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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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At least 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Uncertainty Quantification of Multi-Phase Closures

In the ensemble-averaged dispersed phase formulation used for CFD of multiphase ows in nuclear reactor thermohydraulics, closures of interphase transfer of mass, momentum, and energy constitute, by far, the biggest source of error and uncertainty. Reliable estimators of this source of error and uncertainty are currently non-existent. Here, we report on how modern Validation and Uncertainty Quanti cation (VUQ) techniques can be leveraged to not only quantify such errors and uncertainties, but also to uncover (unintended) interactions between closures of di erent phenomena. As such this approach serves as a valuable aide in the research and development of multiphase closures. The joint modeling of lift, drag, wall lubrication, and turbulent dispersion|forces that lead to tranfer of momentum between the liquid and gas phases|is examined in the frame- work of validation of the adiabatic but turbulent experiments of Liu and Banko , 1993. An extensive calibration study is undertaken with a popular combination of closure relations and the popular k-ϵ turbulence model in a Bayesian framework. When a wide range of super cial liquid and gas velocities and void fractions is considered, it is found that this set of closures can be validated against the experimental data only by allowing large variations in the coe cients associated with the closures. We argue that such an extent of variation is a measure of uncertainty induced by the chosen set of closures. We also nd that while mean uid velocity and void fraction pro les are properly t, uctuating uid velocity may or may not be properly t. This aspect needs to be investigated further. The popular set of closures considered contains ad-hoc components and are undesirable from a predictive modeling point of view. Consequently, we next consider improvements that are being developed by the MIT group under CASL and which remove the ad-hoc elements. We use non-intrusive methodologies for sensitivity analysis and calibration (using Dakota) to study sensitivities of the CFD representation (STARCCM+) of uid velocity pro les and void fraction pro les in the context of Shaver and Podowski, 2015 correction to lift, and the Lubchenko et al., 2017 formulation of wall lubrication.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Adaptive Recovery Model: Designing Systems for Testing Tracing and Vaccination to Support COVID-19 Recovery Planning.

This report documents a new approach to designing disease control policies that allocate scarce testing, contact tracing, and vaccination resources to better control community transmission of COVID19 or similar diseases. The Adaptive Recovery Model (ARM) combines a deterministic compartmental disease model with a stochastic network disease propagation model to enable us to simulate COVID-19 community spread through the lens of two complementary modeling motifs. ARM contact networks are derived from cell-phone location data that have been anonymized and interpreted as individual arrivals to specic public locations. Modeling disease spread over these networks allows us to identify locations within communities conducive to rapid disease spread. ARM applies this model- and data-derived abstractions of community transmission to evaluate the effectiveness of disease control measures including targeted social distancing, contact tracing, testing and vaccination. The architecture of ARM provides a unique capacity to help decision makers understand how best to deploy scarce testing, tracing and vaccination resources to minimize disease-spread potential in a community. This document details the novel mathematical formulations underlying ARM, presents a dynamical stability analysis of the deterministic model components, a sensitivity analysis of control parameters and network structure, and summarizes a process for deriving contact networks from cell-phone location data. An example use case steps through applying ARM to evaluate three targeted social distancing policies using Bernalillo County, New Mexico as an exemplar test locale. This step-by-step analysis demonstrates how ARM can be used to measure the relative performance of competing public health policies. Initial scenario tests of ARM shows that ARMs design focus on resource utilization rather than simple incidence prediction can provide decision makers with additional quantitative guidance for managing ongoing public health emergencies and planning future responses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1983

Development of a Multilevel Optimization Approach to the Design of Modern Engineering Systems

A general algorithm is proposed which carries out the design process iteratively, starting at the top of the hierarchy and proceeding downward. Each subproblem is optimized separately for fixed controls from higher level subproblems. An optimum sensitivity analysis is then performed which determines the sensitivity of the subproblem design to changes in higher level subproblem controls. The resulting sensitivity derivatives are used to construct constraints which force the controlling subproblems into chosing their own designs so as to improve the lower levels subproblem designs while satisfying their own constraints. The applicability of the proposed algorithm is demonstrated by devising a four-level hierarchy to perform the simultaneous aerodynamic and structural design of a high-performance sailplane wing for maximum cross-country speed. Finally, the concepts discussed are applied to the two-level minimum weight structural design of the sailplane wing. The numerical experiments show that discontinuities in the sensitivity derivatives may delay convergence, but that the algorithm is robust enough to overcome these discontinuities and produce low-weight feasible designs, regardless of whether the optimization is started from the feasible space or the infeasible one.

