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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Benchmarking 3D-MHD simulations of electrothermal instability growth by studying z-pinches with engineered defects

The electrothermal instability (ETI) is driven by Joule heating and arises from the dependence of resistivity on temperature. ETI may drive azimuthally correlated surface density variations which seed magneto Rayleigh-Taylor (MRT) instability growth. Liner implosion studies suggest that dielectric surface coatings reduce the amplitude of ETI driven perturbations. Furthermore, previous fundamental physics studies suggest that non-metallic inclusions within the metal can seed ETI growth. In this project, we aimed to (1) determine how dielectric coatings modify ETI growth by varying the coating thickness and the surface structure of the underlying metal, and (2) study overheating from engineered defects—designed lattices of micron-scale pits. Engineered pits divert current density and drive local overheating in a way that can be compared with 3DMHD simulations. All experiments were executed on the Sandia Mykonos Facility. Facility and diagnostic investments enabled high quality data to be gathered in support of project deliverables.

42 ENGINEERING↗
DOE OSTI2021

CFD/FEA Co-Simulation Framework for Analysis of the Thermal Barrier Coating Design and Its Impact on the HD Diesel Engine Performance

Thermal barrier coatings (TBCs) have been investigated both experimentally and through simulation for mixing controlled combustion (MCC) concepts as a method for reducing heat transfer losses and increasing cycle efficiency, but it is still a very active research area. Early studies were inconclusive, with different groups discovering obstacles to realizing the theoretical potential. Nuanced papers have shown that coating material properties, thickness, microstructure, and surface morphology/roughness all can impact the efficacy of the thermal barrier coating and must be accounted for. Adding to the complexities, a strong spatial and temporal heat flux inhomogeneity exists for mixing controlled combustion (diesel) imposed onto the surfaces from the impinging flame jets. In support of the United States Department of Energy SuperTruck II program goal to achieve 55% brake thermal efficiency on a heavy-duty diesel engines, this study sought to develop a deeper insight into the inhomogeneous heat flux from mixing controlled combustion on thermal barrier coatings and to infer concrete guidance for designing coatings. To that end, a co-simulation approach was developed that couples high-fidelity computational fluid dynamics (CFD) modeling of in-cylinder processes and combustion, and finite element analysis (FEA) modeling of the thermal barrier-coated and metal engine components to resolve spatial and temporal thermal boundary conditions. The models interface at the surface of the combustion chamber; FEA modeling predicts the spatially resolved surface temperature profile, while CFD develops insights into the effect of the thermal barrier coating on the combustion process and the boundary conditions on the gas side. The paper demonstrates the capability of the framework to estimate cycle impacts of the temperature swing at the surface, as well as identify critical locations on the piston/thermal barrier coating that exhibit the highest charge temperature and highest heat fluxes. In addition, the FEA results include predictions of thermal stresses, thus enabling insight into factors affecting coating durability. An example of the capability of the framework is provided to illustrate its use for investigating novel coatings and provide deeper insights to guide future coating design.

42 ENGINEERING↗
DOE OSTI2018

Superhigh-Speed Electric Motor with Unipolar Magnetic Bearing

New unipolar magnetic bearings of high-speed and superhigh-speed electric motors for different branches of industry are described. A new design is proposed and investigated by computer simulation. On the basis of the results, a mockup is created, and the computer models are verified.

42 ENGINEERING↗
DOE OSTI2025

Model Form Error Correction for a Black-Box Thermal Battery Heat Transfer Simulation

Thermal batteries are crucial for supplying power to high-consequence engineering applications such as rockets. Computational simulations have been developed to predict thermal battery behavior, but these simulations often suffer from modeling errors, including model form uncertainty. Addressing this uncertainty can be achieved by quantifying either the model discrepancy in the output or the model form error (MFE) in the governing equation. MFE is particularly valuable as it can be better extrapolated beyond observed outputs, which is essential for predictions involving changes in external system loading, system configuration and geometry, or output quantities. This paper employs a state estimation approach to estimate MFE using experimental data and then utilizes machine learning (ML) to model its relationship with state variables. A nonintrusive technique is used to estimate MFE in a black-box thermal battery heat transfer simulation. The trained machine learning model for MFE is then applied to correct simulation predictions under extrapolated initial conditions and battery configurations. In conclusion, the methodology's performance is evaluated using additional experimental data, demonstrating its effectiveness in improving prediction accuracy.

