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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Origins of the change in mechanical strength of silicon/gold nanocomposites during irradiation

Abstract Silicon-based layered nanocomposites, comprised of covalent-metal interfaces, have demonstrated elevated resistance to radiation. The amorphization of the crystalline silicon sublayer during irradiation and/or heating can provide an additional mechanism for accommodating irradiation-induced defects. In this study, we investigated the mechanical strength of irradiated Si-based nanocomposites using atomistic modeling. We first examined dose effects on the defect evolution mechanisms near silicon-gold crystalline and amorphous interfaces. Our simulations reveal the growth of an emergent amorphous interfacial layer with increasing dose, a dominant factor mitigating radiation damage. We then examined the effect of radiation on the mechanical strength of silicon-gold multilayers by constructing yield surfaces. These results demonstrate a rapid onset strength loss with dose. Nearly identical behavior is observed in bulk gold, a phenomenon that can be rooted to the formation of radiation-induced stacking fault tetrahedra which dominate the dislocation emission mechanism during mechanical loading. Taken together, these results advance our understanding of the interaction between radiation-induced point defects and metal-covalent interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Unraveling magneto-structural coupling of Ni 2 MnGa alloy under the application of stress and magnetic field using in situ polarized neutron diffraction

The magneto-structural evolution of a Ni 2 MnGa single crystal was investigated by in situ polarized neutron diffraction under both magnetic field and mechanical loading. The ability to separate the nuclear and magnetic scatterings by polarized neutrons enables simultaneous observations of the twin reorientation (through twin boundary motion) and magnetic moment configuration. It is found that under a 1.0 T saturating magnetic field, twin reorientation can be activated by compressive stress and is accompanied by a realignment of the magnetic moment, which follows the easy axis of the twin variant. On the other hand, compressive stress as small as 1.0 MPa can freeze the twin reorientation. The evolution of the magnetic scattering amplitudes suggests that the change in magnetization was mainly achieved through the magnetic domain wall motion, while the twin boundary motion was blocked.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bifurcation-driven vertical plasma displacement

This paper considers vertical plasma motion resulting from plasma current decay during the disruption event. The presented filament-based model describes the motion in the ideal wall limit as an adiabatically slow evolution of the plasma equilibrium. Here, the equilibrium exhibits a pitchfork bifurcation when the decaying plasma current passes a critical value determined by the external magnetic field. This bifurcation affects the disruption-induced mechanical loads on the first wall.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Application of a Physics-Informed Convolutional Neural Network for Monitoring the Temperature Fields in High-Temperature Gas Reactors

Here, this work presents current advances in applying a physics-informed convolutional neural network (CNN) to evaluate temperature distributions in advanced reactors. Our goal is to demonstrate that the CNN can reconstruct temperature fields within the solid region of a prismatic fuel assembly in a high-temperature gas reactor (HTGR) with sensor data available in only a few cooling channels. Before that, we showcase the superior performance of the physics-informed CNN in comparison to a purely data-driven multilayer perceptron (MLP), considering a canonical heated channel setup. This analysis shows the advantages of our approach and justifies its choice. The datasets employed here are obtained upon numerical simulations performed with codes under the Nuclear Energy Advanced Modeling and Simulation program. This work is important, as industry experience indicates that the assembly material in HTGR concepts is prone to large thermal-mechanical loads nearing operational limits. This makes it crucial to characterize peak temperatures and their distributions near hot spots. Modern thermocouples are unreliable in these types of harsh environments because of the high neutron fluxes and elevated temperatures involved. The CNN-based field reconstruction represents an attractive solution, enabling sensor arrays in less aggressive locations and augmenting indirect predictions for less accessible regions. The results show that the CNN reduces prediction errors by orders of magnitude in comparison to the MLP, considering the simple yet well-representative heated channel case. In the case of the HTGR fuel assembly, the CNN can successfully reconstruct temperature fields over various cooling regimes. Furthermore, we also explore the algorithm’s ability to detect abnormalities. Interestingly, the CNN proves it has the capacity to detect blockage in one of the noninstrumented cooling channels.

