Machine learning enabled reduced-order scenario generation for stochastic analysis of solar power forecasts
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Energy & Fuels↗
SEARCH · Engineering Papers
Results for “uncertainty reduction”
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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Emulator-based Bayesian calibration of a subglacial drainage model
Subglacial drainage models, often motivated by the relationship between hydrology and ice flow, sensitively depend on numerous unconstrained parameters. We explore using borehole water-pressure time series to calibrate the uncertain parameters of a popular subglacial drainage model, taking a Bayesian perspective to quantify the uncertainty in parameter estimates and in the calibrated model predictions. To reduce the computation time associated with Markov Chain Monte Carlo sampling, we construct a fast Gaussian process emulator to stand in for the subglacial drainage model. We first carry out a calibration experiment using synthetic observations consisting of model simulations with hidden parameter values as a demonstration of the method. Using real borehole water pressures measured in western Greenland, we find meaningful constraints on four of the eight model parameters and a factor-of-three reduction in uncertainty of the calibrated model predictions. These experiments illustrate Gaussian process-based Bayesian inference as a useful tool for calibration and uncertainty quantification of complex glaciological models using field data. However, significant differences between the calibrated model and the borehole data suggest that structural limitations of the model, rather than poorly constrained parameters or computational cost, remain the most important constraint on subglacial drainage modelling.
58 GEOSCIENCES↗
Complete quantification of parametric uncertainties in ( d , p ) transfer reactions
Deuteron-induced transfer reactions are a popular probe in nuclear structure and nuclear astro physics studies. The interpretation of these transfer measurements relies on reaction theory that takes as input effective interactions between the nucleons and the target nucleus. Previous work quantified the uncertainty associated with the optical potentials between the nucleons and the target. In this study, we extend that work by also including the parameters of the mean field associated with the overlap function of the final bound state, thus obtaining the full parametric uncertainty on transfer observables. We use Bayesian Markov Chain Monte Carlo simulations to obtain parameter posterior distributions. We use elastic-scattering cross sections to constrain the optical potential parameters and use the asymptotic normalization coefficient of the final state to constrain the bound state interaction. We then propagate these posteriors to the transfer angular distributions and obtain confidence intervals for this observable. We study (d,p) reactions on 14 C, 16 O, and 48 Ca at energies in the range $E$ $d$ = 7 - 24 MeV. Our results show a strong reduction in uncertainty by using the asymptotic normalization coefficient as a constraint, particularly for those reactions most sensitive to ambiguities in the mean field. For those reactions, the importance of constraining the bound state interaction is equal to that of constraining the optical potentials. Furthermore, the case of 14 C is an outlier because the cross section is less sensitive to the nuclear interior. When minimal constraints are used on the parameters of the nucleon-target interaction, the 1σ uncertainties on the differential cross sections are large (~ 500-100%). However, if elastic-scattering data and the asymptotic normalization coefficient are used in the analysis, with an error of 10% (5%), this uncertainty reduces to ~ 30% (~ 15%).
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Uncertainty analysis for techno-economic and life-cycle assessment of wet waste hydrothermal liquefaction with centralized upgrading to produce fuel blendstocks
Wet waste hydrothermal liquefaction is a promising technology for producing transportation fuels with much lower greenhouse gases emissions than petroleum-based fuels. However, its techno-economic and life cycle assessment are primarily based on laboratory scale testing data, subject to considerable uncertainties, and even bias, due to knowledge gaps. Here, a preliminary uncertainty analysis of key economic measures was conducted based on the 2019 state-of-technology model for biocrude production. Building on the preliminary analysis, this work presents a comprehensive uncertainty analysis in both economic and environmental measures of the entire supply chain of wet waste hydrothermal liquefaction to fuel blendstocks including biocrude upgrading based on the 2021 state-of-technology model. The analysis includes the most recent developments in hydrothermal liquefaction and biocrude upgrading technologies and Monte Carlo simulation based on an integrated model system including an improved reactor yield model, reduced-order process model, discounted cash flow economic model and simplified life-cycle assessment model. The estimated biocrude yield ranges from 42.2% to 52.4% with a median of 47.3%. The estimated fuel yield ranges from 34.7% to 42.7% with a median of 38.7%. The estimated minimum fuel selling price ranges from $\$ $2.28/gge to $\$ $3.45/gge with a median of $\$ $2.80/gge. Relative to petroleum-derived diesel, the estimated reduction in supply chain greenhouse gas emissions ranges from 73.4% to 81.8% with a median of 77.7%. Compared to the 2019 state-of-technology analysis, a significant improvement in biocrude selectivity and economic measures and reduction in uncertainties were achieved due to the incorporation of additional continuous experimental data sets, technology development and de-risking, and improvement in model accuracy.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Impacts of EPA’s finalized power plant greenhouse gas standards
Emissions reductions may be met with relatively small costs.
