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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2010

Steps toward fault-tolerant quantum chemistry.

Developing quantum chemistry programs on the coming generation of exascale computers will be a difficult task. The programs will need to be fault-tolerant and minimize the use of global operations. This work explores the use a task-based model that uses a data-centric approach to allocate work to different processes as it applies to quantum chemistry. After introducing the key problems that appear when trying to parallelize a complicated quantum chemistry method such as coupled-cluster theory, we discuss the implications of that model as it pertains to the computational kernel of a coupled-cluster program - matrix multiplication. Also, we discuss the extensions that would required to build a full coupled-cluster program using the task-based model. Current programming models for high-performance computing are fault-intolerant and use global operations. Those properties are unsustainable as computers scale to millions of CPUs; instead one must recognize that these systems will be hierarchical in structure, prone to constant faults, and global operations will be infeasible. The FAST-OS HARE project is introducing a scale-free computing model to address these issues. This model is hierarchical and fault-tolerant by design, allows for the clean overlap of computation and communication, reducing the network load, does not require checkpointing, and avoids the complexity of many HPC runtimes. Development of an algorithm within this model requires a change in focus from imperative programming to a data-centric approach. Quantum chemistry (QC) algorithms, in particular electronic structure methods, are an ideal test bed for this computing model. These methods describe the distribution of electrons in a molecule, which determine the properties of the molecule. The computational cost of these methods is high, scaling quartically or higher in the size of the molecule, which is why QC applications are major users of HPC resources. The complexity of these algorithms means that MPI alone is insufficient to achieve parallel scaling; QC developers have been forced to use alternative approaches to achieve scalability and would be receptive to radical shifts in the programming paradigm. Initial work in adapting the simplest QC method, Hartree-Fock, to this the new programming model indicates that the approach is beneficial for QC applications. However, the advantages to being able to scale to exascale computers are greatest for the computationally most expensive algorithms; within QC these are the high-accuracy coupled-cluster (CC) methods. Parallel coupledcluster programs are available, however they are based on the conventional MPI paradigm. Much of the effort is spent handling the complicated data dependencies between the various processors, especially as the size of the problem becomes large. The current paradigm will not survive the move to exascale computers. Here we discuss the initial steps toward designing and implementing a CC method within this model. First, we introduce the general concepts behind a CC method, focusing on the aspects that make these methods difficult to parallelize with conventional techniques. Then we outline what is the computational core of the CC method - a matrix multiply - within the task-based approach that the FAST-OS project is designed to take advantage of. Finally we outline the general setup to implement the simplest CC method in this model, linearized CC doubles (LinCC).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Machine learning models for estimating contamination across different curbside collection strategies

Contaminated recyclables, which are frequently discarded as waste, pose a significant challenge to the implementation of a circular economy. These contaminated recyclables impede the circulation of resources, resulting in higher processing costs at material recovery facilities (MRFs). Over the past few decades, machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and random forest (RF) have evolved to provide new methods for predicting inbound contamination rates in addition to traditional statistical models. In this study, we applied ML models to predict inbound contamination rates using demographic features from 15 counties in the U.S. with different curbside collection strategies. In general, we found that ML models outperformed linear mixed models. Specifically, SVM models had the highest performance (R 2 = 0.75; mean absolute error (MAE) = 0.06), which may be due to their ability to model nonlinear relationships between features and inbound contamination rates. Further, the key predictor was population, with poverty rate being positively correlated and median age negatively correlated with inbound contamination rates. To improve the management of contamination and enhance the implementation of a circular economy, better models are needed to understand and estimate inbound contamination rates as well as identify critical factors in the present and future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Spherically symmetric cosmological spacetimes with dust and radiation — numerical implementation

We present new numerical cosmological solutions of the Einstein Field Equations. The spacetime is spherically symmetric with a source of dust and radiation approximated as a perfect fluid. The dust and radiation are necessarily non-comoving due to the inhomogeneity of the spacetime. Such a model can be used to investigate non-linear general relativistic effects present during decoupling or big-bang nucleosynthesis, as well as for investigating void models of dark energy with isocurvature degrees of freedom. We describe the full evolution of the spacetime as well as the redshift and luminosity distance for a central observer. After demonstrating accuracy of the code, we consider a few example models, and demonstrate the sensitivity of the late time model to the degree of inhomogeneity of the initial radiation contrast.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Optimal Experimental Design for Inverse Problems in the Presence of Observation Correlations

