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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Load dynamics of double planar foil liners and double planar wire arrays on the UM MAIZE LTD generator

In previous studies using the University of Nevada, Reno's (UNR's) high-impedance Zebra Marx generator (1.9 Ω, 1.7 MA, 100 ns), Double Planar Wire Arrays (DPWAs) proved to be excellent radiators, and Double Planar Foil Liners (DPFLs) proved useful for future inertial confinement fusion applications. This article presents the results of joint UNR/UM (University of Michigan) experiments with aluminum (Al) DPWAs, Al DPFLs, and tungsten (W) DPWAs using UM's Michigan Accelerator for Inductive Z-Pinch Experiments (MAIZE) generator, a low-impedance Linear Transformer Driver (LTD) (0.1 Ω, 0.5–1 MA, and 100–250 ns). The main goals of this study were twofold: the first was a pioneering effort to test whether a relatively heavy Al DPFL could successfully be imploded on a low-impedance university-scale LTD like the MAIZE generator, and, if so, to analyze the results and make comparisons to the optimized, lighter DPWA configurations that have been previously studied. The DPWAs consisted of two planes of micrometer-scale diameter Al or W wires, while the DPFLs consisted of two planes of micrometer-scale thickness Al foils. Diagnostics include filtered Si-diodes, an absolutely calibrated filtered PCD, x-ray pinhole cameras, spectrometers, and gated optical self-emission imaging. The implosion dynamics and radiative properties of Al DPWAs and DPFLs and W DPWAs on the MAIZE LTD are discussed and compared. Time-dependent load inductance calculations derived from measurements of the load current and a MAIZE circuit model provide a relative measurement of pinch strength. In experiments on MAIZE, W planar wire arrays exhibited a higher peak load inductance throughout the pinch than Al DPWAs and DPFLs, while x-ray pulses from Al DPFLs had the longest emission duration.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Temperature dependent interfacial microstructure in HIP-bonded Al6061/Zr joints

Pressing (HIP) at 450 °C and 560 °C under 103 MPa for 90 minutes to evaluate interfacial diffusion and microstructural evolution. Sound metallurgical bonding was achieved at both temperatures, with Zr serving as an effective diffusion barrier. Microstructural analysis revealed the the formation of a (Al,Si)3Zr intermetallic layer at the Al/Zr interface, whose thickness increased from ~100 nm at 450 °C to ~2100 nm at 560 °C, consistent with temperature-driven diffusion kinetics. The thin and continuous reaction layer observed at 450 °C indicates controlled interdiffusion and minimal reaction zone growth, favorable for maintaining interface stability and ductility. At 560 °C, enhanced atomic mobility led to pronounced intermetallic development, confirming diffusion-controlled growth behavior. These results provide valuable insights for optimizing Al/Zr interface design and diffusion barrier performance in advanced structural and energy systems.

Al/Zr interface↗

DOE OSTI2024

Spin-orbit coupling in molecular complexes beyond van der Waals regime: Key factors for further splitting of 2 P 3/2 ground state

Here, we report a joint spectroscopic and theoretical study probing spin-orbit coupling (SOC) in a variety of molecular complexes between an iodine atom and a ligand (L) with L ranging from Ar, HF to formic/acetic acids, and glycine/N-methylated glycine derivatives. Cryogenic photoelectron spectroscopy of L·I - (L=HCOOH, CH 3 COOH) reveals three distinct peaks, identified as three SOC states, denoted as X(1/2), A(3/2), and B(l/2) for the corresponding neutrals. The X and A separation ΔE XA is measured to be 0.10 eV for both, whereas the X and B gap ΔE XB is 0.98 and 0.97 eV for formic and acetic acid, respectively. These new ΔE XA values are compared with the previously reported values for the molecular complexes L·I· with L=Ar, HF, glycine, and A-methylated glycines. All together these complexes encompass a diversity of intermolecular interactions, from van der Waals to weak and strong hydrogen bonding. While the ΔE XB remains similar, the ΔE XA is shown to be extremely sensitive to the type of ligands and interactions, spanning from 5 meV to 150 meV. High-level relativistic quantum calculations including explicit SOC formulism nicely reproduce all experimental SOC splitting. A direct correlation between the magnitude of ΔE XA with the intermolecular interaction strength or bond distance of the neutral complexes—the stronger interaction (shorter bond length), the greater splitting, is established.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

