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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Model predictive control for active insulation in building envelopes

Active insulation systems (AISs) refer to building envelopes with insulation materials that can change their thermal conductivity and are coupled with thermal mass to reduce building energy consumption and peak power. In this research, a novel optimal control approach is proposed to evaluate the maximum theoretical energy and cost-saving potential of AISs. A time-varying model predictive control (TV-MPC) controller was used to optimally select the AIS mode and simultaneously determine the operation of the heating, ventilation and air conditioning (HVAC) system so that the maximum saving potential of the entire system can be realized. To comprehensively evaluate the power shifting flexibility of AISs, two optimization objectives—minimizing weekly electric energy consumption and minimizing weekly electricity cost—were considered. The summer season simulation results show that under the first objective, more than 50% electric and thermal energy was saved when the upper boundary of the indoor air temperature was set to 25 °C. Furthermore under the second optimization objective, 38% of the cost was saved. It can be expected that the developed approach can be easily applied to multiple types of AISs with different mechanisms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems

Modern buildings are increasingly automated and often equipped with multiple building services (e.g., air-conditioning and mechanical ventilation (ACMV), dynamic shading, dimmable lighting). These systems are conventionally controlled individually without considering their interactions, affecting the building’s overall energy inefficiency and occupant comfort. A model predictive control (MPC) system that features a multi-objective MPC scheme to enable coordinated control of multiple building services for overall optimized energy efficiency, indoor thermal and visual comfort, as well as a hybrid model for predicting indoor visual comfort and lighting power is proposed. The MPC system was implemented in a test facility having two identical, side-by-side experimental cells to facilitate comparison with a building management system (BMS) employing conventional reactive feedback control. The MPC system coordinated the control of the ACMV, dynamic façade and automated dimmable lighting systems in one cell while the BMS controlled the building services in the other cell in a conventional manner. The MPC side achieved 15.1–20.7% electricity consumption reduction, as compared to the BMS side. Simultaneously, the MPC system improved indoor thermal comfort by maintaining the room within the comfortable range (-0.5 < predicted mean vote < 0.5) for 98.3% of the time, up from 91.8% of the time on the BMS side. Visual comfort, measured by indoor daylight glare probability (DGP) and horizontal illuminance level at work plane height, was maintained for the entire test period on the MPC side, improving from having visual comfort for 94.5% and 85.7% of the time, respectively, on the BMS side.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Model Predictive Control Tuning by Inverse Matching for a Wave Energy Converter

This paper investigates the application of a method to find the cost function or the weight matrices to be used in model predictive control (MPC) such that the MPC has the same performance as a predesigned linear controller in state-feedback form when constraints are not active. This is potentially useful when a successful linear controller already exists and it is necessary to incorporate the constraint-handling capabilities of MPC. This is the case for a wave energy converter (WEC), where the maximum power transfer law is well-understood. In addition to solutions based on numerical optimization, a simple analytical solution is also derived for cases with a short prediction horizon. These methods are applied for the control of an empirically-based WEC model. The results show that the MPC can be successfully tuned to follow an existing linear control law and to comply with both input and state constraints, such as actuator force and actuator stroke.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Data-Driven Model Predictive Control for Temperature Management of Heat Pipe Microreactor

To enable the self-regulating capability of heat pipe (HP) microreactors, an anticipatory control strategy through model predictive control (MPC) could proactively respond to potential disturbances and deviations in operating setpoints. However, a key factor prohibiting the widespread adoption of MPCs in nuclear applications is the effort and computational costs associated with learning and calibrating first-principles-based process models when the target system is complex and when there are gaps between modeled and target reactor systems. In this paper, we demonstrate data-driven MPC using three approaches for modeling the system dynamics, including a linear state-space model, feedforward neural network, and recurrent neural networks long short-term memory. We present the development and validation process of each model and compare the performance of data-driven MPCs in controlling the temperatures of selected HPs at the evaporator and condenser regions in a 37-HP-monolith system. Our results show that, qualitatively, all data-driven MPCs are producing similar control actions, while quantitatively, with artificial neural nets (especially feedforward neural nets), MPC can better follow drastic changes in setpoints with smallest errors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1989

Cosmological velocity correlations - Observations and model predictions

By applying the present simple statistics for two-point cosmological peculiar velocity-correlation measurements to the actual data sets of the Local Supercluster spiral galaxy of Aaronson et al. (1982) and the elliptical galaxy sample of Burstein et al. (1987), as well as to the velocity field predicted by the distribution of IRAS galaxies, a coherence length of 1100-1600 km/sec is obtained. Coherence length is defined as that separation at which the correlations drop to half their zero-lag value. These results are compared with predictions from two models of large-scale structure formation: that of cold dark matter and that of baryon isocurvature proposed by Peebles (1980). N-body simulations of these models are performed to check the linear theory predictions and measure sampling fluctuations.

