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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A Consistent Spatially Adaptive Smoothed Particle Hydrodynamics Method for fluid-structure interactions

A new consistent, spatially adaptive, smoothed particle hydrodynamics (SPH) method for Fluid-Structure Interactions (FSI) is presented. The method combines several attributes that have not been simultaneously satisfied by other SPH methods. Specifically, it is second-order convergent; it allows for resolutions spatially adapted with moving (translating and rotating) boundaries of arbitrary geometries; and, it accelerates the FSI solution as the adaptive approach leads to fewer degrees of freedom without sacrificing accuracy. The key ingredients in the method are a consistent discretization of differential operators, a posteriori error estimator/distance-based criterion of adaptivity, and a particle-shifting technique. The method is applied in simulating six different flows or FSI problems. The new method's convergence, accuracy, and efficiency attributes are assessed by comparing the results it produces with analytical, finite element, and consistent SPH uniform high-resolution solutions as well as experimental data.

Hu, Wei↗
DOE OSTI2021

End-gas autoignition fraction and flame propagation rate in laser-ignited primary reference fuel mixtures at elevated temperature and pressure

Knock in spark-ignited (SI) engines is initiated by autoignition of the unburned gasses upstream of spark-ignited, propagating, turbulent premixed flames. Knock propensity of fuel/air mixtures is typically quantified using research octane number (RON), motor octane number (MON), or methane number (MN; for gaseous fuels), which are measured using single-cylinder, variable compression ratio engines. In this study, knock propensity of SI fuels was quantified via observations of end-gas autoignition (EGAI) in unburned gasses upstream of laser-ignited, premixed flames at elevated pressures and temperatures in a rapid compression machine. Stoichiometric primary reference fuel (PRF; n-heptane/isooctane) blends of varying reactivity (50 ≤ PRF ≤ 100) were ignited using an Nd:YAG laser over a range of temperatures and pressures, all in excess of 545 K and 16.1 bar. Laser ignition produced outwardly-propagating premixed flames. High-speed pressure measurements and schlieren images indicated the presence of EGAI. The fraction of the total heat release attributed to EGAI (i.e., EGAI fraction) varied with fuel reactivity (i.e., octane number) and the time-integrated temperature of the end-gas prior to ignition. Flame propagation rates, which were measured using schlieren images, were only weakly correlated with octane number but were affected by turbulence caused by variation in piston timing. Under conditions of low turbulence, measured flame propagation rates approached one-dimensional premixed laminar flame speed computations performed at the same conditions. Experiments were simulated with a three-dimensional CONVERGE™ model using reduced chemical kinetics (121 species, 538 reactions). The simulations accurately captured the measured flame propagation rates, as well as the variation in EGAI fraction with fuel reactivity and time-integrated end-gas temperature. The simulations also revealed low-temperature heat release as well as formaldehyde and hydrogen peroxide formation in the end-gas upstream of the propagating flame, which increased the temperature and degree of chain branching in the end-gas, ultimately leading to EGAI.

42 ENGINEERING↗
DOE OSTI2024

In-situ two-dimensional temperature measurements using x-ray fluorescence spectroscopy in laminar flames with high silica particle concentrations

X-ray fluorescence spectroscopy (XRF) was used to measure temperatures and study mixing, for the first time in silica particle synthesis flames. Hexamethyldisiloxane (HMDSO) and trimethylsilanol (TMSO) were the particle precursors. A multi-element diffusion burner was used to produce a flat methane flame, and the precursors, dilute in inert gas, were injected via a central jet. Krypton was the fluorescent medium at 3.2 % concentration by volume. Scans with Kr in the central flow and not in the main flow were made to assess the mixing effects between the central and main gas flows. When HMDSO or TMSO were added, a secondary diffusion flame formed between the jet and the main methane flame. The results revealed a dramatic change in the centerline temperature profile of the jet gases when HMDSO or TMSO were added. The main methane flame stoichiometry also affected the temperature profiles. The results show HMDSO and TMSO reactions are initiated in a lowtemperature and low-oxygen concentration region of the jet where thermal decomposition is not expected to be significant. Reaction of the particle precursors is therefore attributed to radical transport from the main methane flame. In the current work, particle number densities of up to 270 g/m 3 locally are estimated. Thus, the study also demonstrates the capability of the XRF technique for high spatial fidelity measurements in flames with high concentrations of condensed-phase particles, leveraging the attribute that the XRF signal is generally not impacted by condensed-phase interferences. In conclusion, the observations and data obtained in this study inform likely reaction pathways for this important class of siloxane compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Comparing non-destructive 3D X-ray computed tomography with destructive optical microscopy for microstructural characterization of fiber reinforced composites

