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All sources NASA NTRS DOE OSTI

DOE OSTI2016

glmnetLRC f/k/a lrc package: Logistic Regression Classification

Methods for fitting and predicting logistic regression classifiers (LRC) with an arbitrary loss function using elastic net or best subsets. This package adds additional model fitting features to the existing glmnet and bestglm R packages. This package was created to perform the analyses described in Amidan BG, Orton DJ, LaMarche BL, et al. 2014. Signatures for Mass Spectrometry Data Quality. Journal of Proteome Research. 13(4), 2215-2222. It makes the model fitting available in the glmnet and bestglm packages more general by identifying optimal model parameters via cross validation with an customizable loss function. It also identifies the optimal threshold for binary classification.

Source record↗

DOE OSTI2016

glmnetLRC f/k/a lrc package: Logistic Regression Classification

Source record↗

DOE OSTI2021

Identification of crystal plasticity model parameters by multi-objective optimization integrating microstructural evolution and mechanical data

Crystal plasticity models evolve a polycrystalline yield surface using meso-scale descriptions of deformation mechanisms. The activation of deformation mechanisms is governed by crystallography and a set of model parameters, which are typically calibrated through the fitting of mechanical data such as stress–strain curves and elastic lattice strains. Microstructural data such as phase fractions and texture evolution are used for verifying crystal plasticity parameters. In this study, we use a multi-objective genetic algorithm to identify hardening parameters from flow stress curves with an option to incorporate texture into the optimization approach. Robust, generalized objective functions are developed and used to identify sets of parameters pertaining to dislocation density-based hardening laws in visco-plastic and elasto-plastic self-consistent (VPSC and EPSC) homogenization models. First, the parameters are identified for pure Nb directly from texture using an objective function based on generalized spherical harmonics. Since texture evolution is driven by the relative contribution of active slip systems, the parameters governing the evolution of slip resistance ratios can be recovered from fitting discrete textures at a series of strains. Next, a comprehensive set of load reversal data for dual phase (DP) 780 steel is used to fit a hardening law and a back-stress law in EPSC. Finally, parameters pertaining to a complex hardening law for the evolution of slip and twinning in pure α-Ti are identified. Remarkably, using texture as an objective in combination with stress–strain objectives constrains the model of Ti to fully reproduce not only stress–strain and texture evolution but also hierarchical twinning measurements as a function of initial grain size and texture. Furthermore, given an appropriate model fit to representative experimental texture evolution, underlying twin volume fractions contributing to texture evolution can be predicted.

42 ENGINEERING↗

DOE OSTI2021

Sensitivity Analysis of Particle-In-Cell Modeling Parameters in Settling Bed, Bubbling Fluidized Bed and Circulating Fluidized Bed

The objective of the work presented is to perform a preliminary sensitivity analysis of particle-in-cell (PIC) model parameters when applied to settling bed, bubbling fluidized bed, and circulating fluidized bed simulations. These examples correspond to widely different flow conditions commonly seen in chemical engineering applications. Simulations were performed using the PIC method in the open-source software Multiphase Flow with Interphase eXchanges (MFiX) developed by the National Energy Technology Laboratory (NETL). As part of the non-intrusive uncertainty quantification (UQ) analysis, simulation campaigns were generated using Nodeworks. Sampling locations or settings for PIC model parameters were determined using the Latin Hypercube method. Response surfaces were created using radial basis functions (RBF), and Sobol’ indices were estimated to quantify the influence of model parameters on the quantities of interest (QoI). This study marks a first step towards systematically determining optimal ranges for model parameters used in MFiX-PIC. Based on limited experience, it is expected that these values would depend strongly on flow conditions. Given the complexity of the multiphase flow systems under analysis, a non-intrusive UQ based approach is used to identify the most influential parameters in each case. This prior knowledge will help in proposing an effective design of experiments (DoE) and determine optimal parameters through techniques such as deterministic or Bayesian calibration, which will be pursued in the future.

