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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

RBMK Neutron-Physical Characteristics and Nuclear Safety

The neutron-physical characteristics of RBMK at the Kursk, Leningradskaya, and Smolensk NPPs for 2015–2017, as determined from calculations of more than 6000 actual states at nominal power, are presented. The results of computational modeling of shutdown regimes upon actuation of the shutdown systems of the emergency protection and fast power reduction are presented. It is shown that the physical characteristics, efficiency, and fast response of the shutdown systems of the emergency protection and fast power reduction ensure the nuclear safety of RBMK. The effect of repair of the graphite stack with a sharp graphite blocks on the neutron-physical characteristics of the reactor is determined. It is shown that the removal of some of the graphite during the repair of 500–600 graphite columns results in a reduction of the steam coefficient of reactivity and partial or complete removal of additional absorbers from the core while maintaining the neutronic characteristics and nuclear safety of RBMK.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Projective Representations, Bogomolov Multiplier, and Their Applications in Physics

We present a pedagogical review of projective representations of finite groups and their physical applications in quantum many-body systems. Some of our physical results are new. We begin with a self-contained introduction to projective representations, highlighting the role of group cohomology, representation theory, and classification of irreducible projective representations. We then focus on a special subset of cohomology classes, known as the Bogomolov multiplier, which consists of cocycles that are symmetric on commuting pairs but remain nontrivial in group cohomology. Such cocycles have important physical implications: they characterize (1+1)D SPT phases that cannot be detected by string order parameters and give rise, upon gauging, to distinct gapped phases with completely broken non-invertible Rep(G) symmetry. We construct explicit lattice models for these phases and demonstrate how they are distinguished by the fusion rules of local order parameters. We show that a pair of completely broken Rep(G) SSB phases host nontrivial interface modes at their domain walls. As an example, we construct a lattice model where the ground state degeneracy on a ring increases from 32 without interfaces to 56 with interfaces.

Bogomolov multiplier↗
DOE OSTI2022

Physics-Informed and Data-Driven Prediction of Residual Stress in Three-Dimensional Machining

Efficient and reliable prediction of machining-induced residual stress (RS) is a key requirement for truly integrated computational materials engineering (ICME). Currently available process modeling approaches, including empirical, analytical, and numerical methodologies lack predictive power and require substantial calibration and validation data. Moreover, most model-based approaches consider only two-dimensional (2D) (i.e., orthogonal), cutting processes. Meanwhile, industrial processes such as milling, turning, and drilling are inherently three-dimensional (3D). The present work attempts to bridge the gap between 2D and 3D through careful consideration of the process physics, including geometric, kinematic, and size-effect constraints to realize robust prediction of how RS develops in 3D machining. Using a novel in-situ experimental technique and digital image correlation (DIC) to determine equivalent Hertzian contact widths, contact pressures, and friction coefficients, the proposed methodology leverages a discretized conversion algorithm that includes multi-pass shakedown effects. This paper presents a semi-analytical model to predict machining-induced RS in 3D turning operations, which are used representatively for 3D processes more generally. Rather than follow a ‘brute force’ 3D FEM approach or conduct countless experiments to train a purely data-driven machine learning algorithm, the proposed approach builds on previous 2D modeling work. Through careful consideration of the process physics, including complex geometry/kinematic considerations of 3D turning, the authors demonstrated an experimentally calibrated approach, as well as validation based on published RS data. Model predictions and previously published measurement data of RS depth profiles for turning of Inconel 718 were compared for a range of process parameters. Correlation between the proposed 3D model and validation data was found to be within the margin of experimental error for most conditions. The proposed model appears to capture the overall behavior of 3D RS depth profiles with acceptable accuracy, particularly the key metrics of near-surface stress, peak stress magnitude and location, as well as overall stress profile depth. This report presents a physics-informed, data-driven approach for efficient calibration of a 2D model for machining-induced RS through DIC analysis of in-situ characterized subsurface displacement fields.

