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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Industrial battery operation and utilization in the presence of electrical load uncertainty using Bayesian decision theory

Behind the meter battery storage is becoming increasing popular in all sectors, though enthusiasm has recently lagged in the industrial sector. Even though there may be many factors contributing to this including lack of innovation, prohibitive costs, and undesirable rate structures, a difficulty arises in accounting for uncertainty of electrical load in industrial facilities while still attempting to utilize battery storage as much as possible all while trying to achieve fiscal profitability. Here this study utilizes Gaussian process regression and Bayesian decision theory to organize load data and quantify electrical load uncertainty to properly and effectively discharge industrial battery storage. The study employs a simulation model to set battery load setpoints for the span of the utility billing period according to the degree of risk aversion. This combination of economic analysis according to utility billing period and utilization of degree of risk aversion to make decisions on the uncertainty of the data has not before been applied to battery storage. The method resulted in an annual average reduction of peak demand by 3.8 % at the lowest amount of savings and lowest risk aversion. The highest risk aversion resulted in an annual average reduction of peak demand of 7.5 %. The maximum reduction of peak load in any month was 13.8 % in the month of December with a relatively high risk aversion. With a the highest amount risk aversion tested, the model reduced demand ten of the twelve months of the year.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

Distribution of SLS Integrated Load Uncertainty to Surface Pressures and Sectional Loads

Aerodynamic loads that are important to launch vehicle programs such as NASA’s Space Launch System (SLS) include both integrated loads such as the force & moment on the entire vehicle and distributed loads. In this work two examples of distributed loads are considered: the pressure field on the surface of the vehicle and sectional loads, which are one-dimensional distributions along the axis of the launch vehicle. In some modern flight programs, the integrated loads, such as lift and drag, used to design the guidance and control laws for the vehicle come from wind tunnel testing, while distributed loads are produced using Computational Fluid Dynamics(CFD). The first task that is addressed in this paper, then, is to provide a formal method to adjust the distributed loads so that integrating them matches the prescribed integrated load. In addition, the integrated loads in a launch vehicle typically include an uncertainty estimate. The second task is to distribute this prescribed integrated uncertainty to each point in a distributed load. Both tasks are addressed using the same technique, which is to create distributed load profiles that isolate adjustments to one integrated load while leaving the others unaffected. These adjustments are informed by Proper Orthogonal Decomposition (POD) of the entire CFD-based distributed load database. Once applied, the adjusted distributed loads can be used to evaluate any scalar quantity of interest that might be needed by downstream users such as structural analysis or trajectory modelers.

SLS↗
DOE OSTI2022

Influence of sampling frequency and estimation method on phosphorus load uncertainty in the Western Lake Erie Basin, Ohio, USA

Accurate estimates of nutrient loads are necessary to identify critical source areas and quantify the impact of management practices on pollutant export. Previous studies have investigated nutrient load estimate uncertainty, but they often focus on nutrient loads estimated using an interpolation method for large-scale watersheds with short-term datasets. The study objective was to quantify uncertainty in soluble reactive phosphorus (SRP), total phosphorus (TP), and suspended solids (SS) load estimates from two small (<10 3 km 2 ) agricultural watersheds in the western Lake Erie Basin resulting from different sampling frequencies. Each watershed had high temporal resolution datasets of discharge (15 min) and nutrient concentration (1 to 3 samples per day) collected over a 30-year period (1990–2020). Firstly, SRP, TP, and SS loads were calculated using the high temporal resolution datasets, which was assumed as “true loads”. Secondly, the high temporal concentration data were decomposed to semiweekly, weekly, biweekly, and monthly sampling and annual loads were estimated using four common load estimation methods to assess the effect of sampling frequency and load estimation method on load estimate error. Across the four different methods, the composite method had the lowest relative root mean square and absolute bias, but the rectangular interpolation method was the most precise. Furthermore, even with semiweekly sampling, the composite method resulted in an unacceptable level of precision (average imprecision = 39 %), while the interpolation method resulted in an unacceptable bias (average absolute bias = 16 %). Because neither method could provide acceptable accuracy and precision at the lowest decrease in sampling (e.t. semiweekly sampling), continued daily sampling is recommended in these watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Identifying Hydropower Operational Flexibilities in Presence of Streamflow and Net-load Uncertainty (Final Technical report)