High-burnup Experiments in Reactivity Initiated Accidents (HERA)

HERA is a joint experimental program (JEEP) operating within the Nuclear Energy Agency’s (NEA’s) framework for irradiation experiments (FIDES). HERA is dedicated to the understanding of light water reactor (LWR) fuel performance at high burnup under reactivity-initiated accidents (RIA). During RIAs the fuel cladding can be breached by one of two mechanisms; pellet-cladding mechanical interaction or high temperature balloon and rupture. Pellet-cladding mechanical interaction (PCMI) failure occurs in the early phase of an RIA transient where the fuel pellet expands rapidly into the cladding prior to any meaningful heat transfer from the pellet to the cladding. This results in high stresses in the cladding resulting in crack propagation through the base Zircaloy metal and axially along the cladding tube. Failure by PCMI is a brittle failure mode and occurs at low cladding strains, approximately 2% hoop strain. If the cladding survives the PCMI phase of the transient, it may still fail due to ballooning and rupture induced by extended exposure to high temperature. Thermal transport from the fuel pin to the coolant is significantly reduced if a transition to film boiling on the cladding surface occurs. This result in both a rapid increase in fuel temperature, which drives fission gas release, and a spike in cladding temperature, which results in loss of mechanical strength. If the internal pressure of the fuel rod is greater than the system pressure the cladding can inelastically deform and eventually rupture. Failure by swelling and rupture occurs at larger strains, generally greater than 5% hoop strain. While PCMI failures are generally seen as more limiting, the dominance of one failure mode over the other can be affected by both the cladding material conditions, and the transient evolution.

(HERA)↗

DOE OSTI2023

High-burnup Experiments in Reactivity Initiated Accidents (HERA)

(HERA)↗

DOE OSTI2020

Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification

This report summarizes a joint effort between Argonne National Laboratory, Idaho National Laboratory, and Los Alamos National Laboratory to develop and deploy constitutive models targeted at predicting the life of Grade 91 alloy components subjected to high temperature environments typical of those that structural components in advanced nuclear reactors would experience. Two distinct, but complementary constitutive modeling approaches have been taken here. The first employs a phenomenological viscoplastic model for which parameters have been calibrated based on experimental data for a wide range of Grade 91 alloy that has undergone a variety of processing. A Bayesian approach was used to derive distributions of uncertain parameters for this model based on this data set. The second approach is a reduced order model suitable for engineering-scale analysis that is based on the results of a large set of mesoscale simulations. Mesoscale models allow for the microstructure and composition of a particular alloy to be directly taken into account in the computation of the viscoplastic response, but are computationally expensive, which makes it impractical to directly call those models for the material constitutive response in an engineering-scale simulation. The reduced-order representation of the response of the underlying model used here allows for an engineering-scale model to take into account the characteristics of the underlying microstructure, while only incurring a reasonable computational expense. Both of these approaches have different strengths, and are applicable for different parts of the design/analysis process. The phenomenological models can be readily parameterized based on a set of experimental data for a given class of materials and used for scoping calculations. Once a specific material is chosen and adequately characterized, the reduced order models can accurately predict the response of that specific alloy, and because the models are based on predictive models of the underlying microstructure, they can be used to more confidently predict the response under conditions in regions where there is limited experimental data. Both of these models have been integrated in the Grizzly code, which is used here to perform proof-of-concept uncertainty quantification analyses of a simple component under prototypical conditions. The built- in stochastic analysis capabilities in the MOOSE framework that Grizzly is built on are used here to run large sets of simulations for this uncertainty quantification analysis. As would be expected, because the reduced order models are developed for a much more tightly defined alloy, they predict tighter distributions of the time to failure than the phenomenological models, which are calibrated to a broader set of data. Also important is that these simulations demonstrate that a reduced order modeling approach can be successfully deployed to propagate uncertainties from the material scale to practical engineering-scale component simulations.

