Electric sector impacts of renewable policy coordination: A multi-model study of the North American energy system
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Bistline, John E. T.↗
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Multipole responses in fissioning nuclei and their uncertainties
Electromagnetic multipole responses are key inputs to model the structure, decay, and reactions of atomic nuclei. With the introduction of the finite amplitude method (FAM), large-scale calculations of the nuclear linear response in heavy deformed nuclei have become possible. This work provides a detailed study of multipole responses in actinide nuclei with Skyrme energy density functionals. We quantify both systematic and statistical uncertainties induced by the functional parametrization in FAM calculations. We also extend the FAM formalism to perform blocking calculations with the equal filling approximation for odd-mass and odd-odd nuclei, and analyze the impact of blocking configurations on the response. In conclusion, by examining the entire plutonium isotopic chain from the proton to the neutron drip line, we find a large variability of the response with the neutron number and study how it correlates with the deformation of the nuclear ground state.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Physical Review Journal Club: Structural uncertainty assessment for fire-engulfed objects in crosswind: Establishing credibility for a multiphysics wall-modeled large-eddy simulation paradigm
This is an invited presentation for APS's monthly Journal Club where important papers that have recently been accepted for publication are highlighted in a seminar-like setting. The presentation will be "live" (May 1, 2025) and streamed across the world (including an archived recording).
Domino, Stefan Paul [Sandia National Laboratories ↗
Computational methods for structural load and resistance modeling
An automated capability for computing structural reliability considering uncertainties in both load and resistance variables is presented. The computations are carried out using an automated Advanced Mean Value iteration algorithm (AMV +) with performance functions involving load and resistance variables obtained by both explicit and implicit methods. A complete description of the procedures used is given as well as several illustrative examples, verified by Monte Carlo Analysis. In particular, the computational methods described in the paper are shown to be quite accurate and efficient for a material nonlinear structure considering material damage as a function of several primitive random variables. The results show clearly the effectiveness of the algorithms for computing the reliability of large-scale structural systems with a maximum number of resolutions.
Thacker, B. H.↗
The Role of Global Hydrologic Processes in Interannual and Long-Term Climate Variability
The earth's climate and its variability is linked inextricably with the presence of water on our planet. El Nino / Southern Oscillation-- the major mode of interannual variability-- is characterized by strong perturbations in oceanic evaporation, tropical rainfall, and radiation. On longer time scales, the major feedback mechanism in CO2-induced global warming is actually that due to increased water vapor holding capacity of the atmosphere. The global hydrologic cycle effects on climate are manifested through influence of cloud and water vapor on energy fluxes at the top of atmosphere and at the surface. Surface moisture anomalies retain the "memory" of past precipitation anomalies and subsequently alter the partitioning of latent and sensible heat fluxes at the surface. At the top of atmosphere, water vapor and cloud perturbations alter the net amount of radiation that the earth's climate system receives. These pervasive linkages between water, radiation, and surface processes present major complexities for observing and modeling climate variations. Major uncertainties in the observations include vertical structure of clouds and water vapor, surface energy balance, and transport of water and heat by wind fields. Modeling climate variability and change on a physical basis requires accurate by simplified submodels of radiation, cloud formation, radiative exchange, surface biophysics, and oceanic energy flux. In the past, we m safely say that being "data poor' has limited our depth of understanding and impeded model validation and improvement. Beginning with pre-EOS data sets, many of these barriers are being removed. EOS platforms with the suite of measurements dedicated to specific science questions are part of our most cost effective path to improved understanding and predictive capability. This talk will highlight some of the major questions confronting global hydrology and the prospects for significant progress afforded by EOS-era measurements.
Robertson, Franklin R.↗
Neutron star models
The current state of neutron star structure calculations is reviewed. Uncertainties in the equation of state for matter at and above nuclear density remain. The role of the delta resonance, pion condensates, and quark matter is reviewed. It is found that recent models yield stable neutron star masses which are consistent with observational estimates.