42 ENGINEERING↗
DOE OSTI2018

Similarity Criteria for the Study of Removal of Spherical Non-metallic Inclusions in Physical Models of Continuous Casting Tundishes: A More Fundamental Approach

Physical modeling is widely used for studying steelmaking processes. The accuracy of this technique depends on the use of appropriate similarity criteria during experimental design. The present work deduced rigorously the similarity criteria for the study of spherical non-metallic inclusions in isothermal physical models of tundishes. Initially, macroscopic quantities were used to simplify the multiphase problem. Then, the similarity criteria were obtained from the dimensionless form of the exact equation of motion for an inclusion in Stokes regime. The validity range of these criteria was discussed and they were compared with common literature practice. Some practical considerations were also presented. This work clarifies various implicit assumptions usually adopted in literature. Additionally, a restrictive criterion to choose the density of the inclusion simulating particles was obtained, which can be satisfied, for example, by hollow glass particles in water models. This criterion is necessary to adequately describe the inclusion motion in regions where fluid elements suffer strong acceleration (e.g., inside nozzles, near flow controllers or in the entering fluid jet). Moreover, it was established an upper limit for the concentration of particles injected in the inlet nozzle of physical models, which do not simulate the phenomenon of inclusion aggregation.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Numerical Simulation of Recirculating Flow and Physical Model of Slag–Metal Behavior in an RH Reactor: Application to Desulfurization

Computational fluid dynamics (CFD) techniques and a 1:7.5 physical model of an RH degasser have been used to evaluate the flow of gas and metal inside an RH reactor for vacuum degassing of liquid steel. The effect of gas injection on the gas spatial distribution, steel circulation rate and flow field inside the ladle, snorkels and vacuum chamber have been assessed. N-pentane oil was employed to evaluate the average residence time as well as the slag droplet size distribution. The predicted radial gas distribution and liquid circulation rate have been validated against experimental data from a physical model. The results with incorporation of the virtual mass force coefficient of 0.25 and the turbulence dispersion force showed better predictions of gas distribution in the up-snorkel as well as circulation rate. Full-scale simulations were performed, and the predicted circulation rate was significantly affected by the argon bubble expansion. Data from these simulations were used to analyze the degree of desulfurization performed by the addition of desulfurizing agents inside the vacuum chamber. A model of the kinetics of desulfurization based on the results from the physical model and CFD simulation and on slag dispersion inside liquid steel yields degrees of desulfurization similar to the industrial trials reported in the literature.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Accelerating laser ray tracing in high fidelity physics simulations of laser melting using squeeze U-net

Laser melting is a core component of the ongoing industrial revolution, dubbed Industry 4.0, as lasers facilitate fast and precise melting and fusion in advanced manufacturing. There is a strong need to optimize the laser process using simulations. However, this has proven challenging as high fidelity simulations are needed for predictive modeling and this is currently prohibitively expensive even when run on hundreds of processors on high performance computers. The challenge is capturing complex physics of laser material interaction, fluid dynamics, thermal physics and material phase transformations at various length and time scales. To close this technological gap, we modified a squeeze U-net to accelerate the laser ray tracing component of such high fidelity models by ~4x–40x while preserving the core physics principle of conservation of energy with 97% accuracy. This approach enables the accurate modeling of global laser energy absorption as a function of local surface temperatures and complex surface topologies, which govern the reflection directions and energy losses of laser rays upon interacting with the material surface.

Computer science↗
DOE OSTI2018

Reactor physics phenomena in additively manufactured control elements for the High Flux Isotope Reactor