42 ENGINEERING↗

DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models

Annual burned areas in the United States have increased 2-fold during the past decades. With more large fires resulting in more emissions of fine particulate matter, an accurate prediction of fire emissions is critical for quantifying the impacts of fires on air quality, human health, and climate. This study aims to construct a machine learning (ML) model with game-theory interpretation to predict monthly fire emissions over the contiguous US (CONUS) and to understand the controlling factors of fire emissions. The optimized ML model is used to diagnose the process-based models in the Fire Modeling Intercomparison Project (FireMIP) to inform future development. Results show promising performance for the ML model, Community Land Model (CLM), and Joint UK Land Environment Simulator-Interactive Fire And Emission Algorithm For Natural Environments (JULES-INFERNO) in reproducing the spatial distributions, seasonality, and interannual variability of fire emissions over the CONUS. Regional analysis shows that only the ML model and CLM simulate the realistic interannual variability of fire emissions for most of the subregions (r >0.95 for ML and r =0.14~0.70 for CLM), except for Mediterranean California, where all the models perform poorly (r =0.74 for ML and r <0.30 for the FireMIP models). Regarding seasonality, most models capture the peak emission in July over the western US. However, all models except for the ML model fail to reproduce the bimodal peaks in July and October over Mediterranean California, which may be explained by the smaller wind speeds of the atmospheric forcing data during Santa Ana wind events and limitations in model parameterizations for capturing the effects of Santa Ana winds on fire activity. Furthermore, most models struggle to capture the spring peak in emissions in the southeastern US, probably due to underrepresentation of human effects and the influences of winter dryness on fires in the models. As for extreme events, both the ML model and CLM successfully reproduce the frequency map of extreme emission occurrence but overestimate the number of months with extremely large fire emissions. Comparing the fire PM 2.5 emissions from the ML model with process-based fire models highlights their strengths and uncertainties for regional analysis and prediction and provides useful insights into future directions for model improvements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Stellar and weak lensing profiles of massive galaxies in the Hyper-Suprime Cam survey and in hydrodynamic simulations

ABSTRACT We perform a consistent comparison of the mass and mass profiles of massive (M⋆ > 1011.4 M⊙) central galaxies at z ∼ 0.4 from deep Hyper Suprime-Cam (HSC) observations and from the Illustris, TNG100, and Ponos simulations. Weak lensing measurements from HSC enable measurements at fixed halo mass and provide constraints on the strength and impact of feedback at different halo mass scales. We compare the stellar mass function (SMF) and the Stellar-to-Halo Mass Relation (SHMR) at various radii and show that the radius at which the comparison is performed is important. In general, Illustris and TNG100 display steeper values of α where $M_{\star } \propto M_{\rm vir}^{\alpha }$. These differences are more pronounced for Illustris than for TNG100 and in the inner rather than outer regions of galaxies. Differences in the inner regions may suggest that TNG100 is too efficient at quenching in situ star formation at Mvir ≃ 1013 M⊙ but not efficient enough at Mvir ≃ 1014 M⊙. The outer stellar masses are in excellent agreement with our observations at Mvir ≃ 1013 M⊙, but both Illustris and TNG100 display excess outer mass as Mvir ≃ 1014 M⊙ (by ∼0.25 and ∼0.12 dex, respectively). We argue that reducing stellar growth at early times in $M_\star \sim 10^{9-10} \, \mathrm{M}_{\odot }$ galaxies would help to prevent excess ex-situ growth at this mass scale. The Ponos simulations do not implement AGN feedback and display an excess mass of ∼0.5 dex at r < 30 kpc compared to HSC which is indicative of overcooling and excess star formation in the central regions. The comparison of the inner profiles of Ponos and HSC suggests that the physical scale over which the central AGN limits star formation is r ≲ 20 kpc. Joint comparisons between weak lensing and galaxy stellar profiles are a direct test of whether simulations build and deposit galaxy mass in the correct dark matter haloes and thereby provide powerful constraints on the physics of feedback and galaxy growth. Our galaxy and weak lensing profiles are publicly available to facilitate comparisons with other simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