Bistline, John↗
Machine Learning to Select Experiments Driven by Fundamental Science and Applications for Targeted Nuclear Data Improvement
This work describes a blueprint for a process that accelerates progress in science by quantitatively answering the following question: What is the optimal combination of fundamental-science and application-driven experiments to maximally reduce pertinent data uncertainties? Answering this question entails solving a high-dimensional and complex optimization problem that is best solved with advanced statistic techniques often classified as machine learning. We apply this process within the framework of nuclear data with the aim to select an experiment combination that will reduce uncertainties in 239 Pu nuclear data for neutron energies between 1 and 600 keV. In this field, fundamental-physics driven data, called differential, look at one nuclear physics observable at a time. They are contrasted to application-driven, integral, data where one or few resulting values inform a broad set of nuclear data across several nuclides and energies. The candidates for integral experiments are criticality measurements that were refined by a genetic algorithm to be maximally sensitive to 239 Pu fission cross sections in the desired energy range. Twenty-three candidate differential experiments were investigated and span multiple nuclear physics observables (e.g., total, capture cross sections) for isotopes appearing in the integral experiments. The optimal combination among these candidate experiments was investigated via generalized least squares fitting, augmented with Gaussian processes to ameliorate statistical irregularities in data, and the D-optimality criterion. The latter evaluates for each pair of candidates the joint reduction in uncertainties of all 12200 nuclear data appearing in the integral experiments compared to the knowledge we have from 168 past experiments, theory, and nuclear data. We chose as differential measurements those that investigate 63 Cu and 239 Pu total cross sections, based on D-optimality rank and feasibility constraints. Two integral (criticality) experiments were selected: An experiment with Al 2 O 3 and graphite interleaved with Pu and a thick Cu reflector explores 1–30 keV, while we target the 30–600 keV range with an experiment that swaps boron in place of graphite with a different geometry.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
A Diagnosis-Prognosis Feedback Loop for Improved Performance Under Uncertainties
The feed-forward relationship between diagnosis and prognosis is the foundation of both aircraft structural health management and the digital twin concept. Measurements of structural response are obtained either in-situ with mounted sensor networks or offline using more traditional techniques (e.g., nondestructive evaluation). Diagnosis algorithms process this information to detect and quantify damage and then feed this data forward to a prognostic framework. A prognosis of the structure's future operational readiness (e.g., remaining useful life or residual strength) is then made and is used to inform mission- critical decision-making. Years of research have been devoted to improving the elements of this process, but the process itself has not changed significantly. Here, a new approach is proposed in which prognosis information is not only fed forward for decision-making, but it is also fed back to the forthcoming diagnosis. In this way, diagnosis algorithms can take advantage of a priori information about the expected state of health, rather than operating in an uninformed condition. As a feasibility test, a diagnosis-prognosis feedback loop of this manner is demonstrated. The approach is applied to a numerical example in which fatigue crack growth is simulated in a simple aluminum alloy test specimen. A prognosis was derived from a set of diagnoses which provided feedback to a subsequent set of diagnoses. Improvements in accuracy and a reduction in uncertainty in the prognosis- informed diagnoses were observed when compared with an uninformed diagnostic approach.
