Code verification is an essential part of credibility analysis for computational models. It assesses whether the mathematical model is implemented correctly into the code and whether the numerical methods behave consistently, and is done before solution verification and validation. Robust guidance for code verification exists in the literature. However, there is no known, concise guide for selecting the approach based on the model form that also presents an overview of the common elements. This document was written to address this gap as an accessible reference for beginning a code-verification effort.
Microchannel heat exchanger technology is being pursued for next generation CSP concepts for primary power cycle heat addition and power cycle heat recuperation due to the high heat transfer coefficients and pressure containment advantages of small sCO2 channels. The economics of future CSP plants as dictated by the SETO 2020 or 2030 targets depend on a heat exchanger with a 30-year lifetime (resisting creep, fatigue, corrosion, erosion) and operational characteristics such as fast ramping and the ability to withstand thermal shock. However, the lifetime and operational limits of microchannel heat exchangers operating at high temperatures, particularly those constructed from high-nickel alloys, are not well known. This uncertainty has resulted in heat exchanger vendors not being able to accurately forecast heat exchanger lifetime as required by customers, specify operational limits as required by process engineers to prevent premature heat exchanger failure, or overdesign heat exchanger which leads to higher cost than necessary.
This project is intended to support the development of new traction drive systems that meet the targets of 100 kW/L for power electronics and 50 kW/L for electric machines with reliable operation to 300,000 miles. To meet these goals, new designs must be identified that make use of state-of-the-art and next-generation electronic materials and design methods. Designs must exploit synergies between components, for example converters designed for high-frequency switching using wide band gap (WBG) devices and ceramic capacitors. This project included: (1) a survey of available technologies; (2) investigating new technologies, that for example, reduce volume of thermal management or magnetic components; (3) the development of computer aided design tools that consider the converter volume, reliability, and electrical performance; (4) exercising the design software to evaluate performance gaps and predict the impact of certain technologies and design approaches, i.e. GaN semiconductors, ceramic capacitors, ceramic thermal management components, and select topologies; (5) building and testing hardware prototypes to validate models and concepts. The design tools enable co-optimization of the power module and passive elements and provide some design guidance. At the end of the project, new advanced computing methods, such as machine learning approaches, were considered.
The Open-Source Offshore (OSO) airfoils have been developed for research purposes for offshore wind turbines, offering a set of airfoils that align with modern turbine design requirements and industry design practices without proprietary constraints on research use. The eventual airfoil family will target the IEA 22 MW reference wind turbine, which was originally developed with the FFA airfoils. The two airfoils summarized in Table 1 (OSO-21-WT1 and OSO-30-WT1) started development as part of a family of airfoils being designed to target the IEA 22 MW wind turbine. The criteria used to design these airfoils are summarized in Table 1, which aim to encapsulate requirements of modern airfoils for offshore wind turbine applications, and were developed with feedback from industry and research experts. The airfoils were designed using XFOIL and candidate airfoils were then analyzed in RFOIL, which is considered more accurate than XFoil for high lift predictions of thicker airfoils. The design process for a preliminary family of airfoils is available, including a more detailed explanation of the design requirements and metrics similar to those used for these airfoils. Most of the design criteria are met for these two airfoils, with two exceptions. For both airfoils, the L/D Roughness Loss metric is exceeded (42% > 40% goal) and the desired lift coefficient margin over the design value (“CL_Margin”) was moderately exceeded (0.43 > 0.3) while smooth-stall characteristics (computed) were achieved. Note that all of the metrics were computed using RFOIL, and like other new airfoils, these will need to be experimentally validated at a range of Reynolds numbers. The airfoil coordinates will be shared publicly on Sadia National Laboratories’ public Github repository:
Hardware-in-the-Loop (HIL) methodologies for test system development often require simulations capable of running at MHz speeds on FPGAs. The stringent memory and speed constraints necessitate compromises between model fidelity and execution speed. Numerically solving the underlying governing equations represents the highest accuracy, but slowest responding approach. By storing pre-determined results in look-up-tables (LUT), one can balance speed and accuracy.
The purpose of this paper is to qualitatively explore the question of whether as a power system’s sources and energy storage become more distributed, the power system also tends to become more resilient. The paper is divided into two main parts. After presenting introductory material, Part 1 looks at factors that might limit the ability of ‘fully-distributed’ resources to provide the expected resilience, and Part 2 considers factors that might make a more centralized system more resilient.