The role of information structures in game-theoretic multi-agent learning

Multi-agent learning (MAL) studies how agents learn to behave optimally and adaptively from their experience when interacting with other agents in dynamic environments. The outcome of a MAL process is jointly determined by all agents’ decision-making. Hence, each agent needs to think strategically about others’ sequential moves, when planning future actions. The strategic interactions among agents makes MAL go beyond the direct extension of single-agent learning to multiple agents. With the strategic thinking, each agent aims to build a subjective model of others decision-making using its observations. Such modeling is directly influenced by agents’ perception during the learning process, which is called the information structure of the agent’s learning. As it determines the input to MAL processes, information structures play a significant role in the learning mechanisms of the agents. This review creates a taxonomy of MAL and establishes a unified and systematic way to understand MAL from the perspective of information structures. Here we define three fundamental components of MAL: the information structure (i.e., what the agent can observe), the belief generation (i.e., how the agent forms a belief about others based on the observations), as well as the policy generation (i.e., how the agent generates its policy based on its belief). In addition, this taxonomy enables the classification of a wide range of state-of-the-art algorithms into four categories based on the belief-generation mechanisms of the opponents, including stationary, conjectured, calibrated, and sophisticated opponents. We introduce Value of Information (VoI) as a metric to quantify the impact of different information structures on MAL. Finally, we discuss the strengths and limitations of algorithms from different categories and point to promising avenues of future research.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

HFTS-1 Natural Joint Data and Engineering Summary

This report provides an analysis of engineering and geologic data collected from the Hydraulic Fracturing Test Site #1 (HTSF-1) project in the southern Midland Basin, Reagan County, Texas. The site is being studied as part of the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative at the National Energy Technology Laboratory (NETL). The data collected is intended to provide a basis of understanding of the site and to construct simulation models. This report provides a summary analysis of the engineering and geologic data collected from the project to provide a basis of understanding of the site and to construct simulation models. The report examines various possible correlations in engineering properties based on natural fracture data from the program, which consisted of 11 horizontal wells, one vertical well, and one slant well. The horizontal wells are in two horizons: 1) Upper and 2) Middle Wolfcamp formations. Program data include: fracture frequency and fracture orientation data from four core runs in a slant well; fracture spacing and orientation from a vertical pilot well; laboratory triaxial testing and mineralogical determinations; and porosity results from magnetic resonance analyses from various wells, together with observations based on the data collection. In addition, available references were reviewed on the site for additional insights. As the focus of the report is on the natural system, hydraulic fracture data from the site were not examined in detail in this report. Data variability is the chief observation in examination of the database. Fracture frequency in the slant well can range from sections with values as high as five fractures per ft to sections up to 100+ ft in length with no natural observed fractures. Fracture spacing across is typically less than 10 ft, but can range up to hundreds of feet. Apparent fracturing shows the trends in two predominate orientations, E-SW and WNW-ESE, but minor variations exist. The rock units vary across the site from siliceous mudstones to calcareous mudstones, showing a general layering with depth. The laboratory properties such as strength and modulus show no apparent trend with depth, but appear to correlate with rock mineralogy with high strength and modulus values where calcium content is high. In addition, an attempt to examine variability and mineralogy on a larger scale was made using a color-coded system based on gamma ray measurements. A staged colored approach was adopted, presuming that lower gamma ray values indicate higher value of calcium content (blue scale) and that higher gamma ray values indicate higher clay mineral content (orange scale). As provided in report appendices, the system correlated well with visual examination of the slant core and the petrofabric analyses of the vertical pilot well. The results showed large variability in mineral content along the horizontal plane across the site. The change in mineral content was also rapid, on a scale less than that of the average hydraulic fracture stage length of about 180 ft.