Leser, Patrick E.↗
Space Radiation Cancer Risks
Space radiation presents major challenges to astronauts on the International Space Station and for future missions to the Earth s moon or Mars. Methods used to project risks on Earth need to be modified because of the large uncertainties in projecting cancer risks from space radiation, and thus impact safety factors. We describe NASA s unique approach to radiation safety that applies uncertainty based criteria within the occupational health program for astronauts: The two terrestrial criteria of a point estimate of maximum acceptable level of risk and application of the principle of As Low As Reasonably Achievable (ALARA) are supplemented by a third requirement that protects against risk projection uncertainties using the upper 95% confidence level (CL) in the radiation cancer projection model. NASA s acceptable level of risk for ISS and their new lunar program have been set at the point-estimate of a 3-percent risk of exposure induced death (REID). Tissue-averaged organ dose-equivalents are combined with age at exposure and gender-dependent risk coefficients to project the cumulative occupational radiation risks incurred by astronauts. The 95% CL criteria in practice is a stronger criterion than ALARA, but not an absolute cut-off as is applied to a point projection of a 3% REID. We describe the most recent astronaut dose limits, and present a historical review of astronaut organ doses estimates from the Mercury through the current ISS program, and future projections for lunar and Mars missions. NASA s 95% CL criteria is linked to a vibrant ground based radiobiology program investigating the radiobiology of high-energy protons and heavy ions. The near-term goal of research is new knowledge leading to the reduction of uncertainties in projection models. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. The current model for projecting space radiation cancer risk relies on the three assumptions of linearity, additivity, and scaling along with the use of population averages. We describe uncertainty estimates for this model, and new experimental data that sheds light on the accuracy of the underlying assumptions. These methods make it possible to express risk management objectives in terms of quantitative metrics, i.e., the number of days in space without exceeding a given risk level within well defined confidence limits. The resulting methodology is applied to several human space exploration mission scenarios including lunar station, deep space outpost, and a Mars mission. Factors that dominate risk projection uncertainties and application of this approach to assess candidate mitigation approaches are described.
Cucinotta, Francis A.↗
Developing the S 32 ( p , d ) S * 31 ( p ) ( γ ) reaction to probe the P 30 ( p , γ ) S 31 reaction rate in classical novae
Background: The 30 P(p,γ) 31 S reaction rate is one of the largest remaining sources of uncertainty in the final abundances of nuclei created in a classical nova involving a ONe white dwarf. The reaction rate directly influences silicon isotopic ratios, which are used as identifiers of presolar grains with nova origins. Additionally, the uncertainty in the 30 P(p,γ) 31 S reaction rate has been found to limit the use of nova nuclear thermometers based on observations of elemental ratios in nova ejecta. Purpose: Reduce uncertainties in the nuclear data for proton-unbound states in 31 S, which act as resonances for the 30 P(p,γ) 31 S reaction at classical nova temperatures, and develop a technique for high efficiency, high-resolution reaction-decay coincidence measurements. Methods: Here, the 32 S(p,d) 31 S reaction was used to populate the states of interest in 31 S. The experiment was performed at the Texas A&M Cyclotron Institute using the LLNL Hyperion array for the detection of charged particles and γ rays. A downstream silicon telescope was used to select reaction deuterons, and a single upstream silicon detector was used to measure protons emitted in the decay of unbound 31 S levels. Results: Several states in 31 S above the proton separation energy were observed to have been populated. Decay protons from the resonant states in 31 S were identified as events in the upstream silicon detectors that came in coincidence with deuterons in the downstream telescope. Protons emitted from these states were measured and branching ratios extracted. Conclusions: While no new reaction rate is derived, spin-parity assignments for several higher-lying proton unbound states have been confirmed. Measured p 0 branching ratios for these levels have been compared to previous measurements with good agreement, and in some cases provided a reduction in uncertainty. The previously identified T = 3/2 state may have been incorrectly assigned a large p 0 branching ratio in a previous measurement. The technique of measuring reaction-decay coincidences with a particle-gamma setup appears promising.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Global Top-Down Smoke-Aerosol Emissions Estimation Using Satellite Fire Radiative Power Measurements
Fire emissions estimates have long been based on bottom-up approaches that are not only complex, but also fraught with compounding uncertainties. We present the development of a global gridded (1 deg ×1 deg) emission coefficients (Ce) product for smoke total particulate matter (TPM) based on a top-down approach using coincident measurements of fire radiative power (FRP) and aerosol optical thickness (AOT) from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra and Aqua satellites. This new Fire Energetics and Emissions Research version 1.0 (FEER.v1) Ce product has now been released to the community and can be obtained from http://feer.gsfc. nasa.gov/, along with the corresponding 1-to-1 mapping of their quality assurance (QA) flags that will enable the Ce values to be filtered by quality for use in various applications. The regional averages of Ce values for different ecosystem types were found to be in the ranges of 16-21/gMJ−1 for savanna and grasslands, 15-32/gMJ−1 for tropical forest, 9-12/gMJ−1 for North American boreal forest, and 18- 26/MJ−1 for Russian boreal forest, croplands and natural vegetation. The FEER.v1 Ce product was multiplied by time-integrated FRP data to calculate regional smoke TPM emissions, which were compared with equivalent emissions products from three existing inventories. FEER.v1 showed higher and more reasonable smoke TPM estimates than two other emissions inventories that are based on bottom-up approaches and already reported in the literature to be too low, but portrayed an overall reasonable agreement with another top-down approach. This suggests that top-down approaches may hold better promise and need to be further developed to accelerate the reduction of uncertainty associated with fire emissions estimation in air-quality and climate research and applications. Results of the analysis of FEER.v1 data for 2004-2011 show that 65-85 Tg yr−1 of TPM is emitted globally from open biomass burning, with a generally decreasing trend over this short time period. The FEER.v1 Ce product is the first global gridded product in the family of "emission factors", that is based essentially on satellite measurements, and requires only direct satellite FRP measurements of an actively burning fire anywhere to evaluate its emission rate in near-real time, which is essential for operational activities, such as the monitoring and forecasting of smoke emission impacts on air quality.