Batteries↗
DOE OSTI2021

Impact of DER Communication Delay in AGC: Cyber-Physical Dynamic Co-Simulation: Preprint

Distributed energy resource (DER) frequency regulations are promising technologies for future grid operation. Unlike conventional generators, DERs might require open communication networks to exchange signals with control centers, possibly through DER aggregators; therefore, the impacts of the communication variations including the latency on the system frequency stability need to be investigated. This paper develops a cyber-physical dynamic simulation (CPDS) model based on the Hierarchical Engine for Large-Scale Co-Simulation (HELICS) to evaluate the impact of the communication variations, such as delays in DER frequency regulations. The feasible delay range can be obtained under different AGC parameter settings. The results show that the risk of instability generally increases with the communication delay and that the difference of AGC provided from DER and conventional generation units should be recognized when designing frequency regulation controllers.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2019

M3MS-19IN0101201 Modeling Intra-granular Fission Gas Bubble Evolution in Uranium Dioxide during Transients

The SciDAC milestone M3MS-19IN0101201 on Modeling Intra-granular Fission Gas Bubble Evolution in Uranium Dioxide during Transients was completed with the development, testing and implementation in the Bison code of an engineering model for intra-granular fission gas bubble evolution that is applicable to transient conditions. The model extends the previous work on modeling bubble evolution during normal operating conditions to include additional complexity that arises during transient situations, in particular, the so-called intra-granular bubble coarsening. This phenomenon refers to the appearance of a second population of coarsened bubbles with diameters of tens to hundreds of nm, which co-exist with nanometric bubbles present also during normal operating conditions and are responsible for a large portion of transient fuel swelling. The developed model considers growth of bubbles along dislocations, associated with the absorption of vacancies available in the dislocation core region, and favored by pipe diffusion of fission gas atoms. The model also utilizes the formulation for the gas atom resolution rate developed in the present SciDAC project through molecular dynamics simulations by other institutions. The present work therefore demonstrates an operational multiscale approach, based on collaboration within the project. The model is validated to an extensive experimental dataset of ramp tested uranium dioxide fuel samples. Additionally, we present new applications of the base model for bubble evolution during normal operating conditions to Bison engineering simulations of Cr2O3-doped fuel rods irradiated in the Halden reactor. Simulation results in terms of integral fission gas release in the fuel rods are compared to the available experimental data, pointing out significant improvements in the engineering predictions compared to application of the empirical model that was available in Bison before the SciDAC developments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

A continuum damage mechanics (CDM) based Wilshire model for creep deformation, damage, and rupture prediction

In this report, a new constitutive model is derived that combines the Wilshire equations with continuum damage mechanics (CDM) to enable the long-term prediction of creep deformation, damage, and rupture. The Wilshire equations have been demonstrated to accurately extrapolate the stress-rupture, minimum-creep-strain-rate, and time-to-strain of various alloys across decade of life. Recently, the time-to-creep-strain equation has been rearranged to predict creep deformation curves; however, the resulting equation is difficult to calibrate and does not track damage. The CDM-based Sinh constitutive model accurately predicts creep deformation, damage, and stress-rupture for a variety of alloys; however, it lacks an explicit description of stress and temperature co-dependence which limits interpolation and extrapolation ability. In this study, the Wilshire and the CDM-based Sinh equations are combined to create the “WCS” model. The WCS model combines the best features of each model while eliminating their deficiencies. Experimental data for alloy P91 is gathered and the WCS model is calibrated to the data. The WCS model accurately predicts the stress rupture, minimum-creep-strain-rate, creep deformation, and damage of alloy P91 across the experimental stress and temperature range as well as consistency in parametric simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Numerical Tools for Improved Convergence of Meshfree Peridynamic Discretizations

Peridynamic models have been employed to simulate a broad range of engineering applications concerning material failure and damage, with the majority of these simulations using a meshfree discretization. This chapter reviews that meshfree discretization, related issues present in peridynamic convergence studies, and possible remedies proposed in the literature. In particular, we discuss two numerical tools, partial-volume algorithms and influence functions, to improve the convergence behavior of numerical solutions in peridynamics. Numerical studies in this chapter involve static and dynamic simulations for linear elastic state-based peridynamic problems.