42 ENGINEERING↗

DOE OSTI2025

Implementation of Perturbation Theory and Sensitivity Capabilities in Griffin

Griffin is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor Multiphysics analysis application, jointly developed by Argonne and Idaho National Laboratories under the DOE-NE NEAMS program. This fiscal year, capabilities for reactivity and sensitivity evaluation using perturbation methods were implemented and verified. The First Order Perturbation Method (FOPT) was employed to compute reactivity worth resulting from small perturbations in input parameters, while the Generalized Perturbation Theory (GPT) was used to evaluate sensitivities of a range of response types, including reaction rate ratio, k-eigenvalue, neutron generation time, and effective delayed neutron fraction. These perturbation methods enable users to quantify how response quantities change due to a perturbation in a input parameter without explicitly performing an additional transport simulation for each perturbed state. In particular, the GPT formulation accounts for indirect effects arising from flux changes by solving generalized inhomogeneous equations, for which a Neumann series-based iterative solution method was developed and implemented in Griffin. The implemented reactivity and sensitivity evaluation capabilities were verified using two test problems: an infinite homogeneous system and a two-dimensional hexagonal core. The results showed excellent agreement with reference solutions obtained by a direct method based on finite difference approximation as well as GPT-based results from the PERSENT code, confirming the accuracy of both reactivity and sensitivity evaluations. Additionally, preliminary uncertainty quantification (UQ) results were obtained by combining the sensitivity values computed using GPT and external covariance data, demonstrating that the implemented sensitivity results can be reliably used for uncertainty calculations. To further demonstrate the generality and practical strength of the implementation, the sensitivity evaluation capability was successfully applied to the Empire microreactor with a geometrically complex design that poses significant modeling challenges. The results confirm that Griffin enables sensitivity evaluations even for irregular and highly heterogeneous reactor configurations, thereby establishing a foundation for UQ applications in advanced reactor designs and analyses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Technical Performance of Refractory Liners for Molten Chloride Salt Thermal Energy Storage Systems

