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Results for “generalized predictive control”

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 433 records · Page 24

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Dehydroxylation kinetics of kaolinite and montmorillonite examined using isoconversional methods

The use of calcined clays as supplementary cementitious materials (SCMs) in concrete is a promising strategy towards decarbonizing the cement and concrete industry. This is especially relevant considering the ever-increasing demand for concrete. Comprehensive understanding of the kinetics of calcination is essential towards maximizing the potential reactivity of clay minerals while ensuring energy efficiency. In this study, the kinetics of the dehydroxylation of kaolinite and montmorillonite are investigated under non-isothermal conditions at constant heating rate. Activation energies ( E a ) are determined via Friedman differential and advanced Vyazovkin incremental methods over the isoconversional range; these are devoid of computational approximations, thus allowing kinetic analysis without assuming a specific reaction model. Kinetic equations—in the differential form as well as a combination of differential and integral forms are compared against the experimentally determined reaction models to identify the most probable dehydroxylation mechanism for kaolinite and montmorillonite. A reaction order mechanism is established for dehydroxylation of kaolinite, while montmorillonite is noted to undergo dehydroxylation via a single-step reversible diffusion-controlled process. Kinetic triplet—comprising activation energy, reaction model and pre-exponential factor—is used to predict isothermal calcination conditions, which is further verified using analytical techniques. Heat release rates of clay-portlandite blends from isothermal calorimetry are used within a thermodynamic framework to quantify reactivity of the calcined clays. Here, the study demonstrates a general approach based on isoconversional methods to predict calcination conditions for different clays that can be used in efficient and optimized production of blended cements or SCMs.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Dynamic machine learning-based optimization algorithm to improve boiler efficiency

With decreasing computational costs, improvement in algorithms, and the aggregation of large industrial and commercial datasets, machine learning is becoming a ubiquitous tool for process and business innovations. Machine learning is still lacking applications in the field of dynamic optimization for real-time control. This work presents a novel framework for performing constrained dynamic optimization using a recurrent neural network model combined with a metaheuristic optimizer. The framework is designed to augment an existing control system and is purely data-driven, like most industrial Model Predictive Control applications. Several recurrent neural network models are compared as well as several metaheuristic optimizers. Hyperparameters and optimizer parameters are tuned with parameter sweeps, and the resulting values are reported. Further, the best parameters for each optimizer and model combination are demonstrated in closed-loop control of a dynamic simulation, and several recommendations are made for generalizing this framework to other systems. Up to 0.953% improvement is realized over the non-optimized case for a simulated coal-fired boiler. While this is not a large improvement in percentage, the total economic impact is $991,000 per year, and this study builds a foundation for future machine learning with dynamic optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

A general infrastructure for data-driven control design and implementation in tokamaks

A general infrastructure for tokamak controllers based on data-driven neural net models is presented. The paradigm allows for more flexible choices of both the underlying model and the desired controlled variables and targets. The system is implemented and tested on the DIII-D tokamak, enacting simultaneous pressure and temperature control via a finite-set model-predictive controller. Traditional control methods such as proportional–integral–derivative (PID) have proven effective for decoupled control tasks, but scale poorly when trying to achieve more complicated goals such as full state control. This is exactly where model-based controllers succeed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1978

Theory of the control of structures by low authority controllers

The novel idea presented is based on the observation that if a structure is controlled by distributed systems of sensors and actuators with limited authority, i.e., if the controller is allowed to modify only moderately the natural modes and frequencies of the structure, then it should be possible to apply root perturbation techniques to predict analytically the behavior of the total system. Attention is given to the root perturbation formula first derived by Jacobi for infinitesimal perturbations which neglect the induced eigenvector perturbation, a more general form of Jacobi's formula, first-order structural equations and modal state vectors, state-space equations for damper-augmented structures, and modal damping prediction formulas.

Aubrun, J. N.↗

NASA NTRS1981

Sources, control, and effects of noise from aircraft propellers and rotors

Source noise predictions are compared with measurements for conventional low-speed propellers, for new high speed propellers (propfans), and for a helicopter. Results from a light aircraft demonstration program are described, indicating that about 5-dB reduction of flyover noise can be obtained without significant performance penalty. Sidewall design studies are described for interior noise control in light general aviation aircraft and in large transports using propfan propulsion. The weight of the added acoustic treatment is estimated and tradeoffs between weight and noise reduction are discussed. A laboratory study of passenger response to combined broadband and tonal propeller like noise is described. Subject discomfort ratings of combined tone broadband noises are compared with ratings of broadband (boundary layer) noise alone, and the relative importance of the propeller tones is examined.