Optimal experimental design (OED) is the general formalism of sensor placement and decisions on the data collection strategy for engineered or natural experiments. This problem is prevalent in many critical fields such as battery design, numerical weather prediction, geosciences, environmental and urban studies. State-of-the-art computational methods for experimental design do not accommodate correlation structure in observational errors produced by many expensive-to-operate devices such as X-ray machines, radars, and satellites. Discarding evident data correlations leads to biased results, higher expenses, and waste of valuable resources. We present a general formulation of the OED formalism for model-constrained large-scale Bayesian linear inverse problems, where measurement errors are generally correlated. The proposed approach utilizes the Hadamard product of matrices to formulate the weighted-likelihood, and is valid for both finite as well as infinite-dimensional Bayesian inverse problems. Extensive numerical experiments are carried out for empirical verification of the proposed approach using an advection-diffusion model, where the objective is to optimally place a small set of sensors, under a limited budget, to predict the concentration of a contaminant in a closed and bounded domain.

Correlated observations↗
DOE OSTI2023

Monte Carlo goodness-of-fit tests for degree corrected and related stochastic blockmodels

Abstract We construct Bayesian and frequentist finite-sample goodness-of-fit tests for three different variants of the stochastic blockmodel for network data. Since all of the stochastic blockmodel variants are log-linear in form when block assignments are known, the tests for the latent block model versions combine a block membership estimator with the algebraic statistics machinery for testing goodness-of-fit in log-linear models. We describe Markov bases and marginal polytopes of the variants of the stochastic blockmodel and discuss how both facilitate the development of goodness-of-fit tests and understanding of model behaviour. The general testing methodology developed here extends to any finite mixture of log-linear models on discrete data, and as such is the first application of the algebraic statistics machinery for latent-variable models.

Karwa, Vishesh↗
DOE OSTI2010

Storage Viability and Optimization Web Service

Non-residential sectors offer many promising applications for electrical storage (batteries) and photovoltaics (PVs). However, choosing and operating storage under complex tariff structures poses a daunting technical and economic problem that may discourage potential customers and result in lost carbon and economic savings. Equipment vendors are unlikely to provide adequate environmental analysis or unbiased economic results to potential clients, and are even less likely to completely describe the robustness of choices in the face of changing fuel prices and tariffs. Given these considerations, researchers at Lawrence Berkeley National Laboratory (LBNL) have designed the Storage Viability and Optimization Web Service (SVOW): a tool that helps building owners, operators and managers to decide if storage technologies and PVs merit deeper analysis. SVOW is an open access, web-based energy storage and PV analysis calculator, accessible by secure remote login. Upon first login, the user sees an overview of the parameters: load profile, tariff, technologies, and solar radiation location. Each parameter has a pull-down list of possible predefined inputs and users may upload their own as necessary. Since the non-residential sectors encompass a broad range of facilities with fundamentally different characteristics, the tool starts by asking the users to select a load profile from a limited cohort group of example facilities. The example facilities are categorized according to their North American Industry Classification System (NAICS) code. After the load profile selection, users select a predefined tariff or use the widget to create their own. The technologies and solar radiation menus operate in a similar fashion. After these four parameters have been inputted, the users have to select an optimization setting as well as an optimization objective. The analytic engine of SVOW is LBNL?s Distributed Energy Resources Customer Adoption Model (DER-CAM), which is a mixed-integer linear program (MILP) written and executed in the General Algebraic Modeling System (GAMS) optimization software. LBNL has released version 1.2.0.11 of SVOW. Information can be found at http://der.lbl.gov/microgrids-lbnl/current-project-storage-viability-website.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Magnetic properties of metastable honeycomb KCoAsO 4

