Search NASA⌕ Search

SEARCH · Search NASA

Results for “Reliability Prediction”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Pool boiling heat transfer evaluation of next-generation dielectric fluid: Opteon™ 2P50

The growing use of artificial intelligence has led to heavy thermal loads and high heat dissipation rates in data centers. Conventional air-cooled technologies are not able to fulfill these requirements. To overcome these challenges, two-phase immersion cooling (2PIC) has emerged as one of the leading technologies for high power-density chips. 2PIC increases the heat dissipation rate and efficiency of the system while reducing the footprint of the cooling equipment. A fluid with adequate dielectric properties, a suitable normal boiling temperature to maintain chip temperatures, and good material compatibility, is desired for 2PIC system. In this study, the pool boiling heat transfer of a new developmental dielectric fluid, Opteon™ 2P50, was experimentally investigated. The heat transfer coefficients at various heat fluxes (20–150 kW/m 2 ) and the critical heat flux were measured using a smooth aluminum surface. Compared with HFE-7100, Opteon™ 2P50 shows higher heat transfer coefficient (up to 59% higher) and a slightly lower value of critical heat flux (around 5.9% lower). The modified Cooper correlation with the optimized leading constant resulted in reliable prediction accuracy with a 5.3% mean absolute error percentage. Overall, these results indicate that the new dielectric fluid provides similar thermal performance to some legacy fluids.

2P50↗
DOE OSTI2022

Machine learning magnetism classifiers from atomic coordinates

The determination of magnetic structure poses a long-standing challenge in condensed matter physics and materials science. Experimental techniques such as neutron diffraction are resource-limited and require complex structure refinement protocols, while computational approaches such as first-principles density functional theory (DFT) need additional semi-empirical correction, and reliable prediction is still largely limited to collinear magnetism. Here, we present a machine learning model that aims to classify the magnetic structure by inputting atomic coordinates containing transition metal and rare earth elements. By building a Euclidean equivariant neural network that preserves the crystallographic symmetry, the magnetic structure (ferromagnetic, antiferromagnetic, and nonmagnetic) and magnetic propagation vector (zero or non-zero) can be predicted with an average accuracy of 77.8% and 73.6%. In particular, a 91% accuracy is reached when predicting no magnetic ordering even if the structure contains magneticelement(s). Ourworkrepresents onestepforwardtosolvingthegrand challenge of full magnetic structure determination.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Quantification of grain boundary effects on the geometrically necessary dislocation density evolution and strain hardening of polycrystalline Mg 4Al using in situ tensile testing in scanning electron microscope and HR-EBSD

In situ tensile testing in a scanning electron microscope (SEM) in conjunction with high-resolution electron backscatter diffraction (HR-EBSD) under load was used to characterize the evolution of geometrically necessary dislocation (GND) densities at individual grain boundaries as a function of applied strain in a polycrystalline Mg—4Al alloy. The increase in GND density was investigated at plastic strains of 0 %, 0.6 %, 2.2 %, 3.3 % from the area including 76 grains and correlated with (i) geometric compatibility between slip systems across grain boundaries, and (ii) plastic incompatibility. We develop expressions for the grain boundary GND density evolution as a function of plastic strain and plastic incompatibility, from which uniaxial tensile stress-strain response of polycrystalline Mg—4Al are computed and compared with experimental measurement. The findings in this study contribute to understanding the mechanisms governing the strain hardening response of single-phase polycrystalline alloys and more reliable prediction of mechanical behaviors in diverse microstructures.

Geometrically necessary dislocations↗
DOE OSTI2024

Temperature and Composition Dependence Modeling of Viscosity and Electrical Conductivity of Low-Activity Waste Glass Melts

The development of models that accurately relate the properties of a glass melt to its temperature and composition is important for glass formulation, melter control, and modeling the melt flow, refractory corrosion, and production rate. Using a database consisting of more than 4,000 data points measured between 900 °C and 1250 °C for over 600 unique low-activity waste glass compositions, we developed models for the melt viscosity and electrical conductivity. Models based on the Gaussian process regression approach outperformed models based on the Vogel–Fulcher–Tammann equation according to four standard metrics and yielded reliable prediction intervals. The models found primarily linear effects between properties and individual components, except for the effect of the Na 2 O mass fraction on the electrical conductivity. The effects were found to be consistent with current theories on physical processes involved with those properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Temperature-dependent model of helium bubble expansion and bursting in tungsten

