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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Robust Design Optimization via Failure Domain Bounding

This paper extends and applies the strategies recently developed by the authors for handling constraints under uncertainty to robust design optimization. For the scope of this paper, robust optimization is a methodology aimed at problems for which some parameters are uncertain and are only known to belong to some uncertainty set. This set can be described by either a deterministic or a probabilistic model. In the methodology developed herein, optimization-based strategies are used to bound the constraint violation region using hyper-spheres and hyper-rectangles. By comparing the resulting bounding sets with any given uncertainty model, it can be determined whether the constraints are satisfied for all members of the uncertainty model (i.e., constraints are feasible) or not (i.e., constraints are infeasible). If constraints are infeasible and a probabilistic uncertainty model is available, upper bounds to the probability of constraint violation can be efficiently calculated. The tools developed enable approximating not only the set of designs that make the constraints feasible but also, when required, the set of designs for which the probability of constraint violation is below a prescribed admissible value. When constraint feasibility is possible, several design criteria can be used to shape the uncertainty model of performance metrics of interest. Worst-case, least-second-moment, and reliability-based design criteria are considered herein. Since the problem formulation is generic and the tools derived only require standard optimization algorithms for their implementation, these strategies are easily applicable to a broad range of engineering problems.

Crespo, Luis G.↗
DOE OSTI2022

Holocene paleoclimate change in the western US: The importance of chronology in discerning patterns and drivers

We report sediment in lakes and meadows forms a powerful archive that can be used to reconstruct environmental change through time. Reconstructions of lake level, of chemical, biological, and hydrological conditions, and of surrounding vegetation provide detailed information about past climate conditions, both locally and regionally. Indeed, most of our current knowledge of centennial- to millennial-scale climate variability in the arid western United States, where information about past hydroclimate is particularly important, comes from such sediment-based reconstructions. The pressing need for robust, precise predictions of future conditions is a significant motivation for paleoclimate science, and current research questions frequently require Holocene reconstructions to be resolved at sub-centennial timescales. Increasingly, regional syntheses seek to identify synoptic-scale patterns similar to those defined from modern observations (seasonal, interannual, multi-decadal, etc.) or to compare with the output of climate model simulations. However, the age control on existing records, especially those more than about 20 years old, is often sufficient only for millennial-scale interpretation. Here we assess the age control for 84 published and unpublished records from lakes and meadows in the Great Basin, California, and desert southwest, and use Bayesian modeling to evaluate the 95% uncertainty ranges for the 42 best-dated records. In the Late Holocene, about half of the 42 records have <400-year mean uncertainty ranges; however, high-precision age control is especially critical for young records, used to develop an accurate understanding of a proxy’s response to known climate variations. In the Middle Holocene, records vary from 400 to >800-year mean uncertainty and records of the Early Holocene have 600- to >1400-year mean uncertainty ranges. We find that the largest control on modeled uncertainties is dating density, with at least 2 dates/kyr being optimal and suggest obtaining “range-finder” dates at the onset of a study to better predict the total number of dates needed for an adequate age model. Such a density avoids a commonly observed phenomenon of significant peaks in uncertainty arising in gaps between age control points. Analysis of the uncertainties associated with proxy shifts reveal that more than half are >400 years. Although such large uncertainties currently prevent sub-centennial interpretations in most cases, increased dating density, strategic use of limited funds (including budgeting for a 2 date/kyr minimum at the proposal stage), construction of age-depth models with Bayesian methods, and critical evaluation of chronological uncertainty will shed light on past climate variability at finer timescales, enhancing our understanding of global and regional drivers of western U.S. climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression

Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The existing standard calibration framework suffers from inferential issues when the model output and observational data are high-dimensional dependent data, such as large time series, due to the difficulty in building an emulator and the nonidentifiability between effects from input parameters and data-model discrepancy. To overcome these challenges, we, in this work, propose a new calibration framework based on a deep neural network (DNN) with long short-term memory layers that directly emulates the inverse relationship between the model output and input parameters. Adopting the “learning with noise” idea, we train our DNN model to filter out the effects from data-model discrepancy on input parameter inference. We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input parameter estimates. Through a simulation study and real data application with the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro), we show our approach can yield accurate point estimates and well-calibrated interval estimates for input parameters.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Eigenvalue perturbation models for flexible structures

It is pointed out that real parametric modal frequency and damping variation for lightly damped systems does not resemble disks on the complex plane. Using complex uncertainty (and therefore disklike) models can introduce conservativeness in the design method. An alternative means of developing suitable complex uncertainty models is presented. It involves treating the uncertainty as perturbations to the system eigenvalues and using a particular linear fractional transformation to cover this uncertainty. This approach is applicable to modes within the bandwidth of control. It can be used in conjunction with the standard perturbation modeling approaches. A simple SISO (single-input single-output) example, motivated by a flexible truss experiment at the Jet Propulsion Laboratory, is discussed in order to illustrate the proposed approach.

Smith, Roy S.↗
NASA NTRS2005

Probabilistic Analysis Techniques Applied to Complex Spacecraft Power System Modeling

Electric power system performance predictions are critical to spacecraft, such as the International Space Station (ISS), to ensure that sufficient power is available to support all the spacecraft s power needs. In the case of the ISS power system, analyses to date have been deterministic, meaning that each analysis produces a single-valued result for power capability because of the complexity and large size of the model. As a result, the deterministic ISS analyses did not account for the sensitivity of the power capability to uncertainties in model input variables. Over the last 10 years, the NASA Glenn Research Center has developed advanced, computationally fast, probabilistic analysis techniques and successfully applied them to large (thousands of nodes) complex structural analysis models. These same techniques were recently applied to large, complex ISS power system models. This new application enables probabilistic power analyses that account for input uncertainties and produce results that include variations caused by these uncertainties. Specifically, N&R Engineering, under contract to NASA, integrated these advanced probabilistic techniques with Glenn s internationally recognized ISS power system model, System Power Analysis for Capability Evaluation (SPACE).

Hojnicki, Jeffrey S.↗
NASA NTRS2003

Stochastic Control Synthesis of Systems with Structured Uncertainty

This paper presents a study on the design of robust controllers by using random variables to model structured uncertainty for both SISO and MIMO feedback systems. Once the parameter uncertainty is prescribed with probability density functions, its effects are propagated through the analysis leading to stochastic metrics for the system's output. Control designs that aim for satisfactory performances while guaranteeing robust closed loop stability are attained by solving constrained non-linear optimization problems in the frequency domain. This approach permits not only to quantify the probability of having unstable and unfavorable responses for a particular control design but also to search for controls while favoring the values of the parameters with higher chance of occurrence. In this manner, robust optimality is achieved while the characteristic conservatism of conventional robust control methods is eliminated. Examples that admit closed form expressions for the probabilistic metrics of the output are used to elucidate the nature of the problem at hand and validate the proposed formulations.

Padula, Sharon L.↗
DOE OSTI2025

Axion pulsarscope

Electromagnetic fields surrounding pulsars may source coherent ultralight axion signals at the known rotational frequencies of the neutron stars, which can be detected by laboratory experiments (e.g., pulsarscopes). As a promising case study, we model axion emission from the well-studied Crab pulsar, which would yield a prominent signal at 𝑓 ≈ 29.6 Hz regardless of whether the axion contributes to the dark matter abundance. We estimate the relevant sensitivity of future axion dark matter detection experiments such as DMRadio-GUT, Dark SRF, and CASPEr, assuming different magnetosphere models to bracket the uncertainty in astrophysical modeling. For example, depending on final experimental parameters, the Dark SRF experiment could probe axions with any mass 𝑚 𝑎 ≪ 10 −13 eV down to 𝑔 𝑎⁢𝛾⁢𝛾 ∼3 × 10 −13 GeV −1 with one year of data and assuming the vacuum magnetosphere model. These projected sensitivities may be degraded depending on the extent to which the magnetosphere is screened by charge-filled plasma. The promise of pulsar-sourced axions as a clean target for direct detection experiments motivates dedicated simulations of axion production in pulsar magnetospheres.

