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Results for “Function Driven Optimization”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Data-driven Identification of Occupant Thermostat-Behavior Dynamics

Building occupant behavior drives significant differences in building energy use, even in automated buildings. Users’ distrust in the automation causes them to override settings. This results in responses that fail to satisfy both the occupants’ and/or the building automation’s objectives. The transition toward grid-interactive efficient buildings will make this evermore important as complex building control systems optimize not only for comfort, but also changing electricity costs. This paper presents a data-driven approach to study thermal comfort behavior dynamics which are not captured by standard steady-state comfort models such as predicted mean vote. The proposed model captures the time it takes for a user to override a thermostat setpoint change as a function of the manual setpoint change magnitude. The model was trained with the ecobee Donate Your Data dataset of 5 min. resolution data from 27,764 smart thermostats and occupancy sensors. The resulting population-level model shows that, on average, a 2°F override will occur after ~30 mins. and an 8°F override will occur in only ~15 mins., indicating the magnitude of discomfort as a key driver to the swiftness of an override. Such models could improve demand response programs through personalized controls.

Kane, Michael↗
DOE OSTI2023

Discovery of a Hybrid System for Photocatalytic CO 2 Reduction via Attachment of a Molecular Cobalt-Quaterpyridine Complex to a Crystalline Carbon Nitride

While recent reports have demonstrated the attachment of molecular catalysts to amorphous, graphitic carbon nitrides (g-CN) for light-driven CO 2 reduction, approaches to the utilization of crystalline carbon nitrides have remained undiscovered. Herein, a functional hybrid photocatalyst system has been found using a crystalline carbon nitride semiconductor, poly(triazine imide) lithium chloride (PTI-LiCl), with a surface-attached CoCl 2 (qpy-Ph-COOH) catalyst for CO 2 reduction. The molecular catalyst attaches to PTI-LiCl at concentrations from 0.10 to 4.30 wt % and exhibits ∼96% selectivity for CO production in a CO 2 -saturated, aqueous 0.5 M KHCO 3 solution. Optimal loadings were found to be within 0.42–1.04 wt % with rates between 1,400 and 1,550 μmol CO/g·h at an irradiance of 172 mW/cm 2 (λ = 390 nm) and apparent quantum yields of ∼2%. This optimized loading is postulated to represent a balance between maximal turnover frequency (TOF; 300+ h –1 ) and excess catalyst that can limit excited-electron lifetimes, as probed via transient absorption spectroscopy. An increase in the incident irradiance yields a concomitant increase in the TOFs and CO rates only for the higher catalyst loadings, reaching up to 2,149 μmol CO/g·h with a more efficient use of the catalyst surface capacity. The lower catalyst loadings, by comparison, already function at maximal TOFs. Higher surface loadings are also found to help mitigate deactivation of the molecular catalysts during extended catalytic testing (>24 h) owing to the greater net surface capacity for CO 2 reduction, thus representing an effective strategy to extend lifetime. The hybrid particles can be deposited onto an FTO substrate to yield ∼60% Faradaic efficiency for photoelectrochemical CO production at −1.2 V vs Ag/AgCl bias. In conclusion, these results demonstrate the synergistic combination of a crystalline carbon nitride with a molecular catalyst that achieves among the highest known rates in carbon-nitride systems for the light-driven CO 2 reduction to CO in aqueous solution with >95% selectivity.

