor deposition (CVD) using heated W fabrics as substrate. The challenge is that the growing CVD-W can isolate domains from precursor access leading to strength-reducing pores. To deepen the process understanding and to optimize the CVD parameters, models were developed with COMSOL Multiphysics and validated experimentally. W deposition rate equations as function of the temperature and the partial pressures of the precursors H 2 and WF 6 were experimentally validated in previous work. In the present article, these equations are applied to obtain partial pressures within the CVD reactor. The results are taken as input for transient simulations in the microscale, in which W coatings, growing onto multiple adjacent W fibers, were simulated via mesh deformation and remeshing. The surface-to-surface contact of the W coatings and the corresponding potential pore formation were simulated by implementing sophisticated deposition rate stop conditions. Within the measuring uncertainties of ≃ ±1%, the models are validated successfully by experimental comparison regarding the deposition rate, pore structure, and relative densities ranging from 0.6 to 0.9.

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

Self-welding of Inconel 617 under high-pressure-high-temperature conditions for nuclear reactors

In response to continuously declining availability of fossil fuels, nuclear power has become an important alternative source of power generation. Growing application of nuclear power has relieved the shortage of electricity. However, it is also accompanied with new drawbacks, such as radioactive waste, and potential catastrophic consequences in case of major accidents. For nuclear power plants, severe accidents mostly originate from the reactor, making the reliability of the reactor of paramount importance to the overall safety of a nuclear power plant. Massive heat generation through nuclear fission/fusion and heat exchange render the whole reactor operating at extremely high temperatures (i.e., 900–1000 °C), which, in conjunction with large load from subsystems/ components made of special heavy alloys, renders the reactor under the risk of self-welding, a phenomenon that could lead to strong bonding between components and paralyze the operability of the nuclear reactor. This paper investigates the self-welding behavior of Inconel 617, a primary candidate nickel alloy for key components of the next generation gas cooled nuclear plant, inside a controlled atmospheric furnace with preset static load, simulating the high-pressure high-temperature conditions inside nuclear reactors. Therefore, test results show that Inconel 617 experiences self-welding through common oxide zone at the interface, and it has remarkable bonding strength, requiring 4114 N (57.15 MPa) and 4338 N (60.25 MPa) to break apart a mated sample pair, under an apparent contact pressure of 0.341 MPa after 50-h aging, in helium and air atmospheres, respectively. The higher oxygen presence in air results in slightly stronger oxide bond at the interface, compared to helium, and the bond strength increases with dwell time.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

More high-impact atmospheric river-induced extreme precipitation events under warming in a high-resolution model

Extreme precipitation events, as occurred in Europe 2021, or western North America 2023, with an intensity of 75 mm/day or 100 mm/day, respectively, exert a catastrophic impact. Lower-resolution (~100 km) climate models cannot simulate the intensity, nor their response to greenhouse warming. Using a high-resolution (~25 km) model capable of simulating such events, here we show that frequency-weighted area of such events over western Europe and the west coast of North America likely expands by more than 80% under an approximately 4°C of global warming from the historical levels. Along the west coasts of Europe and North America, area impacted by atmospheric rivers-induced extreme precipitation events is projected to double, driven by intensified landfalling atmospheric rivers. Thermodynamic processes drive the increase, whereas dynamic processes reduce the intensity over western Europe but enhance it along the west coast of North America. Here our findings provide policy-relevant information for climate adaptation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Classification and computation of extreme events in turbulent combustion

