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NASA NTRS1989

Optical distributed sensing and computation for a proposed flexible beam experiment

A new technology is being developed that supports distributed sensing and computation wherein crystal mediums are used with real-time holography to gather images that contain vibration and instantaneous geometry data for a spacecraft. Optical computation techniques are employed to process the image data and to compute commands to actuators located on the spacecraft. This paper presents an integrated sensor/processor design for an experiment to demonstrate this technology. A performance estimate for the design is presented in terms of the optical spatial bandwidth of the imaging system, as opposed to the temporal bandwidth. The advantages of an integrated optical sensor/processor relative to control of large spacecraft are addressed.

Welch, S. S.↗

DOE OSTI2022

Estimating the Likelihood of GHG Concentration Scenarios From Probabilistic Integrated Assessment Model Simulations

The climate scenarios that form the basis for current climate risk assessments have no assigned probabilities, and this impedes the analysis of future climate risks. This paper proposes an approach to estimate the probability of carbon dioxide (CO 2 ) concentration scenarios used in key climate change modeling experiments. It computes the CO 2 emissions compatible with the concentrations prescribed by Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 experiments. The distribution of these compatible cumulative emissions is interpreted as the likelihood of future emissions given a concentration pathway. Using Bayesian analysis, the probability of each pathway can be estimated from a probabilistic sample of future emissions. The approach is demonstrated with five probabilistic CO 2 emission simulation ensembles from four Integrated Assessment Models (IAM), leading to independent estimates of the likelihood of the CO 2 concentration of Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP). Results suggest that SSP5-8.5 is unlikely for the second half of the 21st century, but offer no clear consensus on which of the remaining scenarios is most likely. Estimates of likelihoods of CO 2 concentrations associated with RCP and SSP scenarios are affected by sampling errors, differences in emission sources simulated by the IAMs, and a lack of a common experimental framework for IAM simulations. These shortcomings, along with a small IAM ensemble size, limit the applicability of the results presented here. Novel joint IAM and the Earth System Model experiments are needed to deliver actionable probabilistic climate risk assessments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A perspective on Bayesian methods applied to materials discovery and design

For more than two decades, there has been increasing interest in developing frameworks for the accelerated discovery and design of novel materials that could enable promising and transformative technologies. The Integrated Computational Materials Engineering (ICME) program called for integrating computational tools to establish linkages along process-structure-property-performance (PSPP) chains. The Materials Genome Initiative called for integrating experiments and computations within data science frameworks as a strategy to accelerate the materials development cycle. While these frameworks and paradigms have been quite influential, traditional ICME or data science-based approaches tend to have some limitations, mainly when querying the materials space is costly and very little information is available. Bayesian methods are more suitable in this context due to their efficiency gains. To this end, the materials discovery problem is framed as a Bayesian Optimization (BO). Different examples in which BO has been applied to solve materials discovery problems are presented. The methods/examples discussed include BO under model uncertainty, multi-information source BO, multi-objective and multi-constraint BO, and batch BO. Bayesian Materials Discovery is a promising area of research that is likely to become more influential as more attention is put on autonomous materials discovery platforms. Therefore, a discussion is provided on the potential development of such methods to increase the ability of existing platforms in materials discovery. Here, the ultimate goal is to pave the way to autonomous materials discovery.

