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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Probabilistic and Possibilistic Analyses of the Strength of a Bonded Joint

The effects of uncertainties on the predicted strength of a single lap shear joint are examined. Probabilistic and possibilistic methods are used to account for uncertainties. A total of ten variables are assumed to be random, with normal distributions. Both Monte Carlo Simulation and the First Order Reliability Method are used to determine the probability of failure. Triangular membership functions with upper and lower bounds located at plus or minus three standard deviations are used to model uncertainty in the possibilistic analysis. The alpha cut (or vertex) method is used to evaluate the possibility of failure. Linear and geometrically nonlinear finite element analyses are used calculate the response of the joint; fracture in the adhesive and material strength failure in the strap are used to evaluate its strength. Although probabilistic and possibilistic analyses provide significantly more information than do conventional deterministic analyses, they are computationally expensive. A novel scaling approach is developed and used to substantially reduce the computational cost of the probabilistic and possibilistic analyses. The possibilistic approach for treating uncertainties appears to be viable during the conceptual and preliminary design stages when limited data are available and high accuracies are not needed. However, this viability is mixed with several cautions that are discussed herein.

Stroud, W. Jefferson↗
NASA NTRS2007

The X-43A Six Degree of Freedom Monte Carlo Analysis

This report provides an overview of the Hyper-X research vehicle Monte Carlo analysis conducted with the six-degree-of-freedom simulation. The methodology and model uncertainties used for the Monte Carlo analysis are presented as permitted. In addition, the process used to select hardware validation test cases from the Monte Carlo data is described. The preflight Monte Carlo analysis indicated that the X-43A control system was robust to the preflight uncertainties and provided the Hyper-X project an important indication that the vehicle would likely be successful in accomplishing the mission objectives. The X-43A in-flight performance is compared to the preflight Monte Carlo predictions and shown to exceed the Monte Carlo bounds in several instances. Possible modeling shortfalls are presented that may account for these discrepancies. The flight control laws and guidance algorithms were robust enough as a result of the preflight Monte Carlo analysis that the unexpected in-flight performance did not have undue consequences. Modeling and Monte Carlo analysis lessons learned are presented.

Baumann, Ethan↗
NASA NTRS2022

Optimizing Terrain Mapping and Landing Site Detection for Autonomous UAVs

The next generation of Mars rotorcrafts requires on-board autonomous hazard avoidance landing. To this end, this work proposes a system that performs continuous multi- resolution height map reconstruction and safe landing spot detection. Structure-from-Motion measurements are aggregated in a pyramid structure using a novel Optimal Mixture of Gaus- sians formulation that provides a comprehensive uncertainty model. Our multiresolution pyramid is built more efficiently and accurately than past work by decoupling pyramid filling from the measurement updates of different resolutions.To detect the safest landing location, after an optimized hazard segmentation, we use a mean shift algorithm on multiple distance transform peaks to account for terrain roughness and uncertainty. The benefits of our contributions are evaluated on real and synthetic flight data.

Brockers, Roland↗
DOE OSTI2015

A Hybrid Global Ocean Data Assimilation System at NCEP

Seasonal forecasting with a coupled model requires accurate initial conditions for the ocean. A hybrid data assimilation has been implemented within the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) as a future replacement of the operational three-dimensional variational data assimilation (3DVar) method. This Hybrid-GODAS provides improved representation of model uncertainties by using a combination of dynamic and static background error covariances, and by using an ensemble forced by different realizations of atmospheric surface conditions. An observing system simulation experiment (OSSE) is presented spanning January 1991 to January 1999, with a bias imposed on the surface forcing conditions to emulate an imperfect model. The OSSE compares the 3DVar used by the NCEP Climate Forecast System (CFSv2) with the new hybrid, using simulated in situ ocean observations corresponding to those used for the NCEP Climate Forecast System Reanalysis (CFSR). The Hybrid-GODAS reduces errors for all prognostic model variables over the majority of the experiment duration, both globally and regionally. Compared to an ensemble Kalman filter (EnKF) used alone, the hybrid further reduces errors in the tropical Pacific. The hybrid eliminates growth in biases of temperature and salinity present in the EnKF and 3DVar, respectively. A preliminary reanalysis using real data shows that reductions in errors and biases are qualitatively similar to the results from the OSSE. Finally, the Hybrid-GODAS is currently being implemented as the ocean component in a prototype next-generation CFSv3, and will be used in studies by the Climate Prediction Center to evaluate impacts on ENSO prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Proton stopping measurements at low velocity in warm dense carbon