Due to their lightweight, relatively high stiffness properties, and formability into complex shapes, discontinuous fiber composites are advantageous for producing small and medium size components. Improved characterization techniques and post-processing methodologies are required for more reliable quantification of the microstructure and defect distributions in these materials, in order to employ model-based approaches to assess their structural integrity. This work compares a non-destructive X-ray approach with a destructive optical microscopy approach for characterizing the microstructural attributes, specifically the fiber volume fraction, porosity volume fraction, fiber orientation distribution, and fiber length distribution of discontinuous glass fibers in a polypropylene matrix. Additionally, a method for destructively determining the ambiguous components of the orientation tensor (related to the sign ambiguity of the out-of-plane angle in a destructive cross-sectional cut of a fiber) over a large surface area is included. It was found that fiber volume fraction and average fiber aspect ratio matched well, while fiber orientation and porosity detection had small but notable differences. The differences in the detection capabilities of each technique are quantified and discussed shedding light on the specific advantages and disadvantages of each approach, and enabling engineers to quantify uncertainty in their microstructural characterization measurements especially as they relate to model based structural integrity activities.

Non-destructive testing↗
DOE OSTI2025

Corrosion of 316 L stainless steel under the natural circulation of molten NaCl-MgCl 2 salt

The corrosion behavior of 316 L stainless steel (SS) was studied via the natural circulation of molten eutectic NaCl-MgCl 2 salt through a microloop. The post-corrosion tested 316 L SS microloop sections were characterized with microscopy techniques to determine the microstructural and microchemical changes that occurred at the alloy/salt interface. It was found that 316 L SS showed heterogeneous dissolution at the hot-leg, whereas deposition of corrosion products occurred at the cold-leg. For the first time, experimentally obtained molten salt flow-induced corrosion of 316 L SS results were combined with computational thermodynamic-kinetic models to validate the dissolution and deposition in terms of elemental compositional changes at the alloy/salt interface. The thermodynamic-kinetic modeling predicted that the heterogeneous dissolution of Cr and Fe from the hot-leg section of 316 L SS persisted throughout the salt circulation. The model also estimated that, despite Cr deposition starting earlier than Fe, the total redeposition of Fe is expected to be significantly greater than that of Cr over the circulation of salt. Furthermore, the modeling accurately predicted the subsurface enrichment of Mo which is attributed to the reduced Cr activity and the relatively higher diffusion rate of Mo within the alloy matrix. Here, the agreement between modeling and experimental results confirms that Fe chlorides dissolve at the hot-leg and subsequently deposit at the cold-leg due to activity changes driven by the thermal gradient. By contrast, Cr was not detected in the cold-leg deposits, which is attributed to its weaker temperature dependence on activity, limiting its redeposition under these conditions.

36 - MATERIALS SCIENCE↗
DOE OSTI2022

Adaptive workflow for simulation of RF heaters

Accurate RF (Radio Frequency) simulations of fusion systems like ITER require the definition of high-fidelity analysis geometries that include detailed antenna, reactor wall, and physics regions. Here, this paper will describe a workflow for the execution of adaptive high-performance simulations of RF fusion systems. In this workflow, the simulation input consists of a CAD model attributed with the needed analysis attributes. The analysis mesh is automatically generated and the analysis steps are executed using the time-harmonic Maxwell's equations solved using high-order Nédélec finite elements. A patch recovery-based error estimator is used to drive a conforming mesh adaptation procedure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

ENDFtk: A robust tool for reading and writing ENDF-formatted nuclear data

ENDFtk is a recently developed C++ and Python interface to interact with ENDF-6 formatted nuclear data files. It provides a robust and complete interface, allowing the reading and writing of all formats currently part of the ENDF-6 formats manual, as well as some non-ENDF formats used by the NJOY processing code. It provides an interface that mimics the names in the ENDF-6 formats manual as well as an equivalent interface using human-readable attribute names. It is robust and powerful enogh for nuclear data experts to develop complex applications, while also simple enough to be used non-experts to retrieve and manipulate evaluated nuclear data. ENDFtk offers the ability to easily interrogate and manipulate data either in large-scale code projects or in simple Python scripts. Here, in this paper, a brief overview of the interface is given, as well as more substantial examples demonstrating plotting simple data, interacting with more complex data, and writing new data to files. ENDFtk is open source and available for download via GitHub (https://github.com/njoy/ENDFtk).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Timing and extent of crop damage by wild pigs (Sus scrofa Linnaeus) to corn and peanut fields

