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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 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Grid-connected heat pump water heater benefits for low-income households in the Southeastern United States

Energy efficient heat pump water heaters (HPWHs) reduce customer energy costs; grid-connected controls further increase HPWH value by enabling energy storage and shifting demand to cheaper, off-peak periods. However, a HPWH’s first cost premium can put it out of reach for low-income customers. This paper will present results of an ongoing study exploring the benefits of load shifting HPWHs for 24 low-income households, aiming to support increased product deployment in this demographic. The study uses EcoPort modules to shift HPWH load for a study sample consisting primarily of low-income adults and seniors in North Carolina. Building upon prior studies, the study’s HPWH operating schedules are designed to maximize shifted energy and minimize participant electricity costs based on local time-of-use rates. The study documents load profiles of grid-connected HPWHs in a less-studied demographic group and explores how certain groups may have particularly flexible loads due to their unique usage profiles. The results are relevant for HPWH product performance in the Southeast. The study will quantify HPWH load shifting performance during North Carolina’s hot summer, temperate shoulder, and occasionally sub-freezing winter seasons, and the study includes installations in conditioned, semi-conditioned, and unconditioned spaces. Finally, study results will also explore how controlled HPWHs can provide value to the regional grid by reducing seasonal peaks via demand response. Lessons learned include HPWH acceptance for low-income and senior users, best practices for maintaining HPWH connectivity among users without prior product experience, and strategies to leverage remote monitoring to ensure optimal operation and enhance HPWH reliability.

Urigwe, Daniela↗
DOE OSTI2025

EVI-EnSitePy (Electric Vehicle Infrastructure – Energy Estimation and Site Optimization Tool in Python) [EVI-X Modeling Suite] [SWR-25-07]

EVI-EnSitePy is a comprehensive agent-based tool designed for the analysis and design of high-power charging sites, encompassing a wide array of site agents including Electric Vehicles (EVs), chargers, energy storage units (ESS), renewable energy resources (DER), and loads. This versatile tool offers diverse functionalities and a modular modeling approach, allowing detailed configuration of agents based on power ratings, port numbers, energy capacities, demand requirements, charger interfaces, and flexibility to customize the tool for project specific goals. By simulating agent interactions and employing various metrics, EVI-EnSitePy enables the assessment of site performance, exploration of energy management systems (EMS), and implementation of innovative EV charging policies. Utilizing EV charge schedules and arrival states, the tool performs thorough charging site simulations, with outputs consisting of agent and site-level power profiles and statistical metrics. Employing a tree graph structure, EVI-EnSitePy supports nested site structures and power distribution modeling. The tool's ability to generate charging schedules deterministically or via stochastic analysis further enhances its versatility. Through its features and capabilities, EVI-EnSitePy offers a powerful platform for informed decision-making in the realm of high-power charging site design and operation.