Seleson, Pablo D.↗
DOE OSTI2018

Bridging the simulator gap: Measuring motivational bias in digital nuclear power plant environments

Using digital NPP simulator facilities for research in Human Reliability Analysis (HRA) and nuclear power research is increasingly popular. For this work, we propose a method for characterizing and quantifying the gap between data collected in a simulator and data that reflect NPP operations. Using novice operators, we demonstrate how to manipulate and measure the impact of the simulator environment on operator actions. A set of biases are proposed to characterize factors introduced by the simulator environment. There are two categories of simulator bias: Environmental Biases (physical differences between the simulator and the control room), and Motivational Biases (cognitive differences between training in a simulator and operating a NPP). This study examines Motivational Bias. A preliminary causal model of Motivational Biases is introduced and tested in a demonstration experiment using 30 student operators. Data from 41 simulator sessions are analyzed. Data include crew characteristics, operator surveys, and time to recognize and diagnose the accident in the scenario. Quantitative models of the Motivational Biases using Structural Equation Modeling (SEM) are proposed. With these models, we estimate how the effects of the scenario conditions are mediated by simulator bias, and we demonstrate how to quantify the strength of these effects.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Prediction of Cyclic Variability and Knock-Limited Spark Advance (KLSA) in Spark-Ignition (SI) Engine

Engine knock remains one of the major barriers to further improve thermal efficiency of Spark Ignition (SI) engines. Knock can be suppressed by lowering the compression ratio, or retarding the spark ignition timing, however, at an expense of efficiency penalty. SI engine is usually operated at knock-limited spark advance (KLSA) to achieve possibly maximum efficiency with given engine hardware and fuel properties, such as Research Octane Number (RON), Motor Octane Number (MON), and heat of vaporization, etc. Co-optimization of engine design and fuel properties is promising to improve the engine efficiency and predictive CFD models can be used to facilitate this optimization process. However, difficulties exist in predicting KLSA in CFD simulations. First, cyclic variability of SI engine demands that multi-cycle results are required to capture the extreme conditions. Secondly, Mach Courant-Friedrichs-Lewy (CFL) number of 1 is desired to accurately predict the knock intensity (KI), resulting in unaffordable computational cost, especially for multi-cycle simulations. In this study, a new approach to numerically predict KLSA using large Mach CFL number of 50 is proposed. This approach is validated against experimental data for a boosted Direct Injection Spark Ignition (DISI) engine at multiple loads and spark timings. G-equation combustion model coupled with well-mixed chemical kinetic model are used to predict the turbulent flame propagation and end-gas auto-ignition, respectively. Simulations run for 10 consecutive engine cycles at each condition. The results show good agreement between model predictions and experiments in terms of cylinder pressure, combustion phasing and cyclic variation. Engine knock is predicted with early spark ignition timing, indicated by significant pressure wave oscillation and end-gas heat release. Maximum Amplitude of Pressure Oscillation (MAPO) analysis is performed to quantify the KI, and the slope change point in KI extrema is used to indicate the KLSA accurately. Using a smaller Mach CFL number of 5 also results in the same conclusions thus demonstrating that this approach is insensitive to the Mach CFL number. The use of large Mach CFL number allows us to achieve fast turn-around time for multi-cycle engine CFD simulations.

Yue, Zongyu↗
DOE OSTI2020

SAM Plug-in Development (Phase I Final Report)