Gorski, Krzysztof M.↗
NASA NTRS2019

Using Machine Learning to Develop a Predictive Model for Future Fire Seasons

The deep learning model shows promise for predicting areas of high wildfire potential. Full evaluation of the model performance is ongoing. Currently, the developed deep learning model is better overall at predicting the number of fires over the acres burned. Acres burned is dependent on location, suppression plan, and current conditions. Antecedent conditions are only one piece of the equation. In-season changes are not accounted for. An ignition source is required, which further complicates the model training and prediction.

White, Andrew T.↗
DOE OSTI2023

Machine Learning-Based Model Predictive Control of Two-Time-Scale Systems

In this study, we present a general form of nonlinear two-time-scale systems, where singular perturbation analysis is used to separate the dynamics of the slow and fast subsystems. Machine learning techniques are utilized to approximate the dynamics of both subsystems. Specifically, a recurrent neural network (RNN) and a feedforward neural network (FNN) are used to predict the slow and fast state vectors, respectively. Moreover, we investigate the generalization error bounds for these machine learning models approximating the dynamics of two-time-scale systems. Next, under the assumption that the fast states are asymptotically stable, our focus shifts toward designing a Lyapunov-based model predictive control (LMPC) scheme that exclusively employs the RNN to predict the dynamics of the slow states. Additionally, we derive sufficient conditions to guarantee the closed-loop stability of the system under the sample-and-hold implementation of the controller. A nonlinear chemical process example is used to demonstrate the theory. In particular, two RNN models are constructed: one to model the full two-time-scale system and the other to predict solely the slow state vector. Both models are integrated within the LMPC scheme, and we compare their closed-loop performance while assessing the computational time required to execute the LMPC optimization problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Phase-Disposition PWM Enabled Model Predictive Control for a Nine-Level Inner-Interleaved Hybrid Multilevel Converter

This article brings forward a phase-disposition pulse width modulation enabled model predictive control (PDPWM-MPC) for a nine-level inner-interleaved hybrid multilevel converter (9L-IHMC). Firstly, three layers of virtual space vector diagrams (VSVD) are established based on the sign patterns of the original and virtual reference vectors in the abcframe, respectively, to achieve the phase-disposition pulse width modulation (PD-PWM) in a non-independent three-phase way. Then, three adjacent virtual vectors in the third-layer VSVD, together with their optimized duty cycles, are applied to guarantee the optimal current tracking. Finally, through the use of the duty cycle alternation approach, dc-link and floating capacitors voltages are balanced and circulating currents are mitigated as well. The proposed PDPWM-MPC can not only enable the decoupling of the low-and high-frequency stages in the 9L-IHMC, but also reduce both output current ripples and computational burden. In addition, it can achieve a constant equivalent switching frequency and address the disproportion of power losses associated with the PD-PWM. Here, both simulations and experiments on a silicon carbide device-based prototype substantiate the effectiveness of the proposed control strategy.

42 ENGINEERING↗
DOE OSTI2022

A Two-Level Model Predictive Control-Based Approach for Building Energy Management including Photovoltaics, Energy Storage, Solar Forecasting and Building Loads

This paper uses a two-level model predictive control-based approach for the coordinated control and energy management of an integrated system that includes photovoltaic (PV) generation, energy storage, and building loads. Novel features of the proposed local controller include (1) the ability to simultaneously manage building loads and energy storage to achieve different operational objectives such as energy efficiency, economic cost efficiency, demand response and grid optimization through the design of specific power trajectory tracking performance functionals, (2) an energy trim function that minimizes the impact of solar forecasting errors on system performance, and (3) the design of a state of charge controller that uses day-ahead forecast of solar power and building loads to intialize energy storage at the start of each day. The local controller is tested in simulation using an exemplary system with PV generation, energy storage and dispatchable building loads. Two sample days with different PV forecasts and multiple case scenarios are considered, and the performance of the algorithm in managing the real and reactive net building load trajectories and the ramp rate of PV injections into the utility network are evaluated. The simulations are based on actual forecasted and measured PV data, and the results show that the local controller meets the tracking requirements for real and reactive power within the operating constraints of the building.