36 MATERIALS SCIENCE↗

NASA NTRS1994

Navier-Stokes, flight, and wind tunnel flow analysis for the F/A-18 aircraft

Computational analysis of flow over the F/A-18 aircraft is presented along with complementary data from both flight and wind tunnel experiments. The computational results are based on the three-dimensional thin-layer Navier-Stokes formulation and are obtained from an accurate surface representation of the fuselage, leading-edge extension (LEX), and the wing geometry. However, the constraints imposed by either the flow solver and/or the complexity associated with the flow-field grid generation required certain geometrical approximations to be implemented in the present numerical model. In particular, such constraints inspired the removal of the empennage and the blocking (fairing) of the inlet face. The results are computed for three different free-stream flow conditions and compared with flight test data of surface pressure coefficients, surface tuft flow, and off-surface vortical flow characteristics that included breakdown phenomena. Excellent surface pressure coefficient correlations, both in terms of magnitude and overall trend, are obtained on the forebody throughout the range of flow conditions. Reasonable pressure agreement was obtained over the LEX; the general correlation tends to improve at higher angles of attack. The surface tuft flow and the off-surface vortex flow structures compared qualitatively well with the flight test results. To evaluate the computational results, a wind tunnel investigation was conducted to determine the effects of existing configurational differences between the flight vehicle and the numerical model on aerodynamic characteristics. In most cases, the geometrical approximations made to the numerical model had very little effect on overall aerodynamic characteristics.

Ghaffari, Farhad↗

NASA NTRS1997

Machine Phase Fullerene Nanotechnology: 1996

NASA has used exotic materials for spacecraft and experimental aircraft to good effect for many decades. In spite of many advances, transportation to space still costs about $10,000 per pound. Drexler has proposed a hypothetical nanotechnology based on diamond and investigated the properties of such molecular systems. These studies and others suggest enormous potential for aerospace systems. Unfortunately, methods to realize diamonoid nanotechnology are at best highly speculative. Recent computational efforts at NASA Ames Research Center and computation and experiment elsewhere suggest that a nanotechnology of machine phase functionalized fullerenes may be synthetically relatively accessible and of great aerospace interest. Machine phase materials are (hypothetical) materials consisting entirely or in large part of microscopic machines. In a sense, most living matter fits this definition. To begin investigation of fullerene nanotechnology, we used molecular dynamics to study the properties of carbon nanotube based gears and gear/shaft configurations. Experiments on C60 and quantum calculations suggest that benzyne may react with carbon nanotubes to form gear teeth. Han has computationally demonstrated that molecular gears fashioned from (14,0) single-walled carbon nanotubes and benzyne teeth should operate well at 50-100 gigahertz. Results suggest that rotation can be converted to rotating or linear motion, and linear motion may be converted into rotation. Preliminary results suggest that these mechanical systems can be cooled by a helium atmosphere. Furthermore, Deepak has successfully simulated using helical electric fields generated by a laser to power fullerene gears once a positive and negative charge have been added to form a dipole. Even with mechanical motion, cooling, and power; creating a viable nanotechnology requires support structures, computer control, a system architecture, a variety of components, and some approach to manufacture. Additional information is contained within the original extended abstract.

Globus, Al↗

DOE OSTI2018

Computational Design and Discovery of Ni-Based Alloys and Coatings: Thermodynamic Approaches Validated by Experiments

This project developed computational tools that can complement and support experimental efforts in order to enable discovery and more efficient development of Ni-base structural materials and coatings. The project goal was reached through an integrated computation-predictive and experimental-validation approach, including first-principles calculations, thermodynamic CALPHAD (CALculation of PHAse Diagram), and experimental investigations on compositions relevant to Ni-base superalloys and coatings in terms of oxide layer growth and microstructure stabilities. The developed description included composition ranges typical for coating alloys and, hence, allow for prediction of thermodynamic properties for these material systems. The calculation of phase compositions, phase fraction, and phase stabilities, which are directly related to properties such as ductility and strength, was a valuable contribution, along with the collection of computational tools that are required to meet the increasing demands for strong, ductile and environmentally-protective coatings. Specifically, a suitable thermodynamic description for the Ni-Al-Cr-Co-Si-Hf-Y system was developed for bulk alloy and coating compositions. Experiments were performed to validate and refine the thermodynamics from the CALPHAD modeling approach. Additionally, alloys produced using predictions from the current computational models were studied in terms of their oxidation performance. Finally, results obtained from experiments aided in the development of a thermodynamic modeling automation tool called ESPEI/pycalphad - for more rapid discovery and development of new materials.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Recent evolution of the offline computing model of the NOvA experiment