CO2 reduction↗
DOE OSTI2020

Size effect on the growth and pulverization behavior of Si nanodomains in SiO anode

Silicon monoxide (SiO) as a promising silicon-based anode electrode of lithium-ion batteries (LIBs) has been the subject of many recent investigations. However, the complex microstructural features of SiO hinder attempts to link the microstructure changes in SiO with its electrochemical performances upon electrochemical cycling. This study employs X-ray pair distribution function (PDF), with a high-resolution transmission electron microscope (HRTEM), to investigate the microstructure of Si nanodomains in SiO and its structural evolution over the electrochemical cycling process. The experiment results reveal the size evolution of Si nanodomains in SiO during the electrochemical cycling and find it highly dependent on the initial Si domain size in SiO. If the initial Si domain size is too large, the average size of Si nanodomains would increase at first and then decrease after a certain number of cycles, which indicates the pulverization of Si domains after the electrochemical-driven growth of Si nanodomains reaches a critical point. Furthermore, these results suggest that an optimal initial Si nanodomain size of 4-6 nm for SiO anode materials is essential to retard the growth and subsequent pulverization process of Si nanodomains and thus to improve the cyclability of SiO during long-term cycling.

25 ENERGY STORAGE↗
DOE OSTI2026

Smart Energy Storage Integration and Management Platform for Buildings (SESIMP-B)

This project involved the design, development, and testing of a commercial smart service panel. It includes an innovative AI-driven control method to manage smart functions of the panel and a technical-economic feasibility assessment for enhancing building energy resilience and grid-edge technology integration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Joint Optimization for Transport and Bucket Loading Phases of Automated Wheel Loaders

This article investigates optimization of fuel-efficiency and productivity for automated wheel loaders. A control-oriented model for both the transport phase and bucket loading phase is proposed. Here, the vehicle model includes an automatic gear shift schedule that can be incorporated into the optimization problem. Based on the model, the multistage optimization problem is formulated to simultaneously consider all phases of a short cycle with physical constraints. Cycle time and fuel efficiency are used as the weighted performance indexes in a multiobjective cost function. Bucket fill factor is included as a constraint during the bucket loading phase. A nonlinear programming problem is created with collocation using MATLAB and CasADi. The optimization solver IPOPT solves the problem to obtain the optimal state and control trajectories, which can be used as a reference for automated wheel loaders or even as a driver advisory for human-driven wheel loaders.

42 ENGINEERING↗
DOE OSTI2020

Simulation of Inelastic Neutron Scattering Spectra Directly from Molecular Dynamics Trajectories

Inelastic neutron scattering (INS) is a widely used technique to study atomic and molecular vibrations. With the increasing complexity of materials and thus the INS spectra, being able to simulate the spectra from various atomistic models becomes an essential step and also a major bottleneck for INS data analysis. The conventional approach using density functional theory and lattice dynamics often falls short when the materials of interest are complex (e.g., defective, disordered, heterogeneous, amorphous, large-scale), for which molecular dynamics driven by an interatomic force field is a more common approach. In this paper, we demonstrate a method to directly convert molecular dynamics trajectories into simulated INS spectra, including not only fundamental but also higher order excitations. In this work, the results are compared with data collected on various representative samples from different neutron spectrometers. This development will open great opportunities by providing the key tool to perform in-depth analysis of INS data and to validate and optimize computer models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2018

Integration of Information Management System, Workflow and Computational Tools Enabling Multiscale Modeling Within an ICME Paradigm

With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Fortunately, material information management systems and physics-based multiscale modeling methods have kept pace with the growing user demands. Herein, recent efforts to develop a set of Python functions that exchange information between NASA GRC's Integrated multiscale Micromechanics Analysis Code (ImMAC) software toolset and its Integrated Computational Materials Engineering (ICME), Granta MI® database schema is presented. The goal is to enable seamless coupling between both test data and simulation data, which is captured and tracked automatically within Granta MI®, with full model pedigree information. These tools, and this type of linkage, are foundational to realizing the full potential of ICME, in which materials processing, microstructure, properties, and performance are coupled to enable application-driven design and optimization of materials and structures.

