Results for “reversible computing” — Page 53

DOE OSTI2024

Sequential Kalman tuning of the t -preconditioned Crank-Nicolson algorithm: efficient, adaptive and gradient-free inference for Bayesian inverse problems

Ensemble Kalman Inversion (EKI) has been proposed as an efficient method for the approximate solution of Bayesian inverse problems with expensive forward models. However, when applied to the Bayesian inverse problem EKI is only exact in the regime of Gaussian target measures and linear forward models. Here, in this work we propose embedding EKI and Flow Annealed Kalman Inversion, its normalizing flow (NF) preconditioned variant, within a Bayesian annealing scheme as part of an adaptive implementation of the t-preconditioned Crank-Nicolson (tpCN) sampler. The tpCN sampler differs from standard pCN in that its proposal is reversible with respect to the multivariate t-distribution. The more flexible tail behaviour allows for better adaptation to sampling from non-Gaussian targets. Within our Sequential Kalman Tuning (SKT) adaptation scheme, EKI is used to initialize and precondition the tpCN sampler for each annealed target. The subsequent tpCN iterations ensure particles are correctly distributed according to each annealed target, avoiding the accumulation of errors that would otherwise impact EKI. We demonstrate the performance of SKT for tpCN on three challenging numerical benchmarks, showing significant improvements in the rate of convergence compared to adaptation within standard SMC with importance weighted resampling at each temperature level, and compared to similar adaptive implementations of standard pCN. The SKT scheme applied to tpCN offers an efficient, practical solution for solving the Bayesian inverse problem when gradients of the forward model are not available. Code implementing the SKT schemes for tpCN is available at https://github.com/RichardGrumitt/KalmanMC.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Accelerating Quantum Materials Development with Advances in Transmission Electron Microscopy

Quantum materials are driving a technology revolution in sensing, communication, and computing, while simultaneously testing many core theories of the past century. Materials such as topological insulators, complex oxides, superconductors, quantum dots, color center-hosting semiconductors, and other types of strongly correlated materials can exhibit exotic properties such as edge conductivity, multiferroicity, magnetoresistance, superconductivity, single photon emission, and optical-spin locking. These emergent properties arise and depend strongly on the material’s detailed atomic-scale structure, including atomic defects, dopants, and lattice stacking. In this review, we describe how progress in the field of electron microscopy (EM), including in situ and in operando EM, can accelerate advances in quantum materials and quantum excitations. We begin by describing fundamental EM principles and operation modes. We then discuss various EM methods such as (i) EM spectroscopies, including electron energy loss spectroscopy (EELS), cathodoluminescence (CL), and electron energy gain spectroscopy (EEGS); (ii) four-dimensional scanning transmission electron microscopy (4D-STEM); (iii) dynamic and ultrafast EM (UEM); (iv) complementary ultrafast spectroscopies (UED, XFEL); and (v) atomic electron tomography (AET). We describe how these methods could inform structure–function relations in quantum materials down to the picometer scale and femtosecond time resolution, and how they enable precision positioning of atomic defects and high-resolution manipulation of quantum materials. For each method, we also describe existing limitations to solve open quantum mechanical questions, and how they might be addressed to accelerate progress. Among numerous notable results, our review highlights how EM is enabling identification of the 3D structure of quantum defects; measuring reversible and metastable dynamics of quantum excitations; mapping exciton states and single photon emission; measuring nanoscale thermal transport and coupled excitation dynamics; and measuring the internal electric field and charge density distribution of quantum heterointerfaces- all at the quantum materials’ intrinsic atomic and near atomic-length scale. Finally, we conclude by describing open challenges for the future, including achieving stable sample holders for ultralow temperature (below 10K) atomic-scale spatial resolution, stable spectrometers that enable meV energy resolution, and high-resolution, dynamic mapping of magnetic and spin fields. With atomic manipulation and ultrafast characterization enabled by EM, quantum materials will be poised to integrate into many of the sustainable and energy-efficient technologies needed for the 21st century.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Logical Majorana zero modes in a nanowire network