14 SOLAR ENERGY↗
DOE OSTI2022

MPC4CLR (Model-Predictive-Control-for-Critical-Load-Restoration-in-Power-Distribution-Systems) [SWR-22-24]

Model predictive control (MPC) is a system or process control technique for making decisions under uncertainty via rolling look-ahead optimizations at each control step where only the current step decisions are applied, and the rest are discarded. In this work, we developed an MPC for a critical load restoration (CLR) in power distribution systems to recover system service (electricity delivery) following an extreme event-triggered substation outage. The method considers the problem of controlling distributed energy resources (DERs) of the distribution system with the objective of achieving maximum load pick up while satisfying distribution network flow and voltage constraints. A linearized optimal power flow (OPF) model is employed to represent the physics of the network. The problem formulation is augmented with a ramping (up) reserve product for the DERs to ensure improved and upward monotonic load restoration as time evolves. Simulation analysis and performance tests are performed using a modified IEEE 13-bus test feeder integrated with wind, solar, microturbine, and energy storage battery. The software is developed using various software packages in Julia and Python. The MPC model is implemented using the JuMP optimization language in Julia while the data analytics including renewable generation and load demand forecasts, running the MPC simulation and visualizations is performed in Python.

Eseye, Abinet Tesfaye↗
NASA NTRS2011

Predictive Models of Duration of Ground Delay Programs in New York Area Airports

Initially planned GDP duration often turns out to be an underestimate or an overestimate of the actual GDP duration. This, in turn, results in avoidable airborne or ground delays in the system. Therefore, better models of actual duration have the potential of reducing delays in the system. The overall objective of this study is to develop such models based on logs of GDPs. In a previous report, we described descriptive models of Ground Delay Programs. These models were defined in terms of initial planned duration and in terms of categorical variables. These descriptive models are good at characterizing the historical errors in planned GDP durations. This paper focuses on developing predictive models of GDP duration. Traffic Management Initiatives (TMI) are logged by Air Traffic Control facilities with The National Traffic Management Log (NTML) which is a single system for automated recoding, coordination, and distribution of relevant information about TMIs throughout the National Airspace System. (Brickman, 2004 Yuditsky, 2007) We use 2008-2009 GDP data from the NTML database for the study reported in this paper. NTML information about a GDP includes the initial specification, possibly one or more revisions, and the cancellation. In the next section, we describe general characteristics of Ground Delay Programs. In the third section, we develop models of actual duration. In the fourth section, we compare predictive performance of these models. The final section is a conclusion.

Kulkarni, Deepak↗
DOE OSTI2023

Constraining long‐term model predictions for woody growth using tropical tree rings

The strength and persistence of the tropical carbon sink hinges on the long-term responses of woody growth to climatic variations and increasing CO 2 . However, the sensitivity of tropical woody growth to these environmental changes is poorly understood, leading to large uncertainties in growth predictions. Here, we used tree ring records from a Southeast Asian tropical forest to constrain ED2.2-hydro, a terrestrial biosphere model with explicit vegetation demography. Specifically, we assessed individual-level woody growth responses to historical climate variability and increases in atmospheric CO 2 (C a ). When forced with historical C a , ED2.2-hydro reproduced the magnitude of increases in intercellular CO 2 concentration (a major determinant of photosynthesis) estimated from tree ring carbon isotope records. In contrast, simulated growth trends were considerably larger than those obtained from tree rings, suggesting that woody biomass production efficiency (WBPE = woody biomass production:gross primary productivity) was overestimated by the model. The estimated WBPE decline under increasing C a based on model-data discrepancy was comparable to or stronger than (depending on tree species and size) the observed WBPE changes from a multi-year mature-forest CO 2 fertilization experiment. In addition, we found that ED2.2-hydro generally overestimated climatic sensitivity of woody growth, especially for late-successional plant functional types. The model-data discrepancy in growth sensitivity to climate was likely caused by underestimating WBPE in hot and dry years due to commonly used model assumptions on carbon use efficiency and allocation. To our knowledge, this is the first study to constrain model predictions of individual tree-level growth sensitivity to C a and climate against tropical tree-ring data. Our results suggest that improving model processes related to WBPE is crucial to obtain better predictions of tropical forest responses to droughts and increasing C a . Finally, more accurate parameterization of WBPE will likely reduce the stimulation of woody growth by C a rise predicted by biosphere models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