The global expansion of wild pigs over the last few decades has resulted in an increase in extent and distribution of damages to crops, placing a growing strain on agricultural producers and land managers. Despite the extent of wild pig damage to agriculture, there is little data regarding timing and spatial variability of damage to corn (Zea mays Linnaeus) and we found no data regarding the effect of these factors on peanuts (Arachis hypogaea Linnaeus). Our objective was to determine the timing and extent of wild pig damage to corn and peanut fields, as well as the extent to which local habitat attributes are useful predictors of damage to these crops. During 2017–2018 we performed ground-based surveys throughout the growing season for 29 corn and 41 peanut fields in South Carolina, USA to determine the most important growth stages for wild pig depredation in both crops. Damage to corn peaked shortly after planting during the seedling stage, fell to nearly zero during V4–V6 stages, and resumed during the silk and mature stages. Peanut damage was almost exclusively limited to the seedling stage. Landscape models for both crops identified the extent of forested and wetland areas surrounding crop fields as the most important attributes positively associated with wild pig damage, while the amount of adjacent agricultural area and paved roads were associated negatively. The number of wild pigs identified by remote cameras also was an important indicator of the extent of damage to peanut fields. Results suggest management efforts to limit crop depredation by wild pigs should be targeted shortly prior to planting. Further, because damage was positively associated with the availability of wetland and forest habitats, our results suggest agricultural damage by wild pigs may be most severe near areas of preferred native habitats.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Improvement of Li-S battery electrochemical performance with 2D TiS 2 additive

As a multifunctional cathode additive, TiS 2 has been used in Li-S batteries to promote sulfur utilization and improve cell cycle life. The improvement of Li-S cell electrochemical performance in the presence of TiS 2 has been attributed to the ability of TiS 2 in adsorbing polysulfide. To maximize the interaction between polysulfides and TiS 2 , the 2-dimensional (2D) TiS 2 nano-sheets with highly exposed TiS 2 surface area has been successfully synthesized by a two-step sonication synthetic procedure. The 2D TiS 2 particles are micron size in the basal plane direction and are nano-sized along the c-axis direction. 2D TiS 2 nano-sheets may adsorb polysulfides more effectively than the raw TiS 2 powder. Li-S cells with 2D TiS 2 nano-sheets additive exhibit better discharge rate capability and significantly reduced capacity-fading after a long term cell cycling. Here, this improvement can be partially attributed to the uniform distribution of 2D TiS 2 nano-sheets particles within the sulfur cathode matrix, which induces more uniform sulfur and lithium sulfide deposition during the cell charge and discharge.

2D TiS2 nano-sheets↗
DOE OSTI2019

Statistical change detection of building energy consumption: Applications to savings estimation

The surge in interval meter data availability and associated activity in energy data analytics has inspired new interest in advanced methods for building efficiency savings estimation. Statistical and machine learning approaches are being explored to improve the energy baseline models used to measure and verify savings. One outstanding challenge is the ability to identify and account for operational changes that may confound savings estimates. In the measurement and verification (M&V) context, ‘non-routine events’ (NREs) cause changes in building energy use that are not attributable to installed efficiency measures, and not accounted for in the baseline model’s independent variables. In the M&V process NREs must be accounted for as ‘adjustments’ to appropriately attribute the estimated energy savings to the specific efficiency interventions that were implemented. Currently this is a manual and custom process, conducted using professional judgment and engineering expertise. As such it remains a barrier in scaling and standardizing meter-based savings estimation. Here in this work, a data driven methodology was developed to (partially) automate, and therefore streamline the process of detecting NREs in the post-retrofit period and making associated savings adjustments. The proposed NRE detection algorithm is based on a statistical change point detection method and a dissimilarity metric. The dissimilarity metric measures the proximity between the actual time series of the post-retrofit energy consumption and the projected baseline, which is generated using a statistical baseline model. The suggested approach for NRE adjustment involves the NRE detection algorithm, the M&V practitioner, and a regression modeling algorithm. The performance of the detection and adjustment algorithm was evaluated using a simulation-generated test data set, and two benchmark algorithms. Results show a high true positive detection rate (75%-100% across the test cases), higher than ideal false positive detection rates (20%-70%), and low errors in energy adjustment (<0.7%). These results indicate that the algorithm holds for helping M&V practitioners to streamline the process of handling NREs. Moreover, the change point algorithm and underlying statistical principles could prove valuable for other building analytics applications such as anomaly detection and fault diagnostics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Conditional distribution estimation of building characteristics with diffusion models for urban energy modeling

Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks

The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.