We present a scheme to use physical Majorana quasizero modes at each junction of a two-dimensional nanowire network to build a logical Majorana zero mode, the location of which is controllable through gate voltages. The wire network is a way to realize a recent proposal to imprint a Kekulé vortex pattern on a honeycomb lattice via gate voltages. We show that a specific type of junction, other than a naive Y or T junction, is needed to realize, without breaking time-reversal symmetry, an artificial “graphene” system with Majorana fermions instead of complex ones at each site. The junction we propose (i) traps exactly one physical Majorana (quasi)zero mode at each site of either a brick wall or honeycomb lattice and (ii) allows this mode to hybridize with all three neighboring sites. Using a lattice of these junctions and starting from an electronic, tight-binding model for the wires, we imprint the voltage patterns corresponding to the Kekulé vortex and observe the emergence of the logical Majorana zero mode at the vortex core. By suitable adiabatic modulations of gate voltages as a function of time, the positions of individual Kekulé vortices can be moved, and in this manner, non-Abelian braiding of the logical Majoranas bound to the vortex cores can be performed. We provide the range of parameters where these excitations could be realized experimentally.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Topological Analysis of The SPOKE Graph

The SPOKE graph [2, 6] is a sparse decorated semantic graph representing a collection of knowledge collected in many scientific databases from the fields of healthcare, biochemistry, chemistry, biology, et cetera. This knowledge graph is stored as a relational dataset decorated with metadata on each constituent vertex and edge. Formally, the graph is G(V, E, D), where V is a set of n vertices V := {1, ..., n} and edges of the form (i, j) ϵ E for i, j ϵ V, and table D that for any item in V υ E stores unstructured data such as vertex/edge type, nature of a relationship, et cetera. D(i) = {data involving vertex i ϵ V}, and D(i, j) = {data involving edge (i, j) ϵ E}. Here, we treat the graph as undirected in the sense that a direct relationship for (i, j) causes a (possibly opposite) reverse direct relationship for (j, i). The SPOKE graph G(V, E, D) is formed by processing a collection of relational datasets from medicine, chemistry, and biology, connecting many entities. Here, we analyze an instance from 2019, Spoke-20190707, where a graph file contains 6.16M edges and associated metadata and a vertex file contains 2.15M vertices and the associated metadata. There are 12 different types of vertex entities; all edge types used are implicit (see §2). There is other metadata in D on edges and vertices, but we just use the topology and the vertex labels in this report. SPOKE is growing as more knowledge is gained and more datasets are added. SPOKE is likely to grow 10x during the next phase of this project, and we therefore would like to consider topoligical analysis techniques that are scalable to several orders of magnitude larger than the current dataset (say >1B edges).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Phase engineering of layered anode materials during ion-intercalation in Van der Waal heterostructures

Transition metal dichalcogenides (TMDs) are a class of 2D materials demonstrating promising properties, such as high capacities and cycling stabilities, making them strong candidates to replace graphitic anodes in lithium-ion batteries. However, certain TMDs, for instance, MoS 2 , undergo a phase transformation from 2H to 1T during intercalation that can affect the mobility of the intercalating ions, the anode voltage, and the reversible capacity. In contrast, select TMDs, for instance, NbS 2 and VS 2 , resist this type of phase transformation during Li-ion intercalation. This manuscript uses density functional theory simulations to investigate the phase transformation of TMD heterostructures during Li-, Na-, and K-ion intercalation. The simulations suggest that while stacking MoS 2 layers with NbS 2 layers is unable to limit this 2H → 1T transformation in MoS 2 during Li-ion intercalation, the interfaces effectively stabilize the 2H phase of MoS 2 during Na- and K-ion intercalation. However, stacking MoS 2 layers with VS 2 is able to suppress the 2H → 1T transformation of MoS 2 during the intercalation of Li, Na, and K-ions. The creation of TMD heterostructures by stacking MoS 2 with layers of non-transforming TMDs also renders theoretical capacities and electrical conductivities that are higher than that of bulk MoS 2 .