Jackson, Derek [National Renewable Energy Laborato↗
DOE OSTI2018

Improving Flexibility and Performance of Staged Pressurized Oxy-Combustion

Staged pressurized oxy-combustion (SPOC) is a novel coal combustion power technology being developed by Washington University in St. Louis (WUSTL) that offers the potential to deliver low-carbon power at a reduced cost. Oxy-combustion plants operate by removing most of the nitrogen contained in air prior to combustion, thereby burning fuel in a near-pure oxygen stream instead of air. This combustion produces a flue gas containing primarily carbon dioxide (CO 2 ) and water vapor that allows relatively straightforward CO 2 capture. First-generation, atmospheric oxy-combustion technology relies on a flue gas recycle (FGR) diluent to reduce the peak temperatures achieved and the resulting high thermal radiation levels that would otherwise occur in a fuel/oxygen only flame. In contrast, by staging the combustion in several steps, SPOC reduces the peak temperatures of combustion without resorting to a high degree of FGR to control flame temperatures. The SPOC process operates by utilizing two or more pressurized boiler modules connected in series to produce fuel staging; hence, only a portion of the fuel is combusted in any given combustion module. Subsequently the thermal energy released at each stage can be captured and removed from the gases prior to subsequent stages, when more fuel is introduced. This allows the SPOC process to operate with minimal FGR, avoiding the associated efficiency losses and additional costs. While the staging controls the heat release profile in SPOC, operating in a pressurized condition yields a more compact system, reducing modular boiler size and delivering enhanced heat transfer. Additionally, operating at elevated pressures ensures that the latent heat of moisture available from moisture in the fuel and generated by the combustion of the hydrogen content in the fuel can be captured as useful thermal energy for steam turbine integration. This is possible due to elevated dew point from the high partial pressure of the moisture generated in this process, which allows substantial feedwater heater bypassing to be achieved. The inclusion of this thermal energy, normally lost to the atmosphere in traditional combustion processes or to cooling systems in atmospheric oxy-combustion, allows the resultant net efficiency of the SPOC system to be up to 6 percentage points greater than first-generation systems. To begin the process of developing this technology for full-scale application, the Electric Power Research Institute, Inc. teamed up with WUSTL, American Air Liquide, Doosan Babcock, and the U.S. Department of Energy to investigate a practical and workable boiler design concept that would minimize process risks where possible. The SPOC system offers unique process flexibility opportunities due to the staged nature of the combustion, making it a prime candidate for future primary energy delivery where the ability to operate at reduced load in an efficient manner and ramp rapidly will be valuable for balancing intermittent renewable electricity generation. The efficiency of traditional coal-fired power plants is lower at reduced loads below the steam temperature control range due to the challenges in balancing the heat pickup between radiative and convective superheater and reheater surface. Introducing additional heating surface for optimal extended controlled low load operation will likely lead to excessive attemperation at high loads, ultimately leading to a poorer overall cycle efficiency. The SPOC process can facilitate the adjustment of fuel firing rates between stages, targeting heat pickup to where it is most needed and potentially can shut down stages for low-load operation, facilitating an increased steam temperature control range on turndown. The boiler design concept assessment is being carried out to identify the maximum permissible compactness of the SPOC boiler heating surfaces. The assessment will determine the minimum overall height that will deliver appropriate tube operating metal temperatures at full load and achieve rated reheat steam temperatures at low operating loads, balanced against the needs of efficient coal combustion, and the potential resultant slagging and ash environments. The design parameters are being validated by combustion testing in the 100-kWth pressurized combustion test rig at WUSTL. Parameters being investigated include flame stability, fuel burnout, ash composition, radiative heat flux, and temperature profiles. These are being carried out for a range of inlet gas conditions and compositions and at multiple load conditions to investigate combustion stability at reduced load. The results of this testing inform the modeling of the large-scale system, that was sized at 550 MWe to allow direct comparison with the National Energy Technology Laboratory baseline cases for atmospheric oxy-combustion, air firing, and air firing with post-combustion capture.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

A novel machine learning based identification of potential adopter of rooftop solar photovoltaics

With the proliferation of rooftop solar photovoltaic installations, there is a need to proactively predict consumer potential for solar photovoltaic adoption, for improved electric utility planning and operation. Traditional analytical modeling approaches are limited to a few survey features and a larger part of the survey would remain untouched by the decision model. This article presents a novel, data-driven modeling approach that strategically prunes a large set of consumer profile features using a machine learning framework to train a model for predicting potential solar adoption. The approach utilizes the Gradient Boosting Decision Tree model through a Light Gradient Boosting framework that improves significantly over the poor prediction accuracy of the existing approaches. Model training using focal-loss based supervision is used to overcome the difficulty in identifying the potential adopters that is inherent in conventional data-driven models. In addition, to overcome possible data sparsity in a limited survey sample, a Generative Adversarial Network is presented to create synthetic user samples and its effectiveness on model performance is assessed. A Bayesian optimization approach is used to systematically arrive at the hyperparameters of the proposed model. Validation of the presented approach on a survey data collected by the National Rural Electric Cooperative Association in Virginia in 2018 demonstrates the excellent predictive capability of the machine learning based approach to modeling solar adoption reliably.

14 SOLAR ENERGY↗
DOE OSTI2016

High Resolution Dopant Profiles Revealed by Atom Probe Tomography and STEM-EBIC for CdTe Based Solar Cells

Segregated elements and their diffusion profiles within grain boundaries and interfaces resulting from post deposition heat treatments are revealed using atom probe tomography (APT), scanning transmission electron microscopy (STEM), and electron beam induced current (EBIC) techniques. The results demonstrate how these techniques complement each other to provide conclusive evidence for locations of space charge regions and mechanisms that create them at the nanoscale. Most importantly, a Cl dopant profile that extends ~5 nm into CdTe grains interfacing the CdS is shown using APT and STEM synergy, which has been shown to push the pn-junction into the CdTe layer indicative of a homojunction (revealed by STEM EBIC). In addition, Cu and Cl concentrations within grain boundaries within several nms and µms from the CdS/CdTe interface are compared, Na segregation of <0.1% is detected, and S variations of ~1–3% are witnessed between CdTe grains close to the CdS/CdTe interface. The segregation and diffusion of these elements directly impacts on the material properties, such as band gap energy and n/p type properties. Optimization of the interfacial and grain boundary doping will lead to higher efficiency solar cells.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Confinement in electron heated plasmas in Wendelstein 7-X and ASDEX Upgrade; the necessity to control turbulent transport