The DOE Office of Nuclear Energy (NE) has created an extensive set of advanced modeling and simulation tools for nuclear engineering analysis. The advanced capabilities of these newer analysis codes require more in-depth training, skills, and knowledge in order to effectively utilize them for the design, analysis, and licensing of advanced nuclear systems and experiments. A high learning curve for inexperienced users may deter organizations from incorporating these tools into their internal processes. This project involved development of a plug-in to the Symbolic Nuclear Analysis Package (SNAP) for the System Analysis Module (SAM) tool. SAM is an advanced system analysis tool for reactor transient analyses being developed at Argonne National Laboratory under the U.S. DOE Office of Nuclear Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. SAM utilizes an object-oriented application framework (MOOSE), and its underlying meshing and finite-element library (libMesh) and linear and non-linear solvers (PETSc), to leverage modern advanced software environments and numerical methods. SNAP provides a highly flexible framework for creating, modifying and documenting input for engineering analysis codes such as SAM as well as extensive functionality for submitting, monitoring, and interacting with the codes through an intuitive graphical user interface (GUI). The common user interface provided by SNAP minimizes the learning curve for engineers starting with a new analysis code and provides an intuitive framework for transitioning between different analysis codes. SNAP provides a powerful but intuitive interface to facilitate access to advanced modeling and simulation tools for inexperienced users. Unlike many “form based” GUI’s, SNAP maps each engineering code’s component input to an internal database which manages all component input parameters along with component interconnections. This level of abstraction permits SNAP to support several advanced capabilities such as renodalization, model validation and consistency checks, embedded documentation, model notebook generation, data ownership and reviewer tracking, and variable assignment for inputs to name a few. SNAP includes a built-in Python interpreter and is interfaced to several commercial and open source packages including CPython, MATLAB/OCTAVE, Microsoft Office, Open Office, and SANDIA’s DAKOTA package which provides Uncertainty Quantification analysis through the SNAP plug-ins. Phase I of this project involved development a fully functional basic SAM plug-in to SNAP. This plug-in provides the ability to import existing models, graphically construct, edit and submit models using SNAP’s extensive functionality.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Quantum Dynamics at Scale: Ultrafast Control of Emergent Functional Materials

Confluence of extreme-scale quantum dynamics simulations (i.e. quantum@scale) and cutting-edge x-ray free-electron laser experiments are revolutionizing materials science. An archetypal example is the exciting concept of using picosecond light pulses to control emergent material properties on demand in atomically-thin layered materials. This paper describes efforts to scale our quantum molecular dynamics engine toward the United States' first exaflop/s computer, under an Aurora Early Science Program project named "Metascalable layered material genome". Key algorithmic and computing techniques incorporated are: (1) globally-scalable and locally-fast solvers within a linear-scaling divide-conquer-recombine algorithmic framework; (2) algebraic `BLASification' of computational kernels; and (3) data alignment and loop restructuring, along with register and cache blocking, for enhanced vectorization and efficient memory access. The resulting weak-scaling parallel efficiency was 0.93 on 131,072 Intel Xeon Phi cores for a 56.6 million atom (or 169 million valence-electron) system, whereas the various code transformations achieved 5-fold speedup. The optimized simulation engine allowed us for the first time to establish a significant effect of substrate on the dynamics of layered material upon electronic excitation.

Algebraic BLASification↗
DOE OSTI2019

H-Canyon Tunnel Inspection System

Cable Analysis: The crawlers is controlled remotely via a long coax cable. In order to make sure we do not damage the cable which could cut our contact (data/power/control), we had to determine the bend radius of it at certain locations. This was done with dimensional analysis in Creo and inputting the information into a MATLAB program. Engineering Drawings: Since many of the crawlers parts, such as the main body, needed to be machined, part drawings were needed to communicate the details necessary for manufacture. Project Overview: The H-Canyon Ventilation Exhaust Tunnel due both to its long service history and exposure to aggressive process offgas chemistry has experienced degradation. In order to allow visual inspection of the condition of the radioactively-contaminated tunnel, a remote crawler was designed to deliver cameras as needed to return high definition video. My involvement included Vendor Searches, Cable Analysis, Thermal Analysis, and Engineering Drawings. Thermal Analysis: In order to see if internal active cooling is needed for the crawler, simplified thermal models were designed to approximate the internal temperatures: Conduction (assumes all heat sources are thermally-coupled to the crawler body); Convection (assumes no heat sources are thermally-bonded to crawler). Going Forward: Continue refining the cable analysis as the crawler model is updated and refined. Use my knowledge gained here to further my skills in engineering.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Calibration of the Diffusivity Predictions of Centipede Using Approximate Bayesian Computation and Applications in Nyx (Engineering Scale) and Xolotl-MARMOT (Meso-Scale) Simulations