25 ENERGY STORAGE↗

DOE OSTI2023

Using Eye-Tracking to Quantify Reverse Engineering Expertise

Software reverse engineering (RE) requires analysts to closely read and make decisions about code. Little is known about what makes an analyst successful, making it difficult to train new analysts or design tools to augment existing ones. The goal of this project was to quantify the eye movement behaviors supporting RE and code comprehension more generally. We applied eye-tracking methods from the language comprehension literature to understand where analysts direct their attention over time when completing tasks (e.g., function identification, bug detection). Across three studies, we manipulated aspects of code hypothesized to impact comprehension (e.g., variable name meaningfulness, code complexity) and presentation methods (e.g., line-by-line, free viewing, gaze-contingent moving window) to understand effects on accuracy and gaze patterns. Results showed clear benefits of meaningful variable names, and effects of expertise on global and line-specific viewing patterns. Findings could inspire empirically-supported tool or analytic adaptations that help to reduce analyst workload.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2010

AIRS Products Confirm and Explain Recent Negative Trends of OLR as Observed by CERES

Anomalies and trends of OLR serve as important indicators of climate change. Several satellite based instruments currently provide information related to OLR. CERES, on board the EOS Aqua and Terra satellites, contains broad band radiometers that measure total flux and short-wave flux, from which OLR is determined. AIRS is a high spectral resolution IR sounder on EOS Aqua that measures IR radiances covering most of the spectral interval 650/cm to 2670/cm. These observations enable the determination of detailed information about atmospheric temperature, moisture, and ozone profiles, as well as surface skin temperatures and cloud parameters. The AIRS OLR product is the total flux over the spectral interval 2/cm to 2750/cm computed for the surface and atmospheric state determined from AIRS observations. We compared spatial anomalies and trends of OLR, over the seven year period September 2002 through August 2009, as observed by CERES and computed using Version-5 AIRS products. These two sets of OLR anomalies and trends, obtained in very different ways, agree with each other almost perfectly in essentially every detail. This important finding shows that a very stable high spectral infra-red sounder such as AIRS corroborates the anomalies and trends of OLR obtained from CERES. More significantly, anomalies and trends of the individual geophysical parameters derived from AIRS explain the detailed causes of the anomalies and trends of CERES OLR. Both sets of results show that global mean OLR has been decreasing at a rate of 0.12 W/sq m/yr over the seven year time period under study. Both also confirm that the primary cause of this is due to changes in the tropics, in which OLR has been decreasing at a rate of 0.27 W/sq m/yr. AIRS products show that the decrease of tropical OLR is a result of increasing tropical atmospheric water vapor and cloud cover over that time period studied, which in turn is responding to a very strong La Nina; a event starting in late 2007. Equatorial ocean temperatures between 160 E and 120 W cooled considerably during this time period, with corresponding local decreases in mid-tropospheric humidity and cloud cover, resulting in increases in local OLR. This was more than compensated by substantial increases in water vapor and cloud cover elsewhere in the tropics, resulting in a net decrease in tropical OLR. The AIRS Version-5 products currently extend through December 2009. An El Nino event began in late 2009. AIRS products show that tropical OLR began to increase at that time, and there was a corresponding reversal of sign of tropical cloud and water vapor anomalies.