In electron (cyclotron) heated plasmas, in both ASDEX Upgrade (L-mode) and Wendelstein 7-X, clamping of the ion temperature occurs at $T_\mathrm{i}$ ~ 1.5 keV independent of magnetic configuration. The ions in such plasmas are heated through the energy exchange power as ${n}_{\mathrm{e}}^{2}({T}_{\mathrm{e}}-{T}_{\mathrm{i}})/{T}_{\mathrm{e}}^{3/2}$, which offers a broad ion heating profile, similar to that offered by alpha heating in future thermonuclear fusion reactors. However, the predominant electron heating may put an additional constraint on the ion heat transport, as the ratio $T_\mathrm{e}T_\mathrm{i}$ > 1 can exacerbates ITG/TEM core turbulence. Therefore, in practical terms the strongly 'stiff' core transport translates into Ti-clamping in electron heated plasmas. Due to this clamping, electron heated L-mode scenarios, with standard gas fueling, in either tokamaks or stellarators may struggle to reach high normalized ion temperature gradients required in a compact fusion reactor. The comparison shows that core heat transport in neoclassically optimized stellarators is driven by the same mechanisms as in tokamaks. The absence of a strong H-mode temperature edge pedestal in stellarators, sofar (which, like in tokamaks, could lift the clamped temperature-gradients in the core), puts a strong requirement on reliable and sustainable core turbulence suppression techniques in stellarators.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

SU-E-I-80: Quantification of Respiratory and Cardiac Motion Effect in SPECT Acquisitions Using Anthropomorphic Models: A Monte Carlo Simulation Study

Purpose: The purpose of the present study was to quantify, evaluate the impact of cardiac and respiratory motion on clinical nuclear imaging protocols. Common SPECT and scintigraphic scans are studied using Monte Carlo (MC) simulations, comparing the resulted images with and without motion. Methods: Realistic simulations were executed using the GATE toolkit and the XCAT anthropomorphic phantom as a reference model for human anatomy. Three different radiopharmaceuticals based on 99mTc were studied, namely 99mTc-MDP, 99mTc—N—DBODC and 99mTc—DTPA-aerosol for bone, myocardium and lung scanning respectively. The resolution of the phantom was set to 3.5 mm{sup 3}. The impact of the motion on spatial resolution was quantified using a sphere with 3.5 mm diameter and 10 separate time frames, in the ECAM modeled SPECT scanner. Finally, respiratory motion impact on resolution and imaging of lung lesions was investigated. The MLEM algorithm was used for data reconstruction, while the literature derived biodistributions of the pharmaceuticals were used as activity maps in the simulations. Results: FWHM was extracted for a static and a moving sphere which was ∼23 cm away from the entrance of the SPECT head. The difference in the FWHM was 20% between the two simulations. Profiles in thorax were compared in the case of bone scintigraphy, showing displacement and blurring of the bones when respiratory motion was inserted in the simulation. Large discrepancies were noticed in the case of myocardium imaging when cardiac motion was incorporated during the SPECT acquisition. Finally the borders of the lungs are blurred when respiratory motion is included resulting to a dislocation of ∼2.5 cm. Conclusion: As we move to individualized imaging and therapy procedures, quantitative and qualitative imaging is of high importance in nuclear diagnosis. MC simulations combined with anthropomorphic digital phantoms can provide an accurate tool for applications like motion correction techniques’ optimization. This research has been co-funded by the European Union (European Social Fund) and Greek national resources under the framework of the ‘Archimedes III: Funding of Research Groups in TEI of Athens’ project of the ‘Education & Lifelong Learning’ Operational Programme.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Unraveling 1-Hexene Hydrogenation over Dilute Pd-in-Au Alloys

