SEARCH · Engineering Papers

Results for “Optimal Experiment Design”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,657 records · Page 92

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Quantitative x-ray scattering of free molecules

Advances in x-ray free electron lasers have made ultrafast scattering a powerful method for investigating molecular reaction kinetics and dynamics. Accurate measurement of the ground-state, static scattering signals of the reacting molecules is pivotal for these pump-probe x-ray scattering experiments as they are the cornerstone for interpreting the observed structural dynamics. Here, this article presents a data calibration procedure, designed for gas-phase x-ray scattering experiments conducted at the Linac Coherent Light Source x-ray Free-Electron Laser at SLAC National Accelerator Laboratory, that makes it possible to derive a quantitative dependence of the scattering signal on the scattering vector. A self-calibration algorithm that optimizes the detector position without reference to a computed pattern is introduced. Angle-of-scattering corrections that account for several small experimental non-idealities are reported. Their implementation leads to near quantitative agreement with theoretical scattering patterns calculated with ab-initio methods as illustrated for two x-ray photon energies and several molecular test systems.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2016

Spatial Support Vector Regression to Detect Silent Errors in the Exascale Era

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt the executionresults of HPC applications without being detected. In this work, we explore a low-memory-overhead SDC detector, by leveraging epsilon-insensitive support vector machine regression, to detect SDCs that occur in HPC applications that can be characterized by an impact error bound. The key contributions are three fold. (1) Our design takes spatialfeatures (i.e., neighbouring data values for each data point in a snapshot) into training data, such that little memory overhead (less than 1%) is introduced. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show thatour detector can achieve the detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% of false positive rate for most cases. Our detector incurs low performance overhead, 5% on average, for all benchmarks studied in the paper. Compared with other state-of-the-art techniques, our detector exhibits the best tradeoff considering the detection ability and overheads.

Subasi, Omer↗

DOE OSTI2015

Predictive Engineering Tools for Injection-Molded Long-Carbon-Fiber Thermoplastic Composites (FY 2015 Third Quarterly Report)

During the third quarter of FY 2015, the following technical progress has been made toward project milestones: 1) Magna oversaw the tool build and prepared the molding plan for the complex part of Phase II. 2) PlastiComp hosted a visit by Magna and Toyota on April 23rd to finalize the molding scope and schedule. The plan for molding trials including selection of molding parameters for both LFT and D-LFT for the U-shape complex part was established. 3) Toyota shipped the U-shape complex part tool to Magna on May 28th, 2015. 4) Plasticomp provided 30wt% LCF/PP and 30wt% LCF/PA66 compounded pellets to Magna for molding the complex part. 5) Magna performed preliminary molding trials on June 2nd, 2015 to validate wall thickness, fill profile, tool temperature and shot size requirements for the complex part. 6) Magna performed the first complex part run on June 16th and 17th, 2015 at Magna’s Composite Centre of Excellence in Concord, ON, Canada. Dale Roland of Plasticomp, and Umesh Gandhi of Toyota also attended the molding. 7) Magna discussed and finalized the plan with PNNL and the team for cutting samples from molded parts at selected locations for fiber orientation and length measurements. 8) Magna provided the computer-aided design (CAD) files of the complex parts with and without ribs to PNNL and Autodesk to build the corresponding ASMI models for injection molding simulations. Magna also provided the actual parameters used. 9) Plasticomp’s provided knowledge and experience of molding LCF materials essential to the successful molding of the parts including optimization of fill speed, tool temperatures, and plasticizing conditions for the 30wt% LCF/PP and 30wt% LCF/PA66 materials in both rib and non-rib versions. 10) Magna molded additional parts for evaluation of mechanical property testing including torsional stiffness on June 29th and 30th, 2015 at Magna’s Composite Center of Excellence. 11) Toyota began preparation for the torsion test of the specimens. Preparation of a computer-aided engineering (CAE) model to predict the performance is in progress. 12) Autodesk fixed an error in the implementation of the proper orthogonal decomposition (POD) calculation of fiber length that had caused the ASMI solution to crash and provided an updated build of ASMI containing the fix. 13)Autodesk reviewed and provided feedback for the complex part molding and measurement locations. 14) Autodesk provided support to set up the workflow for ASMI-ABAQUS® analysis, and provided a fix and workaround for a bug in the ASMI-ABAQUS® output command. 15) Autodesk helped build ASMI analysis models for the complex parts with and without ribs. 16) Autodesk worked on improving the orientation prediction accuracy in the shearing layer for 3D meshes based on comparison to measured data of the plaque moldings. 17) PNNL installed a new ASMI version received from Autodesk and performed comparative analyses to assess mid-plane versus 3D fiber length predictions using the full fiber length model and the reduced-order model (ROM) using POD. 18) PNNL presented the project scope, accomplishments, significant results and future plans to DOE and the USCAR Materials Tech Team on June 3rd, 2015. 19) PNNL discussed the cutting of samples from molded parts and finalized a plan with Magna and the team suggesting the sample size, locations and number of samples per location. 20) PNNL and Autodesk built ASMI models for the complex parts with and without ribs, and preliminary analyses of the part with ribs were conducted using the actual molding parameters received from Magna. 21) PNNL worked on a procedure to extract fiber orientation and length results from a 3D ASMI analysis to a 3D ABAQUS model. This procedure is essential to import ASMI fiber orientation and length to a 3D ABAQUS model of the part allowing future part structural analysis for weight reduction study.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Mono-Ether and Alcohol Bioblendstocks to Reduce the Fuel Penalty of Mixing Controlled Compression Ignition (MCCI) Engine Aftertreatment