Susskind, Joel↗

DOE OSTI2020

Observation of Weyl fermions in a magnetic non-centrosymmetric crystal

The absence of inversion symmetry in non-centrosymmetric materials has a fundamental role in the emergence of a vast number of fascinating phenomena, like ferroelectricity, second harmonic generation, and Weyl fermions. The removal of time-reversal symmetry in such systems further extends the variety of observable magneto-electric and topological effects. Here we report the striking topological properties in the non-centrosymmetric spin-orbit magnet PrAlGe by combining spectroscopy and transport measurements. By photoemission spectroscopy below the Curie temperature, we observe topological Fermi arcs that correspond to projected topological charges of ±1 in the surface Brillouin zone. In the bulk, we observe the linear energy-dispersion of the Weyl fermions. We further observe a large anomalous Hall response in our magneto-transport measurements, which is understood to arise from diverging bulk Berry curvature fields associated with the Weyl band structure. These results establish a novel Weyl semimetal phase in magnetic non-centrosymmetric PrAlGe.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Variable domain mutational analysis to probe the molecular mechanisms of high viscosity of an IgG 1 antibody

Subcutaneous injection is the preferred route of administration for many antibody therapeutics for reasons that include its speed and convenience. However, the small volume limit (typically ≤2 mL) for subcutaneous delivery often necessitates antibody formulations at high concentrations (commonly ≥100 mg/mL), which may lead to physicochemical problems. For example, antibodies with large hydrophobic or charged patches can be prone to self-interaction giving rise to high viscosity. Here, we combined X-ray crystallography with computational modeling to predict regions of an anti-glucagon receptor (GCGR) IgG 1 antibody prone to self-interaction. An extensive mutational analysis was undertaken of the complementarity-determining region residues residing in hydrophobic surface patches predicted by spatial aggregation propensity, in conjunction with residue-level solvent accessibility, averaged over conformational ensembles from molecular dynamics simulations. Dynamic light scattering (DLS) was used as a medium throughput screen for self-interaction of ~200 anti-GCGR IgG 1 variants. A negative correlation was found between the viscosity determined at high concentration (180 mg/mL) and the DLS interaction parameter measured at low concentration (2–10 mg/mL). Additionally, anti-GCGR variants were readily identified with reduced viscosity and antigen-binding affinity within a few fold of the parent antibody, with no identified impact on overall developability. The methods described here may be useful in the optimization of other antibodies to facilitate their therapeutic administration at high concentration.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2014