Abstract Ion stopping in warm dense matter is a process of fundamental importance for the understanding of the properties of dense plasmas, the realization and the interpretation of experiments involving ion-beam-heated warm dense matter samples, and for inertial confinement fusion research. The theoretical description of the ion stopping power in warm dense matter is difficult notably due to electron coupling and degeneracy, and measurements are still largely missing. In particular, the low-velocity stopping range, that features the largest modelling uncertainties, remains virtually unexplored. Here, we report proton energy-loss measurements in warm dense plasma at unprecedented low projectile velocities. Our energy-loss data, combined with a precise target characterization based on plasma-emission measurements using two independent spectroscopy diagnostics, demonstrate a significant deviation of the stopping power from classical models in this regime. In particular, we show that our results are in closest agreement with recent first-principles simulations based on time-dependent density functional theory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

PV Performance Modeling Methods and Practices: Results from the 4th PV Performance Modeling Collaborative Workshop.

In 2014, the IEA PVPS Task 13 added the PVPMC as a formal activity to its technical work plan for 2014-2017. The goal of this activity is to expand the reach of the PVPMC to a broader international audience and help to reduce PV performance modeling uncertainties worldwide. One of the main deliverables of this activity is to host one or more PVPMC workshops outside the US to foster more international participation within this collaborative group. This report reviews the results of the first in a series of these joint IEA PVPS Task 13/PVPMC workshops. The 4th PV Performance Modeling Collaborative Workshop was held in Cologne, Germany at the headquarters of TÜV Rheinland on October 22-23, 2015.

14 SOLAR ENERGY↗
DOE OSTI2018

Final Technical Report: Mathematical Foundations for Uncertainty Quantification in Materials Design

We developed path-wise information theory-based and goal-oriented sensitivity analysis and parameter identification methods for complex high-dimensional dynamics and in particular of non-equilibrium extended molecular systems. The combination of these novel methodologies provided the first methods in the literature which are capable to handle UQ questions for stochastic complex systems with some or all of the following features: (a) multi-scale stochastic models such as (bio)chemical reaction networks, with a very large number of parameters, (b) spatially distributed systems such as Kinetic Monte Carlo or Langevin Dynamics, (c) non-equilibrium processes typically associated with coupled physico-chemical mechanisms, driven boundary conditions, hybrid micro-macro systems, etc. A particular computational challenge arises in simulations of multi-scale reaction networks and molecular systems. Mathematical techniques were applied to in silico prediction of novel materials with emphasis on the effect of microstructure on model uncertainty quantification (UQ). We outline acceleration methods to make calculations of real chemistry feasible followed by two complementary tasks on structure optimization and microstructure-induced UQ.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Real-time Social Internet Data to Guide Forecasting Models

Our goal is to improve decision support by monitoring and forecasting events using social media, mathematical models, and quantifying model uncertainty. Our approach is real-time, data-driven forecasts with quantified uncertainty: Not just for weather anymore. Information flow from human observations of events through an Internet system and classification algorithms is used to produce quantitatively uncertain forecast. In summary, we want to develop new tools to extract useful information from Internet data streams, develop new approaches to assimilate real-time information into predictive models, validate approaches by forecasting events, and our ultimate goal is to develop an event forecasting system using mathematical approaches and heterogeneous data streams.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

Recent trends in ozone in the upper stratosphere: Implications for chlorine chemistry