Vertical-axis wind turbines (VAWTs) have been the subject of research and development for nearly a century. However, this turbine architecture has fallen in and out of favor on multiple occasions. Beginning in the late 1970s, the U.S. Department of Energy sponsored an extensive experimental program through Sandia National Laboratories which produced a mass of experimental data from several highly instrumented turbines. Turbines designed, built, and tested include the 2 meter, 5 meter, 17 meter, and 34 meter and their respective configurations. This program kicked off a commercial collaboration and resulted in the FloWind turbines. The FloWind turbines had several notable design changes from the experimental turbines that, in conjunction with a general lack of understanding regarding predicting fatigue at the time, led to the majority of the turbines failing prematurely during the late 80s.
This document summarizes common indices used to characterize climate-related impacts to agricultural production. In particular, we consider indices for three phenomena: 1) drought, 2) growing period, and 3) heat stress (Table 1). It should be noted that these indices are far from exhaustive. Instead, they reflect common concerns related to production of agriculture that are emerging across multiple regions (Gunda et al., 2024). Other indices such as rate of return (Shand et al., 2024) can also be leveraged to support agriculture-related climate analyses as well.
The mechanical response of a component is affected by defects, such as porosity, arising from the laser powder bed fusion (LPBF) fabrication process. Thus, it is important to develop accurate and efficient inspection methods for identifying porosity. In this work, porosity identified in an X-ray computed tomography (XCT) volume of a Ti-5553 coupon was compared to pores identified in a serial sectioned volume that represented the ground truth. The porosity of the XCT scan was identified using contrast-based, ISO-based, and machine learning (ML) methods for segmentation. Large inherent porosity was easy to identify, but the ISO thresholding still struggled due to the intensity gradient resulting from both the beam hardening in XCT and the uneven lighting of the serial sectioning panels. Further, the results show that ML-based methods were better suited for identifying small pores and reducing the amount of false positives. Additionally, high strain-rate impact testing was done on some of the XCT samples as well as post-mortem XCT inspection, and the same suite of segmentation and quantification tools were used to identify the large spallation cavities. The comparison of porosity pre- and post-mortem provides insight on the influence of the LPBF porosity on the formation of spall cavities.
This is a self-contained reference document that derives the equations necessary to build a combined inertial navigation system and error-state Kalman filter. Coordinate transform, linear time invariant system, inertial sensing, and error-state Kalman filtering theory is built up from first principles. This theory is then leveraged to derive the system equations for two combined inertial navigation system and error-state Kalman filters: (1) a 15-state system modeling white-noise-integrating accelerometer and gyroscope biases, and (2) a 39-state system modeling static and first-order Gauss-Markov accelerometer and gyroscope biases, scale factor errors, and cross-axis sensitivity errors.
Hydrogen Extremely Low Probability of Rupture (HELPR) is a modular probabilistic fracture mechanics modeling platform developed to assess structural integrity of pipelines for transmission and distribution of hydrogen. HELPR couples fatigue and fracture engineering models with probabilistic methods to generate fast predictions and enables quantification of prediction uncertainty and sensitivity. This user manual serves as a guide through the various analysis features HELPR contains.
Salt formations have been explored for the permanent isolation of spent nuclear fuel based on their high thermal conductivity, self-healing nature, and low hydraulic permeability to brine flow. Vacancy defect concentrations in salt complicate fracture mechanics not driven by dislocation dynamics and can influence the resulting surface structure. Classical molecular dynamic simulations were used to simulate tensile testing of salt crystals (halite) with vacancy defect concentrations of up to 0.5 defects/nm3. Increasing defect concentrations resulted in a decrease in ultimate tensile strength and fracture surface energies, driven by increased surface roughness rather than changes in the amount of surface area. Brine–salt surface energies of the fractured surfaces were 0.22 to 0.26 J/m2, significantly higher than values reported for atomically flat (100) surfaces at the same brine composition. This change in surface energy increased the brine–salt dihedral angle by ~27°. The dihedral angle threshold for percolation in salt is 60°, and a 27° increase due to rough fracture surfaces identifies a reduction in porosity percolation and a decrease in salt permeability. Therefore, bedded salt and salt domes may be even more stable than those previously predicted from dihedral angle calculations.