We present comprehensive neutron scattering data on polycrystalline samples of a new metastable honeycomb material KCoAsO 4 . Below 𝑇 𝑁 = 14 K, the system orders into a zigzag antiferromagnetic state, with spins ordered into alternating ferromagnetic chains similar to the isostructural sister compound KNiAsO 4 . In the case of KCoAsO 4 , we find the moments are robustly canted out of plane closer to the crystallographic 𝑐 axis. A combination of weak interlayer coupling, lattice strain, and inhomogeneities lead to the coexistence of two magnetic ordering wave vectors 𝑘 1 = (1.5 0 0) and 𝑘 2 = (0.5 0 0.5), where the two structures differ only in their layer stacking. Inelastic data show the presence of a spin orbital mode at 24 meV, supporting a pseudospin $\tilde{𝑆}$ = 1/2 Kramer's doublet ground state of the Co 2+ ions. We model the low energy excitations using both a conventional XXZ Hamiltonian and generalized Kitaev Heisenberg Hamiltonian within a linear spin wave limit. While either model can qualitatively reproduce the observed spectra, the lack of fine features and observation of disorder prevent a clear-cut determination of the low energy Hamiltonian. In the case of an XXZ-type model, a large easy-axis anisotropy is necessary to reproduce the gapped spectra and canting of the magnetic moments. For the generalized Kitaev model, despite the large canting of the moments away from the honeycomb layers, we find a noticeable if nondominant Kitaev term persists. In conclusion, the contrast of the magnetic properties of KCoAsO 4 to other cobalt honeycombs highlights the sensitivity of the low energy magnetic properties of Co 2+ to fine details of its crystalline environment.

Honeycomb lattice↗
DOE OSTI2020

A comprehensive conductivity model for drift and micro-tearing modes

The parallel electrical conductivity is a crucial parameter in the study of the linear stability of drift-modes like the micro-tearing mode (MTM). The conductivity enters by closing the electromagnetic tearing layer equations. Recent progress in the understanding of the pedestal suggests that the MTM could play an important role in its structure and evolution. For this reason, we revisit and improve previous model conductivities. This parameter is generally derived from the linearized drift kinetic equation. In the past literature, it has been computed using either simplified collision operators or neglecting the spatial dependence away from the rational surface. Here, a fully consistent expression for the conductivity that would accurately model the pedestal has not been available. By applying a novel variational procedure and with the full Fokker Plank collision operator, including electron–electron collisions, we compute a closed expression for the parallel electrical conductivity in the form of a rational function.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Simultaneous Evolution of Gyre and Atlantic Meridional Overturning Circulation Anomalies as an Eigenmode of the North Atlantic System

Here, the authors observe an interdecadal oscillatory mode of the North Atlantic atmosphere–ocean system in a general circulation model (GFDL CM2.1) via a linear inverse model (LIM). The oscillation mechanism is mostly embedded in the subpolar gyre: anomalous advection generates density anomalies in the eastern subpolar gyre, which propagate along the mean gyre circulation and reach the subpolar gyre center around 10 years later, when associated anomalous advection of the opposite sign starts the other half cycle. While density anomalies reach the Labrador Sea deep convection region, Atlantic meridional overturning circulation (AMOC) anomalies are also induced. Both the gyre and AMOC anomalies then propagate equatorward slowly, following the advection of density anomalies. The oscillation is further demonstrated to be more likely an ocean-only mode while excited by the atmospheric forcing; in particular, it can be approximated as a linearly driven damped oscillator that is partly excited by the North Atlantic Oscillation (NAO). The slowly evolving interdecadal oscillation significantly improves and prolongs the LIM’s prediction skill of sea surface temperature (SST) evolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Discovering the desirable alleles contributing to the lignocellulosic biomass traits in saccharum germplasm collections for energy cane improvement