Machine learning↗
DOE OSTI2026

Primary Heat Transport System Design Considerations for Xcimer Energy’s Athena Fusion Pilot Plant

Fusion energy promises a reliable, carbon-free source of power; however, significant challenges remain before it can be deployed as an economical energy source. In addition to achieving fusion conditions, power plants must operate under extreme temperatures, radiation, and mechanical loads while maintaining high efficiency and availability. These requirements place strong demands on engineering design and plant operation. This work focuses on the engineering challenges associated with balance of plant analysis for inertial fusion energy systems. In particular, this paper examines the design considerations for primary heat transfer systems in fusion pilot plants employing molten fluoride salt coolants, with particular emphasis on system layout optimization and the balance between competing design objectives using the Xcimer Energy Athena inertial pilot plant design as a case study. Through systematic analysis of candidate system configurations and parametric sensitivity studies, we identify key engineering trade-offs governing salt inventory, pumping power requirements, and operational flexibility. The analysis employs system-level modeling tools to explore the design space and establish relationships between geometric parameters and system performance metrics.

Greenwood, Scott [ORNL] (ORCID:0000000333480736)↗
DOE OSTI2025

Magnetic and Mechanical Analysis of Bi-2212 Rutherford Cable in a Cos-Theta Sub-Scale Dipole Coil

The U.S. Magnet Development Program (US-MDP) explores high-field accelerator magnets compatible with operational conditions beyond the limits of Nb$_3$Sn technology. The ongoing R&D High-Temperature Superconductors (HTS) suggests using Bi$_2$Sr$_2$CaCu$_2$O$_{8-x}$ (Bi-2212) as superconducting element. Bi-2212 Rutherford cables maintain a high critical current (I$_C$) when exposed to a large external magnetic field. However, Bi-2212 exhibits an oversensitive stress-strain response when subject to large Lorentz forces. This paper reports on the magnetic and mechanical analysis of the Bi-2212 cosine-theta insert being developed at Fermilab for a hybrid magnet composed of two external layers of Nb$_3$Sn and two internal layers of Bi-2212. We performed a FEM analysis of the insert to estimate the HTS stress state in the coil's strands under magnetic and mechanical loads.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Energy storage in magnetic textures driven by vorticity flow

Here, an experimentally feasible energy-storage concept is formulated based on vorticity (hydro)dynamics within an easy-plane insulating magnet. The free energy associated with the magnetic winding texture is built up in a circular easy-plane magnetic structure by injecting a vorticity flow in the radial direction. The latter is accomplished by electrically induced spin-transfer torque, which pumps energy into the magnetic system in proportion to the vortex flux. The resultant magnetic metastable state with a finite winding number can be maintained indefinitely because the process of its relaxation via phase slips is exponentially suppressed when the temperature is brought well below the Curie temperature. We characterize the vorticity-current interaction underlying the energy-loading mechanism through its contribution to the effective electric inductance in the rf response. Our proposal may open an avenue for naturally powering spintronic circuits and nontraditional magnet-based neuromorphic networks.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Rapid detection of rare events from in situ X-ray diffraction data using machine learning

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics ( e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

Zheng, Weijian↗
DOE OSTI2026

Machine learning approaches for crystallographic classification from synthetic 2D X-ray diffraction data

Crystallographic structure identification is crucial for understanding material properties; however, current methodologies often depend on labor-intensive and time-consuming analyses of 2D X-ray diffraction (XRD) patterns. To address these limitations, this study employs synthetic 2D XRD patterns combined with deep learning (DL) techniques to enable automated and high-throughput classification of the seven crystal systems and 230 space groups. We introduce the novel Auto Diffraction Pipeline, designed to generate synthetic 2D XRD spot patterns from crystallographic information files under diverse conditions, including varying zone axes, atomic substitution, atomic depletion and mechanical loading. These conditions enhance the realism of synthetic data, mitigating the scarcity of experimental datasets and enabling the creation of large representative training sets. Convolutional neural networks were trained and validated on these synthetic datasets to classify crystallographic structures across multiple scenarios. Our results demonstrate that integrating synthetic 2D XRD patterns with DL facilitates rapid, accurate and automated crystallographic classification, promoting the wider adoption of data-driven approaches in materials science.