An integrated approach utilizing catalysis experiments, process systems engineering, fuel property modeling, and engine testing was utilized in this project to optimize the production process and composition of a #2 diesel bioblendstock produced from ethanol consisting primarily of long-chain mono-ethers. The primary objective of the project was to determine the composition and to design the production process for a bioblendstock for #2 diesel fuel with > 50% reduction in greenhouse gas emissions relative to conventional diesel fuel. The desired bioblendstock needed to be blendable with #2 diesel fuel at > 5 vol. % while still meeting ASTM D975 diesel fuel specification properties and achieving improvements in fuel properties: increased cetane number, decreased sooting, and reduced pour point and cloud point temperatures. At the same time, it needed to reduce the fuel energy penalty associated with MCCI engine aftertreatment resulting in improved system efficiency. The findings of the current project demonstrate that primary objective of the work has been met, i.e., to determine the composition and to design the production process for a bioblendstock for #2 diesel fuel with > 50% reduction in greenhouse gas emissions relative to conventional diesel fuel. Additionally, the results indicate that the property objectives (increased cetane number, reduced pour and cloud points, and reduced sooting propensity) for the designed bioblendstock composition have also been met. Engine testing performed has also confirmed that the increased reactivity of the bioblendstock can be used to improve catalyst heating operation and to reduce the fuel penalty associated with this operation mode. The fuel property results demonstrate that >5 vol.% blending is easily achieved while meeting the ASTM D975 #2 diesel fuel property specifications tested in this work, as this was achieved for a blend with 43 vol. % of the bioblendstock. The results provide a foundation for future work to scaleup the catalytic production process designed in this work, with many of the challenges and areas for improvement being identified in this work to enable economic production with low GHG lifecycle emissions.

09 BIOMASS FUELS↗

DOE OSTI2024

A Multiphysics Multiscale Simulation Platform for Damage, Environmental Degradation, and Life Prediction of CMCs in Extreme Environments

This project successfully developed a multiphysics, multiscale computational framework to enhance the design and development of CMCs, with a focus on modeling highly nonlinear, time-dependent damage mechanisms and material degradation under extreme conditions, such as those experienced in turbine service environments. The project made significant advances in improving our understanding of progressive damage, oxidative degradation, and time-dependent inelastic deformation in CMCs, with particular attention to the role of uncertainties in predictions. Key outcomes include the integration of advanced material characterization, uncertainty quantification, and multiphysics constitutive models to predict the behavior of CMCs over their service life. A novel multiscale methodology was employed, which integrated microscale constituent behaviors with structural-scale responses, enabling the manufacturing defects in the microstructure that are prone to damage nucleation. Through the development of DL algorithms, the project advanced the prediction of damage initiation and crack propagation, taking into account the defect morphology and statistical variations across multiple scales. The framework was rigorously validated using thermomechanical experiments, which tested CMCs under various mechanical loadings at elevated temperatures, further enhancing the model's predictive capability. Overall, the research outcomes have provided a more accurate, reliable method for predicting CMC component life, significantly advancing material design, and improving component reliability in extreme environments. This work has strong implications for the optimization of turbine components and other high-performance applications where CMCs are used.