Adaptive Augmenting Control Flight Characterization Experiment on an F/A-18

This paper summarizes the Adaptive Augmenting Control (AAC) flight characterization experiments performed using an F/A-18 (TN 853). AAC was designed and developed specifically for launch vehicles, and is currently part of the baseline autopilot design for NASA's Space Launch System (SLS). The scope covered here includes a brief overview of the algorithm (covered in more detail elsewhere), motivation and benefits of flight testing, top-level SLS flight test objectives, applicability of the F/A-18 as a platform for testing a launch vehicle control design, test cases designed to fully vet the AAC algorithm, flight test results, and conclusions regarding the functionality of AAC. The AAC algorithm developed at Marshall Space Flight Center is a forward loop gain multiplicative adaptive algorithm that modifies the total attitude control system gain in response to sensed model errors or undesirable parasitic mode resonances. The AAC algorithm provides the capability to improve or decrease performance by balancing attitude tracking with the mitigation of parasitic dynamics, such as control-structure interaction or servo-actuator limit cycles. In the case of the latter, if unmodeled or mismodeled parasitic dynamics are present that would otherwise result in a closed-loop instability or near instability, the adaptive controller decreases the total loop gain to reduce the interaction between these dynamics and the controller. This is in contrast to traditional adaptive control logic, which focuses on improving performance by increasing gain. The computationally simple AAC attitude control algorithm has stability properties that are reconcilable in the context of classical frequency-domain criteria (i.e., gain and phase margin). The algorithm assumes that the baseline attitude control design is well-tuned for a nominal trajectory and is designed to adapt only when necessary. Furthermore, the adaptation is attracted to the nominal design and adapts only on an as-needed basis (see Figure 1). The MSFC algorithm design was formulated during the Constellation Program and reached a high maturity level during SLS through simulation-based development and internal and external analytical review. The AAC algorithm design has three summary-level objectives: (1) "Do no harm;" return to baseline control design when not needed, (2) Increase performance; respond to error in ability of vehicle to track command, and (3) Regain stability; respond to undesirable control-structure interaction or other parasitic dynamics. AAC has been successfully implemented as part of the Space Launch System baseline design, including extensive testing in high-fidelity 6-DOF simulations the details of which are described in [1]. The Dryden Flight Research Center's F/A-18 Full-Scale Advanced Systems Testbed (FAST) platform is used to conduct an algorithm flight characterization experiment intended to fully vet the aforementioned design objectives. FAST was specifically designed with this type of test program in mind. The onboard flight control system has full-authority experiment control of ten aerodynamic effectors and two throttles. It has production and research sensor inputs and pilot engage/disengage and real-time configuration of up to eight different experiments on a single flight. It has failure detection and automatic reversion to fail-safe mode. The F/A-18 aircraft has an experiment envelope cleared for full-authority control and maneuvering and exhibits characteristics for robust recovery from unusual attitudes and configurations aided by the presence of a qualified test pilot. The F/A-18 aircraft has relatively high mass and inertia with exceptional performance; the F/A-18 also has a large thrust-to-weight ratio, owing to its military heritage. This enables the simulation of a portion of the ascent trajectory with a high degree of dynamic similarity to a launch vehicle, and the research flight control system can simulate unstable longitudinal dynamics. Parasitic dynamics such as slosh and bending modes, as well as atmospheric disturbances, are being produced by the airframe via modification of bending filters and the use of secondary control surfaces, including leading and trailing edge flaps, symmetric ailerons, and symmetric rudders. The platform also has the ability to inject signals in flight to simulate structural mode resonances or other challenging dynamics. This platform also offers more test maneuvers and longer maneuver times than a single rocket or missile test, which provides ample opportunity to fully and repeatedly exercise all aspects of the algorithm. Prior to testing on an F/A-18, AAC was the only component of the SLS autopilot design that had not been flight tested. The testing described in this paper raises the Technology Readiness Level (TRL) early in the SLS Program and is able to demonstrate its capabilities and robustness in a flight environment.

VanZwieten, Tannen S.↗

DOE OSTI2022

Fatigue performance of laser powder bed fusion hydride-dehydride Ti-6Al-4V powder

We report hydride-dehydride (HDH) Ti-6Al-4V alloy with particle size distribution of 50–120 µm is laser powder bed fusion (L-PBF) processed using optimum processing parameters and a near-fully dense structure with a density of 99.9 % is achieved. Microstructural observations and phase analyses indicate formation of columnar β grains with acicular α/α' phases in as-built condition. The roughness of the as-fabricated samples is significant with an average roughness of R a = 15.71 ± 3.96 µm and a root mean square roughness of R rms = 108.4 ± 24.9 µm, however, both values are reduced to R a = 0.19 ± 0.04 µm and R rms = 4.9 ± 0.6 µm after mechanical grinding. Mechanical tests are carried out on as-fabricated specimens followed by stress relief treatment. All samples are tested to failure in fatigue, under fully-reversed tension-compression conditions of R = –1. The as-built samples failed from the surface with crack initiation mainly at micro-notches, whereas after mechanically grinding, crack initiation changed to subsurface defects such as pores. Minimizing surface roughness by mechanically grinding eliminates surface micro-notches which improves fatigue strength in the high cycle fatigue region. Fatigue notch factor calculations showed that the effect of surface roughness was significantly lower when HDH powder is used compared to standard spherical powder. X-ray diffraction analysis revealed an in-plane compressive stress, micro-strain and grain refinement on the surface of the mechanically ground samples. Fractography observations (macroscale) revealed a fully brittle fracture in the first stage of crack growth with a transition to a dominantly ductile fracture in the third stage of crack growth. On the other hand, at the micro scale, even the brittle fracture regions showed evidence of ductile fracture within the α' martensite laths.