A chloride-based molten-salt system that uses a ternary blend of MgCl2/KCl/NaCl is investigated to provide higher temperature thermal energy storage capability. Despite higher thermal stability, molten chlorides present several unique challenges, including the design of internal refractory-ceramic liners to prevent the corrosion and thermal stress of alloy tank shells. This work discusses issues and potential solutions related to containment of molten chloride salt, specifically the optimization of the refractory material at the molten salt interface (hot face). The down-selected hot face candidate limits permeation of salt through the material and forms a highly stable secondary surface phase in equilibrium with the molten salt. A mortar is created using the corrosion resistant hot face brick. Brick and mortar composite are subjected to mechanical stress/strain analysis, in order to calculate composite material properties and better inform thermomechanical models. The U.S. Department of Energy Generation 3 (DOE Gen3) program seeks to develop higher efficiency CSP plants that can provide cost-competitive, flexible power in the U.S. electric grid. The proposed Gen3 Liquid Pathway CSP plant closely resembles the configuration of current nitrate salt power towers with two-tank storage (Gen2). The differences between Gen2 and Gen3 include the types of compatible materials used in salt storage tank construction. Stainless steel loses strength at Gen3 temperatures, and although nickel superalloys would be capable of withstanding sustained high temperatures, these materials are prohibitively expensive at scale. Uninsulated tank shells also pose a significant risk as common steels are highly susceptible to chemical attack from molten chloride salt. To address these concerns, refractory-ceramic based containment materials are proposed to line the inside of the hot and cold storage tanks. In doing so, stainless or carbon steel shells may be used in construction depending on the level of insulation provided. The composition of the internal liner requires careful consideration to maximize the efficacy of multiple parameters including corrosion resistance, strength at operating temperature, durability, and cost. This is particularly true for the material at the interface with the salt, known as the "hot face", which is responsible for protecting the insulating layers between the tank shell and the hot face brick layer. The molten salt in this system is superheated over 300 °C above its freeze temperature. Therefore, unlike other industrial processes which use refractory-lined vessels, it is not expected that a freeze plane will develop in the hot face. Therefore, the hot face must be designed to withstand chemical corrosion and inhibit permeation of molten salt into the insulating layers. A down selection was performed to identify a hot face candidate best equipped to maintain thermal, mechanical, and chemical integrity when exposed to molten salt over extended periods of time. Long-duration chemical capability experiments were conducted with the down selected hot face refractory fully immersed in molten chloride salt for up to 3000 hours. The average salt penetration does not exceed 100 microns. When extrapolated to 20 and 30 years of continuous exposure, the expected salt penetration depth is approximately 2.0 mm and 2.9 mm, respectively. A magnesium-rich secondary phases develops at the salt/refractory interface. X-ray diffraction identifies the material as forsterite (Mg2SiO4), which is reported to form synthetically in molten chloride salt solutions. These results suggest the selected hot face will adequately inhibit salt permeation. While there is optimism that the hot face brick will inhibit salt permeation, mortar joints are typically the weakest point of a refractory brick liner. From a thermochemical perspective, differing thermal expansion coefficients may result in the mortar and brick to grow independent of each other, creating gaps through which molten salt can penetrate. To address this issue, NREL has developed an in-house mortar composed of the down selected hot face brick that has been shown to be compatible with the salt. Compressive stress/strain analyses have been performed on the brick/ mortar composites to generate stress/strain curves. Modulus of elasticity and Poisson's ratio of the composite may be calculated from the stress/strain curves, in order to provide more representative data to finite element mechanical models for accurate approximation of stress on the tank shell and the amount of thermal expansion expected within the tank liner.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Incidence, scaling relations and physical conditions of ionized gas outflows in MaNGA

ABSTRACT In this work, we investigate the strength and impact of ionized gas outflows within z ∼ 0.04 MaNGA galaxies. We find evidence for outflows in 322 galaxies ($12{{\ \rm per\ cent}}$ of the analysed line-emitting sample), 185 of which show evidence for hosting an active galactic nucleus (AGN). Most outflows are centrally concentrated with a spatial extent that scales sublinearly with Re. The incidence of outflows is enhanced at higher masses, central surface densities, and deeper gravitational potentials, as well as at higher star formation rate (SFR) and AGN luminosity. We quantify strong correlations between mass outflow rates and the mechanical drivers of the outflow of the form $\dot{M}_{\rm out} \propto \rm SFR^{0.97}$ and $\dot{M}_{\rm out} \propto L_{\rm AGN}^{0.55}$. We derive a master scaling relation describing the mass outflow rate of ionized gas as a function of M⋆, SFR, Re, and LAGN. Most of the observed winds are anticipated to act as galactic fountains, with the fraction of galaxies with escaping winds increasing with decreasing potential well depth. We further investigate the physical properties of the outflowing gas finding evidence for enhanced attenuation in the outflow, possibly due to metal-enriched winds, and higher excitation compared to the gas in the galactic disc. Given that the majority of previous studies have focused on more extreme systems with higher SFRs and/or more luminous AGN, our study provides a unique view of the non-gravitational gaseous motions within ‘typical’ galaxies in the low-redshift Universe, where low-luminosity AGN and star formation contribute jointly to the observed outflow phenomenology.