Phenotyping Methods: The accessions (which includes 21 taxa and 1,177 accessions) in the World Collection of Sugarcane and Related Grasses (WCSRG) was evaluated for the following traits: arenchyma, internode length and diameter, pubescence, pith, Brix, stalk number and fiber. A core of 300 accessions that included each species in the World Collection was selected by using the Maximization Strategy in MStrat software. Results: The core had a higher diversity rating than random selections of 300 accessions. The Shannon–Weaver Diversity Index scores of the core and whole collection were similar indicating that the majority of the diversity was captured by the core collection. The ranges and medians between the core and WCSRG were similar; only two of the trait medians were not significant at P = 0.05 using the non-parametric Wilcoxon method and the coincidence rate (CR % = 96.2) was high (>80) indicating that extreme values were retained. Thus, the phenotypic diversity of these traits in the WCSRG was well represented by the core collection. Associations Methods: Genotypic and phenotypic data were collected for 1002 accessions of the WCSRG including 209 SSR markers. Association analysis was performed using both General Linear (GLM) and Maximum Likelihood (MLM) models. Different core collections with 300 accessions each were selected based on genotypic, phenotypic and combined data based on the Maximization Strategy in MStrat software. Results: A major portion of the genotyping involving SNPs is being conducted by Dr. Jianping Wang of the University of Florida under the DOE award DE-FG 02-11ER 65214 and the genotypic and phenotypic associations will be reported separately next year. In the current, study forty one and seventeen markers were found to be associated with traits using the GLM and MLM methods respectively including associations with arenchyma, internode length and diameter, pubescence, pith, and Sugar Cane Yellow Leaf Virus. The data indicates that each of the cores and the World Collection are similar to each other genotypically and phenotypically, but the core that was selected using only genotypic data was significantly different phenotypically. This indicates that there is not enough association between the genotypic and phenotypic diversity as to select using only genotypic diversity and get the full phenotypic diversity. Core Collection: Creation and Phenotyping Methods: To evaluate this germplasm for breeding purposes, a representative diversity panel selected from the WCSRG of approximately 300 accessions was planted at Canal Point, FL in three replications. These accessions were measured for stalk height and stalk number multiple times throughout the growing season and Brix and fresh biomass during harvest in 2013 and, stalk height, stalk number, stalk diameter, internode length, Brix and fresh and dry biomass was determined in the ratoon crop harvest in 2014. Results: In correlations of multiple measurements, there were higher correlations for early measurements of stalk number and stalk height with harvest traits like Brix and fresh weight. Hybrids had higher fresh mass and Brix while Saccharum spontaneum had higher stalk number and dry mass. The heritability of hybrid mass traits was lower in the ratoon crop. According to the principal component analysis, the diversity panel was divided into two groups. One group had accessions with high stalk number and high dry biomass like S. spontaneum and the other groups contained accessions with higher Brix and fresh biomass like S. officinarum. Mass traits correlated with each other as expected but hybrids had lower correlations between fresh and dry mass. Stalk number and the mass traits correlated with each other except in S. spontaneum and hybrids in the first ratoon. There were 110 accessions not significantly different in Brix from the commercial sugarcane checks including 10 S. spontaneum accessions. There were 27 dry and 6 fresh mass accessions significantly higher than the commercial sugarcane checks. Core Collection: Fiber analysis Methods: A biomass sample was taken from each accession then shredded and dried. Fiber analysis was then performed on each sample. The acetyl groups, acid insoluble lignin, acid soluble lignin, arabinan, glucan, holocellulose, total lignin, structural ash, and xylan were quantified on a % fiber basis and nonstructural ash on a % total basis. Results: There were significant, but not large differences between species for holocellulose, lignin, acetyl, acid soluble lignin, nonstructural ash, and glucan. For each trait, S. spontaneum had significantly more holocellulose, glucan, lignin, and nonstructural ash and less acetyl and acid soluble lignin than the other species. In all populations, glucan and was positively correlated with holocellulose were positively correlated and glucan and and holocellulose were negatively correlated with lignin. In hybrids, internode length correlated positively with holocellulose and nonstructural ash and negatively with lignin. The heritability estimates for each of the fiber component traits is low indicating that environment is an important factor in fiber composition. Principal component analysis indicated that a large amount of diversity exists within each of the species.

