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At least 55 records · Page 3

Evaluation of Piloted Inputs for Onboard Frequency Response Estimation

Frequency response estimation results are presented using piloted inputs and a real-time estimation method recently developed for multisine inputs. A nonlinear simulation of the F-16 and a Piper Saratoga research aircraft were subjected to different piloted test inputs while the short period stabilator/elevator to pitch rate frequency response was estimated. Results show that the method can produce accurate results using wide-band piloted inputs instead of multisines. A new metric is introduced for evaluating which data points to include in the analysis and recommendations are provided for applying this method with piloted inputs.

Grauer, Jared A.↗

Flight investigation of various control inputs intended for parameter estimation

NASA's F-8 digital fly-by-wire aircraft has been subjected to stability and control derivative assessments, leading to the proposal of improved control inputs for more efficient control derivative estimation. This will reduce program costs by reducing flight test and data analysis requirements. Inputs were divided into sinusoidal types and cornered types. Those with corners produced the best set of stability and control derivatives for the unaugmented flight control system mode. Small inputs are noted to have provided worse derivatives than larger ones.

Shafer, M. F.↗

Angle of attack estimation using an inertial reference platform

This paper presents the mathematical development and flight test results of an angle of attack estimation system based on inertial navigation system inputs. The estimator uses these inputs to determine the coefficient of lift required at any instant inflight. Angle of attack is then modeled through a regression analysis based on coefficient of lift, altitude and Mach. Overall correlation of the estimator as tested was generally within 0.5 degrees through 17 degrees angle of attack on an F-15A aircraft. A robustness analysis indicates that the system can be used adequately in maneuvering flight.

Zeis, Joseph E., Jr.↗

PMSM Parameter Estimation using a Linear Unknown Input Interval Observer

In this paper, the problem of permanent magnet synchronous motor (PMSM) speed and unknown load torque estimation is addressed. For this purpose, a interval unknown input observer (UIO) for linear time-invariant (LTI) systems is used. First, the PMSM model is linearized in order to make it in a suitable form for the linear interval UIO. Then, the interval UIO is applied to allow the joint estimation of the motor speed and the unknown load torque disturbance. The main advantages of this approach is that it not only allows the joint state and unknown input estimation, but also to take the different uncertainty sources into account. Indeed, taking model and measurement uncertainty into account is crucial. Assuming that the measurement noise and disturbances are bounded, lower and upper bounds are first computed for the unmeasured state (motor speed) and then for the unknown input (load torque). The proposed approach and its limitations are demonstrated with the nonlinear PMSM model derived from its equivalent electrical circuit.

Elinirina I Robinson↗

Estimating Fluctuating Pressures From Distorted Measurements

Two algorithms extract estimates of time-dependent input (upstream) pressures from outputs of pressure sensors located at downstream ends of pneumatic tubes. Effect deconvolutions that account for distoring effects of tube upon pressure signal. Distortion of pressure measurements by pneumatic tubes also discussed in "Distortion of Pressure Signals in Pneumatic Tubes," (ARC-12868). Varying input pressure estimated from measured time-varying output pressure by one of two deconvolution algorithms that take account of measurement noise. Algorithms based on minimum-covariance (Kalman filtering) theory.

Whitmore, Stephen A.↗

Practical input optimization for aircraft parameter estimation experiments

The object of this research was to develop an algorithm for the design of practical, optimal flight test inputs for aircraft parameter estimation experiments. A general, single pass technique was developed which allows global optimization of the flight test input design for parameter estimation using the principles of dynamic programming with the input forms limited to square waves only. Provision was made for practical constraints on the input, including amplitude constraints, control system dynamics, and selected input frequency range exclusions. In addition, the input design was accomplished while imposing output amplitude constraints required by model validity and considerations of safety during the flight test. The algorithm has multiple input design capability, with optional inclusion of a constraint that only one control move at a time, so that a human pilot can implement the inputs. It is shown that the technique can be used to design experiments for estimation of open loop model parameters from closed loop flight test data. The report includes a new formulation of the optimal input design problem, a description of a new approach to the solution, and a summary of the characteristics of the algorithm, followed by three example applications of the new technique which demonstrate the quality and expanded capabilities of the input designs produced by the new technique. In all cases, the new input design approach showed significant improvement over previous input design methods in terms of achievable parameter accuracies.