Additive manufacturing is under investigation as a novel method of fabricating the control elements (CEs) of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory with greater simplicity, eliminating numerous highly complex fabrication steps and thereby offering potential for significant savings in cost, time, and effort. This process yields a unique CE design with lumped absorbers, a departure from traditionally manufactured CEs with uniformly distributed absorbing material. Here, this study undertakes a neutronics analysis of the impact of additively manufactured CEs on the HFIR core physics, seeking preliminary assessment of the feasibility of their practical use. The results of the MCNP transport simulations reveal changes in the HFIR reactor physics arising from geometric and nuclear effects. Absorber lumping in the discrete CEs yields a large volume of unpoisoned material that is not present in the homogeneous design, in turn yielding increases in free thermal flux in the CE absorbing regions and their immediate vicinity. The availability of additional free thermal neutrons in the core yields an increase in fission rate density in the fuel closest to the CEs and a corresponding increase in neutron multiplication on the order of 100 pcm. The absorption behavior exhibited by the discrete CEs is markedly different from the homogeneous CEs due to several competing effects. Self-shielding arising from absorber lumping acts to reduce the effective absorption cross section of the discrete CEs, but this effect is offset by geometric and spectral effects. The operational performance of the discrete CEs is found to be comparable to the homogeneous CEs, with only limited deficiencies in reactivity worth that are expected to be operationally recoverable via limited adjustment of the CE positions and withdrawal rate. On the whole, these results indicate that the discrete CEs perform reasonably similarly to the homogeneous CEs and appear feasible for application in HFIR. In conclusion, the physical phenomena identified in this study provide valuable background for follow-up design studies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Physics analysis and design of heavy water reflected thermal test reactor

Here, this work investigates the option of modifying the Advanced Test Reactor by replacing the current beryllium reflector with heavy water. Such a change may provide some potential benefits for not only increasing the thermal irradiation capabilities but also resolving other problems such as reflector integrity issues due to fast fluence damage, which is always a limiting factor in the lifetime of the current beryllium reflector. This paper presents the analysis and estimation of the ATR core physics parameters by replacing the current beryllium reflector with heavy water (D 2 O). The paper first describes the details of two selected conceptual designs, which are partially reflected with either beryllium or graphite, and how they are derived from the baseline beryllium reflector concept. Then, reactor physics performance parameters for the two new concepts are assessed by comparing with those of the baseline concept. The performance parameters considered in this paper include in-pile tube neutron and gamma fluxes and heating rates, maximum loop voiding reactivity, core power behavior with different power splits, predicted cycle length with a given fuel loading, and thermal hydraulic analysis with a higher lobe power split. It is important to note that this study focuses on the reactor physics aspects and does not delve into the engineering challenges associated with such a design modification.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Uncertainty quantification of a physics-informed model based on sparse identification of a Thermal Energy Distribution System

Integrated energy systems (IES)s are crucial for enhancing the economy and efficiency of power generation sources (e.g., nuclear energy) necessary to unleash American energy dominance. These systems can be integrated with thermal energy storage (TES) and intermittent renewable energies to optimize overall energy use, peak-load regulation, and demand-side responses. However, the stabilization of energy generation, transport, and utilization introduces operational complexities that exceed the challenges of managing each sub-component individually. Currently, though IESs rely on human operators for efficiency and stability, reducing human error risk and enhancing performance through automation is highly desirable. Recent advances at Idaho National Laboratory have demonstrated successful control of the Thermal Energy Distributed System (TEDS). However, the automatic control system depends on a deterministic Sparse Identification of Nonlinear Dynamics with Control (SINDyC) model, which are trained based on simulation data from physics-based simulations. Because of uncertainties in physics-based simulation, SINDyC model results in large discrepancies against experimental data and cannot be reliably used in automatic control. In this paper, we present an innovative approach to address these discrepancies by quantifying uncertainties and developing a more robust model. We first generated trajectories by using first-principles physics codes to encapsulate the experiment. Next, we trained thousands of models by randomly sampling these trajectories. We then collapsed all those models into one probabilistic SINDyC by fitting a multivariate Gaussian distribution onto the resulting coefficient’s distribution. Despite its simplicity, our approach successfully produced 95% confidence intervals that captured the experimental trajectories. It even did so with a higher probability and better U-pooling score across six of the seven relevant quantities of interest (QoIs), as compared to other classical approaches. In conclusion, ongoing research is focusing on generating new experimental trajectories to validate this approach, and on employing Bayesian calibration to refine parametric uncertainties and guide future model development efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Physics-informed graphical neural network for power system state estimation