09 BIOMASS FUELS↗
DOE OSTI2010

Strategic stockpiling of power system supplies for disaster recovery

This paper studies the Power System Stochastic Storage Problem (PSSSP), a novel application in power restoration which consists of deciding how to store power system components throughout a populated area to maximize the amount of power served after disaster restoration. The paper proposes an exact mixed-integer formulation for the linearized DC power flow model and a general column-generation approach. Both formulations were evaluated experimentally on benchmarks using the electrical power infrastructure of the United States and disaster scenarios generated by state-of-the-art hurricane simulation tools similar to those used by the National Hurricane Center. The results show that the column-generation algorithm produces near-optimal solutions quickly and produces orders of magnitude speedups over the exact formulation for large benchmarks. Moreover, both the exact and the column-generation formulations produce significant improvements over greedy approach and hence should yield significant benefits in practice.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Limited accuracy of linearized gravity

Standard cosmological models rely on an approximate treatment of gravity, utilizing solutions of the linearized Einstein equations as well as physical approximations. In an era of precision cosmology, we should ask: are these approximate predictions sufficiently accurate for comparison to observations, and can we draw meaningful conclusions about properties of our Universe from them? In this work we examine the accuracy of linearized gravity in the presence of collisionless matter and a cosmological constant utilizing fully general relativistic simulations. We observe the gauge dependence of corrections to linear theory, and note the amplitude of these corrections. For perturbations whose amplitudes are in line with expectations from the standard Λ cold dark matter model, we find that the full, general relativistic metric is well described by linear theory in Newtonian and harmonic gauges, while the metric in comoving-synchronous gauge is not. Furthermore, for the most extreme observed structures in our Universe, such as supervoids, our results suggest that corrections to linear gravitational theory can reach or surpass the percent level in all gauges.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Probing cosmology and gravity with redshift-space distortions around voids

Cosmic voids in the large-scale structure of the Universe affect the peculiar motions of objects in their vicinity. Although these motions are difficult to observe directly, the clustering pattern of their surrounding tracers in redshift space is influenced in a unique way. This allows to investigate the interplay between densities and velocities around voids, which is solely dictated by the laws of gravity. With the help of N-body simulations and derived mock-galaxy catalogs we calculate the average density fluctuations around voids identified with a watershed algorithm in redshift space and compare the results with the expectation from general relativity and the ΛCDM model. We find linear theory to work remarkably well in describing the dynamics of voids. Adopting a Bayesian inference framework, we explore the full posterior of our model parameters and forecast the achievable accuracy on measurements of the growth rate of structure and the geometric distortion through the Alcock-Paczyński effect. Systematic errors in the latter are reduced from ∼15% to ∼5% when peculiar velocities are taken into account. The relative parameter uncertainties in galaxy surveys with number densities comparable to the SDSS MAIN (CMASS) sample probing a volume of 1h{sup −3}Gpc{sup 3} yield σ{sub f/b}/(f/b).∼2% (20%) and σ{sub D{sub A{sub H}}}/D{sub AH∼0}.2% (2%), respectively. At this level of precision the linear-theory model becomes systematics dominated, with parameter biases that fall beyond these values. Nevertheless, the presented method is highly model independent; its viability lies in the underlying assumption of statistical isotropy of the Universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The MBARI-WEC: a power source for ocean sensing

Abstract Interest in wave energy converters to provide autonomous power to various ocean-bound systems, such as autonomous underwater vehicles, sensor systems, and even aquaculture farms, has grown in recent years. The Monterey Bay Aquarium Research Institute has developed and deployed a small two-body point absorber wave energy device suitable to such needs. This paper provides a description of the system to support future open-source access to the device and further the general development of similar wave energy systems. Additionally, to support future control design and system modification efforts, a set of hydrodynamic models are presented and cross-compared. To test the viability of using a linear frequency-domain admittance model for controller tuning, the linear model is compared against four WEC-Sim models of increasing complexity. The linear frequency-domain model is found to be generally adequate for capturing system dynamics, as the model agreement is good and the degree of nonlinearity introduced in the WEC-Sim models is generally less than 2.5%.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

pyvisco [SWR-22-30]

pyvisco is a Python library that supports the identification of Prony series parameters for linear viscoelastic materials described by a Generalized Maxwell model. The necessary material model parameters are identified by fitting a Prony series to the experimental measurement data. pyvisco allows for the identification of Prony series parameters from experimental data measured in either the frequency-domain (via Dynamic Mechanical Thermal Analysis) or time-domain (via relaxation measurements). The experimental data can be provided as raw measurement sets at different temperatures or as pre-processed master curves. An optional minimization routine is included to reduce the number of Prony elements. This routine is helpful in Finite Element simulations where reducing the computational complexity of the linear viscoelastic material models can shorten the simulation time. See also, https://pypi.org/project/pyvisco/