03 NATURAL GAS↗

NASA NTRS1975

Psychophysical Models for Signal Detection with Time Varying Uncertainty

Psychophysical models for the behavior of the human operator in detection tasks which include change in detectability, correlation between observations and deferred decisions are developed. Classical Signal Detection Theory (SDT) is discussed and its emphasis on the sensory processes is contrasted to decision strategies. The analysis of decision strategies utilizes detection tasks with time varying signal strength. The classical theory is modified to include such tasks and several optimal decision strategies are explored. Two methods of classifying strategies are suggested. The first method is similar to the analysis of ROC curves, while the second is based on the relation between the criterion level (CL) and the detectability. Experiments to verify the analysis of tasks with changes of signal strength are designed. The results show that subjects are aware of changes in detectability and tend to use strategies that involve changes in the CL's.

Gai, E.↗

NASA NTRS1995

Aerothermodynamic testing requirements for future space transportation systems

Aerothermodynamics, encompassing aerodynamics, aeroheating, and fluid dynamic and physical processes, is the genesis for the design and development of advanced space transportation vehicles. It provides crucial information to other disciplines involved in the development process such as structures, materials, propulsion, and avionics. Sources of aerothermodynamic information include ground-based facilities, computational fluid dynamic (CFD) and engineering computer codes, and flight experiments. Utilization of this triad is required to provide the optimum requirements while reducing undue design conservatism, risk, and cost. This paper discusses the role of ground-based facilities in the design of future space transportation system concepts. Testing methodology is addressed, including the iterative approach often required for the assessment and optimization of configurations from an aerothermodynamic perspective. The influence of vehicle shape and the transition from parametric studies for optimization to benchmark studies for final design and establishment of the flight data book is discussed. Future aerothermodynamic testing requirements including the need for new facilities are also presented.

Paulson, John W., Jr.↗

NASA NTRS2022

Uncertainty Estimation for SMAP Level-1 Brightness Temperature Assimilation at Different Timescales

Soil Moisture Active Passive (SMAP) mission brightness temperature (T(b) ) observations are assimilated into NASA’s Catchment Land Surface Model using an Ensemble Kalman filter to update simulations of surface and root-zone soil moisture. Different time series components of the T(b) observations are assimilated including anomalies, inter-annual variations, and high frequency variations. To optimize the weights that the data assimilation (DA) puts on the observations, the ratio between the uncertainties of modeled and observed T(b) is approximated using modeled and observed soil moisture uncertainties estimated using triple collocation analysis. In a benchmark experiment, T(b) observations are assimilated using a spatially constant 4 Kelvin (K) observation uncertainty, as in the operational SMAP Level-4 algorithm. All DA experiments exhibit notable skill improvements in most regions. Improvements are largest for the inter-annual variations in the simulations of both surface and root-zone soil moisture (mean improvements in terms of Pearson correlation (-) are 0.08 and 0.06, respectively). Anomaly simulations improve similarly (0.07), and improvements in the high-frequency variations are only observed for surface soil moisture simulations (0.06). No notable difference in skill - neither improvement nor deterioration - is observed between the experiments that use optimized observation uncertainty parameters and the 4 K benchmark experiment. This may be explained by the presence of large observation operator errors, which are analytically shown to have the potential to render post-update uncertainty insensitive to inaccuracies in estimates of the Kalman gain. These results have important implications for the design of soil moisture DA systems, in particular for parameterizing model and observation uncertainties.