State estimation is highly critical for accurately observing the dynamic behavior of the power grids and minimizing risks from cyber threats. However, existing state estimation methods encounter challenges in accurately capturing power system dynamics, primarily because of limitations in encoding the grid topology and sparse measurements. Here, this paper proposes a physics-informed graphical learning state estimation method to address these limitations by leveraging both domain physical knowledge and a graph neural network (GNN). We employ a GNN architecture that can handle the graph-structured data of power systems more effectively than traditional data-driven methods. The physics-based knowledge is constructed from the branch current formulation, making the approach adaptable to both transmission and distribution systems. The validation results of three IEEE test systems show that the proposed method can achieve lower mean square error more than 20% than the conventional methods.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics

Machine learning has been effective at detecting patterns and predicting the response of systems that behave free of natural laws. Examples include learning crowd dynamics, recommender systems and autonomous mobility. There also have been applications to the search for new materials that draw upon big-data classification problems. Though, when it comes to physical systems governed by conservation laws, the role of machine learning has been more limited. Here, we present our recent work in exploring the role of machine learning methods in discovering, or aiding, the search for physics. Specifically, we focus on using machine learning algorithms to represent high-dimensional free energy surfaces with the goal of identifying precipitate morphologies in alloy systems. Traditionally, this problem has been approached by combining phase field models, which impose first-order dynamics, with elasticity, to traverse a free energy landscape in search of minima. Equilibrium precipitate morphologies occur at these minima. Here, we exploit the machine learning methods to represent high-dimensional data, combined with surrogate optimization, sensitivity analysis and multifidelity modeling as an alternate framework to explore phenomena controlled by energy extremization. This combination of data-driven methods offers an alternative to the imposition of first-order dynamics via phase field methods, and represents one aspect of applying machine learning to materials physics.

42 ENGINEERING↗
DOE OSTI2022

Enriched immersed boundary method (EIBM) for interface-coupled multi-physics and applications to convective conjugate heat transfer

It is challenging to develop numerical methods that simultaneously maintain accuracy of resolving boundary conditions and mesh flexibility to handle the interface in interface-coupled multi-physics problems involving large property discontinuity. On the one hand, boundary-fitted methods possess high accuracy in capturing the interfacial phenomenon but involve complicated volumetric mesh generation, mesh-motion, and even re-meshing procedures. On the other hand, immersed boundary methods (IBM) provide mesh flexibility but sometimes suffer from inferior interface representations, leading to poor enforcement of boundary conditions. This paper presents an enriched immersed boundary method (EIBM) to overcome this challenge and demonstrates its efficacy in conjugate heat transfer, a representative example in interface-coupled multi-physics systems that have implications for many industrial processes. Further, the core technique of the method is to enhance IBM’s accuracy of the fluid–solid interface by enriching the degrees of freedom of the cut elements to enforce temperature and flux compatibilities and resolve all the physical unknowns on the background mesh to simplify the volumetric mesh generation. We implement the EIBM under the framework of a variational multiscale formulation for coupled Navier–Stokes and thermodynamics equations. The enriched DoFs enable better enforcement of temperature and flux compatibilities with large conductivity ratios across the fluid–solid interface. At the same time, the immersed nature of the proposed method still attains mesh flexibility. The EIBM’s accuracy is thoroughly evaluated through a set of examples, ranging from benchmark problems with analytical solutions to real-world cooling processes of a moving metallic structure with complex geometry.

42 ENGINEERING↗
DOE OSTI2015

GenASiS Basics: Object-oriented utilitarian functionality for large-scale physics simulations

Aside from numerical algorithms and problem setup, large-scale physics simulations on distributed-memory supercomputers require more basic utilitarian functionality, such as physical units and constants; display to the screen or standard output device; message passing; I/O to disk; and runtime parameter management and usage statistics. Here we describe and make available Fortran 2003 classes furnishing extensible object-oriented implementations of this sort of rudimentary functionality, along with individual `unit test' programs and larger example problems demonstrating their use. Lastly, these classes compose the Basics division of our developing astrophysics simulation code GenASiS (General Astrophysical Simulation System), but their fundamental nature makes them useful for physics simulations in many fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