Canuto, V.↗
Quantification of modeling uncertainty in the Rayleigh damping model
Understanding and accurately characterizing energy dissipation mechanisms in civil structures during earthquakes is an important element of seismic assessment and design. The most commonly used model is attributed to Rayleigh. This paper proposes a systematic approach to quantify the uncertainty associated with Rayleigh's damping model. Bayesian calibration with embedded model error is employed to treat the coefficients of the Rayleigh model as random variables using modal damping ratios. Through a numerical example, we illustrate how this approach works and how the calibrated model can address modeling uncertainty associated with the Rayleigh damping model.
42 ENGINEERING↗
A Probabilistic Reasoner Based on Bayes Risk for Damage Detection in Structural Systems
Structural health monitoring (SHM) systems are used to inform operation of structural systems subject to loads and environments that may affect their integrity. SHM systems rely on continuous monitoring of the structure to determine its health state. These systems are often coupled with a model of the deployed structure to determine the consequences of changes in the system by forecasting the response to future states. These models, which may be thought of as digital twins, need to be updated to reflect the latest state of the structural system. This work makes use of an uncertainty-aware machine learning model that enforces distance preservation of the original input space to determine deviations from the training data input space distributions. This workflow enables domain shift detection to determine whether damage is present in the structure. The uncertainty metrics generated by this network are then used in a Bayes risk framework to design an optimal damage detector given cost and risk considerations. The approach is demonstrated on a computational example with simulated damage.
Najera-Flores, David [ATA Engineering, Inc.]↗
Performance and Reliability Optimization for Aerospace Systems subject to Uncertainty and Degradation
This report summarizes work performed by the Space Systems Laboratory (SSL) for NASA Langley Research Center in the field of performance optimization for systems subject to uncertainty. The objective of the research is to develop design methods and tools to the aerospace vehicle design process which take into account lifecycle uncertainties. It recognizes that uncertainty between the predictions of integrated models and data collected from the system in its operational environment is unavoidable. Given the presence of uncertainty, the goal of this work is to develop means of identifying critical sources of uncertainty, and to combine these with the analytical tools used with integrated modeling. In this manner, system uncertainty analysis becomes part of the design process, and can motivate redesign. The specific program objectives were: 1. To incorporate uncertainty modeling, propagation and analysis into the integrated (controls, structures, payloads, disturbances, etc.) design process to derive the error bars associated with performance predictions. 2. To apply modern optimization tools to guide in the expenditure of funds in a way that most cost-effectively improves the lifecycle productivity of the system by enhancing the subsystem reliability and redundancy. The results from the second program objective are described. This report describes the work and results for the first objective: uncertainty modeling, propagation, and synthesis with integrated modeling.
Miller, David W.↗
Modeling Meteor Flares for Spacecraft Safety
NASA's Meteoroid Environment Office (MEO) is tasked with assisting spacecraft operators and engineers in quantifying the threat the meteoroid environment poses to their individual missions. A more complete understanding of the meteoroid environment for this application requires extensive observations. One manner by which the MEO observes meteors is with dedicated video camera systems that operate nightly. Connecting the observational data from these video cameras to the relevant physical properties of the ablating meteoroids, however, is subject to sizable observational and theoretical uncertainties. Arguably the most troublesome theoretical uncertainty in ablation is a model for the structure of meteoroids, as observations clearly show behaviors wholly inconsistent with meteoroids being homogeneous spheres. Further complicating the interpretation of the observations in the context of spacecraft risk is the ubiquitous process of fragmentation and the flares it can produce, which greatly muddles any attempts to estimating initial meteoroid masses. In this talk a method of estimating the mass distribution of fragments in flaring meteors using high resolution video observations will be dis- cussed. Such measurements provide an important step in better understanding of the structure and fragmentation process of the parent meteoroids producing these flares, which in turn may lead to better constraints on meteoroid masses and reduced uncertainties in spacecraft risk.