An Average Model Predictive Control of Quasi-Z-Source Modular Cascaded Photovoltaic Converter

An average model predictive control (AMPC) is proposed for dc grid integration of the front-end isolated quasi-Z-source modular cascaded converter (qZS-MCC) photovoltaic (PV) power system. The qZS-MCC deals with PV maximum power point tracking (MPPT), dc grid integration, and de-link voltage balance by the post-stage qZS half-bridge (HB) dc-dc converters, while a unified duty cycle is used in the front-end isolation converters of all submodules (SMs). Thus, it reduces the control resources while overcoming the dc-bus voltage limit because of the PV panel insulation demand. The proposed AMPC of qZS-MCC PV power system predicts the shoot-through duty cycle of each SM for dc-link voltage control, and the total modulation index for dc grid-connected current control; only one proportional-integral (PI) regulator for PV MPPT is required in each SM. Thus, PI regulators are significantly reduced, whereas system dynamic responses are improved, with low computation and simple implementation. Simulation and downscaled experimental results demonstrate the effectiveness of the proposed control.

Liu, Yushan↗
DOE OSTI2018

Applying a predictive modeling methodology to a radiation transport inverse problem - 25588

In this paper, the predictive modeling of coupled multiphysics systems (PM-CMPS) procedure, developed by Cacuci and embodied in the software package MULTIPRED, has been applied to determine unknown parameters of a radioactive system from its gamma-ray and neutron signature. PM-CMPS has been integrated in two different ways with the inverse transport code INVERSE to determine the parameters. The first uses PM-CMPS in the inverse mode to directly estimate the parameters while the second mode uses a traditional nonlinear optimization routine and has PMCMPS complete a final iteration to estimate the uncertainty in the parameter best estimates. A test problem has been created and used to test both methods for varying numbers and types of responses and parameters. The first method was capable of accurately determining parameters but had a number of drawbacks. The second method functioned well at determining the correct parameter values in cases with limited numbers of parameters. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Extremum-Seeking-Based Ultra-local Model Predictive Control and Its Application to Electric Motor Speed Regulation

Electric vehicle (EV) market is rapidly expanding. As a critical component of EV, an electric motor needs to accurately follow a reference speed signal while respecting the electrical current constraint for safety. Those requirements are usually formulated as a model predictive control (MPC) problem. However, the performance of traditional model-based MPC depends on the accuracy of the system model, which may not always be guaranteed in reality. Therefore, we utilize a data-driven, model-free predictive control strategy, called ultra-local MPC (ULMPC), to control the speed of an electric motor. To further enhance the control performance of ULMPC, we employ the extremum-seeking control (ESC) to tune the control gain of the ULMPC online. Simulation and hardware experiments demonstrate the enhancement of the extremum-seeking-based ULMPC over a constant-gain ULMPC.

Zhou, Yujing↗
NASA NTRS1972

Microbial burden prediction model for unmanned planetary spacecraft

The technical development of a computer program for predicting microbial burden on unmanned planetary spacecraft is outlined. The discussion includes the derivation of the basic analytical equations, the selection of a method for handling several random variables, the macrologic of the computer programs and the validation and verification of the model. The prediction model was developed to (1) supplement the biological assays of a spacecraft by simulating the microbial accretion during periods when assays are not taken; (2) minimize the necessity for a large number of microbiological assays; and (3) predict the microbial loading on a lander immediately prior to sterilization and other non-lander equipment prior to launch. It is shown that these purposes not only were achieved but also that the prediction results compare favorably to the estimates derived from the direct assays. The computer program can be applied not only as a prediction instrument but also as a management and control tool. The basic logic of the model is shown to have possible applicability to other sequential flow processes, such as food processing.

Hoffman, A. R.↗