Avery, Charlotte R.↗

DOE OSTI2021

Welding of Haynes 282 to Steels to Enable Modular Rotors for Advanced Ultra Super-Critical Steam Turbines

Steam turbines for an Advanced Ultra Super Critical (AUSC) fossil fired power plant will operate at temperatures well above those of current commercial steam cycles with inlet temperatures more than 760 C , which is beyond the capabilities of alloy steels presently used for steam turbine applications and requires advanced materials. Large components such as steam turbine rotors may be made of nickel based super alloys such as Haynes 282 (H282). However, monolithic forgings of superalloys in the sizes required for large steam turbines rotors can be prohibitively expensive besides many technical challenges. To minimize cost and alleviate the related technical challenges, superalloy use needs to be limited to locations on the steam turbine rotor where strength and temperature requirements cannot be met by conventional steels. This is possible if nickel-based superalloys can be successfully welded to steels and the related technical challenges - machining parts made of dissimilar welded materials, non-destructive examination of such welds for flaw detection; and, material properties of such hybrid components – are sufficiently addressed. In this technology development project, we successfully welded H282 to plates up to ~ 75 mm (~ 3 inches) to a 3.5NiCrMoV steel of similar thickness. Advanced ultrasonic inspection technique called Phased Array Ultrasonic Testing (PAUT) was employed to examine the dissimilar H282-Steel welds, into which flat bottomed side drilled holes (SDH) of various diameters were introduced, to determine the minimum detectable feature sizes; it was shown that with PAUT SDH of dia. down to 0.5 mm could be detected in the base alloys and SDH with dia. down to 2.4 mm could be detected in the weld metal under multiple orientations successfully. An autonomous machining process monitoring system was developed and demonstrated whereby the forces acting on the cutting tool could be actively monitored as the cutting tool transitioned from H282 to Steel across the weld using which the machining parameters can be potentially altered without interruption to extend tool life. This project successfully achieved its objectives of - i. Developing a welding methodology and viable welding geometries, to successfully join H282 to steel 3.5CrMoNiV steel to enable manufacture of modular steam turbine rotors for AUSC applications (conditions of at least 760 °Celsius and 3,100 psia (pounds per square inch absolute pressure) and evaluate the material properties of the welded specimen. ii. Employ the advanced ultrasonic inspection technique to the dissimilar weld metal joint and determine the minimum detectable feature sizes. iii. Develop effective machining techniques to machine such hybrid structures with online tool force monitoring and effect machine state metrics for optimal results.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2025

Rotary Inertia Friction Welding of Dissimilar High-Strength 422 Martensitic Stainless Steel and 4140 Low Alloy Steel for Heavy-Duty Engine Piston Fabrication

AISI 422 martensitic stainless steel with superior high temperature performance (oxidation resistance and strength) is under evaluation for replacing current heavy-duty piston crown materials, AISI 4140 martensitic steel and micro alloyed steel (MAS) 38MnSiVS5, to fabricate a multimaterial piston. This multimaterial piston concept further improved power density and fuel economy by allowing heavyduty diesel engines to operate at higher temperatures and pressures. Joining AISI 422 steel piston crowns with AISI 4140 steel piston skirts is a key manufacturing step for this multimaterial piston. However, the significant differences in strength, elevated temperature flow stress, alloy chemistry, and temper resistance between these two martensitic steels cause some weldability issues (cracking) and metallurgical challenges (alloying element migration/segregation) when using conventional fusion-based welding processes. Rotary inertia friction welding (RIFW), a solid-state welding process, has been the preferred method to join 4140 crowns to 4140 skirts (and MAS crowns to MAS skirts) in high-volume production of current heavy-duty diesel engine pistons. It has been used to join these two materials with relatively comparable alloy chemistry to fabricate pistons with MAS skirts and 4140 crowns. Meanwhile, RIFW has also been a preferred method of dissimilar metal welding. However, RIFW of dissimilar high-strength martensitic steels has yet to be widely pursued. The interfacial microstructure complexities created by the thermomechanical process and highly nonequilibrium phase transformations during RIFW are a significant challenge for understanding and predicting their joining behavior and have not been reported in detail. Here, in this work, defect-free AISI 422 steel-AISI 4140 multimaterial pistons were successfully fabricated using the RIFW process. The interfacial microstructure and mechanical properties of dissimilar 422/4140 steel RIFW in the as-welded condition were experimentally studied in detail. The results provide critical baseline information for understanding RIFW mechanisms and guiding subsequent postweld heat treatment (PWHT) practice.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Determining Compression Characteristics of Honeycomb Material - 19674