42 ENGINEERING↗

DOE OSTI2026

Validation of the GFS model for gyrokinetic stability of NSTX pedestal data

This study presents a large database validation of the gyro fluid system (GFS) model for linear gyrokinetic stability for high-mode (H-mode) edge transport barrier conditions in the national spherical torus experiment (NSTX) tokamak. The database of linear stability calculations with the CGYRO gyrokinetic code was produced using plasma profile measurements from NSTX discharges to identify kinetic ballooning modes (KBM), trapped electron modes (TEM), and micro-tearing modes (MTM) that limit the pressure profile gradient in the H-mode barrier. A novel Bayesian optimization approach determines optimal resolution parameters for GFS specifically for spherical tokamak pedestal conditions. Our results demonstrate that GFS, with optimized resolution, can achieve accurate linear stability analysis in NSTX pedestal conditions for reduced resolution compared to CGYRO. GFS can accurately find the KBM, TEM, and MTM instability branches. Parametric analysis reveals that GFS accuracy in this extreme pedestal parameter range is degraded for low magnetic shear and near the separatrix conditions. These findings establish GFS as a fast linear eigenmode solver for spherical tokamak pedestal gyrokinetic stability and demonstrate a systematic methodology for determining the optimum resolution settings.

Yang, Minglei [Oak Ridge National Laboratory (ORNL↗

DOE OSTI2019

Exceptional points in classical spin dynamics

Abstract Non-conservative physical systems admit a special kind of spectral degeneracy, known as exceptional point (EP), at which eigenvalues and eigenvectors of the corresponding non-Hermitian Hamiltonian coalesce. Dynamical parametric encircling of the EP can lead to non-adiabatic evolution associated with a state flip, a sharp transition between the resonant modes. Physical consequences of the dynamical encircling of EPs in open dissipative systems have been explored in optics and photonics. Building on the recent progress in understanding the parity-time ( $${\mathscr{P}}{\mathscr{T}}$$ P T )-symmetric dynamics in spin systems, we use topological properties of EPs to implement chiral non-reciprocal transmission of a spin through the material with non-uniform magnetization, like helical magnet. We consider an exemplary system, spin-torque-driven single spin described by the time-dependent non-Hermitian Hamiltonian. We show that encircling individual EPs in a parameter space results in non-reciprocal spin dynamics and find the range of optimal protocol parameters for high-efficiency asymmetric spin filter based on this effect. Our findings offer a platform for non-reciprocal spin devices for spintronics and magnonics.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Computation of Direct Sensitivities of Spatial Multibody Systems With Joint Friction

Abstract Friction exists in most mechanical systems and may have a major influence on the dynamic performance of the system. The incorporation of friction in dynamic systems has been a subject of active research for several years owing to its high nonlinearity and its dependence on several parameters. Consequently, optimization of dynamic systems with friction becomes a challenging task. Gradient-based optimization of dynamical systems is a prominent technique for optimal design and requires the computation of model sensitivities with respect to the design parameters. The novel contribution of this paper is the derivation of the analytical methodology for the computation of direct sensitivities for smooth multibody systems with joint friction using the Lagrangian index-1 formulation. System dynamics have been computed using two different friction models; the Brown and McPhee, and the Gonthier et al. model. The methodology proposed to obtain model sensitivities has also been validated using the complex finite difference method. A case study has been conducted on a spatial multibody system to observe the effect of friction on the dynamics and model sensitivities, compare sensitivities with respect to different parameters and demonstrate the numerical and validation aspects. Since design parameters can have very different magnitudes and units, the sensitivities have been scaled with the parameters for comparison. Finally, a discussion has been presented on the interpretation of the case study results. Due to the incorporation of joint friction, ‘jumps’ or discontinuities are observed in the model sensitivities akin to those observed for hybrid dynamical systems.