Hydrology↗

DOE OSTI2014

MO-C-9A-01: Effective Medical Physics Educational Activities: Models and Methods

Medical physics is learned in a combination of activities including classroom sessions, individual study, small-group collaborative problem solving, and direct experience in the laboratory or clinical environment. Each type of learning activity is characterized by its effectiveness in producing the desired knowledge for the learner and the cost in terms of resources and human effort required providing it. While learning and teaching is a human activity, modern technology provides a variety of tools that can be used to enhance human performance. The class or conference room is the common setting for educational sessions in both academic institutions and continuing education conferences and programs such as those sponsored by the AAPM. A major value of a class/conference room program is efficiency by bringing a group of learners together to share in a common learning experience under the guidance of one or more experienced learning facilitators (lecturers or presenters). A major challenge is that the class/conference room is separated from the real world of medical physics. The design of an educational activity needs to take into consideration the desired outcomes with respect to what the learners should be able to do. The distinction is that of being able to apply the knowledge to perform specific physics functions rather than just knowing and being able to recall facts, and perhaps do well on written examinations. These are different types of knowledge structures within the human brain and distinctly different learning activities to develop each. Much of medical physics education, especially at the post-graduate and continuing education level, is for the purpose of enhancing the ability of physicists and other related professionals to perform applied procedures and tasks and requires specific types of knowledge.In this session we will analyze various learning activity models, the values and limitations of each, and how they can be used in medical physics education. An example we will use is optimizing CT image quality and dose which is an important topic for medical physicists, radiologists and residents, along with technologists. The knowledge structure for this is best developed by a combination of learning activities including class/conference discussions, individual study and review, and direct observation and interaction in the clinical setting under the direction of a knowledgeable leader.The function of the human brain will be considered with respect to learning experiences that contribute to effective medical physics knowledge structures. The characteristics of various types of educational activities will be compared with respect to their effectiveness for producing desired outcomes along with their limitations. Emphasis will be given to the design of highly-effective classroom/conference presentations, and activities will be demonstrated with an emphasis on using technology to enhance human performance of both learners and the learning facilitators. Learning Objectives: Develop and provide highly effective medical physics educational sessions. Use technology to enhance human performance in the educational process. Identify and analyze various models of educational activities Select and use educational activities that contribute value to the medical physics profession.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS1993

The HYDICE instrument design and its application to planetary instruments

The Hyperspectral Digital Imagery Collection Experiment (HYDICE) instrument represents a significant advance in the state of the art in hyperspectral sensors. It combines a higher signal-to-noise ratio (SNR) and significantly better spatial and spectral resolution and radio metric accuracy than systems flying on aircraft today. The need for 'clean' data, i.e., data free of sampling artifacts and excessive spatial or spectral noise, is a key driver behind the difficult combination of performance requirements laid out for HYDICE. Most of these involve the sensor optics and detector. This paper presents an optimized approach to those requirements, one that comprises push broom scanning, a single, mechanically cooled focal plane, a double-pass prism spectrometer, and an easily fabricated yet wide-field telescope. Central to the approach is a detector array that covers the entire spectrum from 0.4 to 2.5 microns. Among the major benefits conferred by such a design are optical and mechanical simplicity, low polarization sensitivity, and coverage of the entire spectrum without suffering the spectral gaps caused by beam splitters. The overall system minimizes interfaces to the C-141 aircraft on which it will be flown, can be calibrated on the ground and in flight to accuracies better than those required, and is designed for simple, push-button operation. Only unprocessed data are recorded during flight. A ground data processing station provides quick-look, calibration correction, and archiving capabilities, with a throughput better than the requirements. Overall performance of the system is expected to provide the solid database required to evaluate the potential of hyperspectral imagery in a wide variety of applications. HYDICE can be regarded as a test bed for future planetary instruments. The ability to spectrally image a wide field of view over multiple spectral octaves offers obvious advantages and is expected to maximize science return for the required cost and weight.

Basedow, R.↗

DOE OSTI2015

Performance optimization of a bendable parabolic cylinder collimating X-ray mirror for the ALS micro-XAS beamline 10.3.2

The Advanced Light Source (ALS) beamline (BL) 10.3.2 is an apparatus for X-ray microprobe spectroscopy and diffraction experiments, operating in the energy range 2.4–17 keV. The performance of the beamline, namely the spatial and energy resolutions of the measurements, depends significantly on the collimation quality of light incident on the monochromator. In the BL 10.3.2 end-station, the synchrotron source is imaged 1:1 onto a set of roll slits which form a virtual source. The light from this source is collimated in the vertical direction by a bendable parabolic cylinder mirror. Details are presented of the mirror design, which allows for precision assembly, alignment and shaping of the mirror, as well as for extending of the mirror operating lifetime by a factor of ~10. Assembly, mirror optimal shaping and preliminary alignment were performed ex situ in the ALS X-ray Optics Laboratory (XROL). Using an original method for optimal ex situ characterization and setting of bendable X-ray optics developed at the XROL, a root-mean-square (RMS) residual surface slope error of 0.31 µrad with respect to the desired parabola, and an RMS residual height error of less than 3 nm were achieved. Once in place at the beamline, deviations from the designed optical geometry ( e.g. due to the tolerances for setting the distance to the virtual source, the grazing incidence angle, the transverse position) and/or mirror shape ( e.g. due to a heat load deformation) may appear. Due to the errors, on installation the energy spread from the monochromator is typically a few electron-volts. Here, a new technique developed and successfully implemented for at-wavelength ( in situ ) fine optimal tuning of the mirror, enabling us to reduce the collimation-induced energy spread to ~0.05 eV, is described.