In the existing operations, hydropower contributions to future system flexibility are generally modeled while maintaining traditional operating rules and constraints in supporting grid operation, such as the balancing of variable renewable energy production. Moreover, operation of large scale hydropower systems on major rivers has been investigated for decades, utilizing various systems engineering approaches, with the evolving electric grid, as the result of renewable resources integration, compounded by the changing climate (variability of river flows, intensification of hydrologic cycle resulting in more frequent extreme events) affecting water availability, the need for more advanced stochastic modeling and effective uncertainty analysis approaches have become necessary. The research results supported by this funding and presented in this report provide a new look at hydropower operational flexibility enforced by the changes identified above. Understanding how hydropower operates in response to the underlying uncertainties with respect to the system constraints is crucial in identifying its operational flexibility potentials. In this project, the flexibility of the operating hydropower facility is described by capturing uncertainties in both water and power system and formulating the operations as a multistage stochastic optimization problem. The proposed approach supports short- to seasonal-term operations and planning decision horizons.

13 HYDRO ENERGY↗
DOE OSTI2021

Enriching Load Data Using Micro-PMUs and Smart Meters

In modern distribution systems, load uncertainty can be fully captured by micro-PMUs, which can record high-resolution data; however, in practice, micro-PMUs are installed at limited locations in distribution networks due to budgetary constraints. In contrast, smart meters are widely deployed but can only measure relatively low-resolution energy consumption, which cannot sufficiently reflect the actual instantaneous load volatility within each sampling interval. In this paper, we have proposed a novel approach for enriching load data for service transformers that only have low-resolution smart meters. The key to our approach is to statistically recover the high-resolution load data, which is masked by the low-resolution data, using trained probabilistic models of service transformers that have both high- and low-resolution data sources, i.e., micro-PMUs and smart meters. The overall framework consists of two steps: first, for the transformers with micro-PMUs, a Gaussian Process is leveraged to capture the relationship between the maximum/minimum load and average load within each low-resolution sampling interval of smart meters; a Markov chain model is employed to characterize the transition probability of known high-resolution load. Next, the trained models are used as teachers for the transformers with only smart meters to decompose known low-resolution load data into targeted high-resolution load data. The enriched data can recover instantaneous load uncertainty and significantly enhance distribution system observability and situational awareness. Here, we have verified the proposed approach using real high- and low-resolution load data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2022

NESC Peer Review of Exploration Systems Development (ESD) Integrated Vehicle Modal Test, Model Correlation, Development Flight Instrumentation (DFI) and Flight Loads Readiness; Uncertainty Propagation for Model Validation Sub-task

This report details a sub-task (regarding Uncertainty Propagation for Model Validation) from a NASA Engineering and Safety Center assessment that is a multi-year activity spanning the complete development of the Space Launch System integrated vehicle structural dynamic models, and the development of the certification of flight readiness for the Artemis 1 and Artemis 2 vehicles and their variants.

Exploration Systems Development; Development Fligh↗
DOE OSTI2026

Added value of site load measurements in probabilistic lifetime extension: a Lillgrund case study

Site-specific fatigue estimation is an essential part of wind turbine lifetime extension, with various methods depending on data availability. The present study compares probabilistic lifetime extension assessment results for rotor blades with and without load measurements. It also addresses two key questions in such assessments: the applicability of the Frandsen model for estimating waked turbulence under complex and mixed wake conditions and the extrapolation of mid-term data over longer time periods. The case study wind turbine is SWT-2.3-93, located at the edge of the Lillgrund wind farm, situated in the Øresund Strait between Denmark and Sweden. The turbine is extensively instrumented, with 5 years of data available from its supervisory control and data acquisition (SCADA) system. Although the Frandsen turbulence estimates deviate in a different manner from measurements at below- and above-rated mean wind speeds, the model remains a conservative approach for fatigue load prediction and reliability. In the current case study, the site-specific assessment using strain gauge measurements yields a 33 % higher annual fatigue reliability index after 35 years compared to a scenario based on the Frandsen estimation combined with ambient environmental data and a generic aeroelastic model. The results also demonstrate that the sensitivity of fatigue reliability to load uncertainty is negligible when load measurements are used directly but relatively high when relying on the Frandsen model in combination with a generic aeroelastic model. Overall, the high variability of the lifetime extension in different scenarios of data availability and accuracy shows the importance and added value of high-quality measurements combined with wind-farm-level SCADA and a model updated in real time (digital twins).