axions↗
DOE OSTI2021

Sparse Control Synthesis for Uncertain Responsive Loads With Stochastic Stability Guarantees

In this report, recent studies have demonstrated the potential of flexible loads in providing frequency response services, predominantly due to their availability and cost-effectiveness. However, uncertainty and variability in various weather-related and end-use behavioral factors often impact the reliability of demand-side control performance. This work addresses this problem with the design of a demand-side control to achieve frequency response under load uncertainties. Our approach involves modeling the load uncertainties via stochastic processes that appear as both multiplicative and additive in the power system dynamics. Recently developed mean square exponential stability (MSES) results for continuous-time linear stochastic systems are applied to pose the control synthesis problem which results in an LMI-based optimization problem. Additional costs and constraints are added to the LMI-based controller synthesis to ensure MSES, improve closed-loop transient performance, maximize tolerable uncertainties, and promote sparsity in the controller. Additionally, the fundamental limitations between the tolerable uncertainties and control efforts while ensuring MSES are discussed. Further, the control synthesis problem for the case of the full-state measurement is generalized to the case of partial-state measurements. The proposed control synthesis is illustrated on an IEEE 39 bus system with rigorous studies to demonstrate the role of sparsity, closed-loop transient performance, tolerable uncertainties, and control efforts while ensuring MSES and achieving frequency response.

42 ENGINEERING↗
DOE OSTI2021

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

In-Situ Inference: Bringing Advanced Data Science Into Exascale Simulations

As simulations generate ever-increasing amounts of data, there are correspondingly richer opportunities for analysis and scientific discovery - discoveries that will be missed if most of the data must be discarded before it is analyzed. Because future exascale architectures will be increasingly storage-limited, it will not be possible to save the vast majority of simulation data for later analysis, requiring analysis to occur “in-situ” within the simulation. However, existing in-situ data analysis frameworks provide little or no support for one of the most sophisticated forms of data science: probabilistic statistical modeling or uncertainty quantification (UQ), and the accompanying challenge of inference - fitting those statistical models to massive simulation output. Our goal is to develop the fundamental statistical algorithms and computer science needed to perform statistical inference in-situ (in HPC simulations) to the full stream of data those simulations generate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Nuclear Material Control & Accounting for Pebble Bed Reactors (FY 2023 Summary Report)

This report discusses the work done under the US Department of Energy NE-5 Advanced Reactor Safeguards and Security Program during FY 2023. It provides a summary of material control and accounting (MC&A) for pebble bed reactors (PBRs) and addresses some of the main challenges with current PBR MC&A approaches that will inform safeguards and security by design efforts. The efforts to date have focused on tristructural isotropic (TRISO) pebble fuel material accounting and control including working with partners in industry, loss and production of nuclear material as part of reactor operations, burnup modeling and measurements, uncertainty quantifications for such modeling and measurements, statistical approaches needed, and measurement methods. The unique fuel management and utilization in a PBR, where the fuel in spherical form is introduced and circulates through the reactor, poses special challenges for MC&A. This contrasts with traditional water-cooled reactors in which the fuel is contained in large assemblies and can be easily identified and counted. Even online fueled reactors, such as the CANDU reactors (none of which operate in the United States), are significantly different because the fuel is still contained in relatively large assemblies, is uniquely identified, and the number of assemblies that pass through the core on an annual basis is much fewer than the hundreds of thousands that circulate in a PBR, none of which are uniquely identified. Additionally, the nature of the TRISO fuel results in very low heavy metal loading with each pebble containing less than 10 g of uranium and on the order of less than 1 g of fissile material. This low fuel density and the robustness of the TRISO particles are major features of the TRISO fuel from a safety basis as each TRISO particle and pebble acts as a containment for the nuclear material and fission products during normal and accident conditions. This also results in very low plutonium loading per pebble during normal operations, which is on the order of 0.1 g at full burnup. A major feature of PBRs is that they will allow for significantly higher burnup, on the order of 160 GWd/THM compared to the burnup of traditional LWRs, which is on the order of 45 GWd/THM. This is achieved by monitoring the pebbles as they circulate through the reactor and allowing them to be reintroduced into the core until the desired burnup is achieved and they are removed from the reactor and enter the spent fuel storage areas.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2014

