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DOE OSTI2020

Preliminary Target Selection for the DESI Luminous Red Galaxy (LRG) Sample

The DESI survey will observe more than 8 million candidate luminous red galaxies (LRGs) in the redshift range 0.3 < z < 1.0. In this work, we present a preliminary version of the DESI LRG target section developed using Legacy Surveys Data Release 8 g, r, z and W1 photometry. This selection yields a sample with a uniform surface density of ~600 deg –2 and very low predicted stellar contamination and redshift failure rates. During DESI Survey Validation, updated versions of this selection will be tested and optimized.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Learning planar Ising models

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the best planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Full-Spectrum-Analysis Isotope ID

FSAIsotopeID analyzes gamma ray spectra to identify radioactive isotopes (radionuclides). The algorithm fits the entire spectrum with combinations of pre-computed templates for a comprehensive set of radionuclides with varying thicknesses and compositions of shielding materials. The isotope identification algorithm is suitable for the analysis of spectra collected by gamma-ray sensors ranging from medium-resolution detectors, such a NaI, to high-resolution detectors, such as HPGe. In addition to analyzing static measurements, the isotope identification algorithm is applied for the radiation search applications. The search subroutine maintains a running background spectrum that is passed to the isotope identification algorithm, and it also selects temporal integration periods that optimize the responsiveness and sensitivity. Gain stabilization is supported for both types of applications.

Mitchell, Dean J.↗

DOE OSTI2017

Development of High Temperature Out-of-Pile Experiments for Testing Nuclear Thermal Propulsion Fuel Surrogates

Nuclear thermal propulsion (NTP) technology is designed to provide “in-space” vehicle propulsion by deriving thrust from a hydrogen working fluid that is heated from a nuclear reaction. Initial NTP fuel structures (primarily carbon/carbide based materials) developed by the Rover/NERVA program were designed to withstand temperatures exceeding 2400 K and remain intact after tens of firing cycles. Current programs responsible for development of this technology expect the fuels to withstand temperatures in excess of 2550K under a similar duty cycle. Experiments that subject fuels to these conditions are being developed to provide data and performance information for optimization and potential down-selection of the candidate materials. Initial tests will be designed for laboratory benchtop operation and will not expose surrogate specimens to the hydrogen working fluid or irradiation fields. The primary focus of these out-of-pile experiments will be to study thermal performance and thermal failure limits of the candidate specimen materials.

Howard, Richard↗

DOE OSTI2016

Full-duplex ultrasonic through-wall communication and power delivery system with frequency tracking

A method and apparatus provide simultaneous bi-directional communications through a barrier using a pair of piezoelectric transducers. Outside-to-inside transmission of differentially encoded binary data is only sampled at the inside when a known repeated bit in a Barker sequence is transmitted in the other direction. Differentially encoded data is translated based on the absolute value of differences between each signal envelope sample and the previous signal envelope sample. Inside-to-outside data is transmitted by varying an electrical load on the inside transducer to vary the proportion of a continuous wave signal it reflects. The reflected portion of the continuous wave changes the electrical impedance of the outside transducer and this change is measured and interpreted as binary data. Two frequency selection algorithms select minimally sufficient and then optimal frequencies for power and data transmission by a frequency stepping process.

Lawry, Tristan J.↗

DOE OSTI2023

Systems and methods for hierarchical multi-objective optimization

A method for optimizing objective functions can include selecting an objective function based at least on a hierarchy, applying parameters to the objective function to generate an output, responsive to the output not satisfying a tolerance condition, assigning a penalty to the set of parameters and evaluating a convergence condition using the set of parameters and the penalty, responsive to the output satisfying the tolerance condition, evaluating an additional objective function using the parameters in an order corresponding to the hierarchy or evaluating the convergence condition responsive to the selected objective function being a final objective function, modifying the set of parameters using a genetic algorithm responsive to the set of parameters not satisfying the convergence condition, and outputting the set of parameters responsive to the set of parameters satisfying the convergence condition.