17 WIND ENERGY↗
DOE OSTI2024

A Distributionally Robust Optimization Framework for Stochastic Assessment of Power System Flexibility in Economic Dispatch

Given the complexity of power systems, particularly the high-dimensional variability of net loads, accurately depicting the entire operational range of net loads poses a challenge. To address this, recent methodologies have sought to gauge the maximum range of net load uncertainty across all buses. In this paper, we consider the stochastic nature of the net load and introduce a distributionally robust optimization framework that assesses system flexibility stochastically, accommodating a minimal extent of system violations. We verify the proposed method by solving the flexibility of the economic dispatch problem on four distinct IEEE standard test systems. Compared to traditional deterministic flexibility evaluations, our approach consistently yields less conservative flexibility outcomes.

distributionally robust optimization↗
NASA NTRS1992

Probabilistic evaluation of uncertainties and risks in aerospace components

This paper summarizes a methodology developed at NASA Lewis Research Center which computationally simulates the structural, material, and load uncertainties associated with Space Shuttle Main Engine (SSME) components. The methodology was applied to evaluate the scatter in static, buckling, dynamic, fatigue, and damage behavior of the SSME turbo pump blade. Also calculated are the probability densities of typical critical blade responses, such as effective stress, natural frequency, damage initiation, most probable damage path, etc. Risk assessments were performed for different failure modes, and the effect of material degradation on the fatigue and damage behaviors of a blade were calculated using a multi-factor interaction equation. Failure probabilities for different fatigue cycles were computed and the uncertainties associated with damage initiation and damage propagation due to different load cycle were quantified. Evaluations on the effects of mistuned blades on a rotor were made; uncertainties in the excitation frequency were found to significantly amplify the blade responses of a mistuned rotor. The effects of the number of blades on a rotor were studied. The autocorrelation function of displacements and the probability density function of the first passage time for deterministic and random barriers for structures subjected to random processes also were computed. A brief discussion was included on the future direction of probabilistic structural analysis.

Shah, A. R.↗
DOE OSTI2022

How Can Probabilistic Solar Power Forecasts Be Used to Lower Costs and Improve Reliability in Power Spot Markets? A Review and Application to Flexiramp Requirements

Net load uncertainty in electricity spot markets is rapidly growing. There are five general approaches by which system operators and market participants can use probabilistic forecasts of wind, solar, and load to help manage this uncertainty. These include operator situation awareness, resource risk hedging, reserves procurement, definition of contingencies, and explicit stochastic optimization. We review these approaches, and then provide a case study in which a method for using probabilistic solar forecasts to define needs for reserves is developed and evaluated. The case study has three parts. First, we describe building blocks for enhancing the Watt-Sun solar forecasting system to produce probabilistic irradiance and power forecasts. Second, relationships between Watt-Sun forecasts for multiple sites in California and the system's need for flexible ramp capability (flexiramp) are defined by machine learning and statistical methods. Third, the performance of present methods to defining flexiramp requirements, which are not conditioned on weather and renewables forecasts, is compared with that of probabilistic solar forecast-based requirements, using a multi-timescale production costing model with an 1820-bus representation of the WECC power system. Significant potential savings in fuel and flexiramp procurement costs from using solar-informed reserve requirements are found.

14 SOLAR ENERGY↗
NASA NTRS2013

Uncertainty Quantification in Remaining Useful Life of Aerospace Components using State Space Models and Inverse FORM

This paper investigates the use of the inverse first-order reliability method (inverse- FORM) to quantify the uncertainty in the remaining useful life (RUL) of aerospace components. The prediction of remaining useful life is an integral part of system health prognosis, and directly helps in online health monitoring and decision-making. However, the prediction of remaining useful life is affected by several sources of uncertainty, and therefore it is necessary to quantify the uncertainty in the remaining useful life prediction. While system parameter uncertainty and physical variability can be easily included in inverse-FORM, this paper extends the methodology to include: (1) future loading uncertainty, (2) process noise; and (3) uncertainty in the state estimate. The inverse-FORM method has been used in this paper to (1) quickly obtain probability bounds on the remaining useful life prediction; and (2) calculate the entire probability distribution of remaining useful life prediction, and the results are verified against Monte Carlo sampling. The proposed methodology is illustrated using a numerical example.

