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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Origin of Unusual Acidity and Li+ Diffusivity in a Series of Water-in-Salt Electrolytes.

Superconcentrated aqueous electrolytes ("water-in-salt" electrolytes, or WiSEs) enable various aqueous battery chemistries beyond the voltage limits imposed by the Pourbaix diagram of water. However, their detailed structural and transport properties remain unexplored and could be better understood through added studies. Here, we report on our observations of strong acidity (pH 2.4) induced by lithium bis(trifluoromethane sulfonyl)imide (LiTFSI) at superconcentration (at 20 mol/kg). Multiple nuclear magnetic resonance (NMR) and pulsed-field gradient (PFG) diffusion NMR experiments, density functional theory (DFT) calculations, and molecular dynamics (MD) simulations reveal that such acidity originates from the formation of nanometric ion-rich structures. The experimental and simulation results indicate the separation of water-rich and ion-rich domains at salt concentrations >= 5 m and the acidity arising therefrom is due to deprotonation of water molecules in the ion-rich domains. As such, the ion-rich domain is composed of hydrophobic -CF3 (of TFSI-) and hydrophilic hydroxyl (OH-) groups. At 20 m concentration, the tortuosity and radius of water diffusion channels are estimated to be similar to 10 and similar to 1 nm, respectively, which are close to values obtained from hydrated Nafion membranes that also have hydrophobic polytetrafluoroethylene (PTFE) backbones and hydrophilic channels consisting of SO3- ion cluster networks providing for the transport of ions and water. Thus, we have discovered the structural similarity between WiSE and hydrated Nafion membranes on the nanometer scale.

Han, Kee Sung↗
DOE OSTI2024

Proton Conducting Neuromorphic Materials and Devices

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unveiling the Role of Surface Ir-Oxo Species in O 2 Evolution at IrO 2 Electrocatalysts via Embedded Cluster Multireference Calculations

Understanding the mechanisms driving the oxygen evolution reaction (OER) on iridium oxide (IrO 2 )-based catalysts is essential to improving their performance and enabling an actual scale-up of water-splitting photoelectrochemical cells. The mechanistic pathways at IrO 2 interfaces have been extensively investigated computationally using density functional theory (DFT), which predicts a high-energy barrier for the last step of the OER of molecular oxygen detachment and release from the catalyst surface. Nevertheless, surface O 2 over- and under-binding results by standard generalized gradient approximation and hybrid density functionals, respectively, call for further analysis of this crucial step via multireference methods. Aiming at unveiling the nature of such a barrier, we hereby address the formation of O 2 from the most-stable IrO 2 (110) surface with both periodic DFT and an electrostatic embedded cluster approach at the n-electron valence-state perturbation theory. With this multireference approach, we find a value for the aforementioned energy barrier that is much closer to experimental indications than DFT ones. Here, an in-depth analysis of the involved molecular orbitals suggests that the origin of this barrier is related to the breaking of a π interaction between O 2 and Ir surface atom and to a significant additional O 2 interaction with adjacent electrophilic Ir-oxo species, which is present under experimental operating conditions. Besides shedding light on the mechanism of the OER on IrO 2 , these findings point out the importance of multireference methods for dissecting complex reactions at electrocatalytic interfaces and pave the route for further investigations with effective embedding approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Synthesis of poly(3,4-ethylenedioxythuophene) derivatives using three-armed conjugated cross-linker and its thermoelectric properties

Conducting polymer based thermoelectric systems are very effective at harvesting electricity from waste heat with low-temperature gradients relative to environmental temperature. However, although various studies concerning the doping effect exist, there are insufficient reports on the effect of the change in polymer chain structure on the thermoelectric properties. Here we demonstrate different poly(3,4-ethylenedioxythiophene) (PEDOT)-derivative chain structures (such as linear, two-dimensional, co-polymer), by varying the amount of three-armed conjugated cross-linker (1,3,5-tri(2-thienyl)-benzene (TTB)). Further, at a small amount of TTB addition (until 0.5 mM), the electrochemical charge transport of the PEDOT films increased without shift of the UV–Vis absorbing property. However, from higher than 0.5 mM TTB, electrochemical charge transport decreased with blue shift of the UV–Vis absorbing curve. Moreover, the power factor of the PEDOT-derivative films without TTB to a small amount of TTB (0.5 mM) was improved from (27.03 to 49.43) µWm -1 K -2 , and at higher than 0.5 mM TTB concentration, was diminished, respectively, because a small amount of TTB enhanced the π–π interaction of the polymer chains without disrupting the conjugation, while excess TTB molecules blocked conjugation of the polymer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs

We report that community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that work with existing community codes to provide more functionality and composability than could be achieved with a single program. The Quantum Chemistry Common Driver and Databases (QCDB) project provides such capability through an application programming interface (API) that facilitates interoperability across multiple quantum chemistry software packages. In tandem with the Molecular Sciences Software Institute and their Quantum Chemistry Archive ecosystem, the unique functionalities of several CMS programs are integrated, including CFOUR, GAMESS, NWChem, OpenMM, Psi4, Qcore, TeraChem, and Turbomole, to provide common computational functions, i.e., energy, gradient, and Hessian computations as well as molecular properties such as atomic charges and vibrational frequency analysis. Both standard users and power users benefit from adopting these APIs as they lower the language barrier of input styles and enable a standard layout of variables and data. These designs allow end-to-end interoperable programming of complex computations and provide best practices options by default.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Comparing first-principles density functionals plus corrections for the lattice dynamics of YBa 2 Cu 3 O 6

The enigmatic mechanism underlying unconventional high-temperature superconductivity, especially the role of lattice dynamics, has remained a subject of debate. Theoretical insights have long been hindered due to the lack of an accurate first-principles description of the lattice dynamics of cuprates. Recently, using the r2SCAN meta-generalized gradient approximation (meta-GGA) functional, we have been able to achieve accurate phonon spectra of an insulating cuprate YBa 2 Cu 3 O 6 and discover significant magnetoelastic coupling in experimentally interesting Cu–O bond stretching optical modes [Ning et al., Phys. Rev. B 107, 045126 (2023)]. We extend this work by comparing Perdew–Burke–Ernzerhof and r2SCAN performances with corrections from the on-site Hubbard U and the D4 van der Waals (vdW) methods, aiming at further understanding on both the materials science side and the density functional side. We demonstrate the importance of vdW and self-interaction corrections for accurate first-principles YBa 2 Cu 3 O 6 lattice dynamics. Since r2SCAN by itself partially accounts for these effects, the good performance of r2SCAN is now more fully explained. Finally, in addition, the performances of the Tao–Mo series of meta-GGAs, which are constructed in a different way from the strongly constrained and appropriately normed (SCAN) meta-GGA and its revised version r2SCAN, are also compared and discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Protein Motifs for Proton Transfers That Build the Transmembrane Proton Gradient

Biological membranes are barriers to polar molecules, so membrane embedded proteins control the transfers between cellular compartments. Protein controlled transport moves substrates and activates cellular signaling cascades. In addition, the electrochemical gradient across mitochondrial, bacterial and chloroplast membranes, is a key source of stored cellular energy. This is generated by electron, proton and ion transfers through proteins. The gradient is used to fuel ATP synthesis and to drive active transport. Here the mechanisms by which protons move into the buried active sites of Photosystem II (PSII), bacterial RCs (bRCs) and through the proton pumps, Bacteriorhodopsin (bR), Complex I and Cytochrome c oxidase (CcO), are reviewed. These proteins all use water filled proton transfer paths. The proton pumps, that move protons uphill from low to high concentration compartments, also utilize Proton Loading Sites (PLS), that transiently load and unload protons and gates, which block backflow of protons. PLS and gates should be synchronized so PLS proton affinity is high when the gate opens to the side with few protons and low when the path is open to the high concentration side. Proton transfer paths in the proteins we describe have different design features. Linear paths are seen with a unique entry and exit and a relatively straight path between them. Alternatively, paths can be complex with a tangle of possible routes. Likewise, PLS can be a single residue that changes protonation state or a cluster of residues with multiple charge and tautomer states.