Mixson, J. S.↗

NASA NTRS1981

Sources, control, and effects of noise from aircraft propellers and rotors

Recent NASA and NASA sponsored research on the prediction and control of propeller and rotor source noise, on the analysis and design of fuselage sidewall noise control treatments, and on the measurement and quantification of the response of passengers to aircraft noise is described. Source noise predictions are compared with measurements for conventional low speed propellers, for new high speed propellers (propfans), and for a helicopter. Results from a light aircraft demonstration program are considered which indicates that about 5 dB reduction of flyover noise can be obtained without significant performance penalty. Sidewall design studies are examined for interior noise control in light general aviation aircraft and in large transports using propfan propulsion. The weight of the added acoustic treatment is estimated and tradeoffs between weight and noise reduction are discussed. A laboratory study of passenger response to combined broadband and tonal propeller-like noise is described. Subject discomfort ratings of combined tone broadband noises are compared with ratings of broadband (boundary layer) noise alone and the relative importance of the propeller tones is examined.

Mixson, J. S.↗

NASA NTRS1989

Structural modeling for control design (articulated multibody component representation)

High gain, high frequency flexible responses in gimbaled multibody systems are discussed. Their origin and physical significance are described in terms of detailed mass and stiffness modeling at actuator/sensor interfaces. Guyan Reduction, Generalized Dynamic Reduction, inadequate mass modeling detail, as well as system mode truncation, are shown to suppress the high gain high frequency response and thereby lose system flexibility important for stability and performance predictions. Model validation by modal survey testing is shown to risk similar loss of accuracy. Difficulties caused by high frequency responses in component mode simulations, such as DISCOS, and also linearized system mode simulations, are described, and approaches for handling these difficulties are discussed.

Haugse, E. D.↗

NASA NTRS1993

Trajectory optimization for the National Aerospace Plane

The objective of this second phase research is to investigate the optimal ascent trajectory for the National Aerospace Plane (NASP) from runway take-off to orbital insertion and address the unique problems associated with the hypersonic flight trajectory optimization. The trajectory optimization problem for an aerospace plane is a highly challenging problem because of the complexity involved. Previous work has been successful in obtaining sub-optimal trajectories by using energy-state approximation and time-scale decomposition techniques. But it is known that the energy-state approximation is not valid in certain portions of the trajectory. This research aims at employing full dynamics of the aerospace plane and emphasizing direct trajectory optimization methods. The major accomplishments of this research include the first-time development of an inverse dynamics approach in trajectory optimization which enables us to generate optimal trajectories for the aerospace plane efficiently and reliably, and general analytical solutions to constrained hypersonic trajectories that has wide application in trajectory optimization as well as in guidance and flight dynamics. Optimal trajectories in abort landing and ascent augmented with rocket propulsion and thrust vectoring control were also investigated. Motivated by this study, a new global trajectory optimization tool using continuous simulated annealing and a nonlinear predictive feedback guidance law have been under investigation and some promising results have been obtained, which may well lead to more significant development and application in the near future.

Lu, Ping↗

NASA NTRS2008

Design Guidelines for Quiet Fans and Pumps for Space Vehicles

This document presents guidelines for the design of quiet fans and pumps of the class used on space vehicles. A simple procedure is presented for the prediction of fan noise over the meaningful frequency spectrum. A section also presents general design criteria for axial flow fans, squirrel cage fans, centrifugal fans, and centrifugal pumps. The basis for this report is an experimental program conducted by Hamilton Standard under NASA Contract NAS 9-12457. The derivations of the noise predicting methods used in this document are explained in Hamilton Standard Report SVHSER 6183, "Fan and Pump Noise Control," dated May 1973 (6).