We have studied the implications of recent trends in the annual mean and the amplitude of the annual harmonic of ozone in the upper stratosphere from the 15 years of the combined data from the Nimbus-7 SBUV and the NOAA-11 SBUV/2 instruments. This was done in the context of the GSFC 2D model predictions of these trends which are based on plausible scenarios of anthropogenic Cly increase in the atmosphere. The comparison of the observed and model-estimated annual mean ozone trends show some similarity in their latitude and altitude characteristics. Both the model and data show a maximum ozone decrease of -6 to -10 % per decade at high latitudes in the upper stratosphere. However, there are also significant differences between the observed and computed trends which may be related to both the model uncertainty and the uncertainty in correcting for the long term instrument drift. The observations also suggest a decrease of 10-25 % per decade in the annual amplitude of ozone at 2 mb between 40 deg - 60 deg in both hemispheres, with a relatively larger interannual variability in the northern hemisphere. These values are in general agreement with the model predictions and thus provide additional support in favor of the chlorine induced changes in ozone in the upper stratosphere.

Chandra, Sushil↗
DOE OSTI2023

Effect of Lateral Conduction in a Rectangular Fuel Plate and Optimum Stripe Widths for the OPAL Reactor

The impetus for this work is the Proliferation Resistance Optimization (PRO-X) program, which was created by the National Nuclear Security Administration (NNSA) of the US Department of Energy (DOE) to provide a framework for developing reactor designs that minimize the production of special nuclear materials while maximizing performance for peaceful uses. The Argentine company INVAP and Argonne National Laboratory collaborated to share knowledge and expertise regarding the thermal analysis of research reactors. Working together, they designed a series of test problems based on the Open Pool Australian Light Water (OPAL) reactor, whose fuel is in the form of flat plates cooled by water flowing through narrow rectangular channels. ANL used their PLTEMP/ANL code. INVAP used their TERMIC code (and its new multi-plate version, TERMIC-MP). Then, INVAP and ANL compared the two sets of results. 1-D models were used during the collaboration since for research reactor analysis, largely 1-D thermal-hydraulics models are commonly used because they are simple to apply and technically defendable. The collaboration between the two organizations aims for improvements in thermal-hydraulic models. Improvements to methods and models can reduce modeling uncertainties and excessive conservatism, allowing greater reactor performance without reducing predicted safety margins. Therefore, these improvements potentially expand the design space, making additional designs feasible, which, in turn, may achieve better fuel utilization and proliferation resistance in the designs. This report provides justification for the lateral node size (stripe width) chosen for the 1-D computer models during the collaboration. The lateral heat conduction in OPAL fuel plates is evaluated using the two-dimensional conduction capability of PLTEMP/ANL to determine a representative hot-stripe width that can be conservatively used for one-dimensional steady-state thermal-hydraulic analysis. Basically, a representative hot-stripe width needs to be selected so that the actual heat flux averaged over this stripe width is greater than or equal to the maximum heat flux obtained by the two-dimensional analysis. Detailed results in this report show that a hot-stripe width of 8.125 mm can be conservatively used for the one-dimensional thermal-hydraulics analysis of all plates. Therefore, eight equal stripes (8.125 mm each) are recommended to be modeled in the MCNP and PLTEMP/ANL full core models of the OPAL reactor.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Integrating terrestrial and aquatic processes toward watershed scale modeling of dissolved organic carbon fluxes

Dissolved organic carbon (DOC) is not only a critical component of global and regional carbon budgets, but also an important precursor for carcinogenic disinfection byproducts (DBP) generated during drinking water disinfection process. The lack of process based watershed scale model for carbon cycling has been a limiting factor impeding effective watershed management to control DOC fluxes to source waters. Here, we integrated terrestrial and aquatic carbon processes into the widely tested Soil and Water Assessment Tool (SWAT) watershed model to enable watershed-scale DOC modeling (referred to as SWAT-DOC hereafter). The modifications to SWAT mainly fall into two groups: (1) DOC production in soils and its transport to aquatic environment by different hydrologic processes, and (2) riverine transformation of DOC and their interactions with particular organic carbon (POC), inorganic carbon and algae (floating and bottom). We tested the new SWAT-DOC model in the Cannonsville watershed, which is part of the New York City (NYC) water supply system, using long-term DOC load data (from 1998 to 2012) derived from 1399 DOC samplings. The calibration and verification results indicate that SWAT-DOC achieved satisfactory performance for both streamflow and DOC at daily and monthly temporal scales. The parameter sensitivity analysis indicates that DOC loads in the Cannonsville watershed are controlled by the DOC production in soils and its transport in both terrestrial and aquatic environments. Further model uncertainty analysis indicates high uncertainties associated with peak DOC loads, which are attributed to underestimation of high streamflows. Therefore, future efforts to enhance SWAT-DOC to better represent runoff generation processes hold promise to further improve DOC load simulation. Overall, the wide use of SWAT and the satisfactory performance of SWAT-DOC make it a useful tool for DOC modeling and mitigation at the watershed scale.

