SEARCH · Engineering Papers
Results for “Performance Tuning”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Assessment of DFT methods for the prediction of detachment energies and electronic structures of complex and multiply charged anions
Here, in this work, we assess the ability of different density functional theory methods to reproduce the vertical/adiabatic detachment energies (VDEs/ADEs) of a series of 40 anions, including monoanion versus multiply charged anions (MCAs) and covalent versus noncovalent interaction. The statistical analysis of errors is firstly performed by comparing the theoretical values with the experimental benchmark data obtained from the negative ion photoelectron spectroscopy (NIPES). The proposed optimally tuned range-separated (OTRS) functionals are proved to not only well reproduce the experimental VDEs/ADEs, but also simulate experimental NIPES. The result implies a more serious issue of delocalization error for MCAs and a balanced description of electron-rich/- deficient region of anions is necessary. The radius of the spherically symmetric average electron localization function (ELF) region is demonstrated as a useful descriptor for the characterization of electronic structure of anionic systems and a quasilinear fitting model is proposed to efficiently obtain their OTRS parameters.
Co-design of a wave energy converter for autonomous power
A "bolt-on" wave energy converter is designed to provide power for sensors on an existing oceanographic buoy. The narrow-banded pitch/roll response of the target oceanographic buoy lends itself to a tuned-resonator design, for which we suggest a novel "pitch resonator" wave energy converter concept. Using a pseudo-spectral method, the performance of the proposed wave energy converter is modeled in the range of sea states expected to be present at the target deployment location to study the e
New Concepts in Electrolytes
Over the past decades, Li-ion battery (LIB) has turned into one of the most important advances in the history of technology due to its extensive and in-depth impact on our life. Its omnipresence in all electric vehicles, consumer electronics and electric grids relies on the precisely tuned electrochemical dynamics and interactions among the electrolytes and the diversified anode and cathode chemistries therein. Furthermore, with consumers' demand for battery performance ever increasing, more and more stringent requirements are being imposed upon the established equilibria among these LIB components, and it became clear that the state-of-the-art electrolyte systems could no longer sustain the desired technological trajectory. Driven by such gap, researchers started to explore more unconventional electrolyte systems. From superconcentrated solvent-in-salt electrolytes to solid-state electrolytes, the current research realm of novel electrolyte systems has grown to unprecedented levels. In this review, we will avoid discussions on current state-of-the-art electrolytes but instead focus exclusively on unconventional electrolyte systems that represent new concepts.
Hybrid Integration of GaP Photonic Crystal Cavities with Silicon-Vacancy Centers in Diamond by Stamp-Transfer
Optically addressable solid-state defects are emerging as some of the most promising qubit platforms for quantum networks. Maximizing photon-defect interaction by nanophotonic cavity coupling is key to network efficiency. We demonstrate fabrication of gallium phosphide 1-D photonic crystal waveguide cavities on a silicon oxide carrier and subsequent integration with implanted silicon-vacancy (SiV) centers in diamond using a stamp-transfer technique. The stamping process avoids diamond etching and allows fine-tuning of the cavities prior to integration. After transfer to diamond, we measure cavity quality factors (Q) of up to 8900 and perform resonant excitation of single SiV centers coupled to these cavities. For a cavity with a Q of 4100, we observe a 3-fold lifetime reduction on-resonance, corresponding to a maximum potential cooperativity of C = 2. In conclusion, these results indicate promise for high photon-defect interaction in a platform which avoids fabrication of the quantum defect host crystal.
Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process
As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.
Standardized excitable elements for scalable engineering of far-from-equilibrium chemical networks
Engineered far-from-equilibrium synthetic chemical networks that pulse or switch states in response to environmental signals could precisely regulate the kinetics of chemical synthesis or self-assembly. Currently, such networks must be extensively tuned to compensate for the different activities of and unintended reactions between a network’s various chemical components. Modular elements with standardized performance could be used to rapidly construct networks with designed functions. Here we develop standardized excitable chemical regulatory elements, termed genelets, and use them to construct complex in vitro transcriptional networks. We develop a protocol for identifying >15 interchangeable genelet elements with uniform performance and minimal crosstalk. Furthermore, these elements can be combined to engineer feedforward and feedback modules whose dynamics match those predicted by a simple kinetic model. Modules can then be rationally integrated and organized into networks that produce tunable temporal pulses and act as multistate switchable memories. Standardized genelet elements, and the workflow to identify more, should make engineering complex far-from-equilibrium chemical dynamics routine.
De novo design of protein structure and function with RFdiffusion
Abstract There has been considerable recent progress in designing new proteins using deep-learning methods 1–9 . Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models 10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence–structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.