Dilute Pd-in-Au alloys are valuable materials for selectively hydrogenating alkynes and isomerizing alkenes. By diluting Pd in a Au host, the selectivity toward semihydrogenated alkene isomers can be significantly enhanced and the unfavorable overhydrogenation to alkanes is suppressed. However, a detailed mechanistic study on the origin of the enhanced alkene selectivity over dilute alloy catalysts is still missing. Here, we combine experiment and theory to unravel the reaction mechanism, identifying rate-limiting and selectivity-controlling steps in 1-hexene hydrogenation over dilute Pd-in-Au catalysts. Using isotope-exchange hydrogenation experiments, we show that 1-hexene and hydrogen over a bimetallic Pd4Au96 in silica catalyst preferentially form 1-hexene isomers, (trans and cis) 2- and 3-hexene and only a small amounts of hexane. The reaction is consistent with a Horiuti–Polanyi mechanism, similar to a monometallic Pd nanoparticle catalyst. Computation of the free-energy profiles for 1-hexene hydrogenation and isomerization over a single Pd atom in a Au surface using first principles calculations indicated that the isomerization of 1-hexene to 2-hexene is energetically favorable due to the relatively large barrier for H2 dissociation preventing hydrogenation to n-hexane. Microkinetic modeling established that H2 dissociation on the single-atom Pd sites and H spillover from these sites onto the Au host are rate-limiting and key in steering the selectivity of dilute Pd-in-Au alloys toward the hexene isomers. Furthermore, the mechanistic insights from this study contribute to the rational design of optimized dilute alloy catalysts for selective alkene isomerization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Pomona College Dreamers and Achievers Profile

As an undergraduate at Pomona, Carol Meyers ’00 enjoyed proofs and pure math, but she also had a hankering to solve real-world problems. This eventually led her to discover the field of operations research, in which she earned a PhD from MIT, and from there onto a career at a national laboratory. “National labs are great because they serve a role between academia and industry,” she says, “solving problems too large or too applied for academia, and insufficiently profitdriven for industry.” Her workplace, Lawrence Livermore National Laboratory, is home to the world’s largest laser, frequent home to the world’s fastest supercomputer, and the namesake to element 116 (Livermorium) on the periodic table. During her 10+ years at the laboratory Carol has worked in the areas of energy grid modernization, nuclear counterterrorism, cyber security, stockpile stewardship, and supercomputing, often as a consultant providing mathematical modeling and optimization expertise. Carol has two young kids and serves as co-chair of the New Moms’ Group at her workplace. “Being in a supportive community of moms has really helped me at work,” she says, “because let’s face it – moms know how to get things done!” “People talk a lot about the importance of networking, but this doesn’t have to mean starting technical discussions with strangers. The ability to bond with and relate to other people is far more important, and many women do this very well. Some of my best work networking connections have come through our employer-affiliated daycare.”

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Heat Pump Retrofits for Central Plant Hydronic Heating Systems: A Software Toolkit for Screening and Design

Retrofitting existing central plants with high-efficiency heat pump technologies can play a crucial role in achieving long-term planning goals. Modern heat pump technologies are able to use waste heat recovery to meet a building's heating demand, but there is a lack of accessible tools designed for non-HVAC experts, such as building owners, to quickly and easily conduct what-if analysis, e.g., estimating retrofit costs and payback period for their partial or full equipment replacement. This paper introduces an open-source software toolkit designed to facilitate the initial screening and decision-making of heat pump retrofits in existing central plants using a building's yearly load profile from metered or utility bill data. The toolkit evaluates the technical and economic viability of replacing traditional central plant equipment with various options including water-to-water or air-to-water heat pumps, which can provide efficient and lower-cost heating and cooling. It allows users to compare current central plant configurations with retrofit scenarios, assessing energy consumption, life-cycle costs, and environmental impact. The toolkit offers (1) a web-based tool designed for user-friendly access by a broad audience and (2) Python-based source code for researchers and engineers conducting parametric studies and design parameter optimization. The toolkit compares a typical central plant configuration to a configuration that uses a heat pump to supply hydronic heating and cooling. The output metrics include energy consumption and output of each equipment, life-cycle cost analyses and metrics, and environmental impact of the system.