43 PARTICLE ACCELERATORS↗

NASA NTRS2013

Ongoing Development of a Series Bosch Reactor System

Future manned missions to deep space or planetary surfaces will undoubtedly require highly robust, efficient, and regenerable life support systems that require minimal consumables. To meet this requirement, NASA continues to explore a Bosch‐based carbon dioxide reduction system to recover oxygen from CO2. In order to improve the equivalent system mass of Bosch systems, we seek to design and test a "Series Bosch" system in which two reactors in series are optimized for the two steps of the reaction, as well as to explore the use of in situ materials as carbon deposition catalysts. Here we report recent developments in this effort including assembly and initial testing of a Reverse Water‐Gas Shift reactor (RWGSr) and initial testing of two gas separation membranes. The RWGSr was sized to reduce CO2 produced by a crew of four to carbon monoxide as the first stage in a Series Bosch system. The gas separation membranes, necessary to recycle unreacted hydrogen and CO2, were similarly sized. Additionally, we report results of preliminary experiments designed to determine the catalytic properties of Martian and Lunar regolith simulant for the carbon deposition step.

Abney, Morgan↗

DOE OSTI2024

Redesigning large-scale multimodal transit networks with shared autonomous mobility services

Here, this study addresses a large-scale multimodal transit network design problem, with Shared Autonomous Mobility Services (SAMS) as both transit feeders and an origin-to-destination mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and generates transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, satisfying transit origin-destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each zone are characterized with continuous approximation. The decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. The model is illustrated through a set of experiments with the Chicago network comprised of 50 zones and seven modes, under three scenarios. The computational results present savings in traveler journey time and operator cost demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.