25 ENERGY STORAGE↗
DOE OSTI2025

Anion exchange membranes based on (3-acrylamidopropyl) trimethylammonium chloride (APTA) and phenyl Acrylate: Impact of crosslinker and crosslinker content on physiochemical properties and transport behavior of acetate and formate

CO 2 reduction cells are innovative devices that convert CO 2 into valuable chemicals, such as formate (OFm - ) and acetate (OAc - ), at the cathode. One of the key challenges in these devices is the development of ion exchange membranes that enable the transport of charge carriers between electrodes while minimizing the transfer of CO 2 reduction products. This study focuses on the preparation and characterization of crosslinked anion exchange membranes (AEMs) made of phenyl acrylate (PA) and (3-acrylamidopropyl) trimethylammonium chloride (APTA), crosslinked with either poly(ethylene glycol) diacrylate (PEGDA) or N,N’-methylenebisacrylamide (MBAA). Here, the membranes are characterized to understand their physiochemical properties and corresponding transport behavior through characterization of water volume fraction, mechanical properties, ionic conductivity, ion exchange capacity, water contact angle, glass transition temperature as well as their permeability and solubility to formate and acetate. MBAA crosslinked membranes exhibit higher Young’s modulus and lower strain at break compared to PEGDA-crosslinked membranes, which is attributed to their shorter chain length. Within a series of membranes of varied comonomer content, for either PEGDA or MBAA as crosslinker, permeabilities generally follow free volume theory (increasing permeability with increasing water content where water content increases with decreasing crosslinker content). Interestingly, for membranes with different crosslinkers but analogous water volume fraction significant differences (∼2 orders of magnitude) in permeability are observed which we attribute to differences in chain mobility as characterized through the glass transition temperature.

25 ENERGY STORAGE↗
DOE OSTI2022

Economic and Environmental Analysis to Evaluate the Potential Value of Co-Optima Diesel Bioblendstocks to Petroleum Refiners

The U.S. petroleum refining sector is undergoing a period of historic transformation, catalyzed by the decarbonization of the U.S. economy. Diesel-boiling-range bioblendstocks have gained traction, owing to their superior fuel properties and environmental performance as compared to traditional petroleum fuels. This work couples refinery linear programming models with life cycle assessment to quantify the potential economic and environmental benefits, and trade-offs, of blending diesel-boiling-range bioblendstocks at petroleum refineries. Linear programming models were developed in Aspen Process Industry Modeling Systems (PIMS) for three representative petroleum refinery configurations of differing complexity. Seven diesel-boiling-range bioblendstocks: 4-butoxyheptane, 5-ethyl-4-propylnonane, soy biodiesel, sludge hydrothermal liquefaction diesel, polyoxymethylene ethers, renewable diesel, and hexyl hexanoate, were investigated to identify key fuel properties that influence refineries' economics and to track the effect of adding bioblendstocks on refinery-wide cradle-to-gate greenhouse gases (GHG) emissions. These analyses considered blending levels from 10 to 30 vol% and fuel demand projections over the period 2040 to 2050. This analysis determines that bioblendstock sulfur content and cetane number are the primary fuel attributes with the potential to provide value to refiners. Life cycle assessment results indicate that the use of diesel-boiling-range bioblendstocks can reduce cradle-to-gate refinery GHG emissions by up to ~ 40 % relative to conventional refinery operations when considering carbon uptake in the supply chain of the bioblendstock. Refinery-wide marginal GHG abatement costs range from 120 to 3,600 USD2016/metric tons carbon dioxide equivalent avoided across the scenarios evaluated. Reducing the price of bioblendstocks is identified as a key to their adoption.

ADVANCED PROPULSION SYSTEMS,BIOMASS FUELS,ENERGY P↗
DOE OSTI2022

Production and Catalytic Upgrading of 2,3-Butanediol Fermentation Broth into Sustainable Aviation Fuel Blendstock and Fuel Properties Measurement

