Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “evaluation framework”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Multi-Physics Simulations for Molten Salt Reactor Evaluation: Chemistry Modeling and Database Development

To aid in design and licensing of molten salt reactors, a framework integrating the complex interaction of reactor neutronics, thermal hydraulics, and chemistry is being developed within the Department of Energy Advanced Reactor Technology Program’s Molten Salt Reactor (MSR) campaign. The challenges of integrating thermochemical and thermophysical behavior into a multi-physics reactor simulations include the following: (1) population of data needed for refinement of current models and development of nonexistent models through experimental measurements, first principles calculations, and development of a machine learning approach (2) thermochemical and thermophysical model development, (3) further development of Thermochimica, an open-source efficient equilibrium solver used to link thermochemical models to the multi-physics code, (4) a framework for integrating kinetic phenomena: nucleation, precipitation, mass/heat transport, and corrosion models, and (5) a computational environment to efficiently utilize the data and models within a multi-physics modeling tool. These challenges are being addressed through a collaboration among Oak Ridge National Laboratory, Argonne National Laboratory, the University of South Carolina, and the University of Ontario Institute of Technology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2011

A Framework for Comparative Assessments of Energy Efficiency Policy Measures

When policy makers propose new policies, there is a need to assess the costs and benefits of the proposed policy measures, to compare them to existing and alternative policies, and to rank them according to their effectiveness. In the case of equipment energy efficiency regulations, comparing the effects of a range of alternative policy measures requires evaluating their effects on consumers’ budgets, on national energy consumption and economics, and on the environment. Such an approach should be able to represent in a single framework the particularities of each policy measure and provide comparable results. This report presents an integrated methodological framework to assess prospectively the energy, economic, and environmental impacts of energy efficiency policy measures. The framework builds on the premise that the comparative assessment of energy efficiency policy measures should (a) rely on a common set of primary data and parameters, (b) follow a single functional approach to estimate the energy, economic, and emissions savings resulting from each assessed measure, and (c) present results through a set of comparable indicators. This framework elaborates on models that the U.S. Department of Energy (DOE) has used in support of its rulemakings on mandatory energy efficiency standards. In addition to a rigorous analysis of the impacts of mandatory standards, DOE compares the projected results of alternative policy measures to those projected to be achieved by the standards. The framework extends such an approach to provide a broad, generic methodology, with no geographic or sectoral limitations, that is useful for evaluating any type of equipment energy efficiency market intervention. The report concludes with a demonstration of how to use the framework to compare the impacts estimated for twelve policy measures focusing on increasing the energy efficiency of gas furnaces in the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Bayesian calibration of groundwater models with input data uncertainty

Effective water resources management typically relies on numerical models to analyze groundwater flow and solute transport processes. Groundwater models are often subject to input data uncertainty, as some inputs (such as recharge and well pumping rates) are estimated and subject to uncertainty. Current practices of groundwater model calibration often overlook uncertainties in input data; this can lead to biased parameter estimates and compromised predictions. Through a synthetic case study of surface–ground water interaction under changing pumping conditions and land use, we investigate the impacts of uncertain pumping and recharge rates on model calibration and uncertainty analysis. We then present a Bayesian framework of model calibration to handle uncertain input of groundwater models. The framework implements a marginalizing step to account for input data uncertainty when evaluating likelihood. It was found that not accounting for input uncertainty may lead to biased, overconfident parameter estimates because parameters could be over–adjusted to compensate for possible input data errors. Parameter compensation can have deleterious impacts when the calibrated model is used to make forecast under a scenario that is different from calibration conditions. By marginalizing input data uncertainty, the Bayesian calibration approach effectively alleviates parameter compensation and gives more accurate predictions in the synthetic case study. The marginalizing Bayesian method also decomposes prediction uncertainty into uncertainties contributed by parameters, input data, and measurements. Furthermore, the results underscore the need to account for input uncertainty to better inform postmodeling decision making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

The unexpected virtue of almost: Exploiting MPI collective operations to approximately coordinate checkpoints

Summary Coordinated checkpoint/restart is currently the dominant approach to mitigating the impact of failures on important scientific applications running on large‐scale distributed systems. However, there is widespread evidence that coordinated checkpointing may no longer be viable on next‐generation systems. Uncoordinated checkpoint/restart attempts to address the shortcomings of coordinated checkpoint/restart by allowing application processes to checkpoint their state independently. However, eliminating coordination may significantly degrade application performance. In this paper, we propose an approach that leverages existing coordination in important scientific applications to approximately coordinate checkpoints. Specifically, we propose to extend MPI implementations to force checkpoints to occur immediately after the completion of a collective operation. We evaluate the performance implications of this approach using an existing validated simulation framework. Our results demonstrate that approximately coordinated checkpointing can significantly improve application performance relative to totally uncoordinated checkpointing. We also show that forcing checkpoints to occur following a collective operation has a small impact on the nominal checkpoint interval for several important workloads. As a whole, the results presented in this paper demonstrate that approximately coordinated checkpointing may provide significant performance benefits without significantly increasing the cost of failure recovery.