cytochrome c oxidase↗
DOE OSTI2022

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). Further, the model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II–VI and III–V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III–V and II–VI systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Structure and dynamics of ILs-based gel polymer electrolytes and its enhanced conductive properties with the incorporation of Al 2 O 3 nanofibers

Here, this work reports the enhanced mobility of ions in ionic liquid (IL)-based gel polymer electrolytes (GPEs) with the incorporation of Al 2 O 3 nanofibers. A combination of PVDF-HFP, EMIMTFSI and LiTFSI with 3 wt% Al 2 O 3 nanofibers has been prepared through solution casting technique. The room temperature ionic conductivity of PVDF-HFP: ILs electrolyte (45:55, weight ratio of 0.82) (GPE) is found to be 2.7 × 10 –5 S cm –1 , which increases up to 7.8 × 10 –5 S cm –1 in Al 2 O 3 containing GPE (Al-GPE). Pulsed field gradient (PFG) NMR results validate the increased ionic conductivity observed in Al-GPE. We found that the diffusivity of Li + , TFSI – and EMIM + increases when Al 2 O 3 nanofibers are well-distributed in the GPE matrix. The surface morphology and the amorphicity of GPEs are examined through SEM and XRD analyses. Lastly, the local structure of Al 2 O 3 fibers and the molecular-level interactions of ions with polymer, and their effect on the diffusivity of ions are established through solid-state NMR detecting 27 Al, 1 H, 13 C, 19 F nuclei including 2D 13 C{ 1 H} HETCOR NMR experiments. The 13 C DPMAS and CPMAS experiments highlight the dynamic heterogeneity associated with the ions that are embedded in the rigid and the mobile phase of GPEs. While some of the ionic species strongly interact with polymer chains in the rigid environment, the majority of them reside in the mobile phase and contribute to the overall increased conductivity. Most importantly, Al 2 O 3 nanofibers significantly affect the dynamics of ionic species that are present in the mobile phase between the polymer chains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Reaching the Excitonic Limit in 2D Janus Monolayers by In Situ Deterministic Growth

Abstract Named after the two‐faced Roman god of transitions, transition metal dichalcogenide (TMD) Janus monolayers have two different chalcogen surfaces, inherently breaking the out‐of‐plane mirror symmetry. The broken mirror symmetry and the resulting potential gradient lead to the emergence of quantum properties such as the Rashba effect and the formation of dipolar excitons. Experimental access to these quantum properties, however, hinges on the ability to produce high‐quality 2D Janus monolayers. Here, these results introduce a holistic 2D Janus synthesis technique that allows real‐time monitoring of the growth process. This prototype chamber integrates in situ spectroscopy, offering fundamental insights into the structural evolution and growth kinetics, that allow the evaluation and optimization of the quality of Janus monolayers. The versatility of this method is demonstrated by synthesizing and monitoring the conversion of SWSe, SNbSe, and SMoSe Janus monolayers. Deterministic conversion and real‐time data collection further aid in conversion of exfoliated TMDs to Janus monolayers and unparalleled exciton linewidth values are reached, compared to the current best standard. The results offer an insight into the process kinetics and aid in the development of new Janus monolayers with high optical quality, which is much needed to access their exotic properties.

2D materials↗
DOE OSTI2024

Microscale Thermophoresis (MST) as a Tool to Study Binding Interactions of Oxygen-Sensitive Biohybrids

Microscale thermophoresis (MST) is a technique used to measure the strength of molecular interactions. MST is a thermophoretic-based technique that monitors the change in fluorescence associated with the movement of fluorescent-labeled molecules in response to a temperature gradient triggered by an IR LASER. MST has advantages over other approaches for examining molecular interactions, such as isothermal titration calorimetry, nuclear magnetic resonance, biolayer interferometry, and surface plasmon resonance, requiring a small sample size that does not need to be immobilized and a high-sensitivity fluorescence detection. In addition, since the approach involves the loading of samples into capillaries that can be easily sealed, it can be adapted to analyze oxygen-sensitive samples. In this Bio-protocol, we describe the troubleshooting and optimization we have done to enable the use of MST to examine protein–protein interactions, protein–ligand interactions, and protein–nanocrystal interactions. The salient elements in the developed procedures include 1) loading and sealing capabilities in an anaerobic chamber for analysis using a NanoTemper MST located on the benchtop in air, 2) identification of the optimal reducing agents compatible with data acquisition with effective protection against trace oxygen, and 3) the optimization of data acquisition and analysis procedures. The procedures lay the groundwork to define the determinants of molecular interactions in these technically demanding systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