Shahnazari, Ayoub [Univ. of Rochester, NY (United ↗
DOE OSTI2024

Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway

Lithium-ion batteries, widely used for their durability and high energy storage, face the risk of internal short circuits leading to catastrophic thermal runaway events. These events, triggered by external stimuli like mechanical loads, pose safety concerns in applications such as electric vehicles. Detecting and understanding thermal runaway events is crucial, but physics-driven models struggle to explain the non-linear evolution of battery temperature during these events, considering factors like material composition and state-of-charge. Due to the rarity of these events and the cost of data collection, we propose a deep learning (DL) model to predict battery temperature responses during thermal runaway. The challenge lies in the scarcity of data, making traditional DL models prone to overfitting and learning low-quality representations of the complex process.Our approach introduces a novel few-shot architecture that incorporates an adversarially governed invariant encoding process. This architecture aims to distill "invariant" relationships by addressing distributional shifts in data across various battery properties, facilitating the detection of thermal runaway events. Specifically, our results demonstrate that deep learning models conditioned on these "invariant" representations outperform state-of-the-art baselines, achieving a remarkable 96.8% performance improvement in terms of the popular metric MAPE. This framework presents a promising direction for enhancing battery safety modeling, particularly in the context of rare and complex events like thermal runaway. Our code and code and dataset used for the paper are public1.

Tabassum, Anika [ORNL] (ORCID:0000000254600955)↗
DOE OSTI2020

Methods for In Situ Electroluminescence Imaging of Photovoltaic Modules Under Varying Environmental Conditions

Electroluminescence (EL) imaging is a powerful tool used to identify defects in photovoltaic solar cells. Typically, this type of characterization is performed in the dark using a current injection that equals short-circuit current measured at standard test conditions (STC). Restricting imaging to such a temperature range limits the information obtained about the module and cells. However, it is not trivial to develop a tool that would allow for EL imaging to be performed under a wider range of temperatures. Here we demonstrate an in situ EL imaging capability developed within an environmental chamber that allows for control of sample temperatures between –40 and 90 °C. Additionally, we demonstrate EL imaging of 4-cell mini-modules (MiMo) under front-side mechanical loading. A Raspberry Pi-connected camera with short-pass filter removed is used for EL imaging. The camera is low-cost with a small form-factor, appropriate for use in a harsh, enclosed environment. The camera is installed within a thermally isolating housing mounted within the environmental chamber. Three example cases are given for MiMos that exhibit various forms of degradation including solder fatigue and cell cracking. It is shown that by measuring at conditions above and below STC, different behaviors may be identified. In some cases, restricting characterization to STC may lead to a failure to detect damage existing in the sample.

14 SOLAR ENERGY↗
DOE OSTI2022

Millions of Small Pressure Cycles Drive Damage in Cracked Solar Cells

Here, we applied time-varying air pressure to a PV module containing newly cracked cells. The test used a new dynamic mechanical acceleration (DMX) apparatus. We applied pressure cycles similar to natural, wind-driven cycles. Compared to standard dynamic mechanical load (DML) tests, we applied much lower pressure (10 Pa to 300 Pa RMS) and many more cycles (one million at each of four pressure levels). We present a case study on a single cell in a commercial module. We monitored electrical continuity loss across cracks using electroluminescence (EL) imaging. 10 Pa pressure cycles caused negligible change. 30 Pa pressure cycles caused permanent damage that continued worsening even after tens of thousands of cycles. After one million 30 Pa cycles, a series of 100 Pa cycles still caused new, permanent damage to existing cracks. 300 Pa cycles caused further worsening and introduced new cracks.