Under IER-441, critical experiments were done with and without tantalum test rods within a central test region surrounded by 7uPCX fuel rods. The experiments were done in new critical assembly hardware designed to support the 7uPCX fuel in a 1.02 cm triangular-pitched array. Appendix I is a draft of section 1 of the ICSBEP benchmark evaluation of the experiments.
We demonstrate the capability of a narrow linewidth quantum cascade laser (QCL) to selectively excite a very narrow velocity range of nitric oxide (σ ≤ 7(3) m/s) with a pure ro-vibrational quantum state. By implementing a counter-propagating geometry, the molecules are selectively excited according to the Doppler shift of the ro-vibrational transition frequency such that the velocity width associated with the excited molecules depends only on the QCL linewidth. We demonstrate a velocity distribution limited by the effective linewidth of our free-running QCL (Γ = 3.2 MHz). Our development provides a cost-effective, flexible approach to resolve quantum-state selective chemical dynamics with excellent velocity resolution in a wide variety of molecules with infrared-active transitions. This technique has been formulated to provide ultrahigh collisional energy resolution in molecular beams to delineate final quantum-state product pairs in studies of molecular collisions.
Garcia, Felipe H.; Oliveira, Mario O.; Ferraz, Renato G.; Bretas, Arturo
Here, this paper analyzes the impact of the use of Kron reduction on the state variables of a three-phase electrical system, even when it does not meet the necessary conditions for its application. Reduction is applied to a power line model to eliminate the equation corresponding to the neutral conductor of the line. The ATP program is used to model and simulate the behavior of an electrical system considering different degrees of disequilibrium as a reference for the comparison of results. The results show that under certain conditions of disequilibrium the Kron reduction can lead to significant errors in the state variables of the system.
As quantum computing hardware becomes more complex with ongoing design innovations and growing capabilities, the quantum computing community needs increasingly powerful techniques for fabrication failure root-cause analysis. This is especially true for trapped-ion quantum computing. As trapped-ion quantum computing aims to scale to thousands of ions, the electrode numbers are growing to several hundred, with likely integrated photonic components also adding to the electrical and fabrication complexity, making faults even harder to locate. In this work, we used a high-resolution quantum magnetic imaging technique, based on nitrogen-vacancy centers in diamond, to investigate short-circuit faults in an ion trap chip. We imaged currents from these short-circuit faults to ground and compared them to intentionally created faults, finding that the root cause of the faults was failures in the on-chip trench capacitors. This work, where we exploited the performance advantages of a quantum magnetic sensing technique to troubleshoot a piece of quantum computing hardware, is a unique example of the evolving synergy between emerging quantum technologies to achieve capabilities that were previously inaccessible.
Stochastic collocation (SC) is a well-known non-intrusive method of constructing surrogate models for uncertainty quantification. In dynamical systems, SC is especially suited for full-field uncertainty propagation that characterizes the distributions of the high-dimensional solution fields of a model with stochastic input parameters. However, due to the highly nonlinear nature of the parameter-to-solution map in even the simplest dynamical systems, the constructed SC surrogates are often inaccurate. This work presents an alternative approach, where we apply the SC approximation over the dynamics of the model, rather than the solution. By combining the data-driven sparse identification of nonlinear dynamics framework with SC, we construct dynamics surrogates and integrate them through time to construct the surrogate solutions. We demonstrate that the SC-over-dynamics framework leads to smaller errors, both in terms of the approximated system trajectories as well as the model state distributions, when compared against full-field SC applied to the solutions directly. We present numerical evidence of this improvement using three test problems: a chaotic ordinary differential equation, and two partial differential equations from solid mechanics.
Stochastic collocation (SC) is a well-known non-intrusive method of constructing surrogate models for uncertainty quantification. In dynamical systems, SC is especially suited for full-field uncertainty propagation that characterizes the distributions of the high-dimensional solution fields of a model with stochastic input parameters. However, due to the highly nonlinear nature of the parameter-to-solution map in even the simplest dynamical systems, the constructed SC surrogates are often inaccurate. This work presents an alternative approach, where we apply the SC approximation over the dynamics of the model, rather than the solution. By combining the data-driven sparse identification of nonlinear dynamics framework with SC, we construct dynamics surrogates and integrate them through time to construct the surrogate solutions. We demonstrate that the SC-over-dynamics framework leads to smaller errors, both in terms of the approximated system trajectories as well as the model state distributions, when compared against full-field SC applied to the solutions directly. We present numerical evidence of this improvement using three test problems: a chaotic ordinary differential equation, and two partial differential equations from solid mechanics.