Chan, Henry↗

DOE OSTI2024

Ensemble variational Fokker-Planck methods for data assimilation

Particle flow filters solve Bayesian inference problems by smoothly transforming a set of particles into samples from the posterior distribution. Particles move in state space under the flow of an McKean-Vlasov-Itˆo process. This work introduces the Variational Fokker-Planck (VFP) framework for data assimilation, a general approach that includes previously known particle flow filters as special cases. The McKean-Vlasov-Itˆo process that transforms particles is defined via an optimal drift that depends on the selected diffusion term. It is established that the underlying probability density - sampled by the ensemble of particles - converges to the Bayesian posterior probability density. For a finite number of particles the optimal drift contains a regularization term that nudges particles toward becoming independent random variables. Based on this analysis, we derive computationally-feasible approximate regularization approaches that penalize the mutual information between pairs of particles, and avoid particle collapse. Moreover, the diffusion plays a role akin to a particle rejuvenation approach that aims to alleviate particle collapse. The VFP framework is very flexible. Different assumptions on prior and intermediate probability distributions can be used to implement the optimal drift, and localization and covariance shrinkage can be applied to alleviate the curse of dimensionality. A robust implicit-explicit method is discussed for the efficient integration of stiff McKean- Vlasov-Itˆo processes. Here, the effectiveness of the VFP framework is demonstrated on three progressively more challenging test problems, namely the Lorenz ’63, Lorenz ’96 and the quasi-geostrophic equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Simplified Two-Stage Model Predictive Control for a Hybrid Multilevel Converter With Floating H-Bridge

This paper proposes a simplified two-stage model predictive control (ST-MPC) for a hybrid multilevel converter, which is an active-neutral-point-clamped converter with floating H-bridge (ANPC-H). The objective of the first stage is to select the voltage vector that has the optimal current tracking performance by using a novel geometrical positioning approach in the complex plane. The second stage selects the best switching state among all the available switching states that belong to the same voltage vector obtained in the first stage, to balance dc capacitor voltages and reduce the common mode voltage. The proposed ST-MPC can dramatically reduce the computational burden and ensure the best current tracking by the two-stage structure, such that the execution time is much shorter compared with the conventional MPC. In addition, the geometrical positioning approach in the first stage is generic and can be applicable for any multilevel converters with N-level output; thus, this ST-MPC can be applied for both sevenand nine-level operation of the hybrid ANPC-H converter under different dc voltage ratios. Both simulation results and experimental results obtained on a silicon carbide hybrid ANPC-H converter prototype validate the feasibility and effectiveness of the proposed ST-MPC strategy.

42 ENGINEERING↗

DOE OSTI2026

Trends and limits of CO 2 capture in solid and liquid sorbents at standard conditions

Carbon capture and storage (CCS) plays a critical role in achieving climate change mitigation targets, offering a pathway to decarbonize power generation, industrial processes, and heat production while addressing atmospheric CO 2 removal. While CCS technologies are technically advanced, the widespread adoption of 100 % CO 2 capture capacities such as 1 mol of CO 2 /mol of material and 1 g CO 2 /g storage (targeted by the DARPA, Defense Sciences Office, USA Govt.) has raised questions about the feasibility of achieving higher capture capacities. In the context of limiting global warming to 1.5°C, reaching 100 % CO 2 capture capacity is increasingly necessary, with residual emissions requiring complementary carbon dioxide removal (CDR) technologies. This review exclusively focuses on the CO 2 capture capacities of various sorbents under standard conditions, using different evaluation metrics. This study explores the performance of solid and liquid sorbents under standard conditions, analyzing factors including surface area, pore structure, solvent type, and functionalization to identify materials optimized for industrial-scale CCS applications. Emerging sorbents, including ILs, MOFs, COFs, POPs, DES, RCC, hybrid materials, and reactive sorbents, offer significant potential for enhanced selectivity and energy-efficient regeneration. Through a systematic assessment of gravimetric, volumetric, and molar capacities, the study provides insights into material efficiencies and trade-offs, offering guidance on optimizing sorbent selection for specific applications. The research advances understanding of scalable CCS technologies, contributing to global efforts to achieve net-zero emissions and address the pressing challenge of climate change.

Absorption↗

DOE OSTI2019

Bayesian modeling of inconsistent plastic response due to material variability

The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. To account for material response variability through variations in physical parameters, we adapt a recent Bayesian embedded modeling error calibration technique. We use Bayesian model selection to determine the most plausible of a variety of plasticity models and the optimal embedding of parameter variability. To expedite model selection, we develop an adaptive importance-sampling-based numerical integration scheme to compute the Bayesian model evidence. In conclusion, we demonstrate that the new framework provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.