biomass burning↗
The Energy-Energy Correlation in the back-to-back limit at N 3 LO and N 3 LL'
We present the analytic formula for the Energy-Energy Correlation (EEC) in electron-positron annihilation computed in perturbative QCD to next-to-next-to-next-to-leading order (N 3 LO) in the back-to-back limit. In particular, we consider the EEC arising from the annihilation of an electron-positron pair into a virtual photon as well as a Higgs boson and their subsequent inclusive decay into hadrons. Our computation is based on a factorization theorem of the EEC formulated within Soft-Collinear Effective Theory (SCET) for the back-to-back limit. We obtain the last missing ingredient for our computation — the jet function — from a recent calculation of the transverse-momentum dependent fragmentation function (TMDFF) at N 3 LO. We combine the newly obtained N 3 LO jet function with the well known hard and soft function to predict the EEC in the back-to-back limit. The leading transcendental contribution of our analytic formula agrees with previously obtained results in N = 4 supersymmetric Yang-Mills theory. We obtain the N = 2 Mellin moment of the bulk region of the EEC using momentum sum rules. Finally, we obtain the first resummation of the EEC in the back-to-back limit at N 3 LL' accuracy, resulting in a factor of ~ 4 reduction of uncertainties in the peak region compared to N 3 LL predictions.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Understanding and reducing the uncertainties of land surface energy flux partitioning within CMIP6 land models
Not Available
54 ENVIRONMENTAL SCIENCES↗
Optimal experimental design for prediction based on push-forward probability measures
Incorporating experimental data is essential for increasing the credibility of simulation-aided decision making and design. This paper presents a method which uses a computational model to guide the optimal acquisition of experimental data to produce data-informed predictions of quantities of interest (QoI). Many strategies for optimal experimental design (OED) select data that maximize some utility that measures the reduction in uncertainty of uncertain model parameters, for example the expected information gain between prior and posterior distributions of these parameters. In this paper, we seek to maximize the expected information gained from the pushforward of an initial (prior) density to the push-forward of the updated (posterior) density through the parameter-to-prediction map. The formulation presented is based upon the solution of a specific class of stochastic inverse problems which seeks a probability density that is consistent with the model and the data in the sense that the push-forward of this density through the parameter-to-observable map matches a target density on the observable data. While this stochastic inverse problem forms the mathematical basis for our approach, we develop a one-step algorithm, focused on push-forward probability measures, that leverages inference-for-prediction to bypass constructing the solution to the stochastic inverse problem. A number of numerical results are presented to demonstrate the utility of this optimal experimental design for prediction and facilitate comparison of our approach with traditional OED.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
A CALPHAD-informed approach to modeling constituent redistribution in Zr-based metallic fuels using BISON
Here, a CALPHAD-informed (Computer Coupling of Phase Diagrams and Thermochemistry) constituent redistribution model was developed for Zr-based metallic fuels and incorporated into the BISON fuel performance code. Three uncertain model parameters associated with β and γ phase kinetics were calibrated using integral test data from U-Zr fuel elements irradiated in Experimental Breeder Reactor II. The calibrated constituent redistribution model was shown to predict the behavior of U-Zr fuels with excellent accuracy. Model predictions for U-Pu-Zr fuels were physically reasonable but less accurate. Reduction of uncertainties in the ternary phase transition temperatures and collection of kinetic data for the ζ phase are expected to improve the model’s ternary predictions. Finally, the new model was coupled to existing thermomechanics models in BISON to simulate irradiation of an entire U-Zr fuel element, demonstrating its ability to accurately predict the behavior of U-Zr fuels with realistic geometries and mesh resolutions at the engineering scale.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Process Interactions Can Change Process Ranking in a Coupled Complex System Under Process Model and Parametric Uncertainty
For a complex hydrologic system with multiple processes and process interactions, global sensitivity analysis is often used to identify important or influential parameters for model development and improvement. The identification is complicated by process model uncertainty, when a system process can be represented by multiple process models. This study develops a new total-effect process sensitivity index to identify influential processes under model uncertainty. This is done by extending Sobol's total-effect parameter sensitivity index for one system model to total-effect process sensitivity index for multiple system models to account for uncertainty in process models and model parameters. The total-effect process sensitivity index includes not only the first-order process sensitivity index for measuring the importance of individual processes but also higher-order indices that account for process interactions. The total-effect process sensitivity index can identify an influential process that itself and its interactions with other processes influence a model output. Here, the total-effect process sensitivity index is applied to two numerical examples: (a) Sobol's G*-functions with analytical solutions of first-order and total-effect process sensitivity indices, and (b) groundwater flow models with interactions between recharge, geology, and snowmelt processes. The second evaluation shows that, due to second-order and higher-order process interactions, the first-order and total-effect process sensitivity indices give different process ranking. It is thus necessary to estimate both first-order and total-effect process sensitivity indices to appreciate the difference between the first-order impact of a process alone and the overall total-effect impact of the process itself and its interactions with other processes on a model output.