The objective is to determine compression test characteristics of stainless steel honeycomb material to be able to represent honeycomb structures accurately in analytical models used to simulate hypothetical accident scenarios of shipping packages. Honeycomb is a material used primary in the aerospace industry due to its high strength-to-weight ratio. Because it doesn't have a shelf life and can withstand high heats, it is an excellent candidate for a structural material in package designs. Honeycomb has orthotropic material properties. The honeycomb currently being evaluated is made of metal ribbons spot welded together to form hexagon pattern between metal plates. The hexagon ribbons are brazed to the metal plates. The complexity of the honeycomb significantly lengthens the simulation time, which is further complicated when the brazing, welding, and imperfections of the material is considered. This means to affectively represent the honeycomb in simulation programs, such as Abaqus, the material needs to be approximated as a uniform orthotropic material. This requires material properties in each of the three directions, T, W, and L. The T direction is defined as the 'strong' direction, perpendicular to the honeycomb sheet. The L direction is parallel the ribbon and the W direction is perpendicular to the ribbon. Honeycomb has 3 different compression stages. Stage 1 is the initial compression where the honeycomb maintains its structural integrity and does not permanently deform from loads from normal operations. Stage 2 is when the initial buckling causes irreversible damage to the honeycomb. Stage 2 is the one we are most interested in because it absorbs the most energy from a hypothetical accident scenario. During Stage 2, the honeycomb fails layer-by-layer, indicating that the more layers, the longer material crushing is sustained, and thus the more energy that is absorbed. Stage 3 is final compression, similar to compressing solid metal. During stage 3 the stress increases with a diminishing rate of elongation and the honeycomb is completely failed where the plates between the honeycomb core sandwich the crushed honeycomb ribbon. The stress-strain graph bellow shows compression test of 3 different samples in the T direction. The red lines divide the three stages. The far left is Stage 1, the middle stage 2, and the right stage 3. The data collected is displayed in the table below. The expressions in the table represent the regression formula that represents each stage of the compression on a stress-strain graph. Stage 1 is assumed to intersect with the origin; data for stage 3 for the crush in W and L directions where unable to be gathered due to the nature of failure for those directions. Stage 1 and 2 are linear regressions while stage 3 is a degree 2, to best match the curve. Linear regression is used on stage 2 because ultimately that represents the energy absorbed. With more data, a degree n x 2 regression would be a more appropriate regression for stage 2, where n is the number of layers. The table below is the approximation of each scenario and stage. Each stage starts and ends at the intersection of the next stage. There where two main objectives for these tests: firstly to understand how honeycomb performs under extreme compression, and secondly to be able to numerically represent the honeycomb structure. Both of these where completed to varying degrees. It is important understood how the honeycomb fails. If it is crushed in the T direction it retains its integrity even after being crushed, however when crushed in the L and W crush direction, if it fails, it disintegrates, and loses all integrity. Also, the more layers the more time the material spends in stage 2. Failure is started by buckling, thus if there is any imperfection, the stress will not spike but transition straight into stage 2. The data collected and aggregated can be used for initial simulation of honeycomb used in packages. The initial testing has set the ground work for more data to be collected in order to verify results and to allow more confidence in the simulation results. This is only the initial data collected. The next steps is to continue to collect more data to verify results and to test more variants of honeycomb, with different brazing, layers, and shape.

36 MATERIALS SCIENCE↗