Morelli, Eugene A.↗

gcm_eval (Global Climate Model Evaluation) [SWR-24-37]

The interplay between energy, climate, and weather is becoming more complex as our changing climate continues to affect the weather we experience which in turn drives changes in the ever increasing share of renewable energy generation and energy demand. Energy system analyses commonly rely on meteorological inputs to estimate renewable energy generation and energy demand; however, these inputs rarely represent the estimated impacts of future climate change. Climate models and publicly available climate change datasets can be used for this purpose, but the selection of inputs from the myriad of available models and datasets is a nuanced and often subjective process. There is no single perfect climate model or dataset for all applications. In this repository, we include software and the corresponding assessments of various global climate models (GCMs) from the Coupled Model Intercomparison Project (CMIP6), evaluating their skills with respect to the historical climate and comparing of their future projections of climate change. We focus on variables that directly affect the energy system including the representation of extreme values that can drive grid resilience events. The objective of this repository is not to recommend the best climate model and dataset for a given analysis, but instead to provide a reference to facilitate the selection of climate models and datasets in subsequent work.

Buster, Grant↗

Evaluation of Global Climate Models for Use in Energy Analysis

The interplay between energy, climate, and weather is becoming more complex due to increasing contributions of renewable energy generation, energy storage, electrified end uses, and the increasing frequency of extreme weather events. Energy system analyses commonly rely on meteorological inputs to estimate renewable energy generation and energy demand; however, these inputs rarely represent the estimated impacts of future climate change. Climate models and publicly available climate change datasets can be used for this purpose, but the selection of inputs from the myriad of available models and datasets is a nuanced and subjective process. In this work, we assess datasets from various global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We present evaluations of their skills with respect to the historical climate and comparisons of their future projections of climate change for two climate change scenarios. We present the results for different climatic and energy system regions and include interactive figures in the accompanying software repository. Previous work has presented similar GCM evaluations, but none have presented variables and metrics specifically intended for comprehensive energy systems analysis including impacts on energy demand, thermal cooling, hydropower, water availability, solar energy generation, and wind energy generation. We focus on GCM output meteorological variables that directly affect these energy system components including the representation of extreme values that can drive grid resilience events. The objective of this work is not to recommend the best climate model and dataset for a given analysis, but instead to provide a reference to facilitate the selection of climate models and scenarios in subsequent work.

14 SOLAR ENERGY↗

Optimal input design for aircraft parameter estimation using dynamic programming principles

A new technique was developed for designing optimal flight test inputs for aircraft parameter estimation experiments. The principles of dynamic programming were used for the design in the time domain. This approach made it possible to include realistic practical constraints on the input and output variables. A description of the new approach is presented, followed by an example for a multiple input linear model describing the lateral dynamics of a fighter aircraft. The optimal input designs produced by the new technique demonstrated improved quality and expanded capability relative to the conventional multiple input design method.

Klein, Vladislav↗

Adaptive control of propellant slosh for a launch vehicle with multiple tanks

This paper discusses the use of an adaptive system to stabilize a multi-tank launch vehicle with sloshable fuel. The adaptive system utilizes a sensor to measure the fluid slosh angle, a recursive-least-squares estimator to estimate the natural frequency of the sloshing fuel, and a linear filter which stabilizes the unstable fuel slosh using the frequency estimate. Single-input and multiinput estimators were investigated. The time-varying complexity of the slosh control loop keeps the system persistently excited through flight, even though the attitude is controlled by a conventional PID controller. For the first portion of flight, the adaptive system maintains stability. A modification is proposed for latter portions of flight. The approach requires no tank baffles or partitions.

Saugen, John D.↗

Optimal Input Design for Aircraft Parameter Estimation using Dynamic Programming Principles

A new technique was developed for designing optimal flight test inputs for aircraft parameter estimation experiments. The principles of dynamic programming were used for the design in the time domain. This approach made it possible to include realistic practical constraints on the input and output variables. A description of the new approach is presented, followed by an example for a multiple input linear model describing the lateral dynamics of a fighter aircraft. The optimal input designs produced by the new technique demonstrated improved quality and expanded capability relative to the conventional multiple input design method.