Levy, Scott↗
DOE OSTI2016

Prompt atmospheric neutrino fluxes: perturbative QCD models and nuclear effects

We evaluate the prompt atmospheric neutrino flux at high energies using three different frameworks for calculating the heavy quark production cross section in QCD: NLO perturbative QCD, k T factorization including low-x resummation, and the dipole model including parton saturation. We use QCD parameters, the value for the charm quark mass and the range for the factorization and renormalization scales that provide the best description of the total charm cross section measured at fixed target experiments, at RHIC and at LHC. Using these parameters we calculate differential cross sections for charm and bottom production and compare with the latest data on forward charm meson production from LHCb at 7 TeV and at 13 TeV, finding good agreement with the data. In addition, we investigate the role of nuclear shadowing by including nuclear parton distribution functions (PDF) for the target air nucleus using two different nuclear PDF schemes. Depending on the scheme used, we find the reduction of the flux due to nuclear effects varies from 10% to 50% at the highest energies. Finally, we compare our results with the IceCube limit on the prompt neutrino flux, which is already providing valuable information about some of the QCD models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo

Sequential Monte Carlo (SMC) samplers are powerful tools for Bayesian inference but suffer from high computational costs due to their reliance on large particle ensembles for accurate estimates. We introduce persistent sampling (PS), an extension of SMC that systematically retains and reuses particles from all prior iterations to construct a growing, weighted ensemble. By leveraging multiple importance sampling and resampling from a mixture of historical distributions, PS mitigates the need for excessively large particle counts, directly addressing key limitations of SMC such as particle impoverishment and mode collapse. Crucially, PS achieves this without additional likelihood evaluations-weights for persistent particles are computed using cached likelihood values. This framework not only yields more accurate posterior approximations but also produces marginal likelihood estimates with significantly lower variance, enhancing reliability in model comparison. Furthermore, the persistent ensemble enables efficient adaptation of transition kernels by leveraging a larger, decorrelated particle pool. Experiments on high-dimensional Gaussian mixtures, hierarchical models, and non-convex targets demonstrate that PS consistently outperforms standard SMC and related variants, including recycled and waste-free SMC, achieving substantial reductions in mean squared error for posterior expectations and evidence estimates, all at reduced computational cost. PS thus establishes itself as a robust, scalable, and efficient alternative for complex Bayesian inference tasks.

Karamanis, Minas↗
DOE OSTI2011

A probabilistic approach to microRNA-target binding

Highlights: {yields} A new probabilistic model is introduced for microRNA-target binding. {yields} The new model significantly outperforms RNAHybrid and miRTif. {yields} The experiments can unveil the effects of the type and directions of distinct base pairings. -- Abstract: Elucidation of microRNA activity is a crucial step in understanding gene regulation. One key problem in this effort is how to model the pairwise interactions of microRNAs with their targets. As this interaction is strongly mediated by their sequences, it is desired to set-up a probabilistic model to explain the binding preferences between a microRNA sequence and the sequence of a putative target. To this end, we introduce a new model of microRNA-target binding, which transforms an aligned duplex to a new sequence and defines the likelihood of this sequence using a Variable Length Markov Chain. It offers a complementary representation of microRNA-mRNA pairs for microRNA target prediction tools or other probabilistic frameworks of integrative gene regulation analysis. The performance of present model is evaluated by its ability to predict microRNA-target mRNA interaction given a mature microRNA sequence and a putative mRNA binding site. In regard to classification accuracy, it outperforms two recent methods based on thermodynamic stability and sequence complementarity. The experiments can also unveil the effects of base pairing types and non-seed region in duplex formation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Multiple stakeholders in multi-criteria decision-making in the context of Municipal Solid Waste Management: A review