multiscale modeling; Micromechanics; Computational↗
DOE OSTI2020

TEAM project review, year 1

This note summarizes our research activities within the TEAM project between October 2019 and November 2020, funded by the ASCR Advanced Research in Quantum Computing program. Three research efforts have begun during the review period. The first effort, resulting in the JuQBox software, implemented a novel algorithm for optimal control in closed quantum systems based on symplectic time integration and exact calculations of the gradient of the discretized objective function. In our second effort, resulting in a code called Quandary, we consider the optimal control problem for open quantum systems. Here the evolution of the quantum system is modeled by the Lindblad master equation. In this code, we are considering both the realization of quantum gates as well as the optimal reset problem in which a general mixed quantum state is driven towards its ground state. In our third research effort we have begun the development of computational software for what we call Lindblad learning, i.e. the process of characterizing the equations governing our quantum computing hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

High-shade dryland agrivoltaic conditions enhanced carbon uptake and water-use efficiency in zucchini ( Cucurbita pepo )

Introduction: The increasing global demand for food and energy is intensifying land-use competition. Agrivoltaic systems are a multifunctional land-use approach that vertically integrates the production of agricultural crops and solar power on the same land area. Most food crops are adapted to full-sun conditions, and the physiological responses of these crops to the novel microclimate under solar panels remain poorly understood. We hypothesized that the microclimate beneath the high-density photovoltaic system would influence carbon uptake, water use, and yield outcomes of zucchini summer squash.Methods: We conducted a field experiment in a hot, semi-arid climate on zucchini (Cucurbita pepo). Plants were grown under an agrivoltaic system with a 75% ground cover ratio (GCR) and in a full-sun control plot, each with two irrigation regimes (100 and 50%). We measured leaf-level photosynthesis, microclimate variables, and fruit yield at plant maturity and throughout the growing season.Results: The agrivoltaic array reduced photosynthetically active radiation (PAR) by ~79%, resulting in a cooler (−1.1 °C), more humid environment with higher soil moisture. These microclimatic conditions enhanced midday photosynthesis and daily cumulative carbon uptake. However, fruit yield was consistently lower under the panels, indicating a shift in carbon allocation toward vegetative growth. Photosynthesis was primarily driven by PAR across treatments, while soil moisture significantly influenced photosynthesis only in the control plots, suggesting water limitation was alleviated under the panels.Discussion: These findings highlight a trade-off between improved physiological performance and reduced yield under high-density agrivoltaics. While the system buffered heat and drought stress and improved overall plant function, excessive shade reduced reproductive output. Optimizing panel density or selecting crops cultivated for non-fruit yields will be essential for balancing food production and energy generation in dryland agrivoltaic settings.

14 SOLAR ENERGY↗
NASA NTRS1972

Supervisory sampling and control: Sources of suboptimality in a prediction task

A process supervisor is defined as a person who decides when to sample the process input and what values of a control variable to specify in order to maximize (minimize) a given value function of input sampling period, control setting, and process state. Presented experimental data in such a process where the value function is a time-averaged sampling cost plus mean squared difference between input and control variable. The task was unpaced prediction of the output of a second order filter driven by white noise. Experimental results, when compared to the optical strategy, reveal several consistently suboptimal behaviors. One is a tendency not to choose a long prediction interval even though the optimal strategy dictates that one should. Some results are also interpreted in terms of those input parameters according to which each subjects' behavior would have been nearest optimal. Differences of those parameters from actual input parameters served to quantify how subjects' prediction behavior differed from optimal.

Sheridan, T. B.↗
NASA NTRS1974

Multicyclic jet-flap control for alleviation of helicopter blade stresses and fuselage vibration

Results of wind tunnel tests of a 12 meter-diameter-rotor utilizing multicyclic jet-flap control deflection are presented. Analyses of these results are shown, and experimental transfer functions are determined by which optimal control vectors are developed. These vectors are calculated to eliminate specific harmonic bending stresses, minimize rms levels (a measure of the peak-to-peak stresses), or minimize vertical vibratory loads that would be transmitted to the fuselage. Although the specific results and the ideal control vectors presented are for a specific jet-flap driven rotor, the method employed for the analyses is applicable to similar investigations. A discussion of possible alternative methods of multicyclic control by mechanical flaps or nonpropulsive jet-flaps is presented.

