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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Quantum Computational Determination of the Weak Mixing Angle in the Standard Model

The weak mixing angle $s_W$ is a fundamental constant in the Standard Model (SM) and measured at the $Z$ boson mass to be $\widehat{s}^2_W(m_Z) = 0.23129 \pm 0.00004$ in the $\overline{\rm MS}$ renormalization scheme, where $m_Z=91.2\ \text{GeV}$. On the other hand, non-stabilizerness - the magic - characterizes the computational advantage of a quantum system over classical computers. We consider the production of magic from stabilizer initial states, which carry zero magic, in the 2-to-2 scattering of charged leptons in the SM at the tree level, which is mediated by the photon and the $Z$ boson. Using the second order stabilizer Rényi entropy, and averaging over all 60 initial stabilizer states and the scattering angle, we compute and minimize the magic production as a function of $s^2_W$ in the Møller scattering $e^-e^-\to e^-e^-$, which is free of kinematic thresholds. At the centre-of-mass energy $\sqrt{s}=m_Z$, there is a unique minimum in magic production at $\mathbf{s}^{2}_W(m_Z)=0.2317$, which agrees with the measured $\widehat{s}^2_W(m_Z)$ at the sub-percent level. At higher energies, the magic-minimizing $\mathbf{s}^{2}_W$ continues to agree with the empirical value at the percent level or better, up to 10 TeV. The finding suggests the electroweak sector of the SM tends to generate minimal quantum resources from the computational viewpoint.

Liu, Qiaofeng [Northwestern U.]↗
DOE OSTI2018

Efficient Cytosolic Delivery Using Crystalline Nanoflowers Assembled from Fluorinated Peptoids

The design and synthesis of biocompatible nanomaterials as cargoes for the intracellular delivery of therapeutic proteins or genes have attracted intense attention because of their promise for use in therapeutics. Despite the advances in this area, very few nanomaterials can be efficiently delivered to the cytosol. To address these challenges, we designed and synthesized crystalline nanoflower-like particles from fluorinated sequence-defined peptoids; the crystallinity and fluorination of these particles enabled highly efficient cytosolic delivery with minimal cytotoxicity. We delivered these particles, 80% appear to diffuse in the cytosol. Furthermore, these nanocrystals can carry mRNA for instance and effectively delivered into cytosol for gene transfection, demonstrating the generality of our nanocrystals. Our results indicate that self-assembly of crystalline nanomaterials from fluorinated peptoids paves a new way toward development of nanocargoes with efficient cytosol gene delivery. Y. Song and M. Wang contributed equally to this work. This work was supported by start-up funds from Washington State University. Peptoid synthesis work was supported by the Materials Synthesis and Simulation Across Scales (MS3) Initiative through the LDRD fund at Pacific Northwest National Laboratory (PNNL). Assembly of peptoid particles and their structural characterizations were supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Biomolecular Materials Program at PNNL. XRD work was conducted at the Advanced Light Source with support from the Molecular Foundry, at Lawrence Berkeley National Laboratory, both of which are supported by the Office of Science, under Contrast No. DE-AC02-05CH11231. PNNL is multi-program national laboratory operated for Department of Energy by Battelle under Contract No. DE-AC05-76RL01830.

peptoids, nanocrystal, cytosol delivery, endosome ↗
DOE OSTI2021

A Consistent BGK Model with Velocity-Dependent Collision Frequency for Gas Mixtures

Abstract We derive a multi-species BGK model with velocity-dependent collision frequency for a non-reactive, multi-component gas mixture. The model is derived by minimizing a weighted entropy under the constraint that the number of particles of each species, total momentum, and total energy are conserved. We prove that this minimization problem admits a unique solution for very general collision frequencies. Moreover, we prove that the model satisfies an H-Theorem and characterize the form of equilibrium.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

A descent algorithm for generalized complementarity problems based on generalized Fischer-Burmeister functions

We study an unconstrained minimization approach to the generalized complementarity problem GCP(f, g) based on the generalized Fischer-Burmeister function and its generalizations when the underlying functions are C{sup 1}. Also, we show how, under appropriate regularity conditions, minimizing the merit function corresponding to f and g leads to a solution of the generalized complementarity problem. Moreover, we propose a descent algorithm for GCP(f, g) and show a result on the global convergence of a descent algorithm for solving generalized complementarity problem. Finally, we present some preliminary numerical results. Our results further give a unified/generalization treatment of such results for the nonlinear complementarity problem based on generalized Fischer-Burmeister function and its generalizations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On convergent finite difference schemes for variational–PDE-based image processing