Continuum-scale models that can reliably predict the behavior of helium in tungsten are of interest to the fusion community due to the projected impact of these materials on fusion reactor operation. In this work, we perform molecular dynamics simulations of spherical helium bubbles of various sizes in tungsten at different temperatures and depths with the goal of determining a mathematical model of the pressure and density at which the bubbles initially expand or burst as a function of depth, size, temperature, and surface orientation. The bubbles are small enough that their loop-punching pressures cannot be accurately predicted with continuum mechanics, and their expansion behavior is important, as it appears to cause many of the features observed on helium-irradiated tungsten surfaces. We vary the temperature, bubble size, bubble depth, and surface orientation in each case, recording the bubble pressure and density that result in bubble expansion. An exponential function with three adjustable parameters is found to fit the results well; the parameters that best fit our results are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Understanding the effect of specimen size on the properties of fine-grain isotropic nuclear graphite for irradiation studies: Mechanical properties

Programs at Oak Ridge National Laboratory are investigating the effects of neutron irradiation in the High Flux Isotope Reactor (HFIR) on properties of fine-grain isotropic nuclear graphites. Specimens were irradiated in the HFIR flux trap, which requires using specimens that are smaller than the sizes recommended by ASTM standards. Here, this work has investigated whether subsized specimens can be used to reliably predict strength behavior. The results show that (1) tensile strength was loosely coupled to specimen volume, (2) the compression specimens had a slightly higher strength for larger volumes, (3) the uniaxial flexural strength was nearly independent of specimen effective area for one orientation whereas the other orientation showed a slight strength–size scaling Weibull behavior, and (4) equibiaxial flexural strength followed traditional strength–size scaling Weibull behavior. These results suggest that fine-grain graphite is more similar to ceramics than to medium- and large-grained graphite with respect to both the strength-limiting flaws (Griffith theory) and the effect of machining on the strength behavior. This work shows that using specimens that are smaller than ASTM recommendations are suitable for investigating the strength changes caused by neutron irradiation because the primary interest is the relative change in the strength distribution parameters rather than their absolute values.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Modelling single atom catalysts for water splitting and fuel cells: A tutorial review

Here, in this tutorial review, we report the state-of-the-art of the modeling approaches of Single Atom Catalysts (SAC) for water splitting and fuel cells reactions. The discussion applies for Hydrogen Evolution Reaction (HER), Oxygen Reduction Reaction (OER), Hydrogen Oxidation Reaction (HOR), and Oxygen Reduction Reaction (ORR). The main scope of this work is to underline the relevant aspects of SACs modelling. On the one hand, the review could help computational chemists aiming to start the study of SACs. On the other hand, experimentalists could find the critical analysis of DFT results of interest to understand better the strengths and weaknesses of simulations, and how to interpret computational results. After an introductory section of SACs, we start by briefly presenting the state-of-the-art methodologies. Then, we analyze the critical aspects for a reliable prediction of the electronic properties, and we discuss the robustness of the structural models and ways to validate them. Furthermore, we discuss the main approaches to predict catalytic activity and selectivity, which is the final goal of the computational catalysis. We conclude this review with a critical analysis of the current challenges in the field, and the main limitations of the modeling approaches that are described.

25 ENERGY STORAGE↗
DOE OSTI2026

Influence of fabrication on microstructure and heat affected zone width in weldments of nuclear reactor pressure vessel steel