In the design of practical combustion systems, ensuring safety and reliability is an important requirement. For instance, reliably avoiding lean blowout, flame flashback or inlet unstart is critical for ensuring safe operation. Currently, the science of predicting such events is based on prior experience, limited modeling or diagnostic tools and purely statistical approaches. Even though computational and experimental tools for studying combustion devices have vastly advanced in the last three decades, the analysis of such failure events has not been pursued widely. While the use of data for model development and calibration is being widely accepted, the extension to failure events introduces numerous challenges. In particular, the focus here is on so-called data-poor problems, where the cost of generating data is extremely high and is not easily amenable to existing computational and experimental approaches. Data-poor problems are particularly relevant when related to extreme events (also called anomalous events) that can lead to catastrophic failure of the system. It is argued that transient events that describe such failure can have different causal mechanisms. To develop the scientific inference process, a classification of such problems is used to determine specific modeling paths as well as computational tools needed. Research opportunities in the emerging field of extreme event prediction are highlighted in order to identify critical and immediate needs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Understanding the role of flow dynamics in thermoacoustic combustion instability

Thermoacoustic combustion instability is one of the most challenging operational issues in several high-performance, low-emissions combustion technologies, including gas turbines, aircraft engines, rockets, and industrial boilers. Driven by the coupling between combustor acoustics and flame heat release rate fluctuations, thermoacoustic combustion instability can lead to reduced operability, increased emissions, and, in the most extreme cases, catastrophic failure of combustor components. The feedback loop between acoustics and combustion is often facilitated by fluid mechanic oscillations, referred to as “velocity coupling,” whereby acoustic oscillations drive flow fluctuations, which in turn create fluctuations in the flame. The character of these fluid mechanic oscillations is highly dependent on the structure of the flow field and the receptivity of the flow to external excitation. Combustor flow fields use features like fluid recirculation and shear to enhance flame holding and reduce emissions, but these are also the same features that can make the flow receptive to acoustic excitation or even drive self-excited oscillations. Here, in this paper, we discuss the basics of thermoacoustic instability with a focus on the role of hydrodynamic oscillations in typical combustor flows. To facilitate this discussion, we explore the hydrodynamic instability characteristics of several key combustor unit flows (wakes, swirling jets, etc.) and show how the hydrodynamic stability of a flow is an important consideration in determining a combustor’s propensity for thermoacoustic oscillations. Several examples of coupling between hydrodynamics and thermoacoustics are discussed to illustrate this important link. The paper concludes by discussing the potential for designing flow fields that are thermoacoustic instability resistant, either through a reduction in the receptivity of the flow or through nonlinear coupling mechanisms by which self-excited flow instabilities can suppress velocity-coupled combustion oscillations.

42 ENGINEERING↗
DOE OSTI2025

A survey on degradation modeling, prognosis, and prognostics-driven maintenance in wind energy systems

Wind energy generation proliferated over the past decades, introducing unique challenges and opportunities for failure prediction, operation and maintenance. Decision-makers are continuously looking into new methods to infer failure mechanisms and behaviors of wind turbine components to detect and intervene in the failures before they happen. Evidently, degradation modeling and prognosis become engaging topics for researchers and practitioners to prevent catastrophic failures. Prognostics-driven approaches predict the time of failure for the components (e.g., predicting remaining useful life), which provides significant insights for scheduling of operations and maintenance activities. Integrating these prognostics-driven insights into wind farm operations and maintenance presents a substantial challenge, demanding careful consideration of numerous factors such as accessibility, crew routing, and spare part logistics. This study provides state-of-the-art review for degradation modeling, prognosis, and prognostics-driven maintenance techniques for wind energy systems. The discussed techniques align with the United Nations' sustainable development goals, in particular Goal 7 (Affordable and Clean Energy), by enhancing effectiveness and sustainability of wind energy operations. This work also showcases open research questions related to degradation modeling, prognosis, and prognostics-driven maintenance.