Highlights: • We review Municipal Solid Waste Management studies with focus on multiple stakeholders. • We focus on studies with multi-criteria decision analysis methods and discover their trends. • Most studies do not offer solutions for situations where stakeholders compete for more benefits or have unequal voting powers. • Governments and experts are the most participated stakeholders and AHP is the most dominant method. - Abstract: Municipal Solid Waste Management (MSWM) is a complicated process that involves multiple environmental and socio-economic criteria. Decision-makers look for decision support frameworks that can guide in defining alternatives, relevant criteria and their weights, and finding a suitable solution. In addition, decision-making in MSWM problems such as finding proper waste treatment locations or strategies often requires multiple stakeholders such as government, municipalities, industries, experts, and/or general public to get involved. Multi-criteria Decision Analysis (MCDA) is the most popular framework employed in previous studies on MSWM; MCDA methods help multiple stakeholders evaluate the often conflicting criteria, communicate their different preferences, and rank or prioritize MSWM strategies to finally agree on some elements of these strategies and make an applicable decision. This paper reviews and brings together research on the application of MCDA for solving MSWM problems with more focus on the studies that have considered multiple stakeholders and offers solutions for such problems. Results of this study show that AHP is the most common approach in consideration of multiple stakeholders and experts and governments/municipalities are the most common participants in these studies.

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

Modeling C–H Bond Activation and Oxidations of Alkanes over Cu–MOR Using First-Principles Methods

Direct conversion of methane to methanol can significantly boost the economic value of shale gas utilization. However, the stable C–H bond makes methane partial oxidation a challenging catalysis task. At low temperatures, C–H bond activation is kinetically sluggish. On the other hand, methane is likely oxidized fully into CO2 at high temperatures, which will lower methanol selectivity. Several Cu–oxo complexes such as mono(μ-oxo)dicopper, bis(μ-oxo)dicopper, and copper trioxo anchored in the mordenite (MOR) zeolite framework are shown to balance catalytic activity and product selectivity for the methane-to-methanol conversion. In this work, the thermodynamic stability of Cu–oxo complexes was evaluated as active centers for C–H bond activation using the BEEF-vdW functional within the density functional theory (DFT) framework, and the bis(μ-oxo)dicopper moiety was identified as the most stable configuration. The catalytic reactivity of these complexes was then tested with the activation of the first C–H bond of light hydrocarbons (i.e., methane, ethane, and propane), as well as subsequent oxidation steps to corresponding alcohols. Site II of the Cu–trioxo cluster with highest spin density remains as the most potent active site by enabling the lowest energy barrier for initial methane activation when electronic spin crossing was considered. Energy barriers for the first C–H activation step follow a decreasing order of methane, ethane, and propane.

Xu, Jiayi↗
DOE OSTI2025

Complexions at the iron-magnetite interface

Synthesizing distinct phases and controlling crystalline defects are key concepts in materials design. These approaches are often decoupled, with the former grounded in equilibrium thermodynamics and the latter in nonequilibrium kinetics. By unifying them through defect phase diagrams, we can apply phase equilibrium models to thermodynamically evaluate defects—including dislocations, grain boundaries, and phase boundaries—establishing a theoretical framework linking material imperfections to properties. Using scanning transmission electron microscopy (STEM) with differential phase contrast (DPC) imaging, we achieve the simultaneous imaging of heavy Fe and light O atoms, precisely mapping the atomic structure and chemical composition at the iron-magnetite (Fe/Fe 3 O 4 ) interface. We identify a well-ordered two-layer interface-stabilized phase state (referred to as complexion) at the Fe[001]/Fe 3 O 4 [001] interface. Using density-functional theory (DFT), we explain the observed complexion and map out various interface-stabilized phases as a function of the O chemical potential. The formation of complexions increases interface adhesion by 20% and alters charge transfer between adjacent materials, impacting transport properties. Our findings highlight the potential of tunable defect-stabilized phase states as a degree of freedom in materials design, enabling optimized corrosion protection, catalysis, and redox-driven phase transitions, with applications in materials sustainability, efficient energy conversion, and green steel production.