Advanced manufacturing routes such as electron beam welding and powder metallurgy with hot isostatic pressing are increasingly used across energy and aerospace industries, where the reliable prediction of weld behavior and heat affected zone (HAZ) evolution is critical. This study examines how fabrication routes and post-weld heat treatments influence phase distribution, crystallite size, microstrain, and dislocation density in nuclear reactor pressure vessel steels using synchrotron X-ray diffraction (SXRD). Retained austenite occurs only in samples that did not undergo austenitization, whereas an austenitizing heat treatment fully eliminates retained austenite and produces a more uniform microstructure across the weldment in terms of phase fraction, dislocation density, and microstrain. The Rosenthal solution underestimates the HAZ width for powder metallurgy samples. A newly proposed modified Rosenthal solution, reducing density by accounting for porosity, matches the SXRD-measured HAZ width with a 0.65% error. Structure–property correlations reveal that dislocation density correlates strongly with nanohardness in homogenous microstructures, while in heterogenous weldments nanohardness is further influenced by the presence of dissimilar phase boundaries. These findings provide new insight into the thermal and microstructural response of powder metallurgy fabricated steels and offer a framework for optimizing welding procedures and heat treatments in advanced manufacturing applications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Elevated temperature mechanical properties of Inconel 617 surface oxide using nanoindentation

Inconel 617 is a principal candidate material for helium gas cooled very-high-temperature reactors with outlet temperatures of 700–950 °C. Recent findings showed that this alloy develops unique surface oxide layers especially at high temperature (HT) helium environment with distinctive wear, friction and contact properties. This study investigates the elevated temperature mechanical properties of Inconel 617 top surface layers aged in HT helium environment. Nanoindentation technique is used to obtain load-displacement graphs of the alloy top surface oxide in temperatures ranging from 25 °C up to 600 °C. In addition, using finite element analysis along with an iterative regression technique, a semi-numerical method is developed to further measure and quantify the material parameters and, in particular, time-independent and creep characteristics of the oxide. Although Young's modulus of the oxide is found to be relatively close to the bulk for the tested temperatures, the yield strength and hardness, in comparison to the bulk material, increase significantly as the material is oxidized after aging. The oxide exhibits significant softening as the temperature increases to 600 °C. Unlike the bulk material, diffusion through the grains is found to be the dominant creep mechanism for the oxide. Considerable difference between the mechanical properties of the oxide and the bulk material shows the need for accurate measurements of near surface mechanical properties, if reliable predictive contact and tribological models are sought at elevated temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Fatigue analysis of the Spallation Neutron Source 2 MW target design

Upgrades to the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory are under way with two major projects. The first project is the Proton Power Upgrade (PPU), which will double the power capacity of the SNS accelerator system to enable the operation of a future second neutron source target station—the second project. PPU also will increase power to the SNS first target station – which currently operates at 1.4 MW with 1.0 GeV protons – to 2 MW with 1.3 GeV protons. Final design of the PPU 2 MW target module is finished, including all necessary design and analysis calculations. The fatigue evaluation described herein is an assessment of the fitness of the PPU 2 MW target mercury vessel to resist failure from cyclic loading from the pulsed 60 Hz proton beam and thermal changes from disrupted operation. In addition to the reliability predictions, the method, detail, and depth of this fatigue evaluation are superior to those of past target design assessments. Furthermore, the thermal fatigue life is predicted to exceed 3125 thermal cycles per 1250 h of operation for all load cases. The design also meets the (relative) fatigue design goal for combined thermal and pulse loading of greater than 0.5 times the minimum fatigue life calculated for a jet-flow target design operating at 1.4 MW—the most robust design to date in terms of resistance to fatigue failure. From the onset of the PPU, it has been known that pulse loading requires effective application of helium gas injection into the target mercury to reduce beam pressure loading. For the PPU 2 MW target, none of the pulse load cases requires a maximum strain reduction from gas injection of more than 50% (for base material) to meet the fatigue design goal. This level of reduction is achievable from gas injection, according to historical strain measurements from 1.4 MW target operation. Overall, the PPU 2 MW target design is predicted to have superior resistance to fatigue failure compared with past target designs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Metallic fuel cladding degradation model development and evaluation for BISON