Barthelemy, J. F. M.↗
NASA NTRS1988

Integrated structural-aerodynamic design optimization

This paper focuses on the processes of simultaneous aerodynamic and structural wing design as a prototype for design integration, with emphasis on the major difficulty associated with multidisciplinary design optimization processes, their enormous computational costs. Methods are presented for reducing this computational burden through the development of efficient methods for cross-sensitivity calculations and the implementation of approximate optimization procedures. Utilizing a modular sensitivity analysis approach, it is shown that the sensitivities can be computed without the expensive calculation of the derivatives of the aerodynamic influence coefficient matrix, and the derivatives of the structural flexibility matrix. The same process is used to efficiently evaluate the sensitivities of the wing divergence constraint, which should be particularly useful, not only in problems of complete integrated aircraft design, but also in aeroelastic tailoring applications.

Haftka, R. T.↗
NASA NTRS1998

Analysis of Information Content in High-Spectral Resolution Sounders using Subset Selection Analysis

In this paper, we summarize the results of the sensitivity analysis and data reduction carried out to determine the information content of AIRS and IASI channels. The analysis and data reduction was based on the use of subset selection techniques developed in the linear algebra and statistical community to study linear dependencies in high dimensional data sets. We applied the subset selection method to study dependency among channels by studying the dependency among their weighting functions. Also, we applied the technique to study the information provided by the different levels in which the atmosphere is discretized for retrievals and analysis. Results from the method correlate well with intuition in many respects and point out to possible modifications for band selection in sensor design and number and location of levels in the analysis process.

Velez-Reyes, Miguel↗
NASA NTRS2005

An Improved Analysis of Forest Carbon Dynamics using Data Assimilation

There are two broad approaches to quantifying landscape C dynamics - by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques - which combine stock and flux observations with a dynamic model - improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3-year period, and include eddy flux and soil C02 efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)= -251 f 197g Cm-2 over the 3 years, compared with an estimate of -419 f 29gCm-2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5gCm-2 day-1, but the uncertainty on assimilated estimates averaged 0.47 g Cm-2 day-1, and only exceeded 0.5gC m-2 day-1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long-running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote-sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis - which might be provided indirectly by remotely sensed data - improves the analysis of NEE.

Williams, Mathew↗
DOE OSTI2026

Artificial Intelligence for Event Reconstruction and Higgs Physics at CMS and Future Colliders

This dissertation charts a trajectory in which advances in artificial intelligence (AI) play a central role in pushing the high-energy physics frontier, complementing progress driven by higher collision energies and larger colliders. The discovery potential of the LHC and future colliders relies on accurate reconstruction of increasingly complex particle collision events. In the CMS experiment, this task is performed by the particle-flow (PF) algorithm. This dissertation presents the first implementation of a machine-learning-based particle-flow (MLPF) reconstruction in the CMS detector based on transformer architectures. In simulated top quark--antiquark pair (ttbar) events under LHC Run~3 (2023--2024) conditions, MLPF improves jet energy resolution by 10--20\% compared to standard PF for jets with transverse momentum between 30--100\GeV. Runtime performance is evaluated using simulated multijet events, with a median inference time of 20\unit{ms} per event on an NVIDIA L4 GPU, compa red to approximately 110\unit{ms} for standard PF. The MLPF algorithm is also validated on Run~3 collision data, representing the first data-validated ML-based reconstruction pipeline at any LHC experiment. We then extend MLPF toward future electron--positron colliders and introduce the first full-simulation cross-detector transfer learning workflow for PF reconstruction. The model is pre-trained on simulated events from the Compact Linear Collider detector (CLICdet) and fine-tuned on the CLIC-like detector (CLD) proposed for the Future Circular Collider (FCC). This approach achieves up to a 40\% improvement in jet energy resolution over rule-based reconstruction while reducing the required training dataset size by an order of magnitude, demonstrating the potential of AI to accelerate detector development and optimization. This dissertation also demonstrates how modern AI techniques enhance the sensitivity of LHC physics analyses. A CMS search for highly Lorentz-boosted Higgs bosons decaying to \textrm{W} boson pairs is presented, focusing on the single-lepton final state. A dedicated fine-tuning strategy for \ParT yields an approximately 70\% increase in expected sensitivity relative to the baseline model. The analysis uses proton--proton collision data at a center-of-mass energy of \ensuremath{\sqrt{s}=13\TeV} collected by CMS between 2016 and 2018, corresponding to an integrated luminosity of 138\ensuremath{\ \mathrm{fb}^{-1}}. The expected significance of the search is $1.86\sigma$, with an observed signal strength of $-0.19^{+0.48}_{-0.46}$. Finally, explainable AI techniques are applied to the MLPF and \ParticleNet algorithms using layerwise relevance propagation, showing that both models base their predictions on physically meaningful features consistent with our physics intuition. Together, these results demonstrate how advanced AI methods can enhance reconstruction, analysis sensitivity, and interpretability, shaping the next era of experimental parti cle physics.