36 MATERIALS SCIENCE↗
DOE OSTI2017

GRACE satellite observations reveal the severity of recent water over-consumption in the United States

Changes in the climate and population growth will critically impact the future supply and demand of water, leading to large uncertainties for sustainable resource management. In the absence of on-the-ground measurements to provide spatially continuous, high-resolution information on water supplies, satellite observations can provide essential insight. Here, we develop a technique using observations from the Gravity Recovery and Climate Experiment (GRACE) satellite to evaluate the sustainability of surface water and groundwater use over the continental United States. We determine the annual total water availability for 2003–2015 using the annual variability in GRACE-derived total water storage for 18 major watersheds. The long-term sustainable water quantity available to humans is calculated by subtracting an annual estimate of the water needed to maintain local ecosystems, and the resulting water volumes are compared to reported consumptive water use to determine a sustainability fraction. We find over-consumption is highest in the southwest US, where increasing stress trends were observed in all five basins and annual consumptive use exceeded 100% availability twice in the Lower Colorado basin during 2003–2015. By providing a coarse-scale evaluation of sustainable water use from satellite and ground observations, the established framework serves as a blueprint for future large-scale water resource monitoring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A thermodynamic tank model for studying the effect of higher hydrocarbons on natural gas storage in metal-organic frameworks

Metal-organic frameworks (MOFs) are promising materials for storing natural gas in vehicular applications. Evaluation of these materials has focused on adsorption of pure methane, although commercial natural gas also contains small amounts of higher hydrocarbons such as ethane and propane, which adsorb more strongly than methane. There is, thus, a possibility that these higher hydrocarbons will accumulate in the MOF after multiple operating (adsorption/desorption) cycles, and reduce the storage capacity. To study the net effect of ethane and propane on the performance of an adsorbed natural gas (ANG) tank, we developed a mathematical model based on thermodynamics and mass balance equations that describes the state of the tank at any instant. The required inputs are the pure-component isotherms, and mixture adsorption data are calculated using the Ideal Adsorbed Solution Theory (IAST). We focused on how the "deliverable energy'' provided by the ANG tank to the engine changed over 200 operating cycles for a sample of 120 MOF structures. We found that, with any MOF, the ANG tank performance monotonically declines during early operating cycles until a "cyclic steady state'' is reached. We determined that the best materials when the fuel is 100% methane are not necessarily the best when the fuel includes ethane and propane. Among the materials tested, some top MOFs are MOF-143 > NU-800 > IRMOF-14 > IRMOF-20 > MIL-100 > NU-125 > IRMOF-1 > NU-111. MOF-143 is predicted to deliver 5.43 MJ L-1 of tank to the engine once the cyclic steady state is reached. The model also provided insights that can assist in future work to discover more promising adsorbent materials for natural gas storage.

Zhang, HD↗
DOE OSTI2014

Electrical flicker-noise generated by filling and emptying of impurity states in injectors of quantum-cascade lasers

Free running line-widths (>100 kHz), much broader than intrinsic line-widths ∼100 Hz, of existing quantum-cascade lasers are governed by strong flicker frequency-noise originating from electrical flicker noise. Understanding of microscopic origins of the electrical flicker noises in quantum-cascade lasers is crucially important for the reduction of strength of flicker frequency-noise without assistances of any type of feedback schemes. In this article, an ad hoc model that is based on fluctuating charge-dipoles induced by electron trappings and de-trappings at indispensable impurity states in injector super-lattices of a quantum-cascade laser is proposed, developing theoretical framework based on the model. The validity of the present model is evaluated by comparing theoretical voltage-noise power spectral densities based on the model with experimental ones obtained by using mid-infrared quantum-cascade lasers with designed impurity-positioning. The obtained experimental results on flicker noises, in comparison with the theoretical ones, shed light on physical mechanisms, such as the inherent one due to impurity states in their injectors and extrinsic ones due to surface states on the ridge-walls and due to residual deep traps, for electrical flicker-noise generation in existing mid-infrared quantum-cascade lasers. It is shown theoretically that quasi-delta doping of impurities in their injectors leads to strong suppression of electrical flicker noise by minimization of the dipole length at a certain temperature, for instance ∼300 K and, in turn, is expected to result in substantial narrowing of the free running line-width down below 10 kHz.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Accelerated path integral methods for atomistic simulations at ultra-low temperatures

Path integral methods provide a rigorous and systematically convergent framework to include the quantum mechanical nature of atomic nuclei in the evaluation of the equilibrium properties of molecules, liquids, or solids at finite temperature. Such nuclear quantum effects are often significant for light nuclei already at room temperature, but become crucial at cryogenic temperatures such as those provided by superfluid helium as a solvent. Unfortunately, the cost of converged path integral simulations increases significantly upon lowering the temperature so that the computational burden of simulating matter at the typical superfluid helium temperatures becomes prohibitive. Here we investigate how accelerated path integral techniques based on colored noise generalized Langevin equations, in particular the so-called path integral generalized Langevin equation thermostat (PIGLET) variant, perform in this extreme quantum regime using as an example the quasi-rigid methane molecule and its highly fluxional protonated cousin, CH{sub 5}{sup +}. We show that the PIGLET technique gives a speedup of two orders of magnitude in the evaluation of structural observables and quantum kinetic energy at ultralow temperatures. Moreover, we computed the spatial spread of the quantum nuclei in CH{sub 4} to illustrate the limits of using such colored noise thermostats close to the many body quantum ground state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A generative artificial intelligence framework for long-time plasma turbulence simulations