Fuel cladding chemical interaction (FCCI) and coolant cladding chemical interaction (CCCI), both consume stainless-steel cladding in sodium-cooled fast reactors (SFRs), and effectively degrade cladding mechanical performance through thickness reduction. Therefore, FCCI/CCCI and corresponding cladding degradation models are essential for advanced fuel performance codes, such as BISON, to reliably predict cladding behavior in SFRs. Here, we report the development and implementation efforts for BISON's FCCI/CCCI correlations and cladding degradation models based on U.S. legacy metallic fuel data. The models were evaluated using Integral Fast Reactor (IFR) program X447/A experiment data supported by the ongoing integration project enabling coordinated application of BISON and EBR-II fuel irradiation and physics database (FIPD). Furthermore, the implemented models were demonstrated to improve BISON's capabilities of predicting cladding damage and degradation behavior that is more consistent with post-irradiation examination observations. Additionally, some limitations of current BISON modules are identified, which are to be overcome through the BISON-FIPD integration efforts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Accelerating scientific discoveries through data-driven innovations

Developing artificial intelligence (AI) and machine learning (ML) methods that can accelerate scientific discoveries and advance science has become one of the important research directions for the AI/ML research community. It has been gaining increasing attention from researchers in diverse scientific areas, including biomedical science, materials science, climate science, physics, chemistry, and many others. Data-driven AI/ML innovations to enable reliable predictions and optimal decision making for scientific discoveries face several critical challenges, among which are high system complexity, large search space, incomplete knowledge, and small data, all of which demand novel strategies to effectively address them. Meeting these challenges and thereby accelerating scientific discoveries and industrial innovations, calls for research that can take full advantage of the latest advances in AI/ML to integrate data-driven techniques with scientific knowledge and is able to execute them in modern high-performance computing (HPC) environments at scale. This Patterns special collection "Accelerating scientific discoveries through data-driven innovations" features articles that showcase the promising roles of AI/ML and data-driven modeling in accelerating scientific discoveries and may inspire the next wave of data-driven innovations in various scientific domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Momentum transfer and plasma rotation caused by destabilized eigenmodes in tokamaks

In this work, the influence of magnetohydrodynamic eigenmodes destabilized by energetic ions on the momentum of these ions and concomitant sheared plasma rotation are studied. Two mechanisms affecting rotation are revealed: (i) spatial channelling (SC) – radially separated emission and absorption of the momentum; (ii) mode induced redistribution (MIR) across the magnetic field of the momentum of energetic ions. Forces arising during SC and MIR produce both toroidal and poloidal rotations. In addition, the momentum emission during SC leads to a radial flux of fast ions and generation of a radial electric field. Using the developed theory, estimates were made for the ITER (International Thermonuclear Experimental Reactor) 15 MA baseline scenario. They show that a global toroidicity-induced Alfvén eigenmode destabilized by alpha particles and neutral beam injection can result in significant radial electric field and forces applied to plasma. However, available data are not sufficient for a reliable prediction of the effects of SC and MIR in ITER. In general, one can expect that sheared rotation arising after destabilization of Alfvén modes and fast magnetoacoustic modes by energetic ions will tend to suppress the turbulence and improve plasma performance. The importance of plasma rotation is supported, in particular, by the fact that during the JET DTE1 experimental campaign the best parameters were achieved in a deuterium–tritium discharge where the rotation frequency was largest.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Radiative and continuum dampings of reversed shear Alfvén eigenmodes and perturbative analysis limitations for tokamaks

A careful theoretical analysis of the excitation of Alfvén eigenmodes (AEs), such as TAE (toroidicity-induced AE) and RSAE (reversed shear AE), by superalfvenic energetic particles is required for reliable predictions of energetic ion relaxation in present day fusion experiments. This includes the evaluation of different AE damping mechanisms including radiative and continuum dampings which are the focus of this study. A recent comprehensive benchmark of different eigenmode solvers including gyrokinetic, gyrofluid and hybrid magenetohydrodynamics (MHD) has shown that employed models may have deficiencies when addressing some of them (Taimourzadeh et al., Nucl. Fusion, vol. 59, 2019, 066006). Here, in this paper, we are studying the radiative and continuum dampings of RSAEs in details which were missing in hybrid NOVA/NOVA-C calculations to prepare a NOVA-C package with a substantial upgrade. Both dampings require the finite Larmor radius (FLR) corrections to AE mode structures to be accounted for. Accurately calculating different damping rates and understanding their parametric dependencies, we resolve the limitation coming out of the perturbative approach. In particular, here, the radiative damping is included perturbatively, whereas the continuum damping is computed non-perturbatively. Our comparison leads to the conclusion that the non-perturbative treatment of the unstable RSAE modes is needed to find the agreement with the gyrokinetic calculations. We expect that the RSAE mode structure modification plays a dominant role in determining the RSAE stability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