How to Develop and Interpret a Credibility Assessment of Numerical Models for Human Research: NASA-STD-7009 Demystified

In the wake of the Columbia accident, the NASA-STD-7009 [1] credibility assessment was developed as a unifying platform to describe model credibility and the uncertainties in its modeling predictions. This standard is now being adapted by NASAs Human Research Program to cover a wide range of numerical models for human research. When used properly, the standard can improve the process of code development by encouraging the use of best practices. It can also give management more insight in making informed decisions through a better understanding of the models capabilities and limitations.To a newcomer, the abstractions presented in NASA-STD-7009 and the sheer volume of information that must be absorbed can be overwhelming. This talk is aimed at describing the credibility assessment, which is the heart of the standard, in plain terms. It will outline how to develop a credibility assessment under the standard. It will also show how to quickly interpret the graphs and tables that result from the assessment and how to drill down from the top-level view to the foundation of the assessment. Finally, it will highlight some of the resources that are available for further study.

numerical analysis↗
NASA NTRS2013

Modeling and Evaluation of the Global Sea-Salt Aerosol Distribution: Sensitivity to Emission Schemes and Resolution Effects at Coastal/Orographic Sites

One of the major sources of uncertainty in model estimates of the global sea-salt aerosol distribution is the emission parameterization. We evaluate a new sea-salt aerosol life cycle module coupled to the online multi-scale chemical transport model NMMB/BSC-CTM. We compare 5 year global simulations using five state-of-the-art sea-salt open-ocean emission schemes with monthly averaged coarse aerosol optical depth (AOD) from selected AERONET sun photometers, surface concentration measurements from the University of Miami's Ocean Aerosol Network, and measurements from two NOAA/PMEL cruises (AEROINDOEX and ACE1). Model results are highly sensitive to the introduction of sea-surface-temperature (SST)-dependent emissions and to the accounting of spume particles production. Emission ranges from 3888 teragrams per year to 8114 teragrams per year, lifetime varies between 7.3 hours and 11.3 hours, and the average column mass load is between 5.0 teragrams and 7.2 teragrams. Coarse AOD is reproduced with an overall correlation of around 0.5 and with normalized biases ranging from +8.8 percent to +38.8 percent. Surface concentration is simulated with normalized biases ranging from minus 9.5 percent to plus 28 percent and the overall correlation is around 0.5. Our results indicate that SST-dependent emission schemes improve the overall model performance in reproducing surface concentrations. On the other hand, they lead to an overestimation of the coarse AOD at tropical latitudes, although it may be affected by uncertainties in the comparison due to the use of all-sky model AOD, the treatment of water uptake, deposition and optical properties in the model and/or an inaccurate size distribution at emission.

Bias↗
NASA NTRS1993

Several examples where turbulence models fail in inlet flow field analysis

Computational uncertainties in turbulence modeling for three dimensional inlet flow fields include flows approaching separation, strength of secondary flow field, three dimensional flow predictions of vortex liftoff, and influence of vortex-boundary layer interactions; computational uncertainties in vortex generator modeling include representation of generator vorticity field and the relationship between generator and vorticity field. The objectives of the inlet flow field studies presented in this document are to advance the understanding, prediction, and control of intake distortion and to study the basic interactions that influence this design problem.