Lovell, John S.↗

NASA NTRS2016

Modeling Relationships Between Flight Crew Demographics and Perceptions of Interval Management

The Interval Management Alternative Clearances (IMAC) human-in-the-loop simulation experiment was conducted to assess interval management system performance and participants' acceptability and workload while performing three interval management clearance types. Twenty-four subject pilots and eight subject controllers flew ten high-density arrival scenarios into Denver International Airport during two weeks of data collection. This analysis examined the possible relationships between subject pilot demographics on reported perceptions of interval management in IMAC. Multiple linear regression models were created with a new software tool to predict subject pilot questionnaire item responses from demographic information. General patterns were noted across models that may indicate flight crew demographics influence perceptions of interval management.

Remy, Benjamin↗

NASA NTRSDate not supplied

In-Tunnel Simulations of the NASA Juncture Flow Model

The effect of wind tunnel walls on the development of flow separation in the wing-fuselage juncture area is assessed using FUN3D’s overset and wind tunnel controller capabilities. The simulation data obtained from the in-tunnel runs are compared with the free-air simulations. The simulation results are also evaluated using measurements from the recently completed Phase 2 of the Juncture Flow Experiment. In general, the simulation data compared well with the measurements, however there were no significant differences in the prediction of the separation location, size, and shape, between the free-air and the in-tunnel runs. The results of this study suggest that the effects of tunnel walls on the flow separation in the juncture region are relatively small.

Juncture Flow↗

NASA NTRSDate not supplied

In-Tunnel Simulations of the NASA Juncture Flow Model

Juncture Flow↗

DOE OSTI2023

Controlling Optical Cavities using Machine Learning

The GQuEST experiment endeavors to investigate the unification of Quantum Mechanics and General Relativity within the realm of quantum gravity. This pursuit constitutes a well-established conundrum in the field of physics, characterized by its formidable nature due to the infinitesimal effects manifesting at the Planck scale, which measures a mere 10 35 meters. A remarkable facet of the GQuEST experiment is its capacity to predict observable manifestations of Quantum Gravity on a larger and more accessible scale.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Deep neural network improves the estimation of polygenic risk scores for breast cancer

Polygenic risk scores (PRS) estimate the genetic risk of an individual for a complex disease based on many genetic variants across the whole genome. Here, we compared a series of computational models for estimation of breast cancer PRS. A deep neural network (DNN) was found to outperform alternative machine learning techniques and established statistical algorithms, including BLUP, BayesA, and LDpred. In the test cohort with 50% prevalence, the Area Under the receiver operating characteristic Curve (AUC) were 67.4% for DNN, 64.2% for BLUP, 64.5% for BayesA, and 62.4% for LDpred. BLUP, BayesA, and LPpred all generated PRS that followed a normal distribution in the case population. However, the PRS generated by DNN in the case population followed a bimodal distribution composed of two normal distributions with distinctly different means. This suggests that DNN was able to separate the case population into a high-genetic-risk case subpopulation with an average PRS significantly higher than the control population and a normal-genetic-risk case subpopulation with an average PRS similar to the control population. This allowed DNN to achieve 18.8% recall at 90% precision in the test cohort with 50% prevalence, which can be extrapolated to 65.4% recall at 20% precision in a general population with 12% prevalence. Interpretation of the DNN model identified salient variants that were assigned insignificant p values by association studies, but were important for DNN prediction. These variants may be associated with the phenotype through nonlinear relationships.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1973

Condensed phase details in the time-independent combustion of AP/composite propellants

The problem of nonmetallized ammonium perchlorate (AP)/composite propellant combustion is studied with the aim of coherently interpreting apparently diverse experimental data. Three fundamental hypotheses are introduced: the extent of propellant degradation at the vaporization step has to be specified through a scientific criterion; condensed phase degradation of AP to vaporizable fragments is the overall rate-limiting reaction; the rate of combustion in the gas phase is controlled by diffusive-mixing processes. Theoretical predictions of the regression rates of AP are seen to match well with experimental observations. Theoretical curves of regression rate are presented for a typical composite propellant. It is seen through analyses that either of the two familiar models for the gas phase (flame sheet approximation and uniform combustion) describes the general behavior adequately, hence de-emphasizing the role of gas phase details in propellant combustion.