Autonomous vehicles↗

DOE OSTI2014

Geoelectrical Measurement of Multi-Scale Mass Transfer Parameters

Mass transfer affects contaminant transport and is thought to control the efficiency of aquifer remediation at a number of sites within the Department of Energy (DOE) complex. An improved understanding of mass transfer is critical to meeting the enormous scientific and engineering challenges currently facing DOE. Informed design of site remedies and long-term stewardship of radionuclide-contaminated sites will require new cost-effective laboratory and field techniques to measure the parameters controlling mass transfer spatially and across a range of scales. In this project, we sought to capitalize on the geophysical signatures of mass transfer. Previous numerical modeling and pilot-scale field experiments suggested that mass transfer produces a geoelectrical signature—a hysteretic relation between sampled (mobile-domain) fluid conductivity and bulk (mobile + immobile) conductivity—over a range of scales relevant to aquifer remediation. In this work, we investigated the geoelectrical signature of mass transfer during tracer transport in a series of controlled experiments to determine the operation of controlling parameters, and also investigated the use of complex-resistivity (CR) as a means of quantifying mass transfer parameters in situ without tracer experiments. In an add-on component to our grant, we additionally considered nuclear magnetic resonance (NMR) to help parse mobile from immobile porosities. Including the NMR component, our revised study objectives were to: 1. Develop and demonstrate geophysical approaches to measure mass-transfer parameters spatially and over a range of scales, including the combination of electrical resistivity monitoring, tracer tests, complex resistivity, nuclear magnetic resonance, and materials characterization; and 2. Provide mass-transfer estimates for improved understanding of contaminant fate and transport at DOE sites, such as uranium transport at the Hanford 300 Area. To achieve our objectives, we implemented a 3-part research plan involving (1) development of computer codes and techniques to estimate mass-transfer parameters from time-lapse electrical data; (2) bench-scale experiments on synthetic materials and materials from cores from the Hanford 300 Area; and (3) field demonstration experiments at the DOE’s Hanford 300 Area. In a synergistic add-on to our workplan, we analyzed data from field experiments performed at the DOE Naturita Site under a separate DOE SBR grant, on which PI Day-Lewis served as co-PI. Techniques developed for application to Hanford datasets also were applied to data from Naturita. 1. Introduction The Department of Energy (DOE) faces enormous scientific and engineering challenges associated with the remediation of legacy contamination at former nuclear weapons production facilities. Selection, design and optimization of appropriate site remedies (e.g., pump-and-treat, biostimulation, or monitored natural attenuation) requires reliable predictive models of radionuclide fate and transport; however, our current modeling capabilities are limited by an incomplete understanding of multi-scale mass transfer—its rates, scales, and the heterogeneity of controlling parameters. At many DOE sites, long “tailing” behavior, concentration rebound, and slower-than-expected cleanup are observed; these observations are all consistent with multi-scale mass transfer [Haggerty and Gorelick, 1995; Haggerty et al., 2000; 2004], which renders pump-and-treat remediation and biotransformation inefficient and slow [Haggerty and Gorelick, 1994; Harvey et al., 1994; Wilson, 1997]. Despite the importance of mass transfer, there are significant uncertainties associated with controlling parameters, and the prevalence of mass transfer remains a point of debate [e.g., Hill et al., 2006; Molz et al., 2006] for lack of experimental methods to verify and measure it in situ or independently of tracer breakthrough. There is a critical need for new field-experimental techniques to measure mass transfer in-situ and estimate multi-scale and spatially variable mass-transfer parameters. The current lack of such techniques results in large parameter uncertainty, which in turn translates into enormous prediction uncertainty and cost to DOE. In this project, we considered three hydrogeophysical approaches for providing information about mass-transfer parameters: (1) the combination of electrical-resistivity tomography (ERT) and ionic tracer experiments to explore rates of exchange and relative mobile and immobile porosities; (2) complex resistivity (CR) measurements to infer the distribution of diffusive length scales active in a porous medium; and (3) nuclear magnetic resonance (NMR) to estimate mobile and immobile porosity.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2011

Numerical Homogenization on Approach for Stokesian Suspensions

In this technical report we investigate efficient methods for numerical simulation of active suspensions. The prototypical system is a suspension of swimming bacteria in a Newtonian fluid. Rheological and other macroscopic properties of such suspensions can differ dramatically from the same properties of the suspending fluid alone or of suspensions of similar but inactive particles. Elongated bacteria, such as E. coli or B. subtilis, swim along their principal axis, propelling themselves with the help of flagella, attached at the anterior of the organism and pushing it forward in the manner of a propeller. They interact hydrodynamically with the surrounding fluid and, because of their asymmetrical shape, have the propensity to align with the local flow. This, along with the dipolar nature of bacteria (the two forces a bacterium exerts on a fluid - one due to self-propulsion and the other opposing drag - have equal magnitude and point in opposite directions), causes nearby bacteria to tend to align, resulting in a intermittent local ordering on the mesoscopic scale, which is between the microscopic scale of an individual bacterium and the macroscopic scale of the suspension (e.g., its container). The local ordering is sometimes called a collective mode or collective swimming. Thanks to self-propulsion, collective modes inject momentum into the fluid in a coherent way. This enhances the local strain rate without changing the macroscopic stress applied at the boundary of the container. The macroscopic effective viscosity of the suspension is defined roughly as the ratio of the applied stress to the bulk strain rate. If local alignment and therefore local strain-rate enhancement, are significant, the effective viscosity can be appreciably lower than that of the corresponding passive suspension or even of the surrounding fluid alone. Indeed, a sevenfold decrease in the effective viscosity was observed in experiments with B. subtilis. More generally, local collective swimming resulting from bacterial alignment can significantly alter other macroscopic properties of the suspension, such as the oxygen diffusivity and mixing rates. In order to understand the unique macroscopic properties of active suspensions the connection between microscopic swimming and alignment dynamics and the mesoscopic pattern formation must be clarified. This is difficult to do analytically in the fully general setting of moderately dense suspensions, because of the large number of bacteria involved (approx. 10 10 cm -3 in experiments) and the complex, time-dependent geometry of the system. Many reduced analytical models of bacterial have been proposed, but all of them require validation. While comparison with experiment is the ultimate test of a model's fidelity, it is difficult to conduct experiments matched to these models assumptions. Numerical simulation of the microscopic dynamics is an acceptable substitute, but it runs into the problem of having to discretize the fluid domain with a fine-grained boundary (the bacteria) and update the discretization as the domain evolves (bacteria move). This leads to a prohibitively high number of degrees of freedom and prohibitively high setup costs per timestep of simulation. In this technical report we propose numerical methods designed to alleviate these two difficulties. We indicate how to (1) construct an optimal discretization in terms of the number of degrees of freedom per digit of accuracy and (2) optimally update the discretization as the simulation evolves. The technical tool here is the derivation of rigorous error bounds on the error in the numerical solution when using our proposed discretization at the initial time as well as after a given elapsed simulation time. These error bounds should guide the construction of practical discretization schemes and update strategies. Our initial construction is carried out by using a theoretically convenient, but practically prohibitive spectral basis, which is a Galerkin basis of functions with global support. At the end of this report we propose localization techniques while maintaining acceptable error bounds. No numerical experiments were conducted as part of this study, but we envision that we may undertake such studies and further development of the method, jointly or individually.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Tailor-Made Microporous Metal–Organic Frameworks for the Full Separation of Propane from Propylene Through Selective Size Exclusion