Anderson, Bernhard H.↗
NASA NTRS2012

UQTools: The Uncertainty Quantification Toolbox - Introduction and Tutorial

UQTools is the short name for the Uncertainty Quantification Toolbox, a software package designed to efficiently quantify the impact of parametric uncertainty on engineering systems. UQTools is a MATLAB-based software package and was designed to be discipline independent, employing very generic representations of the system models and uncertainty. Specifically, UQTools accepts linear and nonlinear system models and permits arbitrary functional dependencies between the system s measures of interest and the probabilistic or non-probabilistic parametric uncertainty. One of the most significant features incorporated into UQTools is the theoretical development centered on homothetic deformations and their application to set bounding and approximating failure probabilities. Beyond the set bounding technique, UQTools provides a wide range of probabilistic and uncertainty-based tools to solve key problems in science and engineering.

Kenny, Sean P.↗
NASA NTRS2013

Preindustrial to Present-Day Changes in Tropospheric Hydroxyl Radical and Methane Lifetime from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)

We have analysed time-slice simulations from 17 global models, participating in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), to explore changes in present-day (2000) hydroxyl radical (OH) concentration and methane (CH4) lifetime relative to preindustrial times (1850) and to 1980. A comparison of modeled and observation-derived methane and methyl chloroform lifetimes suggests that the present-day global multi-model mean OH concentration is overestimated by 5 to 10% but is within the range of uncertainties. The models consistently simulate higher OH concentrations in the Northern Hemisphere (NH) compared with the Southern Hemisphere (SH) for the present-day (2000; inter-hemispheric ratios of 1.13 to 1.42), in contrast to observation-based approaches which generally indicate higher OH in the SH although uncertainties are large. Evaluation of simulated carbon monoxide (CO) concentrations, the primary sink for OH, against ground-based and satellite observations suggests low biases in the NH that may contribute to the high north–south OH asymmetry in the models. The models vary widely in their regional distribution of present-day OH concentrations (up to 34%). Despite large regional changes, the multi-model global mean (mass-weighted) OH concentration changes little over the past 150 yr, due to concurrent increases in factors that enhance OH (humidity, tropospheric ozone, nitrogen oxide (NOx) emissions, and UV radiation due to decreases in stratospheric ozone), compensated by increases in OH sinks (methane abundance, carbon monoxide and non-methane volatile organic carbon (NMVOC) emissions). The large inter-model diversity in the sign and magnitude of preindustrial to present-day OH changes (ranging from a decrease of 12.7% to an increase of 14.6%) indicate that uncertainty remains in our understanding of the long-term trends in OH and methane lifetime. We show that this diversity is largely explained by the different ratio of the change in global mean tropospheric CO and NOx burdens (Delta CO/Delta NOx, approximately represents changes in OH sinks versus changes in OH sources) in the models, pointing to a need for better constraints on natural precursor emissions and on the chemical mechanisms in the current generation of chemistry-climate models. For the 1980 to 2000 period, we find that climate warming and a slight increase in mean OH (3.5 +/- 2.2%) leads to a 4.3 +/- 1.9% decrease in the methane lifetime. Analysing sensitivity simulations performed by 10 models, we find that preindustrial to present-day climate change decreased the methane lifetime by about four months, representing a negative feedback on the climate system. Further, we analysed attribution experiments performed by a subset of models relative to 2000 conditions with only one precursor at a time set to 1860 levels. We find that global mean OH increased by 46.4 +/- 12.2% in response to preindustrial to present-day anthropogenic NOx emission increases, and decreased by 17.3 +/-2.3%, 7.6 +/- 1.5%, and 3.1 +/- 3.0% due to methane burden, and anthropogenic CO, and NMVOC emissions increases, respectively.

nitrogen oxides↗
NASA NTRS2023

The optimization of model ensemble composition and size can enhance the robustness of crop yield projections

Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.

Agriculture↗