Engineering↗

NASA NTRS2008

Effect of Weld Tool Geometry on Friction Stir Welded AA2219-T87 Properties

In this study, flat panels of AA2219-T87 were friction stir welded (FSWed) using weld tools with tapered pins The three pin geometries of the weld tools included: 0 (straight cylinder), 30 , and 60 angles on the frustum. For each weld tool geometry, the FSW process parameters were optimized to eliminate defects. A constant heat input was maintained while varying the process parameters of spindle rpm and travel speed. This provided a constant heat input for each FSW weld panel while altering the hot working conditions imparted to the workpiece. The resulting mechanical properties were evaluated from tensile test results of the FSW joint.

Querin, Joseph A.↗

DOE OSTI2017

Recovery of lithium and cobalt from spent lithium-ion batteries using organic acids: Process optimization and kinetic aspects

Highlights: • Li recovery 99.80%, Co recovery 96.46% in citric acid reductive leaching with H{sub 2}O{sub 2.} • Investigating possibility of bioleaching of LIBs by using 4 organic acids. • Comparing cavitation in ultrasonic agitation with mechanical stirrer. • Co kinetics depends on temperature: both diffusion and chemical reaction controls. •Li kinetics, all temperatures: reagent diffusion through the product layer controls. - Abstract: An environmentally-friendly route based on hydrometallurgy was investigated for the recovery of cobalt and lithium from spent lithium ion batteries (LIBs) using different organic acids (citric acid, Dl-malic acid, oxalic acid and acetic acid). In this investigation, response surface methodology (RSM) was utilized to optimize leaching parameters including solid to liquid ratio (S/L), temperature, acid concentration, type of organic acid and hydrogen peroxide concentration. Based on the results obtained from optimizing procedure, temperature was recognized as the most influential parameter. In addition, while 81% of cobalt was recovered, the maximum lithium recovery of 92% was achieved at the optimum leaching condition of 60 °C, S/L: 30 g L{sup −1}, citric acid concentration: 2 M, hydrogen peroxide concentration: 1.25 Vol.% and leaching time: 2 h. Furthermore, results displayed that ultrasonic agitation will enhance the recovery of lithium and cobalt. It was found that the kinetics of cobalt leaching is controlled by surface chemical reaction at temperatures lower than 45 °C. However, diffusion through the product layer at temperatures higher than 45 °C controls the rate of cobalt leaching. Rate of lithium reaction is controlled by diffusion through the product layer at all the temperatures studied.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

An unstructured mesh based neutronics optimization workflow

We have developed a fully automated workflow to optimize the neutronics performance of the Second Target Station (STS) at the Oak Ridge National Laboratory’s Spallation Neutron Source. The optimization workflow starts with the parametrized solid CAD engineering models and converts them into the unstructured mesh (UM) models for the neutronics calculations with MCNP6.2. Calculations are executed and their results are loaded into the Dakota optimization toolkit. Dakota analyzes the results and proposes new geometry parameters for the next design iteration. The cycle repeats until the optimal parameters are found. The automated CAD to MCNP conversion, the use of high-fidelity UM models, and the use of modern optimizer are the key elements that advance the entire optimization workflow in comparison with the original workflow. The original workflow was based on a simplified constructive solid geometry (CSG) modeling with MCNPX, mcnp_pstudy tool, and an in-house optimizer. Herein to demonstrate the new workflow, we present a case of neutronics optimization of the moderator–reflector assembly (MRA). Apart from the MRA, the workflow can optimize other major STS components, such as the spallation target, neutron beamlines, radiation shielding, and various accelerator components. Importantly, the new workflow opens the door to the advanced multi-physics multi-parameter optimization and has the potential for use in other nuclear physics and accelerator applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Attribute-Guided Adversarial Training for Robustness to Natural Perturbations