We study an adaptive anisotropic Huber functional-based image restoration scheme. Using a combination of L2–L1 regularization functions, an adaptive Huber functional-based energy minimization model provides denoising with edge preservation in noisy digital images. We study a convergent finite difference scheme based on continuous piecewise linear functions and use a variable splitting scheme, namely the Split Bregman (In: Goldstein and Osher, SIAM J Imaging Sci 2(2):323–343, 2009) algorithm, to obtain the discrete minimizer. Experimental results are given in image denoising and comparison with additive operator splitting, dual fixed point, and projected gradient schemes illustrates that the best convergence rates are obtained for our algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Sparse approximation of fitting surface by elastic net

The goal of this paper is to develop a computational model and corresponding efficient algorithm for obtaining a sparse representation of the fitting surface to the given scattered data. The basic idea of the model is to utilize the principal shift invariant space and the balanced l{sub 1},l{sub 2} norm minimization (named elastic net). The elastic net can be solved efficiently by an adapted split Bregman iteration algorithm. Numerical experiments indicate that by choosing appropriate regularization parameters, the model can efficiently provide an acceptable compromise between the minimization of the data mismatch term and the sparsity of the solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Indirect Probe of Quantum Gravity using Molecular Wave-packets

The most obvious obstacle behind a direct test of quantum gravity (QG) is its energy scale (1019 GeV), which remains well outside of any human made machine. The next best possible approach is to provide indirect tests on effective theories of QG which can be performed in a lower energy scale. This paper is aimed in this direction, and shows a promising path to test the existence of the fundamental minimal length scale of Nature by measuring the dispersion of free, large molecular wave-packets. The existence of the minimal length is believed to be the reason for a modified commutation relationship between the position and momentum operators and, in this paper, we show that such a modification of the commutator has a profound effect on the dispersion rate of free wave-packets, and precise measurement on the broadening times of large molecular wave-packets (such as C 60, C 176 and large organic molecules) provide a promising path for an indirect test of quantum gravity, in a laboratory setting.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

LongBondEliminator: A Molecular Simulation Tool to Remove Ring Penetrations in Biomolecular Simulation Systems

We develop a workflow, implemented as a plugin to the molecular visualization program VMD, that can fix ring penetrations with minimal user input. LongBondEliminator, detects ring piercing artifacts by the long, strained bonds that are the local minimum energy conformation during minimization for some assembled simulation system. The LongBondEliminator tool then automatically treats regions near these long bonds using multiple biases applied through NAMD. By combining biases implemented through the collective variables module, density-based forces, and alchemical techniques in NAMD, LongBondEliminator will iteratively alleviate long bonds found within molecular simulation systems. Through three concrete examples with increasing complexity, a lignin polymer, an viral capsid assembly, and a large, highly glycosylated protein aggrecan, we demonstrate the utility for this method in eliminating ring penetrations from classical MD simulation systems. The tool is available via gitlab as a VMD plugin, and has been developed to be generically useful across a variety of biomolecular simulations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Geometric Estimation of Multivariate Dependency

This paper proposes a geometric estimator of dependency between a pair of multivariate random variables. The proposed estimator of dependency is based on a randomly permuted geometric graph (the minimal spanning tree) over the two multivariate samples. This estimator converges to a quantity that we call the geometric mutual information (GMI), which is equivalent to the Henze–Penrose divergence. between the joint distribution of the multivariate samples and the product of the marginals. The GMI has many of the same properties as standard MI but can be estimated from empirical data without density estimation; making it scalable to large datasets. The proposed empirical estimator of GMI is simple to implement, involving the construction of an minimal spanning tree (MST) spanning over both the original data and a randomly permuted version of this data. We establish asymptotic convergence of the estimator and convergence rates of the bias and variance for smooth multivariate density functions belonging to a Hölder class. We demonstrate the advantages of our proposed geometric dependency estimator in a series of experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM

Demand response (DR) serves an important role in improving the efficiency and stability of power systems. In recent years, with advances in communication and smart device technologies, many aggregators have emerged to facilitate end customer participation in DR programs. These aggregators, equipped with customized optimal control algorithms, are capable of providing various grid services. Among them is load scheduling during DR events, namely following a load signal provided by the utility company while minimizing overall customer discomfort. However, as the number of aggregators keeps increasing, it becomes challenging for utility companies to conduct load scheduling for multiple aggregators and generate reference signals for each of them. This paper proposes an optimization framework using hierarchical alternating direction method of multipliers (H-ADMM) to optimally generate load following signals for multiple aggregators. Under this framework, utility and multiple aggregators work in a cooperative manner, aiming at minimizing an overall system cost from different levels of the power system hierarchy, while protecting user privacy. A case study has been conducted in a system with multiple aggregators, based on control of HVAC loads. Experimental results validate the effectiveness of the proposed algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Optimizing the Pyrolysis of Polypropylene and High-Density Polyethylene for Contaminated Solid Waste Treatment - 18537

Organic waste is generally characterized by high volume-to-weight ratio. Organic waste contaminated with radionuclides may further pose a hazard due to radiolysis and generation of combustible and explosive gases. Thus, minimizing waste volume as well as forming a chemically stable and conditioned matrix are of high importance in the treatment of contaminated polymers. In the present study, the pyrolytic decomposition of the commonly used polyolefins, polypropylene (PP) and high-density polyethylene (HDPE), was explored as a method for waste minimization by obtaining high gaseous and low char and liquid yields. The effects of two aluminosilicate catalysts, Zeolite Y and ZSM-5, and reaction temperature on decomposition efficiency were evaluated based on product conversions and gas composition. Under pyrolytic conditions, PP decomposition resulted in higher decomposition efficiency than HDPE. Over the catalysts, pyrolysis of the polymers over ZSM-5 resulted in higher gas conversions than over Zeolite Y, with a more significant impact on HDPE than PP. Increasing the reaction temperature from 450 deg. C to 525 deg. C also resulted in increased polymer decomposition. However, differences in gas composition, including olefinic to paraffinic ratio and carbon number distribution, support that the catalysts and the temperature impact polymer decomposition by different and opposing mechanisms. This conclusion was further supported by the finding that the decomposition efficiency was not significantly enhanced in the presence of the catalyst at 525 deg. C. Taken together, our study highlights the advantage of using ZSM-5 and optimizing the reaction temperature for treatment of contaminated organic solid waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Stakeholder Engaged Structured Decision Making to Improve Performance and Reduce Costs While Protecting Human Health and the Environment - 18645

The objective of the Best Practice is to facilitate a paradigm shift when it comes to approaches to the decision making process for nuclear waste management, disposal, and remediation decisions. The stakeholder-engaged structured decision making (SDM) paradigm shift provides a transparent framework for developing optimal solutions to complex problems (Keeney, 1992, Gregory et al, 2012). This is a deliberative-analytical process. The deliberative side centers on understanding stakeholder concerns and values, and developing objectives while identifying options that may achieve those objectives. SDM is implemented with the aid of computer tools that are aimed specifically at capturing these deliberative aspects of a decision analysis. SDM captures this information in a structured format that allows access to the values, objectives, and options that have been identified to help. Through this approach we achieve transparency, traceability, and reproducibility. The goal of SDM at this stage is to provide a formal structure for capturing and processing deliberative information. The deliberative part sets the stage for the analytical part of SDM. A variety of Subject matter experts (SMEs) are engaged in how to evaluate the options through the objectives. Objectives often include minimizing human health risk and minimizing cost, but in the full scope of a sustainability-based approach to decision making, it can include objectives related to economic, environmental and social issues. Subject matter experts might provide other options for achieving the objectives, but their primary role is to evaluate/model the options to the endpoint defined by the objectives or in other words perform a consequence analysis. The structure of SDM makes it clear exactly what is needed from the SMEs, because the options are identified, and the endpoint (objectives) are defined - evaluations and/or models are needed to connect options to objectives. This consequence analysis completes the evaluation, and directly addresses which of the options is the best option. The same SDM approach to finding the best options can also be used for prioritization and resource allocation. Because all of the information is captured in a formal system with the help of computer tools (a software framework for implementing SDM), the decision models that are developed for an application can be fully evaluated numerically and for the insights gained from using a formal process for managing the multiple factors that need to be considered for complex decisions. This includes uncertainty and sensitivity analysis that can be used to guide the need for further data/information collection if the optimal decision is not adequately supported - that is, if there is insufficient confidence in the decision. Also, this feeds directly into adaptive management therefore if more data/information are collected then the efficacy of the current decision can be evaluated, potentially leading to a change in decisions if warranted. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Water Management Successes at the DoE's Environmental Management Waste Management Facility - 19194