36 MATERIALS SCIENCE↗

NASA NTRS2014

Modeling the Deterioration of Engine and Low Pressure Compressor Performance During a Roll Back Event Due to Ice Accretion

The main focus of this study is to apply a computational tool for the flow analysis of the engine that has been tested with ice crystal ingestion in the Propulsion Systems Laboratory (PSL) of NASA Glenn Research Center. A data point was selected for analysis during which the engine experienced a full roll back event due to the ice accretion on the blades and flow path of the low pressure compressor. The computational tool consists of the Numerical Propulsion System Simulation (NPSS) engine system thermodynamic cycle code, and an Euler-based compressor flow analysis code, that has an ice particle melt estimation code with the capability of determining the rate of sublimation, melting, and evaporation through the compressor blade rows. Decreasing the performance characteristics of the low pressure compressor (LPC) within the NPSS cycle analysis resulted in matching the overall engine performance parameters measured during testing at data points in short time intervals through the progression of the roll back event. Detailed analysis of the fan-core and LPC with the compressor flow analysis code simulated the effects of ice accretion by increasing the aerodynamic blockage and pressure losses through the low pressure compressor until achieving a match with the NPSS cycle analysis results, at each scan. With the additional blockages and losses in the LPC, the compressor flow analysis code results were able to numerically reproduce the performance that was determined by the NPSS cycle analysis, which was in agreement with the PSL engine test data. The compressor flow analysis indicated that the blockage due to ice accretion in the LPC exit guide vane stators caused the exit guide vane (EGV) to be nearly choked, significantly reducing the air flow rate into the core. This caused the LPC to eventually be in stall due to increasing levels of diffusion in the rotors and high incidence angles in the inlet guide vane (IGV) and EGV stators. The flow analysis indicating compressor stall is substantiated by the video images of the IGV taken during the PSL test, which showed water on the surface of the IGV flowing upstream out of the engine, indicating flow reversal, which is characteristic of a stalled compressor.

Veres, Joseph P.↗

NASA NTRS2014

Modeling the Deterioration of Engine and Low Pressure Compressor Performance During a Roll Back Event Due to Ice Accretion

The main focus of this study is to apply a computational tool for the flow analysis of the engine that has been tested with ice crystal ingestion in the Propulsion Systems Laboratory (PSL) of NASA Glenn Research Center. A data point was selected for analysis during which the engine experienced a full roll back event due to the ice accretion on the blades and flow path of the low pressure compressor. The computational tool consists of the Numerical Propulsion System Simulation (NPSS) engine system thermodynamic cycle code, and an Euler-based compressor flow analysis code, that has an ice particle melt estimation code with the capability of determining the rate of sublimation, melting, and evaporation through the compressor blade rows. Decreasing the performance characteristics of the low pressure compressor (LPC) within the NPSS cycle analysis resulted in matching the overall engine performance parameters measured during testing at data points in short time intervals through the progression of the roll back event. Detailed analysis of the fan-core and LPC with the compressor flow analysis code simulated the effects of ice accretion by increasing the aerodynamic blockage and pressure losses through the low pressure compressor until achieving a match with the NPSS cycle analysis results, at each scan. With the additional blockages and losses in the LPC, the compressor flow analysis code results were able to numerically reproduce the performance that was determined by the NPSS cycle analysis, which was in agreement with the PSL engine test data. The compressor flow analysis indicated that the blockage due to ice accretion in the LPC exit guide vane stators caused the exit guide vane (EGV) to be nearly choked, significantly reducing the air flow rate into the core. This caused the LPC to eventually be in stall due to increasing levels of diffusion in the rotors and high incidence angles in the inlet guide vane (IGV) and EGV stators. The flow analysis indicating compressor stall is substantiated by the video images of the IGV taken during the PSL test, which showed water on the surface of the IGV flowing upstream out of the engine, indicating flow reversal, which is characteristic of a stalled compressor.