We report while existing work in robust deep learning has focused on small pixel-level norm-based perturbations, this may not account for perturbations encountered in several real world settings. In many such cases although test data might not be available, broad specifications about the types of perturbations (such as an unknown degree of rotation) may be known. We consider a setup where robustness is expected over an unseen test domain that is not i.i.d. but deviates from the training domain. While this deviation may not be exactly known, its broad characterization is specified a priori, in terms of attributes. We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space, without having access to the data from the test domain. Our adversarial training solves a min-max optimization problem, with the inner maximization generating adversarial perturbations, and the outer minimization finding model parameters by optimizing the loss on adversarial perturbations generated from the inner maximization. We demonstrate the applicability of our approach on three types of naturally occurring perturbations --- object-related shifts, geometric transformations, and common image corruptions. Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations. We demonstrate the usefulness of the proposed approach by showing the robustness gains of deep neural networks trained using our adversarial training on MNIST, CIFAR-10, and a new variant of the CLEVR dataset.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

MODELLING OF ADDITIVE MANUFACTURING – COMPRESSION MOLDING PROCESS USING COMPUTATIONAL FLUID DYNAMICS

A computational fluid dynamics model has been developed to predict the behavior of a printed strands during a novel material extrusion additive manufacturing and compression molding process. While the traditional additive manufacturing process enables control over the fiber orientation within the part, it would also result in high void content. On the other hand, compression molding produces parts with low porosity levels at rapid processing cycle time but lacks control over the microstructure. The novel additive manufacturing – compression molding (AM-CM) integrating both these processes offers control over both the microstructure and porosity to manufacturing high performance composite parts. The numerical model developed here enables to analyze the effect of processing parameters on the behaviour of printed layer that is subsequently compressed and to determine the optimal printing parameters for high-performance composite part design.

Seta, Berin↗

DOE OSTI2015

Adsorbent Alkali Conditioning for Uranium Adsorption from Seawater. Adsorbent Performance and Technology Cost Evaluation

The Fuel Resources program of the Fuel Cycle Research and Development program of the Office of Nuclear Energy (NE) is focused on identifying and implementing actions to assure that nuclear fuel resources are available in the United States. An immense source of uranium is seawater, which contains an estimated amount of 4.5 billion tonnes of dissolved uranium. This unconventional resource can provide a price cap and ensure centuries of uranium supply for future nuclear energy production. NE initiated a multidisciplinary program with participants from national laboratories, universities, and research institutes to enable technical breakthroughs related to uranium recovery from seawater. The goal is to develop advanced adsorbents to reduce the seawater uranium recovery technology cost and uncertainties. Under this program, Oak Ridge National Laboratory (ORNL) has developed a new amidoxime-based adsorbent of high surface area, which tripled the uranium capacity of leading Japanese adsorbents. Parallel efforts have been focused on the optimization of the physicochemical and operating parameters used during the preparation of the adsorbent for deployment. A set of parameters that need to be optimized are related to the conditioning of the adsorbent with alkali solution, which is necessary prior to adsorbent deployment. Previous work indicated that alkali-conditioning parameters significantly affect the adsorbent performance. Initiated in 2014, this study had as a goal to determine optimal parameters such as base type and concentration, temperature, and duration of conditioning that maximize the uranium adsorption performance of amidoxime functionalized adsorbent, while keeping the cost of uranium production low. After base-treatment at various conditions, samples of adsorbent developed at ORNL were tested in this study with batch simulated seawater solution of 8-ppm uranium concentration, batch seawater spiked with uranium nitrate at 75-100 ppb uranium, and continuous-flow natural seawater at the Pacific Northwest National Laboratory (PNNL). Fourier Transform Infrared (FTIR) spectroscopy, Nuclear Magnetic Resonance (NMR) spectroscopy, Scanning Electron Microscopy (SEM), and elemental analysis were used to characterize the adsorbent at different stages of adsorbent preparation and treatment. The study can be divided into two parts: (A) investigation of optimal parameters for KOH adsorbent conditioning and (B) investigation of other possible agents for alkali conditioning, including cost analysis on the basis of uranium production. In the first part of the study, tests with simulated seawater containing 8 ppm uranium showed that the uranium adsorption capacity increased with an increase in the KOH concentration and conditioning time and temperature at each of the KOH concentrations used. FTIR and solid state NMR studies indicated that KOH conditioning converts the amidoxime functional groups into more hydrophilic carboxylate. The longer the KOH conditioning time, up to three hours, the higher was the loading capacity from the simulated seawater solution which is composed of only uranyl, sodium, chloride, and carbonate ions. Marine testing with natural seawater, on the other hand, showed that the uranium adsorption capacity of the adsorbent increased with KOH conditioning temperature, and gradually decreased with increasing KOH conditioning time from one hour to three hours at 80 C. This behavior is due to the conversion of amidoxime to carboxylate. The carboxylate groups are needed to increase the hydrophilicity of the adsorbent; however, conversion of a significant amount of amidoxime to carboxylate leads to loss in selectivity toward uranyl ions. Thus, there is an optimum KOH conditioning time for each temperature at which an optimum ratio between amidoxime and carboxylate is reached. For the case of base conditioning with 0.44 M KOH at 80 C, the optimal conditioning time is 1 hour, with respect to the highest uranium loading capacity from natural seawater. Uptake of other metal ions such as V, Fe, and Cu follows the same trend as that of uranium. Also, the uptake of Ca, Mg, and Zn ions increased with increasing KOH conditioning time, probably due to formation of more carboxylates, which leads to conversion of uranium-selective binding sites to less selective sites. In the second part of the study, inorganic based reagents such as sodium hydroxide (NaOH), sodium carbonate (Na 2 CO 3 ), cesium hydroxide (CsOH), as well as organic based reagents such as ammonium hydroxide (AOH), tetramethylammonium hydroxide (TMAOH), tetraethylammonium hydroxide (TEAOH), triethylmethylammonium hydroxide (TEMAOH), tetrapropylammonium hydroxide (TPAOH) and tetrabutylammonium hydroxide (TBAOH), in addition to KOH, were used for alkaline conditioning. NaOH has emerged as a better reagent for alkaline conditioning of amidoxime-based adsorbent because of higher uranium uptake capacity, higher uranium uptake selectivity ...