General H2O Outgassing Model Applicable to Silica-Filled Silicones

Moisture-induced agining and degradation are continuing concerns in long-term storage and use of silicone materials. The silica fillers frequently used to provide mechanical reinforcement in silicone formulations have a high affinity for H2O and act as a source of moisture outgassing later when placed in dry or vacuum applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation

In the field of computational chemistry, predicting bond dissociation energies (BDEs) presents well-known challenges, particularly due to the multireference character of reactive systems. Many chemical reactions involve configurations where single-reference methods fall short, as the electronic structure can significantly change during bond breaking. As generating training data for partially broken bonds is a challenging task, even state-of-the-art reactive machine learning interatomic potentials (MLIPs) often fail to predict reliable BDEs and smooth dissociation curves. By contrast, simple and inexpensive physics-based models, such as the well-established Morse potential, do not suffer from any such limitations. This work leverages the Morse potential to improve reactive MLIPs by augmenting the training data set with inexpensive Morse data along the dissociation pathways. Further, this physics-constrained data augmentation (PCDA) approach results in MLIPs with smooth bond dissociation curves as well as near coupled-cluster level BDEs, all without requiring any expensive multireference quantum mechanical calculations. A case study for methane combustion demonstrates how the PCDA approach can improve an existing reactive MLIP, namely, ANI-1xnr. In conclusion, not only are the BDEs and bond dissociation curves for all radicals and molecules significantly improved compared to ANI-1xnr but the PCDA-trained MLIP retains the reliability of ANI-1xnr when performing reactive molecular dynamics simulations.

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

Recent Advances toward Efficient Calculation of Higher Nuclear Derivatives in Quantum Chemistry

In this article, we provide an overview of state-of-the-art techniques that are being developed for efficient calculation of second and higher nuclear derivatives of quantum mechanical (QM) energy. Calculations of nuclear Hessians and anharmonic terms incur high costs and memory and scale poorly with system size. Three emerging classes of methods—machine learning (ML), automatic differentiation (AD), and matrix completion (MC)—have demonstrated promise in overcoming these challenges. We illustrate studies that employ unsupervised ML methods to reduce the need for multiple Hessian calculations in dynamics simulations and those that utilize supervised ML to construct approximate potential energy surfaces and estimate Hessians and anharmonic terms at reduced cost. By extension, if electronic structure operations could be written in a manner similar to functions underlying ML methods, rapid differentiation or AD routines can be employed to inexpensively calculate higher arbitrary-order derivatives. While ML approaches are typically black-box, we describe methods such as compressed sensing (CS) and MC, which explicitly leverage problem-specific mathematical properties of higher derivatives such as sparsity and low-rank, to complete higher derivative information using only a small, incomplete sample. The three classes of methods facilitate reliable predictions of observables ranging from infrared spectra to thermal conductivity and constitute a promising way forward in accurately capturing otherwise intractable higher-order responses of QM energy to nuclear perturbations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Screening Ionic Liquids Based on Ionic Volume and Electrostatic Potential Analyses

Ionic liquids are known to have tunable solvation properties, based on the pairing of different anions and cations, but the compositional landscape is vast and challenging to navigate efficiently. Some computational screening protocols are available, but they can be either time-consuming or difficult to implement. Herein, we perform a detailed investigation of the fundamental role of electrostatic interactions in these systems. We effectively develop a bridge between the previous volume-based approach (VBA) with a quantum structure–property relationship (QSPR) approach to create fast, simple screening guidelines. We propose a new parameter that is applicable to both monovalent and multivalent ions, the ionic polarity index (IPI), which is defined as the ratio of the average electrostatic surface potential ($\bar{V}$) of the ion to the net charge of the ion (q). The IPI correlation has been tested on a diverse dataset of 121 ions and reliable predictions can be obtained within a homologous series of ionic liquid compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