Mokhtar, Farouk [UC, San Diego]↗
DOE OSTI2014

Evaluating sub-national building-energy efficiency policy options under uncertainty: Efficient sensitivity testing of alternative climate, technolgical, and socioeconomic futures in a regional intergrated-assessment model.

Improving the energy efficiency of the building stock, commercial equipment and household appliances can have a major impact on energy use, carbon emissions, and building services. Subnational regions such as U.S. states wish to increase their energy efficiency, reduce carbon emissions or adapt to climate change. Evaluating subnational policies to reduce energy use and emissions is difficult because of the uncertainties in socioeconomic factors, technology performance and cost, and energy and climate policies. Climate change may undercut such policies. Assessing these uncertainties can be a significant modeling and computation burden. As part of this uncertainty assessment, this paper demonstrates how a decision-focused sensitivity analysis strategy using fractional factorial methods can be applied to reveal the important drivers for detailed uncertainty analysis.

climate change↗
DOE OSTI2015

Metrological analysis of a virtual flowmeter-based transducer for cryogenic helium

The metrological performance of a virtual flowmeter-based transducer for monitoring helium under cryogenic conditions is assessed. At this aim, an uncertainty model of the transducer, mainly based on a valve model, exploiting finite-element approach, and a virtual flowmeter model, based on the Sereg-Schlumberger method, are presented. The models are validated experimentally on a case study for helium monitoring in cryogenic systems at the European Organization for Nuclear Research (CERN). The impact of uncertainty sources on the transducer metrological performance is assessed by a sensitivity analysis, based on statistical experiment design and analysis of variance. In this way, the uncertainty sources most influencing metrological performance of the transducer are singled out over the input range as a whole, at varying operating and setting conditions. This analysis turns out to be important for CERN cryogenics operation because the metrological design of the transducer is validated, and its components and working conditions with critical specifications for future improvements are identified.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

A novel method to predict internal relative humidity in cementitious materials by 1H NMR

In sealed cementitious materials, the internal relative humidity (RH) decrease is mainly due to the water activity depression caused by menisci formation in partially-saturated pores. This process can be conveniently described with the evolution of the Kelvin radius. To obtain the Kelvin radius, a novel method based on the evolution of pore volumes quantified by {sup 1}H nuclear magnetic resonance (NMR) and mercury intrusion porosimetry (MIP) was developed. This approach was validated against experimental results for cementitious materials with a range of water to cement ratios from 0.30 to 0.46. A comparison between the Kelvin radius calculated with this approach with a previously published method using data obtained from MIP and chemical shrinkage was presented. A sensitivity analysis for the new prediction method was performed using a bootstrapping technique.

36 MATERIALS SCIENCE↗
DOE OSTI2015

User's Guide for RESRAD-OFFSITE

The RESRAD-OFFSITE code can be used to model the radiological dose or risk to an offsite receptor. This User’s Guide for RESRAD-OFFSITE Version 3.1 is an update of the User’s Guide for RESRAD-OFFSITE Version 2 contained in the Appendix A of the User’s Manual for RESRAD-OFFSITE Version 2 (ANL/EVS/TM/07-1, DOE/HS-0005, NUREG/CR-6937). This user’s guide presents the basic information necessary to use Version 3.1 of the code. It also points to the help file and other documents that provide more detailed information about the inputs, the input forms and features/tools in the code; two of the features (overriding the source term and computing area factors) are discussed in the appendices to this guide. Section 2 describes how to download and install the code and then verify the installation of the code. Section 3 shows ways to navigate through the input screens to simulate various exposure scenarios and to view the results in graphics and text reports. Section 4 has screen shots of each input form in the code and provides basic information about each parameter to increase the user’s understanding of the code. Section 5 outlines the contents of all the text reports and the graphical output. It also describes the commands in the two output viewers. Section 6 deals with the probabilistic and sensitivity analysis tools available in the code. Section 7 details the various ways of obtaining help in the code.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2014