Ehlert, Steven↗
Our Sun IV: The Standard Model and Helioseismology: Consequences of Uncertainties in Input Physics and in Observed Solar Parameters
Helioseismic frequency observations provide an extremely accurate window into the solar interior; frequencies from the Michaelson Doppler Imager (MDI) on the Solar and Heliospheric Observatory (SOHO) spacecraft, enable the adiabatic sound speed and adiabatic index to be inferred with an accuracy of a few parts in 10(exp 4) and the density with an accuracy of a few parts in 10(exp 3). This has become a Serious challenge to theoretical models of the Sun. Therefore, we have undertaken a self-consistent, systematic study of the sources of uncertainties in the standard solar models. We found that the largest effect on the interior structure arises from the observational uncertainties in the photospheric abundances of the elements, which affect the sound speed profile at the level of 3 parts in 10(exp 3). The estimated 4% uncertainty in the OPAL opacities could lead to effects of 1 part in 10(exp 3); the approximately 5%, uncertainty in the basic pp nuclear reaction rate would have a similar effect, as would uncertainties of approximately 15% in the diffusion constants for the gravitational settling of helium. The approximately 50% uncertainties in diffusion constants for the heavier elements would have nearly as large an effect. Different observational methods for determining the solar radius yield results differing by as much as 7 parts in 10(exp 4); we found that this leads to uncertainties of a few parts in 10(exp 3) in the sound speed int the solar convective envelope, but has negligible effect on the interior. Our reference standard solar model yielded a convective envelope position of 0.7135 solar radius, in excellent agreement with the observed value of 0.713 +/- 0.001 solar radius and was significantly affected only by Z/X, the pp rate, and the uncertainties in helium diffusion constants. Our reference model also yielded envelope helium abundance of 0.2424, in good agreement with the approximate range of 0.24 to 0.25 inferred from helioseismic observations; only extreme Z/X values yielded envelope helium abundance outside this range. We found that other current uncertainties, namely, in the solar age and luminosity, in nuclear rates other than the pp reaction, in the low-temperature molecular opacities, and in the low-density equation of state, have no significant effect on the quantities that can be inferred from helioseismic observations. The predicted pre-main-sequence lithium depletion is uncertain by a factor of 2. The predicted neutrino capture rate is uncertain by approximately 30% for the Cl-27 experiment and by approximately 3% for Ga-71 experiments, while the B-8 neutrino flux is uncertain by approximately 30%.
Boothroyd, Arnold I.↗
OC6 Project Phase IV: Validation of Numerical Models for Novel Floating Offshore Wind Support Structures
The fourth phase of the Offshore Code Comparison, Collaboration, Continued, with Correlation and unCertainty (OC6) aims to assess the ability of the tools to model a novel floating wind design. The design that was selected for this project is the TetraSpar designed by Stiesdal Offshore Technologies. The platform is a unique design that features a spar like stability with a very low center of gravity, but does not require deep-water ports. This combination is achieved with a system made of two separate steel structures, a hull at the water’s surface that supports the wind turbine tower, and a keel that is suspended below the main body. This lower keel has a very high density and drives the total platform’s low center of gravity. Scaled model tests of the TetraSpar design were performed in 2018-2019 by the University of Maine. The tests were performed with 1:43 Froude scaling. The data collected in the model tests provide a detailed description of the response of the platform in various controlled and relatively well understood conditions. Comparison with these data provides a good opportunity for validation of numerical models. The OC6 Phase IV work will focus on the specific parameters used in these scaled model tests. The load cases (LC) considered in this project are as follows: LC 1.X - Equilibrium and Static Offset Tests LC 2.X - Platform Free Decays LC 3.X - Turbine Thrust (Wind Only) LC 4.X - Regular and Irregular Waves (Wave only) LC 5.X - Combined Wind and Wave Loading
17 WIND ENERGY↗
Numerical simulation of asteroid geometry variance on airburst threat
For an atmospheric airburst the primary source of concern when assessing uncertainty is the size and velocity. Determining these properties provides the basis for threat assessment, as the total energy of the asteroid may then be estimated, and the threat investigated thoroughly. Even with clarity as to how much energy an asteroid may deposit, a great deal of uncertainty still exists for the actual energy deposition process. One such source of uncertainty is the geometry of the incoming asteroid. The geometry of an asteroid will alter the stress distribution during entry, which adds uncertainty to when fracture will occur. Here, in this study, we use Smoothed Particle Hydrodynamics to model the atmospheric airburst of Tunguska-scale asteroids with varying geometric profiles, including a sphere, ellipsoid, binary and superellipsoid. Each asteroid is modeled as a homogenous structure with strength. We assess uncertainty through a series of planar 2D simulation cases for each geometry, comparing the source of stochasticity across geometries. A single 3D airburst simulation for each geometry is also analyzed. Additionally, the 3D cases are compared to the highly uncertain Tunguska event, predicting variance in burst height across geometries, but all bounded by theoretical burst heights proposed for Tunguska.