The phase transitions and thermodynamics of stoichiometric α-, β-, and γ-UO2(OH)2 polymorphs are investigated using density functional perturbation theory. The pressure-induced β(Pbca) → α(Cmca) phase transition is reproduced by calculations, with a volume reduction of ΔV/V = −14.7% similar to experiment. Consistent with observation, a temperature-driven γ(P21/c) → β(Pbca) phase transition is predicted near 533 K. At 298.15 K, the computed standard molar heat capacity of α-UO2(OH)2 is Cp0 = 112.1 J mol−1 K−1, only 1.6% smaller than the value of Cp0 = 113.96 ± 0.12 J mol−1 K−1 measured by calorimetry. Cp0 = 112.4 and 104.8 J mol−1 K−1 are predicted for the β- and γ-UO2(OH)2 polymorphs, respectively. The calculated molar enthalpy and Gibbs energy functions of the α-, β-, and γ-UO2(OH)2 polymorphs are also reported.
Rare-earth metal-organic frameworks (REMOFs) based on polynuclear metal clusters are an emerging class of materials that have shown promise for CO2 capture and conversion. In this work, copper nanoparticles (CuNPs) were successfully installed on a cluster-based Y(III) MOF to yield a composite material, CuNP-Y-TBAP. The abundance of Cu binding sites on the Y(III) clusters allowed a remarkably high Cu loading to be achieved, and electron microscopy demonstrated that the MOF-supported CuNPs are exceptionally small and monodisperse. CuNP-Y-TBAP was found to be an active heterogeneous catalyst for electrochemical reduction of CO2, yielding CO and CH4 as the primary CO2 reduction products.
Magnetic insulation of electrons prevents losses and can be applied to generating radiation or electron sources for high current and high power applications. Ion emission from the anode may degrade magnetic insulation. We develop equilibrium theory, self-consistently coupling magnetically insulated electron flow with free-flowing injected ions. Generally, ion injection is self-limiting from space charge; however, once insulation strength drops below about 1.2x the magnetic insulation threshold, ion space-charge limits vanish. Further, the gap effectively short-circuits and the electron flow layer asymptotically approaches the anode. In this regime a quasineutral, nonthermal plasma manifests, effectively reducing gap distance and suggesting quasiequilibrium gap closure evolution.
Statistical analysis of tensor-valued data has largely used the tensor-variate normal (TVN) distribution that may be inadequate for data arising from distributions with heavier or lighter tails. We study a general family of elliptically contoured (EC) TV distributions and derive its characterizations, moments, marginal, and conditional distributions. We describe procedures for maximum likelihood estimation from data that are (1) uncorrelated draws from an EC distribution, (2) from a scale mixture of the TVN distribution, and (3) from an underlying but unknown EC distribution, for which we extend Tyler’s robust estimator. A detailed simulation study highlights the benefits of choosing an EC distribution over the TVN for heavier-tailed data. We develop TV classification rules using discriminant analysis and EC errors and show that they better predict cats and dogs from images in the Animal Faces-HQ dataset than the TVN-based rules. A novel tensor-on-tensor regression and TV analysis of variance (TANOVA) framework under EC errors is also demonstrated to better characterize gender, age, and ethnic origin than the usual TVN-based TANOVA in the celebrated labeled faces of the wild dataset.
Scanning electron microscopy (SEM), a century-old technique, is today a ubiquitous method of imaging the surface of nanostructures. However, most SEM detectors simply count the number of secondary electrons from a material of interest, and thereby overlook the rich material information contained within them. Here, by simple modifications to a standard SEM tool, we resolve the momentum and energy information on secondary electrons by directly imaging the electron plume generated by the electron beam of the SEM. Leveraging these spectroscopic imaging capabilities, our technique is able to image lateral electric fields across a prototypical silicon p-n junctions and to distinguish differently doped regions, even when buried beyond depths typically accessible by SEM. Intriguingly, the subsurface sensitivity of this technique reveals unexpectedly strong surface band bending within nominally passivated semiconductor structures, providing useful insights for complex layered component designs, in which interfacial dynamics dictate device operation. These capabilities for noninvasive, multimodal probing of complicated electronic components are crucial in today’s electronic manufacturing but is largely inaccessible even with sophisticated techniques. These results show that seemingly simple SEM can be extended to probe complex and useful material properties.