Excell, L↗
DOE OSTI2017

Fine-tuning the release of molecular guests from mesoporous silicas by controlling the orientation and mobility of surface phenyl substituents

Phenyl-functionalized mesoporous silica materials were used to explore the effect of non-covalent interactions on the release of Ibuprofen into simulated body fluid. Variations in orientation and conformational mobility of the surface phenyl groups were introduced by selecting different structural precursors: a rigid upright orientation was obtained using phenyl groups directly bound to surface Si atoms (Ph-MSN), mobile groups were produced by using ethylene linkers to connect phenyl groups to the surface (PhEt-MSN), and groups co-planar to the surface were obtained by synthesizing a phenylene-bridged periodic mesoporous organosilica (Ph-PMO). The Ibuprofen release profiles from these materials and non-functionalized mesoporous silica nanoparticles (MSN) were analyzed using an adsorption-diffusion model. The model provided kinetic and thermodynamic parameters that evidenced fundamental differences in drug-surface interactions between the materials. All phenyl-bearing materials show lower Ibuprofen initial release rates than bare MSN. The conformationally locked Ph-MSN and Ph-PMO have stronger interactions with the drug (negative ΔG of adsorption) than the flexible PhEt-MSN and bare MSN (positive ΔG of adsorption). These differences in strength of adsorption are consistent with differences between interaction geometries obtained from DFT calculations. B3LYP-D3-optimized models show that π-π interactions contribute more to drug adsorption than H-bonding with silanol groups. Here, the results suggest that the type and geometry of interactions control the kinetics and extent of drug release, and should therefore serve as a guide to design new drug delivery systems with precise release behaviors customized to any desired target.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Minimum magnetic curvature for resilient divertors using Compact Toroidal Hybrid geometry

The properties of resilient divertors are explored using equilibria derived from Compact Toroidal Hybrid (CTH) geometries. Resilience is defined here as the robustness of the strike point patterns as the plasma geometry and/or plasma profiles are changed. The addition of plasma current in the CTH configurations significantly alters the shape of the last closed flux surface and the rotational transform profile, however, it does not alter the strike point pattern on the target plates, and hence has resilient divertor features. The limits of when a configuration transforms to a resilient configuration is then explored. New CTH-like configurations are generated that vary from a perfectly circular cross section to configurations with increasing amounts of toroidal shaping. It is found that even small amounts of toroidal shaping lead to strike point localization that is similar to the standard CTH configuration. Lastly, these results show that only a small degree of three-dimensional shaping is necessary to produce a resilient divertor, implying that any highly shaped optimized stellarator will possess the resilient divertor property.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗
DOE OSTI2025

Design and Implementation of Surface Eroding Thermocouples for Stage-One Modular Divertor Integration at the DIII-D Tokamak

The DIII-D has upgraded its upper divertor to a modular system using copper alloy pedestals to alter the divertor geometry without changing the vessel structure. Six new graphite tiles were designed for the shape and volume rise (SVR) divertor. Here, these new SVR tiles facilitated the formation of a poloidal array of 27 surface eroding thermocouples (SETCs) in the upper divertor region. At one location, a specialized recessed SETC, paired with a standard flush SETC, was installed in one of the ceiling tiles to provide comprehensive heat flux measurements, distinguishing between charged and noncharged particle contributions. Upgrades were made to the SETC system in the small-angle slot (SAS) divertor to improve overall performance. These upgrades included optimizing the feedthrough system to double the thermocouple cable capacity and reallocating cables from the SAS area to the SVR divertor. A compact isolation amplifier system with a fixed gain of 41 was employed to improve the signal level and minimize interference. Additionally, two analog-to-analog fiber systems were implemented for transmitting thermocouple signals over a single fiber, significantly reducing both noise levels and costs. The newly installed SETCs in the SVR divertor successfully completed initial commissioning testing. The SETCs captured the in-out asymmetry in the power distribution between the inner and outer strike points and demonstrated the dependency of the heat flux profile on the outer strike point location. During divertor detachment, heat flux mitigation was noted at the outer strike point location, while significant heat flux contributions from neutral particles were measured in the SVR divertor.

Divertor↗
DOE OSTI2026

Accelerated creep profiling: a high-throughput thermal- and stress-gradient approach applied to additive and wrought stainless steel

Here, this study investigates logarithmic creep behavior at temperatures near room temperature in wrought and additively manufactured (AM) 316 L austenitic stainless steel. A novel high-throughput (HiTp) methodology employed thermal gradients to obtain stress- and temperature-dependence of creep behavior. The findings, based on 104 two-week creep tests, reveal significant differences in creep response between wrought and AM materials. Compared to the wrought material, the AM material exhibited a higher activation energy and a lower activation volume. These differences were attributed to the materials’ distinct microstructural features, e.g., dislocation cell structures and precipitate distributions, which alter the susceptibility to dislocation glide creep, particularly at stresses below the yield stress. The HiTp methodology provides both an accelerated determination of low-temperature creep mechanisms, offering critical data for the design and optimization of alloys in near-ambient, high stress applications.

316 L Stainless steel↗