Substrate co-doping modulates electronic metal–support interactions and significantly enhances single-atom catalysis
Transitional metal nanoparticles or atoms deposited on appropriate substrates can lead to highly economical, efficient, and selective catalysis. One of the greatest challenges is to control the electronic metal–support interactions (EMSI) between the supported metal atoms and the substrate so as to optimize their catalytic performance. Here, from first-principles calculations, we show that an otherwise inactive Pd single adatom on TiO 2 (110) can be tuned into a highly effective catalyst, e.g. for O 2 adsorption and CO oxidation, by purposefully selected metal–nonmetal co-dopant pairs in the substrate. Such an effect is proved here to result unambiguously from a significantly enhanced EMSI. A nearly linear correlation is noted between the strength of the EMSI and the activation of the adsorbed O 2 molecule, as well as the energy barrier for CO oxidation. Particularly, the enhanced EMSI shifts the frontier orbital of the deposited Pd atom upward and largely enhances the hybridization and charge transfer between the O 2 molecule and the Pd atom. Upon co-doping, the activation barrier for CO oxidation on the Pd monomer is also reduced to a level comparable to that on the Pd dimer which was experimentally reported to be highly efficient for CO oxidation. The present findings provide new insights into the understanding of the EMSI in heterogeneous catalysis and can open new avenues to design and fabricate cost-effective single-atom-sized and/or nanometer-sized catalysts.
Synthesis, modular composition, and electrochemical properties of lamellar iron sulfides
Transition metal chalcogenides with layered structures are promising materials for energy storage, catalysis, and electronics, among other areas. We have identified a new layered phase of iron sulfide containing interlayer solvated cations. We present an optimized synthesis for the Li + -containing material from an Fe(III) xanthate complex. Structure and composition data indicate the material consists of poorly-ordered iron sulfide layers separated by solvated cations. The lamellar spacing in these materials can be tuned by changing the identity of the cation. Furthermore, the lamellar spacing can also be reversibly tuned by the degree of solvation of the material. The material is electrically conductive and can serve as a pseudocapacitor with comparable performance to commercial materials such as MnO 2 . Finally, these materials also show promise as lithium or sodium ion battery cathodes with good capacity and reversibility.
Protecting against damage from refraction of high power microwaves in the DIII-D tokamak
Here, several new protective systems are being installed on the DIII D tokamak to increase the safety margins for plasma operations with injected ECH power at densities approaching cutoff. Inadvertent overdense operation has previously resulted in reflection of an rf beam back into a launcher causing extensive arcing and melt damage on one waveguide line. Damage to microwave diagnostics, which are located on the same side of the tokamak as the ECH launchers, also has occurred. Developing a reliable microwave based interlock to protect the many vulnerable systems in DIII-D has proved to be difficult. Therefore, multiple protective steps have been taken to reduce the risk of damage in the future. Among these is a density interlock generated by the plasma control system, with setpoint determined by the ECH operators based on rf beam trajectories and plasma parameters. Also installed are enhanced video monitoring of the launchers, and an ambient light monitor on each of the waveguide systems, along with a Langmuir probe at the mouth of each launcher. Versatile rf monitors, measuring forward and reflected power in addition to the mode content of the rf beams, have been installed as the last miter bends in each waveguide line. As these systems are characterized, they are being incorporated in the interlock chains, which enable the ECH injection permits. The diagnostics most susceptible to damage from the ECH waves have also been fitted with a variety of protective devices including stripline filters, thin resonant notch filters tuned to the 110 GHz injected microwave frequency, blazed grating filters and shutters. Calculations of rf beam trajectories in the plasmas are performed using the TORAY ray tracing code with input from kinetic profile diagnostics. Using these calculations, strike points for refracted beams on the vacuum vessel are calculated, which allows evaluation of the risk of damage to sensitive diagnostics and hardware.
Microstructure actuation and gas sensing by the Knudsen thermal force
The generation of forces and moments on structures immersed in rarefied non-isothermal gas flows has received limited practical implementation since first being discovered over a century ago. The formation of significant thermal stresses requires both large thermal gradients and characteristic dimensions which are comparable to the gas molecular mean free path. For macroscopic geometries, this necessitates impractically high temperatures and very low pressures. At the microscale, however, these conditions are easily achieved, allowing the effects to be exploited, namely, for gas-property sensing and microstructure actuation. In this letter, we introduce and experimentally evaluate performance of a microelectromechanical in-plane Knudsen radiometric actuator, a self-contained device having Knudsen thermal force generation, sensing, and tuning mechanisms integrated onto the same platform. Sensitivity to ambient pressure, temperature gradient, as well as gas composition is demonstrated. Results are presented in terms of a non-dimensional force coefficient, allowing measurements to be directly compared to the previous experimental and computational data on out-of-plane cantilevered configurations.