With the increasing demand for sustainable supplies of aviation fuel and need to address climate change, new conversion technologies are needed to efficiently process biomass, produce high quality jet fuel blendstock, and meet carbon emission targets. This study demonstrates the synthesis, conditioning, and catalytic upgrading of 2,3-butanediol (BDO) fermentation broth into a jet fuel blendstock candidate. A high-titer 2,3-BDO fermentation broth (i.e., ~90 g/L) was produced at a 100-L scale and pretreated via nanofiltration to decrease the impurities level in the broth from 4.6 to 0.6 wt%. A novel process for catalytic upgrading of aqueous 2,3-BDO into a jet fuel blendstock candidate was developed, and each step was efficiently demonstrated. The catalytic steps include 1) 2,3-BDO dehydration into methyl ethyl ketone (MEK) over AlPO4, 2) MEK conversion into olefins over Zn1Zr10Ox, 3) oligomerization of olefins over a zeolite beta, and 4) hydrogenation over platinum/carbon. Both the model feed and real 2,3-BDO fermentation broth were tested for upgrading 2,3-BDO to MEK. With the real feed, a continuous loss of conversion (i.e., >50% loss over ~140 h time-on-stream [TOS]) was partly attributed to reversible deactivation from coking species. However, the conversion remained stable with the model feed, which demonstrates the efficiency of the first step for converting aqueous 2,3-BDO (10 wt% in water). For upgrading MEK to olefins, high selectivity to olefins (i.e., 82.5%) was obtained at high conversion levels (i.e., 93-98%) with stable conditions being achieved for > 70-hours TOS. Oligomerization of light olefins, which was demonstrated for > 270 h TOS, mainly led to the formation of dimers (C8-10) and trimers (C13-14). The oligomerized product was hydrogenated and distilled to recover the jet fraction (35 mass% or 40.9% carbon based yield), which consists mostly of desired isoalkanes (31.7 wt%), n-alkanes (24.5 wt%), and cycloalkanes (29.6 wt%). While some improvement is still needed to meet ASTM D7566 specifications for viscosity and final boiling point temperature, freezing point, density, aromatics content, and sulfur content of the jet blendstock candidate were within acceptable ranges, thus highlighting the potential of this process for production of jet fuel blendstock.

ADVANCED PROPULSION SYSTEMS,BIOMASS FUELS↗
DOE OSTI2024

Microstructural effects on the rotating bending fatigue behavior of Ti–6Al–4V produced via laser powder bed fusion with novel heat treatments

The rotating bending fatigue (RBF) behavior (fully reversed, R = -1) of additively manufactured (AM) Ti–6Al–4V alloy produced via laser powder bed fusion (PBF-L) was investigated with respect to different microstructures achieved through novel heat treatments. The investigation herein seeks to elucidate the effect of microstructure by controlling variables that can affect fatigue behavior in Ti–6Al–4V, such as chemistry, porosity, and surface roughness. In order to control these variables, different hot isostatic pressing (HIP) treatments at 800 °C, 920 °C, and 1050 °C with a 920 °C temper were applied to three sets of Ti–6Al–4V cylinders that originated from the same PBF-L build, such that there were 30 tests per condition. After HIP treatment, the specimens were machined and tested. The highest runout stress was achieved after sub-β transus HIP at 800 °C for 2 h at 200 MPa of pressure. A significant drop in fatigue strength was attributed to large prior-β grains and grain boundary α resulting from super-β transus HIP treated specimens. In conclusion, for the sub-β transus HIP specimens, differences in fatigue strength were attributed to α lath thickness, relative dislocation density, and dislocation boundary strengthening.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effect of synthesis temperature on the phase formation of NiTiAlFeCr compositionally complex alloy thin films

Here, the synthesis temperature dependent phase formation of Ni 10 Ti 10 Al 25 Fe 35 Cr 20 thin films is compared to a bulk processed sample of identical composition. The as-cast alloy exhibits a dual-phase microstructure which is composed of a disordered BCC phase and AlNiTi-based B2- and/or L2 1 -ordered phase(s). Formation of the BCC phase as well as an ordered AlNi-based B2 phase is observed for a thin film synthesised at 500 °C (ratio of synthesis temperature of thin film to melting temperature of bulk alloy: T/T m = 0.49), which is attributed to both surface and bulk diffusion mediated growth. Post deposition annealing at 900 °C (T/T m = 0.75) of a thin film deposited without intentional heating results in the formation of NiAlTi-based B2 and/or L2 1 -phase(s) similar to the bulk sample, which is attributed to bulk diffusion. Depositions conducted at room temperature without intentional substrate heating (T/T m = 0.20) resulted in the formation of an X-ray amorphous phase, while a substrate temperature increase to 175 °C (T/T m = 0.28) causes the formation of a BCC phase. Atom probe tomography of the thin films deposited without intentional substrate heating and at 175 °C indicates the formation of ~5 nm and ~10 nm FeAl-rich domains, respectively. This can be rationalized based on the activation energy for surface diffusion, as Ti and Ni exhibt 2.5 to 4 times larger activation energy barriers than Al, Fe and Cr. It is evident from the homologous temperature that the phase formation observed at 500 °C (T/T m = 0.49) is a result of both surface and bulk diffusion. As the temperature is reduced, the formation of FeAl-rich domains can be understood based on the differences in activation energy for surface diffusion and is consistent with kinetically limited thin film growth.

36 MATERIALS SCIENCE↗