URS-CH2M Oak Ridge LLC (UCOR) manages the cleanup of the 2,200-acre East Tennessee Technology Park (ETTP) for its client, the U.S. Department of Energy (DOE), in Oak Ridge, Tennessee. The ETTP site was contaminated with radioactive, hazardous and industrial wastes generated by more than 40 years of national defense and energy missions. Stormwater management is a critical activity at the DoE's Oak Ridge Reservation Environmental Management Waste Management Facility (EMWMF) due to the high average rainfall and the consequences if inadequately managed. EMWMF is located in East Tennessee with an average rainfall of 54.25 inches/year. EMWMF has implemented a successful, multipart strategy to maximize the margin of safety, reduce the volume of stormwater requiring management, and otherwise minimize operational impacts from large volumes of landfill water. The strategy consists of: - Pre-planning and preparation; - Landfill storm water prevention; - Inventory minimization; - Accelerated storm water removal. Since implementation of this aggressive water management strategy in 2017, EMWMF has reduced the volume of leachate requiring management by approximately 27%. This reduction increased the safety and compliance for the EMWMF, while at the same time, reduced costs. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Panel Session 38: Featured Country Canada: Nuclear Power Plant Waste Management

This panel focused on waste characterization, minimization, treatment, packaging, and management of Canadian NPP operational wastes and NPP SNF/UNF storage and management. Presentations proposed include: 1 - Waste Minimization and Innovation applied to Ontario Power Generation's Low and Intermediate Level Waste (Lise Morton, Ontario Power Generation (Canada)); 2 - Waste Prevention Strategy Discussion (Kent Anderson, UniTech Services Group Inc (USA)); 3 - Energy Solutions Canada (Tim Ryder, EnergySolutions (Canada))

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Characterization Techniques for TRU Waste from the Analytical Laboratory Hot Cells at Idaho National Laboratory - 19610

Since the first reactor went hot in 1942, there has been a never ending desire to improve the nuclear reactor and the fuel that it burns. The Idaho National Laboratory has been a key player in reactor and fuel development. As more research projects begin, the spectrum of waste parameters grows. Over the years, the Analytical laboratory (AL) at the INL Materials and Fuels Complex (MFC) has supported a large range of various research projects. Each project brings its own challenge and analytical technique that, in turn, yields its own unique waste parameters. The AL Hot Cells at INL consist of 6 small cells that can support around 12 different fuel research projects at a time along with infrastructure and environmental samples. Each different project yields different levels of transuranic (TRU) material, RCRA constituents, dose, and contamination levels. The waste parameters from each project dictate a path forward for the waste as Low-Level Waste (LLW), Contact Handled TRU (CH-TRU), Mixed Contact Handled TRU (M-CH-TRU), Remote Handled TRU (RH-TRU), or Mixed Remote Handled TRU (M-RHTRU). With visual examination techniques and process knowledge as an aid, there have been waste handling methods developed that allow debris and sample material to be segregated. Unique characterization mechanisms have been applied for each debris and sample material that are generally more accurate than smear and surface contamination data. For debris found in the Hot Cells, analytical data is gathered and conservatively summarized for each fuel type and research method. This consists of data from Quadrupole Inductively Coupled Plasma Mass Spectrometry (Q-ICP-MS), Gamma Spectroscopy, and some data verification through Thermal Ionization Mass Spectrometry (TIMS) and Multi-Collector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS). This data gives a range of TRU isotope concentrations. Fuel age is taken into account to determine the dominant TRU activity isotopes. Cesium concentrations are also gathered. Each parameter/fuel type then ties a measured Cesium number to TRU content based on historical analytical data. Debris in the hot cells is segregated by project and packaged in individual 2 gallon metal paint cans. Each can is counted by gamma spectroscopy in a calculated and measured geometry. Using process knowledge to determine the fuel or project type, newly counted cesium numbers can be applied to the historic analytical data and TRU content can be estimated. The TRU content allows waste debris to be segregated between CH-TRU and RH-TRU. Packaging techniques can allow for the minimization of RHTRU waste. Sample material received into the AL must be dissolved into a liquid matrix in order to be analyzed in most cases. When analysis is complete, these liquids go through a consolidation, complexation, neutralization, and solidification process where real time analytical data can be used to calculate TRU content and RCRA constitutes. These calculations allow for no further analytical data to be gathered. The waste matrix is considered stable and acceptable by all Acceptable Knowledge (AK) documents. The unique techniques used in characterizing and handling the Analytical Laboratories Hot Cell waste can be applied to various situations and various laboratories. They allow for more accurate data for waste constituents and also for the minimization of RH-TRU waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Discrete Regularization for Calibration of Geologic Facies Against Dynamic Flow Data