Altinpulluk, Nur Banu↗
DOE OSTI2020

Impact of krypton irradiation on a single crystal tungsten: Multi-modal X-ray imaging study

Understanding microstructural and strain evolutions induced by noble gas production in the nuclear fuel matrix or plasma-facing materials is crucial for designing next generation nuclear reactors, as they are responsible for volumetric swelling and catastrophic failure. In this paper we describe a multimodal approach combining synchrotron-based nanoscale X-ray imaging techniques with atomic-scale electron microscopy techniques for mapping chemical composition, morphology and lattice distortion in a single crystal W induced by Kr irradiation. We report that Kr-irradiated single crystal W undergoes surface deformation, forming Kr containing cavities. Furthermore, positive strain fields are observed in Kr-irradiated regions, which lead to compression of underlying W matrix.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Porosity evolution under increasing tension in wire-arc additively manufactured aluminum using in-situ micro-computed tomography and convolutional neural network

Internal defects such as porosities are often formed in additively manufactured metal components. The pores nucleate, grow, and coalesce to form cracks under loads, leading to eventual catastrophic failure. Here, in this paper, the full-field porosity evolution, including pore growth and coalescence in a wire-arc additively manufactured (WAAM) aluminum alloy cylinder under tension is observed with in-situ X-ray micro-computed tomography (μCT). The pore size distribution, density, and tensile stress are calculated from the volumetric images analyzed by a convolutional neural network (CNN) algorithm, which provides rapid analysis of 12,950 slice images from μCT volumetric images at the reference state and 13 tensile strains. The results show the quantitative evolution of the growth and coalescence of macropores under tension. A strong correlation is found between the local pore volume fraction and the true tensile stress when the tensile strain is larger than 5%.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Volumetric and shear strain localization throughout triaxial compression experiments on rocks

Deformation localization is a widely observed, but rarely quantified process in the crust. Recent observations suggest that the localization of seismicity and fracture networks can help identify the approach to catastrophic failure. Here, we quantify the localization processes of the volumetric and deviatoric strain components in twelve triaxial compression experiments imaged with X-ray tomography. We capture three-dimensional images of the rock cores during triaxial compressing toward failure, and then calculate the local strain components using digital volume correlation. The divergence and curl of the incremental displacement vector field provide the volumetric and deviatoric components of the strain field. We quantify localization using the proportion of the rock occupied by high magnitudes of the volumetric and deviatoric strains, and the Gini coefficient of these high magnitude strains, which measures the deviation from a uniform process. We find that the vast majority, but not all, of the experiments experience strain localization toward failure. The rocks typically experience their maximum degree of strain localization not immediately preceding failure, but on average at 90% of the failure stress. The volumetric strain tends to localize more than the deviatoric strain. Finally, these observations support using the localization of the volumetric strain, along with the deviatoric strain, to identify the evolution of the precursory phase preceding earthquakes.

58 GEOSCIENCES↗
DOE OSTI2022

Chromoanagenesis in plants: triggers, mechanisms, and potential impact

Chromoanagenesis is a single catastrophic event that involves, in most cases, localized chromosomal shattering and reorganization, resulting in a dramatically restructured chromosome. First discovered in cancer cells, it has since been observed in various other systems, including plants. In this review, we discuss the origin, characteristics, and potential mechanisms underlying chromoanagenesis in plants. Here, we report that multiple processes, including mutagenesis and genetic engineering, can trigger chromoanagenesis via a variety of mechanisms such as micronucleation, breakage–fusion–bridge (BFB) cycles, or chain-like translocations. The resulting rearranged chromosomes can be preserved during subsequent plant growth, and sometimes inherited to the next generation. Because of their high tolerance to genome restructuring, plants offer a unique system for investigating the evolutionary consequences and potential practical applications of chromoanagenesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Microbial inoculants and invasions: a call to action

Microbial inoculants are increasingly used for beneficial purposes in agriculture, bioremediation, and medicine, but they can carry risks of generating invasive microbes. Here, we present a roadmap for guarding against these invasions, proposing developing (i) coherent mechanistic understandings of how microbial inoculants can effect invasions, (ii) predictive models forecasting microbial invasion risks, and (iii) effective management strategies. To guide mechanistic understandings, we distill 17 guiding hypotheses. For predictive modeling, we highlight data collection needs and qualitative approaches. For management strategies, we stress the importance of accurately weighing the risks against benefits. The unified approach presented here provides a route toward an effective research and management infrastructure for microbial inoculants in order to avoid potentially catastrophic microbial invasions.