Jorgenson, Philip C. E.↗

DOE OSTI2023

Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties

Here we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generative models that use diffusion-based dynamics to gradually denoise images and generate realistic synthetic samples. By learning the reverse of a Markov diffusion process, we design an artificial intelligence to efficiently manipulate the topology of microstructures to generate a massive number of prototypes that exhibit constitutive responses sufficiently close to designated nonlinear constitutive behaviors. To identify the subset of microcstructures with sufficiently precise fine-tuned properties, a convolutional neural network surrogate is trained to replace high-fidelity finite element simulations to filter out prototypes outside the admissible range. Results of this study indicate that the denoising diffusion process is capable of creating microstructures of fine-tuned nonlinear material properties within the latent space of the training data. More importantly, this denoising diffusion algorithm can be easily extended to incorporate additional topological and geometric modifications by introducing high-dimensional structures embedded in the latent space. Numerical experiments are conducted on the open-source mechanical MNIST data set (Lejeune, 2020). Consequently, this algorithm is not only capable of performing inverse design of nonlinear effective media, but also learns the nonlinear structure–property map to quantitatively understand the multiscale interplay among the geometry, topology, and their effective macroscopic properties.

42 ENGINEERING↗

DOE OSTI2024

BioPhotovoltaics: New paradigm towards high-efficiency and high-stability cells

In this project, we demonstrated significant progress in the development of Bio-Photovoltaic (BioPV) technology, with a particular focus on the transition from the initial success with Artemisinin (ART) to the development of the E1 compound. This journey began with the exploration of less conformationally restricted analogs of ART, leading to the discovery of E1. The initial success in the first quarter with ART set a precedent for the project, guiding our approach in molecular selection and design. Our computational studies provided a solid rationale for selecting specific biomolecules, with density functional theory calculations revealing the potential of certain molecules to form beneficial interactions with perovskite. This was a crucial step in narrowing down the candidate molecules from a broader selection. Subsequently, our approach involved simplifying these molecules to refine their properties and enhance their performance in bioPV applications. The ART-MAPbI3 films, for example, showcased not only high carrier mobility and hydrophobicity but also a significant increase in PCE. The evolution from ART to E1 was marked by a thorough understanding of molecular interactions and their impact on the material’s performance. This progression, from the complexity of lead candidates to the modeling and testing of simplified compounds, has culminated in the development of next-generation biomolecules with vastly improved properties. The link between E1 and ART, through this enhanced understanding, has been compelling and instrumental in achieving the milestones set forth in our project. The success in material and device performance underscores the importance of fundamental molecular design parameters, pointing towards future potential in the field of bioPV technology.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

Enhancements to Linear Stability-Based, CFD-integrated Transition Prediction for High-Speed Flows

Combining linear stability calculations with computational fluid dynamics (CFD) simulations has great potential for the automated modeling of high-speed flows, especially when adequate information about the configuration and the disturbance environment is available. However, a significant impediment to the applicability of this technique is the lack of an efficient method to calculate the crucial amplification ratio corresponding to the onset of transition in hypersonic flows. This ratio, also known as the "transition N-factor," is dependent upon the freestream disturbance environment as well as the surface properties of the test article. In response to the need for an engineering solution to predict the transition N-factor within conventional hypersonic wind tunnels, this paper presents a data-driven correlation that expands the existing correlations from straight circular cones with a narrow range of half angles to a broader array of axisymmetric configurations. Furthermore, when tested against a chosen dataset that was not used in its calibration, the suggested correlation shows good predictive accuracy with an RMS error of only 6.9%. Although similar accuracy may also be achieved via existing correlations based on similar datasets, predictions based on the proposed correlation have the advantage of not requiring an extensive amount of configuration-specific data. Practical applications often have access to the input parameters needed for this correlation, such as the freestream disturbance intensity, Mach number, and body-based slenderness Reynolds number. Additionally, this correlation outperforms the traditional assumption of a constant N-factor, particularly for configurations with blunted nose geometries. The development of this correlation is grounded in an extensive dataset encompassing conical models with body half-angles varying between 5 degrees and 16 degrees, Mach numbers ranging from 5 to 14, and nosetip-based Reynolds numbers approaching the transition reversal limit for blunt-nosed cones.