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1971

Two examples in the time optimal control theory of distributed parameter systems.

The behavior of hyperbolic and parabolic partial differential equations is contrasted by studying the point-to-point time-optimal control problem for the equation of heat conduction and the equation of motion of a vibrating string. A maximal principle is obtained for the time-optimal control of the one-dimensional heat equation, and it is proven that time optimal controls are weakly bang-bang. The bang-bang principle is proven to be invalid for hyperbolic equations because of the finite speed of wave propagation. In the case of boundary value control of the vibrating spring, the latter is demonstrated by deriving an explicit formula for the time optimal control.

Quinn, J. P.↗

DOE OSTI2021

Chemical looping air separation with Sr 0.8 Ca0.2Fe 0.9 Co 0.1 O 3-δ perovskite sorbent: Packed bed modeling, verification, and optimization

Chemical looping air separation (CLAS) represents a promising approach for efficient O 2 production from the air. This present study aims at optimizing the absorber/desorber operations and the separation process with extensive experimental validation. Specifically, a one-dimensional packed bed model was developed to investigate the CLAS operation with a Sr 0.8 Ca 0.2 Fe 0.9 Co 0.1 O 3-δ perovskite sorbent. The redox thermodynamics of perovskite sorbent was measured by TGA and then incorporated into a linear driving force model to describe the O 2 absorption and desorption rates. Both 4-step and 5-step air separation cycle configurations, with various cyclic structures, were performed in a subpilot-scale packed bed. The model predicted O2 purity and productivity were consistent with experimental results, supporting its accuracy and applicability. Parametric analysis and multi-objective optimization were further carried out to assess the performance of CLAS. Both O 2 purity and recovery increased monotonically with the cycle time, airflow rate, steam flow rate, and absorption pressure. Meanwhile, optimal O 2 productivity and power consumption can only be achieved by specific combinations of these parameters. The optimized results showed that CLAS can be highly competitive when compared to conventional pressure swing adsorption (PSA) or cryogenic distillation. The 5-step cycle configuration achieved a minimum power consumption of 118 kW·h for producing 1 ton O 2 with ≥ 95% purity. The maximum O 2 productivity reached 0.0932 g O2 /(g sorbent ·h) with 390 kW·h/ton O 2 of energy consumption (95% pure). The optimization results also indicate that CLAS can potentially be more efficient than cryogenic distillation even when the required O 2 purity is above 99%.