Kumar, R. N.↗

NASA NTRS1996

Micromechanics Analysis Code (MAC). User Guide: Version 2.0

The ability to accurately predict the thermomechanical deformation response of advanced composite materials continues to play an important role in the development of these strategic materials. Analytical models that predict the effective behavior of composites are used not only by engineers performing structural analysis of large-scale composite components but also by material scientists in developing new material systems. For an analytical model to fulfill these two distinct functions it must be based on a micromechanics approach which utilizes physically based deformation and life constitutive models and allows one to generate the average (macro) response of a composite material given the properties of the individual constituents and their geometric arrangement. Here the user guide for the recently developed, computationally efficient and comprehensive micromechanics analysis code's (MAC) who's predictive capability rests entirely upon the fully analytical generalized method of cells (GMC), micromechanics model is described. MAC is a versatile form of research software that 'drives' the double or triply periodic micromechanics constitutive models based upon GMC. MAC enhances the basic capabilities of GMC by providing a modular framework wherein (1) various thermal, mechanical (stress or strain control) and thermomechanical load histories can be imposed, (2) different integration algorithms may be selected, (3) a variety of constituent constitutive models may be utilized and/or implemented, and (4) a variety of fiber and laminate architectures may be easily accessed through their corresponding representative volume elements.

Wilt, T. E.↗

NASA NTRS2009

The Need for Technology Maturity of Any Advanced Capability to Achieve Better Life Cycle Cost (LCC)

Programs such as space transportation systems are developed and deployed only rarely, and they have long development schedules and large development and life cycle costs (LCC). They have not historically had their LCC predicted well and have only had an effort to control the DDT&E phase of the programs. One of the factors driving the predictability, and thus control, of the LCC of a program is the maturity of the technologies incorporated in the program. If the technologies incorporated are less mature (as measured by their Technology Readiness Level - TRL), then the LCC not only increases but the degree of increase is difficult to predict. Consequently, new programs avoid incorporating technologies unless they are quite mature, generally TRL greater than or equal to 7 (system prototype demonstrated in a space environment) to allow better predictability of the DDT&E phase costs unless there is no alternative. On the other hand, technology development programs rarely develop technologies beyond TRL 6 (system/subsystem model or prototype demonstrated in a relevant environment). Currently the lack of development funds beyond TRL 6 and the major funding required for full scale development leave little or no funding available to prototype TRL 6 concepts so that hardware would be in the ready mode for safe, reliable and cost effective incorporation. The net effect is that each new program either incorporates little new technology or has longer development schedules and costs, and higher LCC, than planned. This paper presents methods to ensure that advanced technologies are incorporated into future programs while providing a greater accuracy of predicting their LCC. One method is having a dedicated organization to develop X-series vehicles or separate prototypes carried on other vehicles. The question of whether such an organization should be independent of NASA and/or have an independent funding source is discussed. Other methods are also discussed. How to make the choice of which technologies to pursue to the prototype level is also discussed since, to achieve better LCC, first the selection of the appropriate technologies.

Robinson, John W.↗

DOE OSTI2020

Evaluating the WFIP2 updates to the HRRR model using scanning Doppler lidar measurements in the complex terrain of the Columbia River Basin

The wind-energy (WE) industry relies on numerical weather prediction (NWP) forecast models as foundational or base models for many purposes, including wind-resource assessment and wind-power forecasting. During the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River Basin of Oregon and Washington, a significant effort was made to improve NWP forecasts through focused model development, to include experimental refinements to the High Resolution Rapid Refresh (HRRR) model physics and horizontal grid spacing. In this study, the performance of an experimental version of HRRR that includes these refinements is tested against a control version, which corresponds to that of the operational HRRR run by National Oceanic and Atmospheric Administration/National Centers for Environmental Protection at the outset of WFIP2. Furthermore, the effects of horizontal grid resolution were also tested by comparing wind forecasts from the HRRR (with 3-km grid spacing) with those from a finer-resolution HRRR nest with 750-m grid spacing. Model forecasts are validated against accurate wind-profile measurements by three scanning, pulsed Doppler lidars at sites separated by a total distance of 71 km. Model skill and improvements in model skill, attributable to physics refinements and improved horizontal grid resolution, varied by season, by site, and during periods of atmospheric phenomena relevant to WE. In general, model errors were the largest below 150 m above ground level (AGL). Experimental HRRR refinements tended to reduce the mean absolute error (MAE) and other error metrics for many conditions, but degradation in skill (increased MAE) was noted below 150 m AGL at the two lowest-elevation sites at night. Finer resolution was found to produce the most significant reductions in the error metrics.

17 WIND ENERGY↗