ABINIT: Overview and focus on selected capabilities

ABINIT is probably the first electronic-structure package to have been released under an open-source license about 20 years ago. It implements density functional theory, density-functional perturbation theory (DFPT), many-body perturbation theory (GW approximation and Bethe–Salpeter equation), and more specific or advanced formalisms, such as dynamical mean-field theory (DMFT) and the “temperature-dependent effective potential” approach for anharmonic effects. Relying on planewaves for the representation of wavefunctions, density, and other space-dependent quantities, with pseudopotentials or projector-augmented waves (PAWs), it is well suited for the study of periodic materials, although nanostructures and molecules can be treated with the supercell technique. The present article starts with a brief description of the project, a summary of the theories upon which ABINIT relies, and a list of the associated capabilities. It then focuses on selected capabilities that might not be present in the majority of electronic structure packages either among planewave codes or, in general, treatment of strongly correlated materials using DMFT; materials under finite electric fields; properties at nuclei (electric field gradient, Mössbauer shifts, and orbital magnetization); positron annihilation; Raman intensities and electro-optic effect; and DFPT calculations of response to strain perturbation (elastic constants and piezoelectricity), spatial dispersion (flexoelectricity), electronic mobility, temperature dependence of the gap, and spin-magnetic-field perturbation. The ABINIT DFPT implementation is very general, including systems with van der Waals interaction or with noncollinear magnetism. Community projects are also described: generation of pseudopotential and PAW datasets, high-throughput calculations (databases of phonon band structure, second-harmonic generation, and GW computations of bandgaps), and the library libpaw. ABINIT has strong links with many other software projects that are briefly mentioned.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Structure–Function Relationships in Sequence-Controlled Copolymers for Rare Earth Element Chelation

The ability to tune material function through primary sequence is a defining feature of biological macromolecules, allowing precise control over structure and target interactions in complex aqueous environments. However, translating sequence–structure–function relationships to synthetic macromolecules is challenging due to their dispersity in sequence, conformation, and composition. Here, we report systematic studies of amphiphilic polymer chelators designed to probe how composition and patterning influence binding affinity and selectivity for rare earth elements (REEs), a series of technologically relevant metals with challenging separation profiles. A library of copolymers varying hydrophobic monomer composition and patterning was synthesized via reversible addition–fragmentation chain transfer (RAFT) polymerization, spanning statistical, gradient, and block architectures. REE binding was quantified using a high-throughput colorimetric assay, and reconstruction of polymer ensembles using kinetic stochastic simulations enabled quantitative comparisons of sequence heterogeneity, linking local monomer colocalization to emergent REE binding. Further, we investigated the role of different hydrophobic comonomers in tuning metal coordination, with binding trends linked to structural features that influence binding site desolvation. Complementary dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS) measurements showed that both polymer and monomer architecture modulate metal-induced conformational changes, and that multichain assembly behavior emerges beyond critical hydrophobic thresholds. Sequence control also altered REE selectivity, with nonmonotonic differences observed across compositionally identical polymers with different sequence architectures. Together, these findings establish design principles that connect polymer sequence and structure to binding performance, guiding the design of macromolecular chelators with enhanced affinity and selectivity for applications in separations, sensing, and catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone

The spatial distribution of organics in geological samples can be used to determine when and how these organics were incorporated into the host rock. Mass spectrometry (MS) imaging can rapidly collect a large amount of data, but ions produced are mixed without discrimination, resulting in complex mass spectra that can be difficult to interpret. Here, we apply unsupervised and supervised machine learning (ML) to help interpret spectra from time-of-flight-secondary ion mass spectrometry (ToF-SIMS) of an organic-carbon-rich mudstone of the Middle Jurassic of England (UK). It was previously shown that the presence of sterane molecular biomarkers in this sample can be detected via ToF-SIMS (Pasterski, M. J. et al., Astrobiology 2023, 23, 936). We use unsupervised ML on scanning electron microscopy–electron dispersive spectroscopy (SEM-EDS) measurements to define compositional categories based on differences in elemental abundances. We then test the ability of four ML algorithms─k-nearest neighbors (KNN), recursive partitioning and regressive trees (RPART), eXtreme gradient boost (XGBoost), and random forest (RF)─to classify the ToF-SIM spectra using (1) the categories assigned via SEM-EDS, (2) organic and inorganic labels assigned via SEM-EDS, and (3) the presence or absence of detectable steranes in ToF-SIMS spectra. In terms of predictive accuracy and balanced accuracy, KNN was the best performing model and RPART the worst. The feature importance, or the specific features of the ToF-SIM spectra used by the models to make classifications, cannot be determined for KNN, preventing posthoc model interpretation. Nevertheless, the feature importance extracted from the other models was useful for interpreting spectra. In conclusion, we determined that some of the organic ions used to classify biomarker containing spectra may be fragment ions derived from kerogen which is abundant in this mudstone sample.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Ion-specific phenomena limit energy recovery in forward-biased bipolar membranes

The ability for bipolar membranes (BPMs) to interconvert voltage and pH makes them attractive materials for use in energy conversion and storage. Reverse-biased BPMs, which use electrical voltage to dissociate water into acid and base, have become increasingly well studied. However, forward-biased BPMs (FB-BPMs), in which voltage is extracted from pH gradients through recombination, require further study. Here physics-based modeling elucidates how the complex coupling of transport and kinetics dictates the performance of FB-BPMs in electrochemical devices. Simulations reveal that the open-circuit potential of FB-BPMs is dictated by the balance of ion recombination and crossover, where recombination of buffering counter-ions attenuates the open-circuit potential. Counter-ion mass-transport limitations and uptake of ionic impurities limit achievable current densities by reducing the applied pH gradient or the available fixed-charge sites that mediate recombination. Further, the model highlights the importance of selective ion management in mitigating energy losses and provides insight into the rational material design of FB-BPMs for energy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Tropospheric aerosols over the western North Atlantic Ocean during the winter and summer deployments of ACTIVATE 2020: life cycle, transport, and distribution

The Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) is a NASA mission to characterize aerosol–cloud interactions over the western North Atlantic Ocean (WNAO). Such characterization requires understanding of life cycle, composition, transport pathways, and distribution of aerosols over the WNAO. This study uses the GEOS-Chem model to simulate aerosol distributions and properties that are evaluated against aircraft, ground-based, and satellite observations during the winter and summer field deployments in 2020 of ACTIVATE. Transport in the boundary layer (BL) behind cold fronts was a major mechanism for the North American continental outflow of pollution to the WNAO in winter. Turbulent mixing was the main driver for the upward transport of sea salt within and ventilation out of BL in winter. The BL aerosol composition was dominated by sea salt, which increased in the summer, followed by organics and sulfate. Aircraft in situ aerosol measurements provided useful constraints on wet scavenging in GEOS-Chem. The model generally captured observed features such as continental outflow, land–ocean gradient, and mixing of anthropogenic aerosols with sea salt. Model sensitivity experiments with elevated smoke injection heights to the mid-troposphere (versus within BL) better reproduced observations of smoke aerosols from the western US wildfires over the WNAO in the summer. Model analysis suggests strong hygroscopic growth of sea salt particles and their seeding of marine BL clouds over the WNAO (< 35° N). Future modeling efforts should focus on improving parameterizations for aerosol wet scavenging, implementing realistic smoke injection heights, and applying high-resolution models that better resolve vertical transport.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed

Chemical looping is a near-zero emission process for generating power from coal. It is based on a multi-phase gas-solid flow and has extremely challenging nonlinear, multi-scale dynamics with jumps, producing large dynamic model uncertainty, which renders traditional robust control techniques, such as linear parameter varying H ∞ design, largely inapplicable. This process complexity is addressed in the present work through the temporal and the spatiotemporal multiresolution modeling along with the corresponding model-based control laws. Namely, the nonlinear autoregressive with exogenous input model structure, nonlinear in the wavelet basis, but linear in parameters, is used to identify the dominant temporal chemical looping process dynamics. The control inputs and the wavelet model parameters are calculated by optimizing a quadratic cost function using a gradient descent method. The respective identification and tracking error convergence of the proposed self-tuning identification and control schemes, the latter using the unconstrained generalized predictive control structure, is separately ascertained through the Lyapunov stability theorem. The rate constraint on the control signal in the temporal control law is then imposed and the control topology is augmented by an additional control loop with self-tuning deadbeat controller which uses the spatiotemporal wavelet riser dynamics representation. The novelty of this work is three-fold: (1) developing the self-tuning controller design methodology that consists in embedding the real-time tunable temporal highly nonlinear, but linearly parametrizable, multiresolution system representations into the classical rate-constrained generalized predictive quadratic optimal control structure, (2) augmenting the temporal multiresolution loop by a more complex spatiotemporal multiresolution self-tuning deadbeat control loop, and (3) demonstrating the effectiveness of the proposed methodology in producing fast recursive real-time algorithms for controlling highly uncertain nonlinear multiscale processes. The latter is shown through the data from the implemented temporal and augmented spatiotemporal solutions of a difficult chemical looping cold flow tracking control problem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2013

Analysis of Handling Qualities Design Criteria for Active Inceptor Force-Feel Characteristics

The force-feel system characteristics of the cyclic inceptors of most helicopters are set based on the characteristics of the mechanical components in the control system (mass, springs, friction dampers, etc.). For these helicopters, the force-feel characteristics typically remain constant over the entire flight envelope, with perhaps a trim release to minimize control forces while maneuvering. With the advent of fly-by-wire control systems and active inceptors in helicopters, the force-feel characteristics are now determined by the closed-loop response of the active inceptor itself as defined by the inertia, force/displacement gradient, damping, breakout force and detent shape configuration parameters in the inceptor control laws. These systems give the flexibility to dynamically prescribe different feel characteristics for different control modes or flight conditions, and the ability to provide tactile cueing to the pilot through the actively controlled side-stick or center-stick cyclic inceptor. For rotorcraft, a few studies have been conducted to assess the effects of cyclic force-feel characteristics on handling qualities in flight. An early study provided valuable insight into the static force-deflection characteristics (force gradient) and the number of axes controlled by the side-stick controller for the U.S. Army's Advanced Digital/Optical Control System (ADOCS) demonstrator aircraft [1]. The first of a series of studies providing insight on the inceptor dynamic force-feel characteristics was conducted on the NASA/Army CH-47B variable-stability helicopter [2]. This work led to a proposed requirement that set boundaries based on the cyclic natural frequency and inertia, with the stipulation of a lower damping ratio limit of 0.3 [3]. A second study was conducted by the Canadian Institute for Aerospace Research using their variable-stability Bell 205A helicopter [4]. This research suggested boundaries for stick dynamics based on natural frequency and damping ratio. While these two studies produced boundaries for acceptable/unacceptable stick dynamics for rotorcraft, they were not able to provide guidance on how variations of the stick dynamics in the acceptable region impact handling qualities. More recently, a ground based simulation study [5] suggested little benefit was to be obtained from variations of the damping ratio for a side-stick controller exhibiting high natural frequencies (greater than 17 rad/s) and damping ratios (greater than 2.0). A flight test campaign was conducted concurrently on the RASCAL JUH-60A in-flight simulator and the ACT/FHS EC-135 in flight simulator [6]. Upon detailed analysis of the pilot evaluations the study identified a clear preference for a high damping ratio and natural frequency of the center stick inceptors. Side stick controllers were found to be less sensitive to the damping. While these studies have compiled a substantial amount of data, in the form of qualitative and quantitative pilot opinion, a fundamental analysis of the effect of the inceptor force-feel system on flight control is found to be lacking. The study of Ref. [6] specifically concluded that a systematic analysis was necessary, since discrepancies with the assigned handling qualities showed that proposed analytical design metrics, or criteria, were not suitable. The overall goal of the present study is to develop a clearer fundamental understanding of the underlying mechanisms associated with the inceptor dynamics that govern the handling qualities using a manageable analytical methodology.

Malpica, Carlos A.↗