Morelli, Eugene A.↗

Optimal input design for aircraft instrumentation systematic error estimation

A new technique for designing optimal flight test inputs for accurate estimation of instrumentation systematic errors was developed and demonstrated. A simulation model of the F-18 High Angle of Attack Research Vehicle (HARV) aircraft was used to evaluate the effectiveness of the optimal input compared to input recorded during flight test. Instrumentation systematic error parameter estimates and their standard errors were compared. It was found that the optimal input design improved error parameter estimates and their accuracies for a fixed time input design. Pilot acceptability of the optimal input design was demonstrated using a six degree-of-freedom fixed base piloted simulation of the F-18 HARV. The technique described in this work provides a practical, optimal procedure for designing inputs for data compatibility experiments.

Morelli, Eugene A.↗

Sequential Precipitation Input Tagging (SPIT) to Estimate Water Transit Times and Hydrologic Tracer Dynamics Within Water‐Tagging Enabled Hydrologic Models

Determining the age distribution of water exiting a catchment is important for understanding groundwater storage and mixing. New water-tagging capabilities within models track precipitation events as they move through simulated storages, yet forward modeling of individual events may not systematically capture the full transit time distribution (TTD). Here, we present a “sequential precipitation input tagging” (SPIT) framework to tag all input precipitation at regular intervals during extended model simulations. Monthly tags over 7 years were applied at six National Ecological Observatory Network sites to calculate TTDs and derive mean virtual tracer age, $\overline{T_{V}}$, fractions of young water, F yw , and hydrologic tracer concentrations (water isotopes δ 18 O and δ 2 H) within a tagging enabled version of the Weather Research and Forecast hydrologic model (WRF-Hydro). Throughout seven simulation years, the fraction of simulated discharge derived from tagged events, F tag , increased each year, with the final year's F tag ranging from 66% to 100% and highlights the need to apply SPIT over many years to understand TTDs. When the F tag was >75%, simulated $\overline{T_{V}}$ ranged 179–923 days and F yw 0.6%–23.9%, with daily values exhibiting a power-law relationship with precipitation, discharge, and groundwater. Through implementation of SPIT, we find this hydrologic model configuration performs poorly in estimation of $\overline{T_{V}}$ and F yw (root mean squared error of 469 days and 14.4% respectively), suggesting it misrepresents subsurface mixing. Thus, the SPIT framework provides a reproducible approach to calculate watershed transit times within tagging enabled models and thereby assess and improve representation of hydrologic processes.

fraction of young water↗

Evaluation of crop acreage estimation methods using Landsat data as auxiliary input

The regression and ratio estimators are studied in the context of improving upon the ground survey estimates of crop acreages by utilizing Landsat data. The approach is to formulate analytically the estimation problem that utilizes ground survey data, as collected by the U.S. Department of Agriculture, and Landsat data, which provide a complete coverage for an area of interest, and then to conduct simulation studies. It is shown over a wide range of conditions that the regression estimator is the most efficient unless there is a low correlation between the actual and estimated crop acreages in the sampled area segments, in which case a ratio type estimator is superior. Estimation of the variance of the regression estimator is also investigated.

Chhikara, R. S.↗

Labor Estimation and Trending in an Operations Environment

Accurate cost estimation for future work is important for ensuring that organizations have the personnel and resources needed to complete a task successfully. Operations support in particular provides additional challenges for estimating cost. Unlike manufacturing-based work, it may be difficult to derive a "cost per product," and projects that appear to be similar may have drastically different underlying assumptions. In addition, for critical operations, a certain level of adaptability to changing circumstances is expected. Responding to contingencies or anomalies requires additional support and troubleshooting, and could result in increases to project cost. Labor estimation is one of the most important inputs for estimating the cost of an operations-based project, especially in a pre-established facility. Various methods are needed to develop reliable estimates. These methods include a bottom-up summarization of known or expected work items, and a top-down estimate generated by trending and scaling actual labor from past analogous operations support.

Laing, Jason↗

INDES User's guide multistep input design with nonlinear rotorcraft modeling

The INDES computer program, a multistep input design program used as part of a data processing technique for rotorcraft systems identification, is described. Flight test inputs base on INDES improve the accuracy of parameter estimates. The input design algorithm, program input, and program output are presented.

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