MFC 5.0: An exascale many-physics flow solver

Many problems of interest in engineering, medicine, and the fundamental sciences rely on high-fidelity flow simulation, making performant computational fluid dynamics solvers a mainstay of the open-source software community. Previous work MFC 3.0 was made a published, documented, and open-source solver via Bryngelson et al. Comp. Phys. Comm. (2021) with numerous physical features, numerical methods, and scalable infrastructure. MFC 5.0 is a significant update to MFC 3.0, featuring a broad set of well-established and novel physical models and numerical methods, as well as the introduction of GPU and APU (or superchip) acceleration. Here, we exhibit state-of-the-art performance and ideal scaling on the first two exascale supercomputers, OLCF Frontier and LLNL El Capitan. Combined with MFC’s single-accelerator performance, MFC achieves exascale computation in practice, and achieved the largest-to-date public CFD simulation at 200 trillion grid points as a 2025 ACM Gordon Bell Prize finalist. New physical features include the immersed boundary method, N-fluid phase change, Euler–Euler and Euler–Lagrange sub-grid bubble models, fluid-structure interaction, hypo- and hyper-elastic materials, chemically reacting flow, two-material surface tension, magnetohydrodynamics (MHD), and more. Numerical techniques now represent the current state-of-the-art, including general relaxation characteristic boundary conditions, WENO variants, Strang splitting for stiff sub-grid flow features, and low Mach number treatments. Weak scaling to tens of thousands of GPUs on OLCF Summit and Frontier and LLNL El Capitan achieves efficiencies within 5% of ideal to over 90% of their respective system sizes. Strong scaling results for a 16-times increase in device count show parallel efficiencies over 90% on OLCF Frontier. MFC’s software stack has undergone further improvements, including continuous integration, which ensures code resilience and correctness through over 300 regression tests; metaprogramming, which reduces code length while maintaining performance portability; and code generation for computing chemical reactions

Computational fluid dynamics↗
DOE OSTI2024

Physics-informed hybrid modeling methodology for building infiltration

Infiltration is responsible for one-third to one-half of the space conditioning load of a typical residential home, but the modeling of infiltration for building energy modeling is either represented by over-simplified equations or dependent on over-generalized rules of thumb. Here, this paper develops a physics-informed data-driven methodology for modeling infiltration using building-specific empirical measurements. The developed hybrid methodology combines machine-learning categorization and grey-box sub-modeling to improve the accuracy and generalization of commonly used grey-box infiltration models. The developed methodology excels at predicting infiltration by improving the ability to predict infiltration under unseen environmental conditions using machine learning algorithms with physical significance. In a case study conducted using the iUnit, a modular studio apartment experimental test facility located at the National Renewable Energy Laboratory, we use empirical airtightness measurements to fit an infiltration model using the developed methodology. We find that the developed methodology can improve the overall model accuracy by 43% and improve extrapolation by 38%, compared with the model based on the common grey-box infiltration equation. We also notice that the selected features can improve the performance of a pure machine-learning model, indicating that our methodology identifies the features with the most physical significance to infiltration modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Relationships between physical–geographical factors and soil degradation on agricultural land

Highlights: • Connection of soil degradation to seven physical-geographical variables was revealed. • New method of threshold data analysis was proposed. • Precise treshold values where soil degradation starts and ends were revealed. • Study area covered the entire area of Czech agricultural land – 13027 cadastres (78,866 km{sup 2}). It is a well-known fact that soil degradation is dramatically increasing and currently threatens agricultural soils all around the world. The objective of this study was to reveal the possible connection between soil degradation and seven physical-geographical factors - slope steepness, altitude, elevation differences, rainfall, temperature, soil texture and solar radiation – in the form of threshold values (if these exist), where soil degradation begins and ends. The analysis involved the whole area of the Czech Republic which consists of 13,027 cadasters (78,866 km{sup 2}). The greatest total degradation threat occurs in areas with slope steepness >7 degrees, average annual temperature 10.54, altitude >766 m a.s.l. Similarly, the results for water erosion, wind erosion, soil compaction, loss of organic matter, acidification and heavy metal contamination were processed. The results enable us to identify the relationships of different levels of threats which could consequently be used in various ways – for classification of threatened areas, for more effective implementation of anti-degradation measures, or purely for a better understanding of the role of physical geographical factors in soil degradation in the Czech Republic, and thus could increase the chances of reducing vulnerability to land degradation not only in the Czech Republic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evolving physical and mechanical rock properties during exposure at Earth's surface