Springer, Martin↗
DOE OSTI2015

Characteristic operator functions for quantum input-plant-output models and coherent control

We introduce the characteristic operator as the generalization of the usual concept of a transfer function of linear input-plant-output systems to arbitrary quantum nonlinear Markovian input-output models. This is intended as a tool in the characterization of quantum feedback control systems that fits in with the general theory of networks. The definition exploits the linearity of noise differentials in both the plant Heisenberg equations of motion and the differential form of the input-output relations. Mathematically, the characteristic operator is a matrix of dimension equal to the number of outputs times the number of inputs (which must coincide), but with entries that are operators of the plant system. In this sense, the characteristic operator retains details of the effective plant dynamical structure and is an essentially quantum object. We illustrate the relevance to model reduction and simplification definition by showing that the convergence of the characteristic operator in adiabatic elimination limit models requires the same conditions and assumptions appearing in the work on limit quantum stochastic differential theorems of Bouten and Silberfarb [Commun. Math. Phys. 283, 491-505 (2008)]. This approach also shows in a natural way that the limit coefficients of the quantum stochastic differential equations in adiabatic elimination problems arise algebraically as Schur complements and amounts to a model reduction where the fast degrees of freedom are decoupled from the slow ones and eliminated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

The Generality of the GUGA MRCI Approach in COLUMBUS for Treating Complex Quantum Chemistry

The core part of the program system COLUMBUS allows highly efficient calculations using variational multireference (MR) methods in the framework of configuration interaction with single and double excitations (MR-CISD) and averaged quadratic coupled-cluster calculations (MR-AQCC), based on uncontracted sets of configurations and the graphical unitary group approach (GUGA). The availability of analytic MR-CISD and MR-AQCC energy gradients and analytic nonadiabatic couplings for MR-CISD enables exciting applications including, e.g., investigations of π-conjugated biradicaloid compounds, calculations of multitudes of excited states, development of diabatization procedures, and furnishing the electronic structure information for on-the-fly surface nonadiabatic dynamics. With fully variational uncontracted spin-orbit MRCI, COLUMBUS provides a unique possibility of performing high-level calculations on compounds containing heavy atoms up to lanthanides and actinides. Crucial for carrying out all of these calculations effectively is the availability of an efficient parallel code for the CI step. Configuration spaces of several billion in size now can be treated quite routinely on standard parallel computer clusters. Emerging developments in COLUMBUS, including the all configuration mean energy multiconfiguration self-consistent field method and the graphically contracted function method, promise to allow practically unlimited configuration space dimensions. Spin density based on the GUGA approach, analytic spin-orbit energy gradients, possibilities for local electron correlation MR calculations, development of general interfaces for nonadiabatic dynamics, and MRCI linear vibronic coupling models conclude this overview.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Generalized Bayesian Framework for Evaluation of Integral Benchmark Experiments

A recently published generalized Bayesian optimization framework has provided a way to retract any or all of the three common assumptions underlying the conventional Generalized Linear Least Squares (GLLS) optimization method based on the concepts introduced in reference two. These assumptions are: 1. Perfection: The model used for data evaluation and the prior probability distribution function (PDF) of generalized* data are perfect; 2. Normality: The prior and posterior PDF are normal; and 3. Linearity: The model is linear. In this work we outline how the framework in 1 could be directly adopted for improved evaluation of nuclear criticality integral benchmark experiments (IBEs) by: 1. Removing the first assumption alone by utilizing the concept of imperfections introduced in 1 to enable evaluation in the presence of discrepancies between the data and model or of missing covariance information by a GLLS method that will be seen as a generalization of the conventional GLLS method employed by the TSURFER code, and by 2. Removing the remaining two assumptions by implement- ing a Markov Chain Monte Carlo method for computation of the posterior PDF in the SAMPLER code, where TSURFER and SAMPLER are the uncertainty quantification (UQ) codes for IBEs in the SCALE code system based on the GLLS, and the stochastic method, respectively.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