Hierarchical modeling of molecular energies using a deep neural network
In this work, we introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. Finally, with minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.
Evolution-inspired design of multicolored photoswitches from a single cyanobacteriochrome scaffold
Significance Cyanobacteriochromes (CBCRs) are small cyanobacterial photoreceptors which are highly diversified and categorized into many lineages based on their primary sequences. In this study, we identified an atypical CBCR exhibiting an orange/green reversible photocycle. Step-by-step site-directed mutagenesis was performed on this native CBCR, and seven new photoconvertible variants were created. During this process, we identified residues crucial for each color tuning event. These seven molecules covering the shorter-wavelength blue-to-orange region would contribute to the future development of multicolored optogenetic tools and are complementary to recently developed molecules sensing longer wavelengths of light.
A kinetic-based regularization method for data science applications
We propose a physics-based regularization technique for function learning, inspired by statistical mechanics. By drawing an analogy between optimizing the parameters of an interpolator and minimizing the energy of a system, we introduce corrections that impose constraints on the lower-order moments of the data distribution. This minimizes the discrepancy between the discrete and continuum representations of the data, in turn allowing to access more favorable energy landscapes, thus improving the accuracy of the interpolator. Our approach improves performance in both interpolation and regression tasks, even in high-dimensional spaces. Unlike traditional methods, it does not require empirical parameter tuning, making it particularly effective for handling noisy data. We also show that thanks to its local nature, the method offers computational and memory efficiency advantages over Radial Basis Function interpolators, especially for large datasets.
Controlling cantilevered adaptive X-ray mirrors
Modeling the behavior of a prototype cantilevered X-ray adaptive mirror (held from one end) demonstrates its potential for use on high-performance X-ray beamlines. Similar adaptive mirrors are used on X-ray beamlines to compensate optical aberrations, control wavefronts and tune mirror focal distances at will. Controlled by 1D arrays of piezoceramic actuators, these glancing-incidence mirrors can provide nanometre-scale surface shape adjustment capabilities. However, significant engineering challenges remain for mounting them with low distortion and low environmental sensitivity. Finite-element analysis is used to predict the micron-scale full actuation surface shape from each channel and then linear modeling is applied to investigate the mirrors' ability to reach target profiles. Using either uniform or arbitrary spatial weighting, actuator voltages are optimized using a Moore–Penrose matrix inverse, or pseudoinverse, revealing a spatial dependence on the shape fitting with increasing fidelity farther from the mount.
Polyolefin blends with co-continuous architectures enabled by dynamic covalent crosslinking
Blending polymers produces brittle materials due to macrophase separation and poor interfacial adhesion, which is exemplified by mixtures of polyolefins. This presents a formidable challenge for the mechanical recycling of mixed plastic waste. Here, we demonstrate that dynamic covalent crosslinking of immiscible polyolefin blends creates macrophase separated co-continuous architectures, yet they display excellent mechanical properties, which challenges the conventional wisdom regarding morphology-property relationships in polymer blend compatibilization. We find that the position and orientation of dynamic crosslinks and their influence on crystallinity are key to understanding the structure-morphology-property relationships. In particular, high-resolution microscopy imaging reveals alignment of crystallite planes with strong orientational preference, particularly at polymer-polymer interfaces, which contribute to material performance. We further demonstrate that changes in crosslinker density and valency allow the properties of binary and ternary polyolefin blends to be tuned in a modular fashion.
Orchestrated trios: compiling for efficient communication in Quantum programs with 3-Qubit gates
Current quantum computers are especially error prone and require high levels of optimization to reduce operation counts and maximize the probability the compiled program will succeed. These computers only support operations decomposed into one- and two-qubit gates and only two-qubit gates between physically connected pairs of qubits. Typical compilers first decompose operations, then route data to connected qubits. We propose a new compiler structure, Orchestrated Trios, that first decomposes to the three-qubit Toffoli, routes the inputs of the higher-level Toffoli operations to groups of nearby qubits, then finishes decomposition to hardware-supported gates. This significantly reduces communication overhead by giving the routing pass access to the higher-level structure of the circuit instead of discarding it. A second benefit is the ability to now select an architecture-tuned Toffoli decomposition such as the 8-CNOT Toffoli for the specific hardware qubits now known after the routing pass. Furthermore, we perform real experiments on IBM Johannesburg showing an average 35% decrease in two-qubit gate count and 23% increase in success rate of a single Toffoli over Qiskit. We additionally compile many near-term benchmark algorithms showing an average 344% increase in (or 4.44x) simulated success rate on the Johannesburg architecture and compare with other architecture types.