54 ENVIRONMENTAL SCIENCES↗
Wetter California Projected by CMIP6 Models With Observational Constraints Under a High GHG Emission Scenario
As the world's fifth-largest economy entity, California (CA) is vulnerable to climate changes especially the magnitude of winter (wet season) precipitation that is closely linked to regional drought severity, vegetation growth, and wildfire activities. However, the fate of CA precipitation in the future remains highly uncertain, for example, the state-of-the-art Earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) projected –3% to +42% and –27% to +63% precipitation changes in northern and central-southern CA, respectively, under the high greenhouse gas (GHG) emission scenario (SSP585). In this work, we applied the Pareto optimality concept and used observed teleconnection patterns (causal relationships between ocean regions and CA precipitation) to mechanistically constrain CMIP6 projected CA precipitations. We estimated that precipitation will robustly increase by 0.4–1.3 mm d –1 (10–34%) over northern CA and increase by 0.1–0.5 mm d –1 (7–32%) over central-southern CA by the end of the 21st century compared with present-day. Up to 71% of ESM projection uncertainties were reduced mainly due to the strong and consistent causal relationship between North American west coast sea level pressure and CA precipitation in both observations and CMIP6 models. Our results suggest that teleconnection patterns are powerful mechanistic constraints that can help explain and reduce uncertainties in ESM projections.
54 ENVIRONMENTAL SCIENCES↗
Probing the limits of cosmological information from the Lyman- α forest 2-point correlation functions
The standard cosmological analysis with the Lyα forest relies on a continuum fitting procedure that suppresses information on large scales and distorts the three-dimensional correlation function on all scales. In this work, we present the first cosmological forecasts without continuum fitting distortion in the Lyα forest, focusing on the recovery of large-scale information. Using idealized synthetic data, we compare the constraining power of the full shape of the Lyα forest auto-correlation and its cross-correlation with quasars using the baseline continuum fitting analysis versus the true continuum. We find that knowledge of the true continuum enables a ∼ 10% reduction in uncertainties on the Alcock-Paczyński (AP) parameter and the matter density, Ω m . We also explore the impact of large-scale information by extending the analysis up to separations of 240 h -1 Mpc along and across the line of sight. The combination of these analysis choices can recover significant large-scale information, yielding up to a ∼ 15% improvement in AP constraints. This improvement is analogous to extending the Lyα forest survey area by ∼ 40%.
Lyman alpha forest↗
Latent bone modelling for estimation of plutonium concentration in skeleton of former nuclear workers
Abstract The skeleton is a major plutonium retention site in the human body. Estimation of the total plutonium activity in the skeleton is a challenging problem. For most tissue donors at the United States Transuranium and Uranium Registries, a limited number of bone samples is available. The skeleton activity is calculated using plutonium activity concentration (Cskel) and skeleton weight. In this study, latent bone modelling was used to estimate Cskel from the limited number of analysed bone samples. Data from 13 non-osteoporotic whole-body donors were used to develop latent bone model (LBM) to estimate Cskel for seven cases with four to eight analysed bone samples. LBM predictions were compared to Cskel estimated using an arithmetic mean in terms of accuracy and precision. For the studied cases, LBM offered a significant reduction of uncertainty of Cskel estimate.
Environmental Sciences & Ecology↗