Fission gas evolution and release in UO 2 nuclear fuel are important fuel performance metrics and occur in several distinct stages: 1) nucleation, growth and resolution of intra-granular bubbles, 2) diffusion to grain boundaries and 3) nucleation and growth of bubbles at grain boundaries, which eventually form a connected network (percolation) enabling release of gas from grain boundaries through connections to triple junctions, grain edges or free surfaces. The NE-SciDAC project is developing several computational tools to model this problem, which are connected in a hierarchical multi-scale framework. The information transfer in the multi-scale framework is a critical step that, in addition to best-estimates, should include uncertainty quantification. Despite taking a first-principles multi-scale approach, there is a need to perform parameter calibration to ensure consistency with available experimental data. In the present study, uncertainty quantification (UQ) and parameter calibration is demonstrated for one of the lower length scale codes in the multi-scale framework (Centipede) and then the results, including instances of the propagated uncertainties, are used in other codes within the framework, specifically Nyx and Xolotl-MARMOT. We calibrated the model parameters in Centipede, a computer code used to predict diffusivities of uranium (U) and xenon (Xe) in the context of the simulation of fission gas in uranium oxide (UO 2 ) nuclear fuel. The Centipede code depends on 183 parameters, all of which are subject to uncertainty. The three data sets used in our calibration effort are taken from the literature. This data is available as a set of measurements, including measurement errors. Our goal is to calibrate a statistical model that predicts both the value of the measurement and the uncertainty associated with the measurement. We perform a Bayesian calibration of the model parameters using a dedicated approximate Bayesian computation (ABC) likelihood function. To avoid excessive computational costs, we replace the expensive Centipede simulation code by a higher-order surrogate model, constructed using only the 9 most important parameters. These important parameters are identified by a preliminary global sensitivity analysis (GSA) study. Among the important parameters are T0 (the temperature at which UO 2 is perfectly stoichiometric) and Hf_pO2 (the temperature dependence of the oxygen (O) partial pressure) that should be considered as operating conditions to be estimated along with the other parameters. We consider two different cases: one where we define one set of these operating conditions for all data sets, and one where we define distinct operating condition parameters for each data set. The Xe diffusivities predicted by the latter case show distinct features that could not be observed in the former. Next, we use the diffusivity predictions by Centipede as input to Nyx, a reduced order fuel performance code focused on gas behavior alone, in order to estimate quantities associated with inter-granular bubble formation at conditions specified by the experiments. Finally, the diffusivities obtained from the calibrated Centipede runs were used in coupled Xolotl-MARMOT simulations of intra- and inter-granular gas evolution. The results are compared to simulations using the baseline diffusivities from Turnbull et al.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

DME-Propane Ignition Delay Time Measurements at Mixing Controlled Compression Ignition Engine-Relevant Conditions

The blend of dimethyl ether (DME, CH 3 OCH 3 ) and propane (C 3 H 8 ) is a potentially renewable fuel mixture that has the potential to replace diesel in compression ignition engines. The combination can potentially reduce particulate and greenhouse gas emissions compared to a conventional diesel engine operating under similar conditions. However, detailed conceptual and simulation studies must be conducted before adopting a new fuel on a compression ignition engine. For these simulations, accurate chemical kinetic models are necessary. However, the validity of chemical kinetic mechanisms in the literature is unknown for mixing controlled compression ignition (MCCI) engine operating conditions. Hence, in this work, we studied the ignition of dimethyl ether (DME) and propane blends in a shock tube at MCCI engine conditions. Ignition delay time (IDT) data was collected behind the reflected shock for DME-propane mixtures for heavy-duty compression ignition (CI) engine parameters. Undiluted experiments spanning temperatures of 700 to 1100 K and pressures of 55 to 84 bar for various blends (100% CH 3 OCH 3 / 0% C 3 H 8, 100% C 3 H 8 / 0% CH 3 OCH 3 , 60% CH 3 OCH 3 / 40% C 3 H 8 ) of DME and propane were combusted in synthetic air (21% O 2 / 79% N 2 ). Some experiments were conducted at higher pressures (90-120 bar) to understand model performance at these conditions. Comparisons of IDT were made with the predictions of recent chemical kinetic mechanisms for DME-propane mixture, including the Aramco3.0, NUIG, and Dames et al. mechanisms. All mechanisms overpredicted IDT compared to experimental values. Sensitivity analysis was conducted with Dames et al. model, and critical reactions sensitive to IDT of DME-propane mixture near 100 bar are outlined.

Mohammed, Zuhayr Pasha↗