Rocks at Earth's surface experience progressive damage due to exposure to environmental conditions. Although environmental damage to rocks is recognized as being time-dependent, our quantitative understanding of its effects on physical and mechanical properties remains limited by field and laboratory studies constrained to short timescales. In this study, we aim to rigorously quantify how these properties evolve under longer natural environmental exposure spanning from the Holocene to the late Pleistocene. We conducted a ‘time series’ analysis of key physical and mechanical rock properties using multiple interior samples from 10 cogenetic granitoid boulders exposed for different amounts of time at two sites in Eastern California, USA. We find that over the late Pleistocene (~30 ka), rocks exhibit porosity increases of 40 ± 4 % and seismic velocity decreases of 7 ± 1 % at both sites. Similarly, over the same time period, rock tensile and compressive strengths declined by an average of 11 ± 4 %, and rock permeability increased on average by 104 ± 50 %. Microscopic analysis of thin sections of the same rocks indicates that microfracture density and intensity also progressively increased over the same timescales. These results suggest that time-dependent fracture propagation due to environmental exposure is likely a central contributor to the observed changes in rock properties. This study provides quantitative documentation that pronounced changes in the physical and mechanical properties of rock progressively occur within thousands of years of exposure.

Critical zone↗
DOE OSTI2016

Comparison of the safety-related physical and combustion properties of liquid hydrogen and liquid natural gas in the context of the SF-BREEZE high-speed fuel-cell ferry

Here, we review liquid hydrogen (LH 2 ) as a maritime vessel fuel, from descriptions of its fundamental properties to its practical application and safety aspects, in the context of the San Francisco Bay Renewable Energy Electric Vessel with Zero Emissions (SF-BREEZE) high-speed fuel-cell ferry. Since marine regulations have been formulated to cover liquid natural gas (LNG) as a primary propulsion fuel, we frame our examination of LH 2 as a comparison to LNG, for both maritime use in general, and the SF-BREEZE in particular. Due to weaker attractions between molecules, LH 2 is colder than LNG, and evaporates more easily. We describe the consequences of these physical differences for the size and duration of spills of the two cryogenic fuels. The classical flammability ranges are reviewed, with a focus on how fuel buoyancy modifies these combustion limits. We examine the conditions for direct fuel explosion (detonation) and contrast them with initiation of normal (laminar) combustion. Direct fuel detonation is not a credible accident scenario for the SF-BREEZE. For both fuels, we review experiments and theory elucidating the deflagration to detonation transition (DDT). LH 2 fires have a shorter duration than energy-equivalent LNG fires, and produce significantly less thermal radiation. The thermal (infrared) radiation from hydrogen fires is also strongly absorbed by humidity in the air. Hydrogen permeability is not a leak issue for practical hydrogen plumbing. We describe the chemistry of hydrogen and methane at iron surfaces, clarifying their impact on steel-based hydrogen storage and transport materials. These physical, chemical and combustion properties are pulled together in a comparison of how a LH 2 or LNG pool fire on the Top Deck of the SF-BREEZE might influence the structural integrity of the aluminum deck. Neither pool fire scenario leads to net heating of the aluminum decking. Overall, LH 2 and LNG are very similar in their physical and combustion properties, thereby posing similar safety risks. For ships utilizing LH 2 or LNG, precautions are needed to avoid fuel leaks, minimize ignition sources, minimize confined spaces, provide ample ventilation for required confined spaces, and to monitor the enclosed spaces to ensure any fuel accumulation is detected far below the fuel/air mix threshold for any type of combustion.

08 HYDROGEN↗