Airburst↗
Mid-frequency Band Dynamics of Large Space Structures
High and low intensity dynamic environments experienced by a spacecraft during launch and on-orbit operations, respectively, induce structural loads and motions, which are difficult to reliably predict. Structural dynamics in low- and mid-frequency bands are sensitive to component interface uncertainty and non-linearity as evidenced in laboratory testing and flight operations. Analytical tools for prediction of linear system response are not necessarily adequate for reliable prediction of mid-frequency band dynamics and analysis of measured laboratory and flight data. A new MATLAB toolbox, designed to address the key challenges of mid-frequency band dynamics, is introduced in this paper. Finite-element models of major subassemblies are defined following rational frequency-wavelength guidelines. For computational efficiency, these subassemblies are described as linear, component mode models. The complete structural system model is composed of component mode subassemblies and linear or non-linear joint descriptions. Computation and display of structural dynamic responses are accomplished employing well-established, stable numerical methods, modern signal processing procedures and descriptive graphical tools. Parametric sensitivity and Monte-Carlo based system identification tools are used to reconcile models with experimental data and investigate the effects of uncertainties. Models and dynamic responses are exported for employment in applications, such as detailed structural integrity and mechanical-optical-control performance analyses.
dynamics↗
Uncertainty-aware mixed-variable machine learning for materials design
Abstract Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.
36 MATERIALS SCIENCE↗
Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections
Abstract Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state‐of‐the‐art knowledge in an internally self‐consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain‐specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low‐emissions SSP1‐1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long‐term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.
54 ENVIRONMENTAL SCIENCES↗
Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections
Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modelling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850-1900, using an observationally-based historical warming estimate of 0.8°C between 1850-1900 and 1995-2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above pre-industrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.
Climate↗
Higher Flux from the Young Sun as an Explanation for Warm Temperatures for Early Earth and Mars
Observations indicate that the Earth was at least warm enough for liquid water to exist as far back as 4 Gyr ago, namely, as early as half a billion years after the formation of the Earth; in fact, there is evidence suggesting that Earth may have been even warmer then than it is now. These relatively warm temperatures required on early Earth are in apparent contradiction to the dimness of the early Sun predicted by the standard solar models. This problem has generally been explained by assuming that Earth's early atmosphere contained huge amounts of carbon dioxide (CO2), resulting in a large enough greenhouse effect to counteract the effect of a dimmer Sun. However, recent work places an upper limit of 0.04 bar on the partial pressure of CO2 in the period from 2.75 to 2.2 Gyr ago, based on the absence of siderite in paleosols; this casts doubt on the viability of a strong CO2 greenhouse effect on early Earth. The existence of liquid water on early Mars has been even more of a puzzle; even the maximum possible CO2 greenhouse effect cannot yield warm enough Martian surface temperatures. These problems can be resolved simultaneously for both Earth and Mars, if the early Sun was brighter than predicted by the standard solar models. This could be accomplished if the early Sun was slightly more massive than it is now, i.e., if the solar wind was considerably stronger in the past than at present. A slightly more massive young Sun would have left fingerprints on the internal structure of the present Sun. Today, helioseismic observations exist that can measure the internal structure of the Sun with very high precision. The task undertaken here was to compute solar models with the highest precision possible at this time, starting with slightly greater initial masses. These were evolved to the present solar age, where comparisons with the helioseismic observations could be made. Our computations also yielded the time evolution of the solar flux at the planets - a key input to the climates of early Earth and Mars. Early solar mass loss is not the only influence that can alter the internal structure of the present Sun. There are minor uncertainties in the physics of the solar models and in the key observed solar parameters that also affect the present Sun's internal structure. It was therefore imperative to obtain an understanding of the effects of these other uncertainties, in order to disentangle them from the fingerprints that might be left by early solar mass loss. From these considerations, our work was divided into two parts: (1) We first computed the evolution of standard solar models with input parameters varied within their uncertainties, to determine their effect on the observable helioseismic quantities; (2) We then computed non-standard solar models with higher initial masses to test against the helioseismological observations.
Sackmann, I.-Juliana↗