Dissolved Organic Carbon, non-point source polluti↗
NASA NTRS2008

A Verification-Driven Approach to Control Analysis and Tuning

This paper proposes a methodology for the analysis and tuning of controllers using control verification metrics. These metrics, which are introduced in a companion paper, measure the size of the largest uncertainty set of a given class for which the closed-loop specifications are satisfied. This framework integrates deterministic and probabilistic uncertainty models into a setting that enables the deformation of sets in the parameter space, the control design space, and in the union of these two spaces. In regard to control analysis, we propose strategies that enable bounding regions of the design space where the specifications are satisfied by all the closed-loop systems associated with a prescribed uncertainty set. When this is unfeasible, we bound regions where the probability of satisfying the requirements exceeds a prescribed value. In regard to control tuning, we propose strategies for the improvement of the robust characteristics of a baseline controller. Some of these strategies use multi-point approximations to the control verification metrics in order to alleviate the numerical burden of solving a min-max problem. Since this methodology targets non-linear systems having an arbitrary, possibly implicit, functional dependency on the uncertain parameters and for which high-fidelity simulations are available, they are applicable to realistic engineering problems..

Crespo, Luis G.↗
NASA NTRS2010

A Computational Framework to Control Verification and Robustness Analysis

This paper presents a methodology for evaluating the robustness of a controller based on its ability to satisfy the design requirements. The framework proposed is generic since it allows for high-fidelity models, arbitrary control structures and arbitrary functional dependencies between the requirements and the uncertain parameters. The cornerstone of this contribution is the ability to bound the region of the uncertain parameter space where the degradation in closed-loop performance remains acceptable. The size of this bounding set, whose geometry can be prescribed according to deterministic or probabilistic uncertainty models, is a measure of robustness. The robustness metrics proposed herein are the parametric safety margin, the reliability index, the failure probability and upper bounds to this probability. The performance observed at the control verification setting, where the assumptions and approximations used for control design may no longer hold, will fully determine the proposed control assessment.

Crespo, Luis G.↗
DOE OSTI2020

Impact of climate change on adaptive management decisions in the face of water scarcity

Reoccurring drought through the early 2000s has caused a serious water scarcity issue in the Colorado River Basin. Previous modeling studies have focused on the impact of climate change without considering the adaptive behaviors of farmers and under-utilized Indian water rights. Here, we use a coupled agent-based water resource model (ABM) to investigate how the adaptive decisions of farmers can affect water resource management under both climate change impacts and fully utilized Indian water right conditions. We used five General Circulation Model projections with RCP8.5 scenarios for the study. The results of farm-level decision-making showed different responses in irrigated areas that were changing due to climate change impact. While winter precipitation changes might partially explain the behavior changes, no specific pattern could be concluded based on their location. Also, farmers’ responses about annual water diversion showed more significant inter-year variation compared to irrigated areas. Basin-level metrics showed that climate change impacts will generally worsen water scarcity issues as measured in Navajo Reservoir storage, flow to Lake Powell, and instream flow requirement. But these basin-level water scarcity metrics cannot reflect individual farm-level impacts under climate change, which is why modeling the bottom-up management actions is necessary. When the under-utilized Indian water rights are fully used, it is more likely to trigger the shortage sharing agreement due to the higher tribal water depletion. Evaluation of model uncertainty and a more realistic setup for adaptive actions under drought contingency plans are suggested for future research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Towards Robustness Guarantees for Feedback-Based Optimization