36 MATERIALS SCIENCE↗

DOE OSTI2017

CYCLE MODELING AND OPTIMIZATION OF AN INTEGRALLY GEARED sCO2 COMPANDER

This paper presents the selection of a system configuration and off-design control method for an integrally geared compander based on cycle modeling and optimization. The goal of the cycle modeling was to determine a cycle configuration that would reach an efficiency of 50% at design conditions and to optimize off-design control to maintain high efficiencies. The compander is being developed for use in a concentrated solar power supercritical carbon dioxide power plant with expected turbine inlet temperatures of 705°C and utilizing dry cooling leading to compressor inlet temperature varying between 35°C and 55°C. The compander is unique as it consists of eight turbomachinery stages on four pinions all being driven by a single bull gear. The separate stages offer the opportunity to consider a variety of flow splits and cycle configurations including intercooling and multiple stages of reheat. Cycle modeling was conducted in two stages: on-design and off-design. On-design modeling was simulated with all components operating at their design point. This was used to compare the performance of different cycle configurations and design temperatures. Off-design modeling was then performed to investigate the temperature dependence of the cycle efficiency and power output and to develop a control strategy. Strategies considered and discussed include: turbine bypass, compressor recycle, inlet guide vanes, and inventory control. To determine the best operating conditions for each configuration and control strategy, a genetic algorithm was implemented to optimize the cycle performance across the range of operating temperatures being considered. The final selection of cycle configuration, design temperature and control strategy is also presented.

bennett, jeffrey↗

DOE OSTI2019

Ideal isotropic auxetic networks from random networks

We design and create isotropically auxetic networks with Poisson's ratio close to −1 in experiments. To achieve this, we selectively prune bonds from disordered networks, and then optimize the properties of individual bonds and nodes.

Reid, Daniel R.↗

DOE OSTI2018

Current capabilities of the residual stress diffractometer at the high flux isotope reactor

The engineering diffractometer 2nd Generation Neutron Residual Stress Facility (NRSF2) at the Oak Ridge National Laboratory’s High Flux Isotope Reactor was built specifically for the mapping of residual strains. NRSF2 is optimized to investigate a wide range of engineering materials by providing the user a selection of monochromatic neutron wavelengths to maintain the selected Bragg reflection near 2θ = 90°, which is the optimal scattering geometry for strain mapping. Here in this work, details of the instrument configuration and operation are presented, and considerations for experimental planning are also discussed. Selected examples of recent residual stress work completed with NRSF2 are presented to highlight capabilities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant

Abstract. Optimizing turbine layout is a challenging problem that has been extensively researched in the literature. However, optimizing the number of turbines within a given boundary has not been studied as extensively and is a difficult problem because it introduces discrete design variables and a discontinuous design space. An essential step in performing wind power plant layout optimization is to define the objective function, or value, that is used to express what is valuable to a wind power plant developer, such as annual energy production, cost of energy, or profit. In this paper, we demonstrate the importance of selecting the appropriate objective function when optimizing a wind power plant in a land-constrained site. We optimized several different wind power plants with different wind resources and boundary sizes. Results show that the optimal number of turbines varies drastically depending on the objective function. For a simple, one-dimensional, land-based scenario, we found that a wind power plant optimized for minimal cost of energy produced just 72 % of the profit compared to the wind power plant optimized for maximum profit, which corresponded to a loss of about USD 2 million each year. This paper also compares the performance of several different optimization algorithms, including a novel repeated-sweep algorithm that we developed. We found that the performance of each algorithm depended on the number of design variables in the problem as well as the objective function.

17 WIND ENERGY↗

DOE OSTI2023

A Comparative Study of Electrical Double Layer Effects for CO Reduction Reaction Kinetics

Solvation models describe how the interactions between the solutes and solvents affect the reactivity and selectivity in electrochemical processes. In this study, we developed a framework for evaluating the effects of applied potential and electrical double layer on CO reduction (COR), comparing fully explicit, implicit, and hybrid solvation models at the standard hydrogen electrode (SHE) scale. We analyzed all crucial intermediates leading to the production of C 1 and C 2 products and found good agreement across these models. Some notable differences were observed in the implicit description of *C and *CO adsorption at higher overpotentials and overall trends in the adsorption energies within the hybrid model. Using this unified SHE framework, we built comparable microkinetic models for COR kinetics and rates. Despite small differences in thermodynamics, solvation-model-based microkinetic simulations showed good agreement for onset potentials against the benchmark experiment. Only qualitative difference was observed for C 1+ versus hydrogen evolution at high overpotentials for the implicit model. Finally, we constructed a generalized C 2 selectivity map in descriptor space (U SHE , ΔG electrolyte CO ), which highlights the limitations of a copper-based COR catalyst and guides the search for optimal descriptor parameters to maximize C 2 selectivity. In conclusion, these findings demonstrate the importance of considering electrical double layer effects in reduction reactions and offer a useful framework for comparing solvation models and predicting optimal electrochemical conditions for specific applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