42 ENGINEERING↗

DOE OSTI2022

Modeling perennial bioenergy crops in the E3SM land model (ELMv2)

Perennial bioenergy crops are increasingly important for the production of ethanol and other renewable fuels, and as part of an agricultural system that alters the climate through its impact on biogeophysical and biogeochemical properties of the terrestrial ecosystem. Few earth system models (ESMs) represent such crops, however. In this study, we expand the Energy Exascale Earth System Model (E3SM) Land Model (ELMv2) to include perennial bioenergy crops with a high potential for mitigating climate change. We focus on high-productivity miscanthus and switchgrass, estimating various parameters associated with their different growth stages and performing a global sensitivity analysis to identify and optimize these parameters. The sensitivity analysis identifies five parameters associated with phenology, carbon/nitrogen allocation, stomatal conductance, and maintenance respiration as the most sensitive parameters for carbon and energy fluxes. We calibrated and validated the model against observations and found that the model closely captures the observed seasonality and the magnitude of carbon fluxes. The validated model represents the latent heat flux fairly well, but sensible heat flux for miscanthus is not well captured. Finally, we validated the model against observed leaf area index (LAI) and harvest amount and found modeled LAI captured observed seasonality , although the model underestimates LAI and harvest amount. This work provides a foundation for future ESM analyses of the interactions between perennial bioenergy crops and carbon, water, and energy dynamics in the larger earth system, and sets the stage for studying the impact of future biofuel expansion on climate and terrestrial systems.

09 BIOMASS FUELS↗

DOE OSTI2017

Nearly optimal quantum control: an analytical approach

We propose here nearly optimal control strategies for changing the states of a quantum system. We argue that quantum control optimization can be studied analytically within some protocol families that depend on a small set of parameters for optimization. This optimization strategy can be preferred in practice because it is physically transparent and does not lead to combinatorial complexity in multistate problems. Finally, as a demonstration, we design optimized control protocols that achieve switching between orthogonal states of a naturally biased quantum two-level system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1994

Integrated design and manufacturing for the high speed civil transport

In June 1992, the School of Aerospace Engineering at Georgia Tech was awarded a three year NASA University Space Research Association (USRA) Advanced Design Program (ADP) grant to address issues associated with the Integrated Design and Manufacturing of High Speed Civil Transport (HSCT) configurations in its graduate Aerospace Systems Design courses. This report provides an overview of the on-going Georgia Tech initiative to address these design/manufacturing issues during the preliminary design phases of an HSCT concept. The new design methodology presented here has been incorporated in the graduate aerospace design curriculum and is based on the concept of Integrated Product and Process Development (IPPD). The selection of the HSCT as a pilot project was motivated by its potential global transportation payoffs; its technological, environmental, and economic challenges; and its impact on U.S. global competitiveness. This pilot project was the focus of each of the five design courses that form the graduate level aerospace systems design curriculum. This year's main objective was the development of a systematic approach to preliminary design and optimization and its implementation to an HSCT wing/propulsion configuration. The new methodology, based on the Taguchi Parameter Design Optimization Method (PDOM), was established and was used to carry out a parametric study where various feasible alternative configurations were evaluated. The comparison criterion selected for this evaluation was the economic impact of this aircraft, measured in terms of average yield per revenue passenger mile ($/RPM).

Lee, Jae Moon↗