invasive species↗
DOE OSTI2023

Freeze range of a condensing gas propagating in a liquid helium-cooled tube

Understanding air propagation and condensation following a catastrophic vacuum break in particle accelerator beamlines cooled by liquid helium is crucial for maintaining the operational safety of these facilities. Previous experimental investigations on nitrogen gas propagation in both normal liquid helium (He I) and superfluid helium (He II) cooled copper tubes unveiled a nearly exponential deceleration of the gas propagation. A comprehensive theoretical model incorporating gas dynamics, heat transfer, and condensation mechanisms has been developed, which effectively reproduces various key experimental observations. An intriguing phenomenon uncovered in our model simulation is that the gas propagation appears to nearly stop beyond a certain distance from the location where condensation starts. We refer to this distance as the freeze range. In this paper, we present our systematic study of the freeze range at various inlet mass fluxes and tube diameters. We show that the results can be well described by a simple correlation. The underlying physical mechanism that supports this useful correlation is explained. Knowing the freeze range may allow accelerator engineers to develop protocols for controlling frost-layer contamination in the beamline tubes, which is of great practical importance.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Investigation of electrode-electrolyte interfaces to enable non-flammable Li-ion batteries operating up to 125°C with liquid electrolyte

Non-flammable and high-temperature stable ionic liquid (IL)-based electrolytes could eliminate catastrophic battery failures and improve battery safety, but their poor electrochemical interaction with the LiNi x Mn y Co z O 2 (NMC) family of cathodes is a long-standing problem due to severe parasitic reactions at high temperature. Understanding surface and bulk structural mechanisms of NMC-type cathodes at elevated operational temperature is of paramount importance to facilitate stable electrochemical performance. Here, we report a non-flammable phosphonium IL-based cell chemistry that offers stable electrode-electrolyte interfaces, leading to electrochemical performance up to 125°C and high-temperature safety. We combine electrochemistry with multimodal X-ray spectroscopy methods to understand interfaces at elevated temperature (100°C). This nanoscale understating enables a proof-of-concept high-temperature cylindrical cell (14500), and the design achieves an average Coulombic efficiency of ≈99.5% up to 300 cycles at 100°C. The results ascertain the significance of depth-dependent degradation at the interface, guiding room-temperature Li-ion technology toward extreme-temperature applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

4D insights into lithium-ion battery sidewall rupture during thermal runaway

Thermal runaway (TR), characterized by rapid exothermic reactions, presents a serious safety risk in lithium-ion batteries (LiBs). External triggers such as high temperatures, mechanical abuse, or internal short circuits (ISCs) can initiate TR, often resulting in sidewall rupture, which may escalate to catastrophic battery pack failure. In this study, we developed and applied high-speed synchrotron imaging techniques to investigate sidewall rupture mechanisms in LiBs subjected to different triggering scenarios. Using in situ 4D tomographic imaging, we visualized the dynamic evolution of sidewall rupture with high spatial and temporal resolution. The results revealed distinct failure behaviors linked to each trigger, underscoring the complex and condition-specific nature of sidewall breach and battery failure. These insights highlight the critical importance of implementing tailored safety strategies across diverse applications. Our findings demonstrate the powerful potential of synchrotron high-speed tomography as a diagnostic tool for advanced safety testing and cell qualification.

25 ENERGY STORAGE↗
DOE OSTI2021

Dispersion curve regression – assisted wideband local wavenumber analysis for characterizing three-dimensional (3D) profile of hidden corrosion damage