14 SOLAR ENERGY↗
DOE OSTI2020

Movement of Cracked Silicon Solar Cells During Module Temperature Changes

Cracks in crystalline silicon solar cells can lead to substantial power loss. While the cells' metal contacts can initially bridge these cracks and maintain electrical connections, the bridges are damaged by mechanical loads, including those due to temperature changes. We investigated the metallization bridges that form over cracks in encapsulated silicon solar cells. Microscopic characterization showed that the crack in the silicon can immediately propagate through the metal grid, but the grid can maintain electrical contact once the load is removed. We also quantified the movement of the cell fragments separated by a crack as a function of temperature. Cell fragments are free to move diagonally and to rotate, so the change in gap across the crack during a temperature change varies along the length of the crack. In one sample, we showed that a 10 degrees C temperature change, causing a 2 um increase in the separation of cell fragments, was sufficient to cause a reversible electrical disconnection of metallization bridging a crack.

14 SOLAR ENERGY↗
DOE OSTI2023

Co-Simulation Model for Optimal Wind-Hydro Coordination Using Wind Farm Control Dynamics

The growing share of Variable Renewable Energy sources (VRES) in power systems presents challenges for regula- tors, grid operators and energy producers. The VRES’ operation has limited flexibility in their operations, as they are highly dependent on ambient environments. To address these challenges, decision-makers must consider multiple objectives, among these are revenue, power system services and mechanical load on wind turbines. Coordinated operation of power plants and different wind farm control strategies are examples of measures that can benefit these objectives. This study proposes a Multi-Objective Linear Programming (MOLP) model to simulate the optimum operation of wind and hydropower plants that share limited transmission capacity. Further, wind farm control dynamics are included to obtain realistic output power and accumulated damage. From this, a case study based on a relevant location in Norway is presented to analyze the improved effect of wind- hydro coordination and wind farm control in achieving the objectives of accumulated wind turbine damage and total revenue of the hybrid power system. In addition, the study considers the potential advantages of adding a variable-speed pump to the hydropower plant. The results demonstrate that by considering multiple objectives in the optimization, one may achieve better overall performance of the objectives. By utilizing the flexibility of hydro storage, the decision maker may adjust to obtain the most desired outcome. Moreover, the added flexibility of utilizing a pump for hydro storage shows great improvements for the combined revenue of the power plants and reduced curtailment of the wind farm. However, less impact is observed from using a variable speed pump compared to a fixed speed pump.

13 HYDRO ENERGY↗
DOE OSTI2020

Interactions Observed Between Torus and Solenoid Superconducting Magnets at JLab

The Jefferson Lab 12 GeV Upgrade of Experimental End Station Hall B required a new detector system that would be more sensitive to forward going particles and able to handle higher luminosity. This new detector is CLAS12 and includes two superconducting iron-free magnets – a torus and an actively-shielded solenoid. The torus magnet consists of 6-trapezoidal racetrack-type coils while the solenoid is an actively shielded 5 T magnet. The torus and the solenoid are located in close proximity to one another and are surrounded by sensitive particle detectors. In this work, the torus and solenoid, operating at 3770 A and 2416 A respectively, were commissioned successfully and are operating normally. This paper presents observed electromagnetic interactions which include induced static mechanical loads and inductive coupling as well as a summary of some of the cryogenic interactions and how they are mitigated.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