Adsorptive separation of olefin/paraffin mixtures by porous solids can greatly reduce the energy consumption associated with the currently employed cryogenic distillation technique. Here, the complete separation of propane and propylene by a designer microporous metal–organic framework material is reported. The compound, Y 6 (OH) 8 (abtc) 3 (H 2 O) 6 (DMA) 2 (Y-abtc, abtc = 3,3',5,5'-azobenzene-tetracarboxylates; DMA = dimethylammonium), is rationally designed through topology-guided replacement of inorganic building units. Y-abtc is both thermally and hydrothermally robust, and possesses optimal pore window size for propane/propylene separation. It adsorbs propylene with fast kinetics under ambient temperature and pressure, but fully excludes propane, as a result of selective size exclusion. Multicomponent column breakthrough experiments confirm that polymer-grade propylene (99.5%) can be obtained by this process, demonstrating its true potential as an alternative sorbent for efficient separation of propane/propylene mixtures.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Size and Temperature Dependence of Electron Transfer between CdSe Quantum Dots and a TiO 2 Nanobelt

Understanding charge transfer reactions between quantum dots (QD) and metal oxides is fundamental for improving photocatalytic, photovoltaic and electronic devices. The complexity of these processes makes it difficult to find an optimum QD size with rapid charge injection and low recombination. We combine time-domain density functional theory with nonadiabatic molecular dynamics to investigate the size and temperature dependence of the experimentally studied electron transfer and charge recombination at CdSe QD-TiO 2 nanobelt (NB) interfaces. The electron injection rate shows strong dependence on the QD size, increasing for small QDs. The rate exhibits Arrhenius temperature dependence, with the activation energy of the order of millielectronvolts. The charge recombination process occurs due to coupling of the electronic subsystem to vibrational modes of the TiO 2 NB. Inelastic electron-phonon scattering happens on a picosecond time scale, with strong dependence on the QD size. Our simulations demonstrate that the electron-hole recombination rate decreases significantly as the QD size increases, in excellent agreement with experiments. The temperature dependence of the charge recombination rates can be successfully modeled within the framework of the Marcus theory through optimization of the electronic coupling and the reorganization energy. Our simulations indicate that by varying the QD size, one can modulate the photoinduced charge separation and charge recombination, fundamental aspects of the design principles for high efficiency devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Curiosity driven exploration to optimize structure–property learning in microscopy

Rapidly determining structure–property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. In microscopy, imaging data provides a direct measurement of the local structure, while spectroscopic measurements provide relevant functional property information. Deep kernel active learning approaches have been utilized to rapidly map local structure to functional properties in microscopy experiments, but are computationally expensive for multi-dimensional and correlated output spaces. Here, we present an alternative lightweight curiosity algorithm which actively samples regions with unexplored structure–property relations, utilizing a deep-learning based surrogate model for error prediction. We show that the algorithm outperforms random sampling for predicting properties from structures, and provides a convenient tool for efficient mapping of structure–property relationships in materials science.

36 MATERIALS SCIENCE↗