Corrosion as common damage in civil, petrochemical, nuclear, and aerospace structures affects the integrity and safety of the structures and may lead to catastrophic failures. This paper presents dispersion curve regression-assisted local wavenumber analysis method, which can analyze the time–space wavefield containing wideband information of wave-damage interaction and further extract the structural information carried by such wavefield for characterizing hidden corrosion damage in an isotropic plate. To acquire the time–space wavefield, a noncontact scanning laser Doppler vibrometer is used. In our analysis method, the acquired time–space wavefield is firstly processed to generate a local wavenumber function. The derived wavenumber function is further analyzed with a dispersion curve regression step, which searches in a set of theoretical frequency-wavenumber dispersion curves for different plate thicknesses and identifies the optimal plate thickness whose theoretical curve best matches the frequency-wavenumber relation contained in the local wavenumber function. By this means, a 3D profile including both in plane and thickness dimensions of the structure can be constructed for corrosion visualization and quantification. The experimental study demonstrates that our method can quantify the profile of hidden uniform corrosion in metal plates in terms of its in-plane shape and size as well as its out-of-plane depth in the subwavelength scale. Moreover, compared to previous Lamb wave-based corrosion inspection methods, our technique allows for noncontact 3D characterization of hidden corrosion from a far distance to the structure. Finally, we believe this work will inspire new 3D damage quantification methods that are based on wavefield analysis as well as enable potential applications for the quantification of hidden corrosion in civil, petrochemical, nuclear, and aerospace structures.

42 ENGINEERING↗
DOE OSTI2023

Measuring photometric redshifts for high-redshift radio source surveys

With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to z = 3 before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify ≈ 76% of the highest redshift sources—sources at redshift z > 2.51 —as being in either the highest bin (z > 2.51) or second highest (z = 2.25).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Lifetime risk of suicide among survivors of the atomic bombings of Japan

Abstract Aims The long-term physical health effects of the atomic bombings of Hiroshima and Nagasaki are well characterised, but the psychological effects remain unclear. Therefore, we sought to determine whether measures of exposure severity, as indirect measures of psychological trauma arising from exposure to the atomic bombings, are associated with suicide mortality among atomic bomb survivors. Methods The Life Span Study is a prospective cohort study of 93 741 Japanese atomic bomb survivors who were located within 10 km of the hypocentre in Hiroshima or Nagasaki at the time of the bombings in 1945, and 26 579 residents of Hiroshima and Nagasaki who were not in either city at the time of the bombings, matched to survivors on city, sex and age. Measures of exposure severity included: proximity to the hypocentre, type of shielding between the survivor and the blast and self-reported occurrence of acute radiation and thermal injuries. Date of death was obtained from the Japanese National Family Registry system. Cause of death was obtained from death certificates. Adjusted hazard ratios (HRs) were estimated from Cox regression models overall and stratified by sex and age. Results During the 60-year follow-up period (1950–2009), 1150 suicide deaths were recorded among 120 231 participants (23.6 per 100 000 person-years): 510 among 70 092 women (17.2 per 100 000 person-years) and 640 among 50 139 men (33.6 per 100 000 person-years). Overall, there was no association of proximity, type of shielding or the occurrence of acute injuries with suicide mortality. Among those <25 years of age at the time of the bombings, increased suicide risk was observed for survivors outside v . shielded inside any structure (HR: 1.24; 95% confidence interval (CI): 1.03, 1.48; interaction p = 0.054) and for those who reported flash burns (HR: 1.32; 95% CI: 1.00, 1.73; interaction p = 0.025). Sex-stratified analyses indicated that these associations were limited to men. Among women, closer proximity to the hypocentre was associated with a non-significant increase in suicide risk, with a positive association between proximity and suicide risk observed among women <15 years of age (HR: 1.09 per km; 95% CI: 1.00, 1.18; interaction p = 0.067). Conclusions Proximity to the hypocentre, shielding and acute injury presence do not generally appear to influence suicide mortality among atomic bomb survivors. However, heterogeneity may exist by age and sex, with younger survivors potentially more sensitive to psychological trauma. Coupled with other studies, our results suggest the importance of long-term monitoring of mental health among young populations exposed to catastrophic events or mass trauma.

Psychiatry↗
DOE OSTI2021

Data-Driven Strategies for Accelerated Materials Design

The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

36 MATERIALS SCIENCE↗