Sankararaman, Shankar↗
NASA NTRS1995

Uncertainty In Measuring Loads On Launch-Pad Supports

Report describes study of uncertainty in measurements of loads in structural members of launch pad of space shuttle. Measurements taken by strain gauges installed on members. Calibration data for study obtained by removing each thus-instrumented member from structure and measuring individual response on test stand.

Townsend, J. S.↗
NASA NTRSDate not supplied

Aerothermal Uncertainty Quantification of Deployable Entry Technologies Using Multi-Fidelity Modeling

The objective of this work was to investigate the use of a co-Kriging based multi-fidelity modeling approach with a Monte Carlo uncertainty quantification analyses of surface heating on hypersonic inflatable aerodynamic decelerator vehicles and adaptable, deployable entry placement technology vehicles in Mars entry. A previously developed co-Kriging based multi-fidelity modeling approach was used to model the laminar and turbulent convective and radiative heat fluxes along the vehicle. Monte Carlo analyses of surface heat load for nine different vehicle nose radii was performed for both vehicles, assuming the same thermal protection system is used for both vehicles. The maximum heat loads and heat load uncertainties were found to occur at either the stagnation point or the vehicle shoulder. The maximum 95% confidence intervals for the total heat load for the hypersonic inflatable aerodynamic decelerator vehicle were found to be [-9.27, 7.81] % of the nominal value and [-6.09, 13.1] % of the nominal value for laminar and turbulent flow, respectively. The maximum 95% confidence intervals for the total heat load for the adaptable, deployable entry placement technology vehicle were found to be [-8.85, 7.22] % of the nominal value and [-9.33, 9.35] % of the nominal value for laminar and turbulent flow, respectively. The difference in heating uncertainties based on vehicle geometry between the two vehicle technologies was found to be minimal.

Mario Santos↗
NASA NTRS2011

Linear Quadratic Tracking Design for a Generic Transport Aircraft with Structural Load Constraints

When designing control laws for systems with constraints added to the tracking performance, control allocation methods can be utilized. Control allocations methods are used when there are more command inputs than controlled variables. Constraints that require allocators are such task as; surface saturation limits, structural load limits, drag reduction constraints or actuator failures. Most transport aircraft have many actuated surfaces compared to the three controlled variables (such as angle of attack, roll rate & angle of side slip). To distribute the control effort among the redundant set of actuators a fixed mixer approach can be utilized or online control allocation techniques. The benefit of an online allocator is that constraints can be considered in the design whereas the fixed mixer cannot. However, an online control allocator mixer has a disadvantage of not guaranteeing a surface schedule, which can then produce ill defined loads on the aircraft. The load uncertainty and complexity has prevented some controller designs from using advanced allocation techniques. This paper considers actuator redundancy management for a class of over actuated systems with real-time structural load limits using linear quadratic tracking applied to the generic transport model. A roll maneuver example of an artificial load limit constraint is shown and compared to the same no load limitation maneuver.

Burken, John J.↗
DOE OSTI2021

Network-Cognizant Time-Coupled Aggregate Flexibility of Distribution Systems Under Uncertainties

Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. To exploit this flexibility and to use the capacity potential of aggregate DERs, feasible substation power injection trajectories need to be efficiently characterized. This paper provides an ellipsoidal inner approximation of the set of feasible power injection trajectories at the substation such that for any point in the set, there exists a feasible disaggregation strategy of DERs for any load uncertainty realization. The problem is formulated as one of finding the robust maximum volume ellipsoid inside the flexibility region under uncertainty. Though the problem is NP-hard even in the deterministic case, this paper derives novel approximations of the resulting adaptive robust optimization problem based on optimal second-stage policies. The proposed approach yields less conservative flexibility characterization than existing flexibility region approximation formulations. The efficacy of the proposed method is demonstrated on a realistic distribution feeder.

aggregates↗