Study of nitrogen flowing afterglow with mercury vapor injection

The reaction kinetics in nitrogen flowing afterglow with mercury vapor addition was studied by optical emission spectroscopy. The DC flowing post-discharge in pure nitrogen was created in a quartz tube at the total gas pressure of 1000 Pa and discharge power of 130 W. The mercury vapors were added into the afterglow at the distance of 30 cm behind the active discharge. The optical emission spectra were measured along the flow tube. Three nitrogen spectral systems – the first positive, the second positive, and the first negative, and after the mercury vapor addition also the mercury resonance line at 254 nm in the spectrum of the second order were identified. The measurement of the spatial dependence of mercury line intensity showed very slow decay of its intensity and the decay rate did not depend on the mercury concentration. In order to explain this behavior, a kinetic model for the reaction in afterglow was developed. This model showed that the state Hg(6 {sup 3}P{sub 1}), which is the upper state of mercury UV resonance line at 254 nm, is produced by the excitation transfer from nitrogen N{sub 2}(A{sup 3}Σ{sup +}{sub u}) metastables to mercury atoms. However, the N{sub 2}(A{sup 3}Σ{sup +}{sub u}) metastables are also produced by the reactions following the N atom recombination, and this limits the decay of N{sub 2}(A{sup 3}Σ{sup +}{sub u}) metastable concentration and results in very slow decay of mercury resonance line intensity. It was found that N atoms are the most important particles in this late nitrogen afterglow, their volume recombination starts a chain of reactions which produce excited states of molecular nitrogen. In order to explain the decrease of N atom concentration, it was also necessary to include the surface recombination of N atoms to the model. The surface recombination was considered as a first order reaction and wall recombination probability γ = (1.35 ± 0.04) × 10{sup −6} was determined from the experimental data. Also sensitivity analysis was applied for the analysis of kinetic model in order to reveal the main control parameters in the model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Sensitivity of blackbody effective emissivity to wavelength and temperature: By genetic algorithm

A variable-temperature blackbody (VTBB) is used to calibrate an infrared radiation thermometer (pyrometer). The effective emissivity (ε{sub eff}) of a VTBB is dependent on temperature and wavelength other than the geometry of the VTBB. In the calibration process the effective emissivity is often assumed to be constant within the wavelength and temperature range. There are practical situations where the sensitivity of the effective emissivity needs to be known and correction has to be applied. We present a method using a genetic algorithm to investigate the sensitivity of the effective emissivity to wavelength and temperature variation. Two matlab® programs are generated: the first to model the radiance temperature calculation and the second to connect the model to the genetic algorithm optimization toolbox. The effective emissivity parameter is taken as a chromosome and optimized at each wavelength and temperature point. The difference between the contact temperature (reading from a platinum resistance thermometer or liquid in glass thermometer) and radiance temperature (calculated from the ε{sub eff} values) is used as an objective function where merit values are calculated and best fit ε{sub eff} values selected. The best fit ε{sub eff} values obtained as a solution show how sensitive they are to temperature and wavelength parameter variation. Uncertainty components that arise from wavelength and temperature variation are determined based on the sensitivity analysis. Numerical examples are considered for illustration.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Evaluation of Maximum Radionuclide Groundwater Concentrations for Basement Fill Model. Zion Station Restoration Project

The objectives of this report are; To present a simplified conceptual model for release from the buildings with residual subsurface structures that can be used to provide an upper bound on contaminant concentrations in the fill material; Provide maximum water concentrations and the corresponding amount of mass sorbed to the solid fill material that could occur in each building for use in dose assessment calculations; Estimate the maximum concentration in a well located outside of the fill material; and Perform a sensitivity analysis of key parameters.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Evaluation of Rapid for a UNF cask benchmark problem