Abstract Subsurface flow model calibration involves many more unknowns than measurements, leading to ill‐posed problems with nonunique solutions. To alleviate nonuniqueness, the problem is regularized by constraining the solution space using prior knowledge. In certain sedimentary environments, such as fluvial systems, the contrast in hydraulic properties of different facies types tends to dominate the flow and transport behavior, making the effect of within facies heterogeneity less significant. Hence, flow model calibration in those formations reduces to delineating the spatial structure and connectivity of different lithofacies types and their boundaries. A major difficulty in calibrating such models is honoring the discrete, or piecewise constant, nature of facies distribution. The problem becomes more challenging when complex spatial connectivity patterns with higher‐order statistics are involved. This paper introduces a novel formulation for calibration of complex geologic facies by imposing appropriate constraints to recover plausible solutions that honor the spatial connectivity and discreteness of facies models. To incorporate prior connectivity patterns, plausible geologic features are learned from available training models. This is achieved by learning spatial patterns from training data, e.g., ‐SVD sparse learning or the traditional Principal Component Analysis. Discrete regularization is introduced as a penalty functions to impose solution discreteness while minimizing the mismatch between observed and predicted data. An efficient gradient‐based alternating directions algorithm is combined with variable splitting to minimize the resulting regularized nonlinear least squares objective function. Numerical results show that imposing learned facies connectivity and discreteness as regularization functions leads to geologically consistent solutions that improve facies calibration quality.

Khaninezhad, Mohammad‐Reza↗
DOE OSTI2020

Extremum seeking for optimal control problems with unknown time-varying systems and unknown objective functions

We consider the problem of optimal feedback control of an unknown, noisy, time-varying, dynamic system that is initialized repeatedly. Examples include a robotic manipulator which must perform the same motion, such as assisting a human, repeatedly and accelerating cavities in particle accelerators which are turned on for a fraction of a second with given initial conditions and vary slowly due to temperature fluctuations. In this paper, we present an approach that applies to systems of practical interest. The method presented here is model independent; does not require knowledge of the objective function; is robust to measurement noise; is applicable for any set of initial conditions; is applicable to simultaneously controlling an arbitrary number of parameters; and may be implemented with a broad range of continuous or discontinuous functions such as sine or square waves. For systems with convex cost functions we prove that our algorithm will produce controllers that approach the minimal cost. For linear systems we reproduce the cost minimizing linear quadratic regulator optimal controller that could have been designed analytically had the system and cost function been known. We demonstrate the effectiveness of the algorithm with simulation studies of noisy and time-varying systems.

42 ENGINEERING↗
DOE OSTI2018

Encapsulating Various Sulfur Allotropes within Graphene Nanocages for Long-Lasting Lithium Storage

The encapsulation of sulfur within carbon matrices is widely utilized in the cathode of a rechargeable lithium-sulfur battery, whose energy density largely depends on the design of the carbon structure. Here, an advanced graphene nanocage structure with the capability of hosting both cyclo-S-8 and smaller sulfur molecules (S2-4) is reported. The cage inner cavity is partially filled with S-8 to form a yolk-shell structure that enables free volumetric variation of S-8 during (de)lithiation. In the graphene shell of the cage, S-8 are downsized to S2-4 to activate extra sulfur loading sites within graphene layers. Importantly, the graphene shell exhibits inward volumetric variation upon (de)lithiation of the loaded S2-4, and the overall electrode strain is thus minimized. This prototyped design promises an ultimate solution to maximize sulfur loading in carbon matrices as well as to circumvent the polysulfide dissolution problem and boost the commercialization of lithium-sulfur batteries in the future.

Yuan, Yifei↗