Mccloud, J. L., III↗
NASA NTRS1974

Multicyclic jet-flap control for alleviation of helicopter blade stresses and fuselage vibration

Results of wind tunnel tests of a 12-meter-diameter rotor utilizing multicyclic jet-flap control deflection are presented. Analyses of these results are shown, and experimental transfer functions are determined by which optimal control vectors are developed. These vectors are calculated to eliminate specific harmonic bending stresses, minimize rms levels (a measure of the peak-to-peak stresses), or minimize vertical vibratory loads that would be transmitted to the fuselage. Although the specific results and the ideal control vectors presented are for a specific jet-flap driven rotor, the method employed for the analyses is applicable to similar investigations. A discussion of possible alternative methods of multicyclic control by mechanical flaps or nonpropulsive jet-flaps is presented.

Mccloud, J. L., III↗
DOE OSTI2021

Identification of crystal plasticity model parameters by multi-objective optimization integrating microstructural evolution and mechanical data

Crystal plasticity models evolve a polycrystalline yield surface using meso-scale descriptions of deformation mechanisms. The activation of deformation mechanisms is governed by crystallography and a set of model parameters, which are typically calibrated through the fitting of mechanical data such as stress–strain curves and elastic lattice strains. Microstructural data such as phase fractions and texture evolution are used for verifying crystal plasticity parameters. In this study, we use a multi-objective genetic algorithm to identify hardening parameters from flow stress curves with an option to incorporate texture into the optimization approach. Robust, generalized objective functions are developed and used to identify sets of parameters pertaining to dislocation density-based hardening laws in visco-plastic and elasto-plastic self-consistent (VPSC and EPSC) homogenization models. First, the parameters are identified for pure Nb directly from texture using an objective function based on generalized spherical harmonics. Since texture evolution is driven by the relative contribution of active slip systems, the parameters governing the evolution of slip resistance ratios can be recovered from fitting discrete textures at a series of strains. Next, a comprehensive set of load reversal data for dual phase (DP) 780 steel is used to fit a hardening law and a back-stress law in EPSC. Finally, parameters pertaining to a complex hardening law for the evolution of slip and twinning in pure α-Ti are identified. Remarkably, using texture as an objective in combination with stress–strain objectives constrains the model of Ti to fully reproduce not only stress–strain and texture evolution but also hierarchical twinning measurements as a function of initial grain size and texture. Furthermore, given an appropriate model fit to representative experimental texture evolution, underlying twin volume fractions contributing to texture evolution can be predicted.

42 ENGINEERING↗
DOE OSTI2025

Safe Physics-Informed Machine Learning for Dynamics and Control

This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learning techniques enhance the modeling and control of complex dynamical systems, ensuring safety and stability remains a critical challenge, especially in safety-critical applications like autonomous vehicles, robotics, medical decision-making, and energy systems. We explore various approaches for embedding and ensuring safety constraints, including structural priors, Lyapunov and Control Barrier Functions, predictive control, projections, and robust optimization techniques. Additionally, we delve into methods for uncertainty quantification and safety verification, including reachability analysis and neural network verification tools, which help validate that control policies remain within safe operating bounds even in uncertain environments. The paper includes illustrative examples demonstrating the implementation aspects of safe learning frameworks that combine the strengths of data-driven approaches with the rigor of physical principles, offering a path toward the safe control of complex dynamical systems.