THERMAL STRESS ANALYSIS OF A SPENT NUCLEAR FUEL CANISTER

The potential for chloride induced stress corrosion cracking (CISCC) in spent nuclear fuel dry storage canisters is a current topic of research by the US Department of Energy Spent Fuel and Waste Science and Technology program. One of the important prerequisites for CISCC is a tensile residual stress state. This study utilizes computational models to provide an initial analysis of thermal stresses in a generic spent nuclear fuel canister. A STAR-CCM+ thermal fluid model provides a temperature profile analysis of the canister based on four different ambient temperature conditions. An ANSYS APDL finite element model incorporates the temperature profiles to analyze the thermal stresses in the canister. The calculated thermal stress magnitudes are in the range of 10 MPa to 80 MPa, which could be significant for crack propagation through the canister wall. The maximum thermal stress is not a concern for the structural failure of the canister, but it is high enough that it is a significant contribution to the total stress state of the canister wall, which also includes residual stress from fabrication, internal pressure from helium cover gas, and potential transient mechanical loads, such as earthquakes. This paper describes the initial finite element thermal stress analysis that was completed in 2020, reports the initial findings, and identifies areas where model refinement is still needed to complete this work in the future.

Jensen, Ben J.↗
DOE OSTI2021

Structural dynamics modeling of spent nuclear fuel during hypothetical package drop events

The response of spent nuclear fuel (SNF) to hypothetical package drop events is of particular interest in the scope of spent fuel storage and transportation because of the mechanical shock encountered in such scenarios. Previous testing and modeling by the U.S. Department of Energy has demonstrated that the shock and vibration environment of normal shipping and handling conditions (excluding package drop events) is relatively benign and does not challenge the integrity of spent nuclear fuel. Cask drop events are worth considering because SNF packages are required to withstand free drops onto unyielding surfaces as part of their licensing basis. The acceleration experienced during drop events can be orders of magnitude higher, and thus more advanced models are needed to encompass potential nonlinear behavior of the fuel, such as spacer grid buckling and rod-to-rod impact. This work describes a number of finite element models developed to calculate the response of spent nuclear fuel to various hypothetical drop events that have been validated by package and fuel assembly drop tests conducted in the last decade. Sensitivity of the model response to factors such as package drop orientation, secondary impacts, and irradiated material properties as well as their potential impacts to fuel cladding integrity, was also investigated. Cask drops are not expected as a regular occurrence during SNF transportation, but this work helps raise the understanding of SNF mechanical loads to the point of consistency with the package design requirements.

Kadooka, Kevin↗
DOE OSTI2023

MODELING A SPENT NUCLEAR FUEL CASK SEISMIC TEST

The US Department of Energy Spent Fuel and Waste Science and Technology (SFWST) program is planning to conduct a series of full-scale shake table tests to simulate hypothetical earthquake conditions and record the response of surrogate spent nuclear fuel (SNF) assemblies in a canister dry storage system mockup. The shake table motions will represent a range of hypothetical earthquake conditions at hypothetical locations in the continental US to generally define the range of mechanical loads that SNF can be expected to experience during extended dry storage periods. The earthquake conditions will represent seismic hazards in the 2,000-to-20,000-year return period range. The test will use instrumented pressurized water reactor fuel assemblies with surrogate mass inside the fuel rods instead of radioactive fuel pellets. Dummy assemblies with similar mass and dimensions to actual fuel assemblies will occupy the rest of the fuel assembly locations in a SNF canister that holds 32 fuel assemblies. The canister will be located inside a mockup vertical concrete cask. Instrumentation will record the motion of the major components of the complex dynamic system, and strain gauges will be used to record the cladding strain at select locations. Preparations for the test require modeling predictions to identify the range of response of the system and to help select specific earthquake cases to be simulated on the shake table from a large set of potential cases. This paper describes the pretest nonlinear finite element modeling efforts that have been completed to date, including cask system level modeling and fuel assembly modeling in LS-DYNA. The cask system level models are critical for anticipating sliding or tipping of an unanchored cask during the test. The fuel assembly model is needed to estimate the range of cladding strain response and fuel assembly structural response to be expected during the test and cladding strain measurements will be key metrics for model validation and the development of modeling best practices after the test is concluded.

Klymyshyn, Nicholas A.↗