CFD↗

NASA NTRSDate not supplied

Enhancements to Linear Stability-Based, CFD-integrated Transition Prediction for High-Speed Flows

Combining linear stability calculations with computational fluid dynamics (CFD) simulations has great potential for the automated modeling of high-speed flows, especially when adequate information about the configuration and the disturbance environment is available. However, a significant impediment to the applicability of this technique is the lack of an efficient method to calculate the crucial amplification ratio corresponding to the onset of transition in hypersonic flows. This ratio, also known as the "transition N-factor," is dependent upon the freestream disturbance environment as well as the surface properties of the test article. In response to the need for an engineering solution to predict the transition N-factor within conventional hypersonic wind tunnels, this paper presents a data-driven correlation that expands the existing correlations from straight circular cones with a narrow range of half angles to a broader array of axisymmetric configurations. Furthermore, when tested against a chosen dataset that was not used in its calibration, the suggested correlation shows good predictive accuracy with an RMS error of only 6.9%. Although similar accuracy may also be achieved via existing correlations based on similar datasets, predictions based on the proposed correlation have the advantage of not requiring an extensive amount of configuration-specific data. Practical applications often have access to the input parameters needed for this correlation, such as the freestream disturbance intensity, Mach number, and body-based slenderness Reynolds number. Additionally, this correlation outperforms the traditional assumption of a constant N-factor, particularly for configurations with blunted nose geometries. The development of this correlation is grounded in an extensive dataset encompassing conical models with body half-angles varying between 5 degrees and 16 degrees, Mach numbers ranging from 5 to 14, and nosetip-based Reynolds numbers approaching the transition reversal limit for blunt-nosed cones.

CFD↗

DOE OSTI2024

Defect Properties, Anion Ordering, and Photochromic Mechanism in Yttrium Oxyhydride

Yttrium oxyhydride (YHO) undergoes a reversible photochromic transition when exposed to ultraviolet light. However, the mechanism for this transformation is not fully understood, and the structure and precise chemical composition of YHO remain under debate. Here, we use first-principles density functional theory calculations with a hybrid functional to study the structure, chemical stability, and point defect properties of YHO. As experiments have shown, we find that YHO prefers a cubic structure, with H and O anions present in equal concentrations and located on tetrahedral sites. Stoichiometric and ordered YHO is chemically stable, but it has a wide band gap of 5.01 eV, considerably larger than that measured in experiments (2.4–3.8 eV). On the other hand, Y4H10O has a smaller band gap of 2.97 eV and also has a region of chemical stability; thus, the actual material may include some fraction of this H-rich structure. The defect chemistry of YHO is dominated by anionic antisite species (H O and O H ), with hydrogen interstitials (H i ) and vacancies (V H ) also present in reasonably high concentrations. We show that antisite disorder lowers the band gap relative to the perfectly ordered structure, bringing the magnitude of the gap into closer agreement with experiment. Based on our calculations of defect migration and the positions of defect states relative to the band edges, we link the onset of photochromic behavior to the reaction H O – → V O 0 + H i – , which follows photoexcitation of a H O + defect. H i – can subsequently migrate away and be trapped by additional H O + defects, contributing to the persistence of the reaction, while the resultant oxygen vacancy, V O 0 , introduces an occupied defect state that leads to optical absorption at visible wavelengths. Our results can explain reported discrepancies between experimental and computational results for YHO, and they allow us to propose specific atomic-scale processes that can lead to photochromism. In conclusion, understanding these mechanisms is key for unlocking YHO’s application in devices ranging from smart windows and optoelectronics to electrochemical synapses for neural networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