Feedback-based online optimization algorithms have gained traction in recent years because of their simple implementation, their ability to reject disturbances in real time, and their increased robustness to model mismatch. While the robustness properties have been observed both in simulation and experimental results, the theoretical analysis in the literature is mostly limited to nominal conditions. In this work, we propose a framework to systematically assess the robust stability of feedback-based online optimization algorithms. We leverage tools from monotone operator theory, variational inequalities and classical robust control to obtain tractable numerical tests that guarantee robust convergence properties of online algorithms in feedback with a physical system, even in the presence of disturbances and model uncertainty. The results are illustrated via an academic example and a case study of a power distribution system.

approximation algorithms↗
DOE OSTI2022

A scalable planning framework of energy storage systems under frequency dynamics constraints

As the penetration of renewables increases in power systems, the declining system inertia can cause frequency stability issues. Battery energy storage systems (BESSs) respond fast and therefore can relieve the low inertia difficulty but need to be appropriately sized considering the associated cost. This paper presents a novel stochastic optimization model for economically planning BESS capacity while considering the spatial–temporal correlation of wind generation and generator outages under frequency stability constraints, which include the rate-of-change of frequency (RoCoF), frequency nadir (FN), and quasi-steady-state (QSS) frequency. A set of new FN constraints that can be easily linearized is developed. To account for renewable uncertainties, a realistic uncertainty modeling approach, Random Field, is adopted to generate wind generation scenarios by considering both spatial and temporal evolutions of wind speed profiles. The ESS sizing is formulated as a mixed-integer linear programming problem and solved by using a scalable decomposition-and-coordination approach, Surrogate Absolute Value Lagrangian Relaxation (SAVLR). To further improve the scalability and reduce computational burdens, a rolling-horizon-based update is developed and incorporated into SAVLR for providing a practical solution to the long-term planning of very large-scale power systems. Finally, a modified IEEE 118-bus system and the Polish system are used to validate the effectiveness and scalability of the model and solution methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Efficient Monte Carlo With Graph-Based Subsurface Flow and Transport Models

Simulating flow and transport in fractured porous media frequently involves solving numerical discretizations of partial differential equations with a large number of degrees of freedom using discrete fracture network (DFN) models. Uncertainty in the properties of the fracture network that controls flow and transport requires a large number of DFN simulations to statistically describe quantities of interest. However, the computational cost of solving more than a few realizations of a large DFN can be intractable. As a means of circumventing this problem, we utilize both a high-fidelity DFN model and a graphbased model of flow and transport in combination with a multifidelity Monte Carlo (MC) method to reduce the number of high-fidelity simulations that are needed to obtain an accurate estimate of the quantity of interest. We demonstrate the approach by estimating quantiles of the breakthrough time for a conservative tracer in an ensemble of fractured porous media. Our results demonstrate that a multifidelity MC estimate, whose computational cost is equal to the cost of 10 DFN simulations, can be as accurate as a standard MC estimate that utilizes 1,000 DFN simulations. Thus the combination of our graphbased model with multifidelity MC estimates effectively reduces the computational cost of the problem by a factor of approximately 100.

58 GEOSCIENCES↗
DOE OSTI2018

CERF – A Geospatial Model for Assessing Future Energy Production Technology Expansion Feasibility

Electricity system expansion planning faces significant challenges not only because of commonly modeled uncertainties such as future demand growth, fuel prices, and technology cost and performance, but also because of challenges such as climate change and carbon policies that can affect supply and demand. Changes in energy technology, natural resource availability, and population migration and growth will affect the feasibility of power plant siting, potentially hindering the achievement of policy goals. New modeling approaches are needed that couple future projections of energy system expansion with “on the ground” feasibility modeling—where both are informed by a consistent set of assumptions about future climate trends, resource availability, climate policy, socioeconomics, and environmental regulations. This research describes the development and evaluation of the Capacity Expansion Regional Feasibility (CERF) model, a geospatial tool for scenario analysis of electricity system expansions.

grid expansion, water-energy nexus, power plant si↗