In Vitro Selection of Antibodies Targeting Yersinia pestis Membrane Lipids Using Nanodisc-Based Antigen Presentation

Proteins are the most common targets for antibody discovery and vaccine development, but their sequence variability can limit the breadth of resulting antigens. Lipids represent an alternative class of antigens due to their structural conservation and roles in host–pathogen interactions. Here, we describe the development and optimization of an in vitro antibody selection workflow using lipid-containing nanodiscs as antigen presentation platforms to enable phage and yeast display selections under conditions adapted for these non-protein targets. Lipopolysaccharide (LPS) nanodiscs were first used as a model system to evaluate selection strategies, including competitive and subtractive approaches to reduce non-specific binders, yielding peptide and single-chain variable fragment (scFv) binders that were affinity matured to improve binding signals. The same approach was subsequently used to select scFv antibodies that recognize lipid nanodiscs prepared from Yersinia pestis membrane lipid extracts. These antibodies show binding to lipid nanodiscs derived from Y. pestis, with evidence of selectivity relative to control nanodiscs. Overall, this work establishes a workflow for antibody selection against lipid-containing nanodisc antigens and highlights practical considerations associated with these targets. The approach may be useful for generating affinity reagents to membrane-associated lipids, although further characterization is required to define antigen specificity and functional activity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Understanding Selection And Diversity For Evolution Of Spiking Recurrent Neural Networks

Evolutionary optimization or genetic algorithms have been used to optimize a variety of neural network types, including spiking recurrent neural networks, and are attractive for many reasons. However, a key impediment to their widespread use is the potential for slow training times and failure to converge to a good fitness value in a reasonable amount of time. In this work, we evaluate the effect of different selection algorithms on the performance of an evolutionary optimization method for designing spiking recurrent neural networks, including those that are meant to be deployed in a neuromorphic system. We propose a selection approach that utilizes a richer understanding of the fitness of an individual network to inform the selection process and to promote diversity in the population. We show that including this feature can provide a significant increase in performance over utilizing a standard selection approach.

Schuman, Catherine↗

DOE OSTI2019

Passivation, conductivity, and selectivity in solar cell contacts: Concepts and simulations based on a unified partial-resistances framework

Passivation, conductivity, and selectivity are often acknowledged as the three requirements for optimal contacts to photovoltaic solar cells. Although there are generally accepted definitions and metrics for passivation and conductivity, a common understanding of the concept of selectivity is emerging only now. In this contribution, we present a generalized model of solar cell contacts based on the distinct lumped resistances encountered by electrons and holes traversing a contact, which we refer to as partial specific contact resistances. The relations between electron and hole partial current densities, quasi-Fermi level separation, and external voltage are derived from these partial specific contact resistances, leading to simple metrics for the aforementioned contact properties: the sum of the electron and hole resistances is a metric for passivation, their ratio is a metric for selectivity, and the majority-carrier resistance is a metric for conductivity. Using PC1D, we validate our model by simulating 10 500 cases of homojunction contacts to crystalline silicon solar cells, although our framework is material agnostic and can be equally applied to any other type of absorber. In these simulations, the hole contact and absorber are assumed to be ideal, whereas we vary the partial specific contact resistances in the electron contact by orders of magnitude by adjusting the electron and hole mobilities, their densities (through variations of the donor doping density), and the contact thickness. The simulations confirm the finding of the model that, when the contact fraction cannot be adjusted—as is the case with full-area contacts—combined passivation and conductivity are necessary and sufficient for optimal solar cell performance, and they imply selectivity. However, the reciprocal is not true: contacts can be selective but lack conductivity—causing a deleterious drop in fill factor—or can be selective but provide poor passivation—leading to a reduction in implied open-circuit voltage and, hence, actual open-circuit voltage. Furthermore, selectivity is a meaningful metric in the sole case of partial-area contacts, where the contact fraction can be adjusted arbitrarily.

14 SOLAR ENERGY↗