Drgona, Jan↗
DOE OSTI2021

Engineering local strain for single-atom nuclear acoustic resonance in silicon

Mechanical strain plays a key role in the physics and operation of nanoscale semiconductor systems, including quantum dots and single-dopant devices. In this report we describe the design of a nanoelectronic device, where a single nuclear spin is coherently controlled via nuclear acoustic resonance (NAR) through the local application of dynamical strain. The strain drives spin transitions by modulating the nuclear quadrupole interaction. We adopt an AlN piezoelectric actuator compatible with standard silicon metal–oxide–semiconductor processing and optimize the device layout to maximize the NAR drive. We predict NAR Rabi frequencies of order 200 Hz for a single 123Sb nucleus in a wide region of the device. Spin transitions driven directly by electric fields are suppressed in the center of the device, allowing the observation of pure NAR. Using electric field gradient-elastic tensors calculated by the density-functional theory, we extend our predictions to other high-spin group-V donors in silicon and to the isoelectronic 73Ge atom.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Computational synthesis of a new generation of 2D-based perovskite quantum materials

Perovskite-based optoelectronic devices have emerged as a promising energy source due to their potential for scalable production. This study introduces “perovskene,” a novel class of 2D materials derived from the ABC3-like perovskites, synthesized via a data-driven, high-throughput computational strategy. We harness machine learning and multitarget deep neural networks to systematically investigate the structure–property relations, paving the way for targeted material design and optimization in fields such as renewable energy, electronics, and catalysis. The characterization of over 1500 synthesized structures shows that more than 500 structures are stable, revealing properties such as ultra-low work function and large magnetic moment, underscoring the potential for advanced technological applications.

2D materials↗
NASA NTRS1993

Optimization of actuator arrays for aircraft interior noise control

A numerical procedure for grouping actuators in order to reduce the number of degrees of freedom in an active noise control system is evaluated using experimental data. Piezoceramic actuators for reducing aircraft interior noise are arranged into groups using a nonlinear optimization routine and clustering algorithm. An actuator group is created when two or more actuators are driven with the same control input. This procedure is suitable for active control applications where actuators are already mounted on a structure. The feasibility of this technique is demonstrated using measured data from the aft cabin of a Douglas DC-9 fuselage. The measured data include transfer functions between 34 piezoceramic actuators and 29 interior microphones and microphone responses due to the primary noise produced by external speakers. Control inputs for the grouped actuators were calculated so that a cost function, defined as a quadratic pressure term and a penalty term, was a minimum. The measured transfer functions and microphone responses are checked by comparing calculated noise reductions with measured noise reductions for four frequencies. The grouping procedure is then used to determine actuator groups that improve overall interior noise reductions by 5.3 to 15 dB, compared to the baseline experimental configuration.

Cabell, R. H.↗
DOE OSTI2025

Microstructure-sensitive mechanical behavior of an additively manufactured psuedoelastic shape memory alloy

The additive manufacturing of shape memory alloys into complex geometries enables fabrication of advanced functional systems across a variety of fields and domains. This work presents results focused on the mechanical behavior of additively manufactured shape memory pseudoelastic NiTi. The deformation induced solid state phase transformation from austenite to martensite allows this system to accommodate large recoverable strains. This deformation behavior is fundamentally driven by crystal-scale transformation physics. Laser powder bed fusion processing reveals that the resulting microstructure, both grain morphology and crystallographic texture, is strongly dependent on the manufacturing processing history. Exhaustive mechanical testing demonstrates that these microstructural factors strongly impact both tensile and cyclic stress–strain behavior. Cyclic dissipative behavior, however, is similar across all tested microstructures following an initial transient period. Remarkably, analysis of spatial strain fields during tensile loading reveals two distinctly different localization “modes”. The first is initiation of localized deformation bands which continuously propagate through the tensile bar during loading. In the second mode localization is observed but lacks propagation; instead additional localization cites nucleate during subsequent loading. The latter phenomena is suspected to be driven by grain-scale deformation physics as the localized band morphologies coincide with grain morphologies. These phenomena strongly impact the resulting aggregate stress–strain behavior. Hence, manufacturers and designers of psuedoelastic functional components must at the very least consider the potential variability in properties when considering additive manufacturing processing. More ideally the process–structure–property relations can be used to further tailor and optimize final functional performance.

Additive manufacturing↗