Generative deep learning techniques are employed in a novel framework for the construction of surrogate models capturing the spatiotemporal dynamics of 2D plasma turbulence. The proposed Generative Artificial Intelligence Turbulence (GAIT) framework enables the acceleration of turbulence simulations for long-time transport studies. GAIT leverages a convolutional variational auto-encoder and a recurrent neural network to generate new turbulence data from existing simulations, extending the time horizon of transport studies with minimal computational cost. The application of the GAIT framework to plasma turbulence using the Hasegawa–Wakatani (HW) model is presented, evaluating its performance via various analyses. Very good agreement is found between the GAIT and the HW models in the spatiotemporal Fourier and Proper Orthogonal Decomposition spectra, the flow topology characterized by the Okubo–Weiss parameter, and the time autocorrelation function of turbulent fluctuations. Excellent agreement has also been obtained in the probability distribution function of particle displacements and the effective turbulent diffusivity. In-depth analyses of the latent space of turbulent states, choice of hyperparameters and alternative deep learning models for the time prediction are presented. Our results highlight the potential of Artificial Intelligence-based surrogate models to overcome the computational challenges in turbulence simulation, which can be extended to other situations such as geophysical fluid dynamics.

Artificial intelligence↗
DOE OSTI2025

Derivative-free stochastic optimization via adaptive sampling strategies

In this paper, we present a novel derivative-free framework for solving unconstrained stochastic optimization problems. Many problems in fields ranging from simulation optimization to reinforcement learning to quantum computing involve settings where only stochastic function values are obtained via a zeroth-order oracle, which has no available gradient information and necessitates the usage of derivative-free optimization methodologies. Our approach includes estimating gradients using stochastic function evaluations and integrating adaptive sampling techniques to control the accuracy in these stochastic approximations. Our framework encapsulates several gradient estimation techniques, including standard finite-difference, Gaussian smoothing, sphere smoothing, randomized coordinate finite-difference, and randomized subspace finite-difference methods. We provide theoretical convergence guarantees for our framework and analyze the worst-case iteration and sample complexities associated with each gradient estimation method. Finally, we demonstrate the empirical performance of the methods on logistic regression and nonlinear least squares problems.

Adaptive sampling↗
DOE OSTI2024

Analytical noise bias correction for precise weak lensing shear inference

Noise bias is a significant source of systematic error in weak gravitational lensing measurements that must be corrected to satisfy the stringent standards of modern imaging surveys in the era of precision cosmology. This paper reviews the analytical noise bias correction method and provides analytical derivations demonstrating that we can recover shear to its second order using the ‘renoising’ noise bias correction approach introduced by METACALIBRATION. We implement this analytical noise bias correction within the AnaCal shear estimation framework and propose several enhancements to the noise bias correction algorithm. We evaluate the improved AnaCal using simulations designed to replicate Rubin Legacy Survey of Space and Time (LSST) imaging data. These simulations feature semi-realistic galaxies and stars, complete with representative distributions of magnitudes and Galactic spatial density. We conduct tests under various observational challenges, including cosmic rays, defective CCD columns, bright star saturation, bleed trails, and spatially variable point spread functions. Our results indicate a multiplicative bias in weak lensing shear recovery of less than a few tenths of a per cent, meeting LSST Dark Energy Science Collaboration requirements without requiring calibration from external image simulations. Additionally, our algorithm achieves rapid processing, handling one galaxy in less than a millisecond.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Gluon tomography from deeply virtual Compton scattering at small x

We present a full evaluation of the deeply virtual Compton scattering cross section in the dipole framework in the small-x region. The result features the cosφ and cos2φ azimuthal angular correlations, which have been missing in previous studies based on the dipole model. In particular, the cos2φ term is generated by the elliptic gluon Wigner distribution of which the measurement at the planned electron-ion collider provides important information about the gluon tomography at small x. Here, we also show the consistency with the standard collinear factorization approach based on the quark and gluon generalized parton distributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