To ensure criticality safety and maintain a high level of security of Used Nuclear Fuel (UNF), it is necessary to perform detailed Particle Transport simulations. The commonly used technique is the 3-D, continuous energy Monte Carlo method that is time-consuming and, for eigenvalue calculations, may have difficulties with the source convergence. Wenner and Haghighat and Dufek demonstrated that in criticality problems with high dominance ratio and/or under-sampling possibilities, the fission matrix (FM) methodology should be used in order to be able to obtain unbiased solution. The RAPID (Real-time Analysis for Particle transport and In-situ Detection) code system is based on the Multi-stage Response-function Transport (MRT) methodology, and uses the FM formulation to determine eigenvalue, subcritical multiplication and fission density distribution of nuclear systems. This paper uses a benchmark problem similar to the GBC-32 Cask to evaluate the accuracy and performance of RAPID against the results of a reference MCNP solution. We performed a detailed sensitivity analysis on a single-assembly model in order to determine the MCNP eigenvalue parameters necessary to obtain a reliable reference solution. These results provided a guide for the selection of an appropriate set of parameters for the eigenvalue calculation for the whole cask. The paper is organized as follows: initially, the RAPID code system is briefly discussed; then, the benchmark problem is introduced. The methodology used to analyze results is proposed and some of the results of different cases are analyzed to identify the best set of eigenvalue parameters for a reference MCNP calculation. The selected case is then compared with the RAPID calculation for both a single assembly and a full cask model. In the end some conclusions and future work perspectives are drawn.

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

Nonparametric Prediction Intervals of Wind Power via Linear Programming

This letter proposes a machine learning-based linear programming model that quickly establishes the nonparametric prediction intervals of wind power by integrating extreme learning machine and quantile regression. The proportions of quantiles can be adaptively determined via sensitivity analysis. The proposed method has been proven to be significantly efficient and reliable, with a high application potential in power systems.

extreme learning machine↗
DOE OSTI2017

Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

With the development of and advances in smartphones and global positioning system (GPS) devices, travelers’ long-term travel behaviors are not impossible to obtain. This study investigates the pattern of individual travel behavior and its correlation with social-demographic features. For different social-demographic groups (e.g., full-time employees and students), the individual travel behavior may have specific temporal-spatial-mobile constraints. The study first extracts the home-based tours, including Home-to-Home and Home-to-Non-Home, from long-term raw GPS data. The travel behavior pattern is then delineated by home-based tour features, such as departure time, destination location entropy, travel time, and driving time ratio. The travel behavior variability describes the variances of travelers’ activity behavior features for an extended period. After that, the variability pattern of an individual’s travel behavior is used for estimating the individual’s social-demographic information, such as social-demographic role, by a supervised learning approach, support vector machine. In this study, a long-term (18-month) recorded GPS data set from Puget Sound Regional Council is used. The experiment’s result is very promising. The sensitivity analysis shows that as the number of tours thresholds increases, the variability of most travel behavior features converges, while the prediction performance may not change for the fixed test data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Safety Assessment for the Kozloduy National Disposal Facility in Bulgaria - 13507

Due to the early decommissioning of four Water-Water Energy Reactors (WWER) 440-V230 reactors at the Nuclear Power Plant (NPP) near the city of Kozloduy in Bulgaria, large amounts of low and intermediate radioactive waste will arise much earlier than initially scheduled. In or-der to manage the radioactive waste from the early decommissioning, Bulgaria has intensified its efforts to provide a near surface disposal facility at Radiana with the required capacity. To this end, a project was launched and assigned in international competition to a German-Spanish consortium to provide the complete technical planning including the preparation of the Intermediate Safety Assessment Report. Preliminary results of operational and long-term safety show compliance with the Bulgarian regulatory requirements. The long-term calculations carried out for the Radiana site are also a good example of how analysis of safety assessment results can be used for iterative improvements of the assessment by pointing out uncertainties and areas of future investigations to reduce such uncertainties in regard to the potential radiological impact. The computer model used to estimate the long-term evolution of the future repository at Radiana predicted a maximum total annual dose for members of the critical group, which is carried to approximately 80 % by C-14 for a specific ingestion pathway. Based on this result and the outcome of the sensitivity analysis, existing uncertainties were evaluated and areas for reasonable future investigations to reduce these uncertainties were identified. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