The NOvA experiment at Fermilab is a long-baseline neutrino experiment designed to study ν e appearance in a ν μ beam. Over the last few years there has been intense work to streamline the computing infrastructure in preparation for data, which started to flow in from the far detector in Fall 2013. Major accomplishments for this effort include migration to the use of off-site resources through the use of the Open Science Grid and upgrading the file-handling framework from simple disk storage to a tiered system using a comprehensive data management and delivery system to find and access files on either disk or tape storage. NOvA has already produced more than 6.5 million files and more than 1 PB of raw data and Monte Carlo simulation files which are managed under this model. In addition, the current system has demonstrated sustained rates of up to 1 TB/hour of file transfer by the data handling system. NOvA pioneered the use of new tools and this paved the way for their use by other Intensity Frontier experiments at Fermilab. Most importantly, the new framework places the experiment's infrastructure on a firm foundation, and is ready to produce the files needed for first physics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Localized harmonic characteristic basis functions for multiscale finite element methods

We solve elliptic systems of equations posed on highly heterogeneous materials. Examples of this class of problems are composite structures and geological processes. We focus on a model problem which is a second-order elliptic equation with discontinuous coefficients. These coefficients represent the conductivity of a composite material. We assume a background with a low conductivity that contains inclusions with different thermal properties. Under this scenario, we design a multiscale finite element method to efficiently approximate solutions. The method is based on an asymptotic expansion of the solution in terms of the ratio between the conductivities. The resulting method constructs (locally) finite element basis functions (one for each inclusion). These bases generate the multiscale finite element space where the approximation of the solution is computed. Numerical experiments show the good performance of the proposed methodology.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Understanding Adsorption and Reactions at Aqueous Oxide Interfaces with Neural Network Potential Molecular Dynamics

Chemical processes at metal oxide−water interfaces are of central importance in geochemistry, biology, and energy technologies. A better understanding of these processes would allow us to make a significant step toward optimizing and controlling them, which could in turn lead to broader impacts. Computational modeling is indispensable to accomplishing this task because complexity and disorder often make it difficult to extract atomistic information from experiments. Balancing computational cost and accuracy, simulation schemes based on efficient machine learning representations of the potential energy surface (PES) predicted by ab initio calculations have become increasingly popular over the past decade. In particular, several studies have demonstrated the ability of machine learning models to accurately reproduce the complex ab initio PESs of aqueous oxide interfaces, allowing simulations of systems and processes that are not accessible with ab initio methods. In this Account, we review our recent efforts to understand adsorption processes and reactions at aqueous oxide interfaces using deep potential molecular dynamics (DPMD), a simulation scheme employing deep neural networks (DNNs), which has proven to be quite successful in accurately describing many different systems in the condensed phase. After summarizing the DPMD methodology, we first review our work on the acid−base chemistry of oxide surfaces in contact with water, a fundamental characteristic that controls proton transfer and surface charge at the interface. We focus on the aqueous interface of rutile IrO 2 , an oxide material thus far considered the best catalyst for the oxygen evolution reaction (OER). We show that this interface is characterized by a large fraction of dissociated water and a strong Brønsted acidity of the surface sites, in good agreement with the experimentally measured value of the point of zero proton charge. In our second example, we investigate how the adsorption of organic species from ambient air or water affects the structure and wettability of the aqueous interfaces of TiO 2 , a prototypical photocatalytic material. This is a question that is relevant to understanding the UV-induced hydrophilicity of TiO 2 surfaces, a property at the basis of self-cleaning windows and related applications. Specifically focusing on formic and acetic acids, the two most common atmospheric organic acids, our simulations reveal that these acids control the wettability of TiO 2 largely through acid−base chemistry at the interface rather than chemisorption on the oxide surface, a finding that could help improve the design of self-cleaning surfaces and photocatalytic devices. Finally, we review our recent study of methanol at TiO 2 −water interfaces, a system whose interest is largely motivated by the role of methanol in enhancing photocatalytic hydrogen evolution on TiO 2 . Our simulations provide mechanistic insights into the coupled roles of the organic adsorbate and water at the TiO 2 interface, with implications for how methanol enhances the activity of H 2 evolution.

adsorption↗

DOE OSTI2021

Theoretical and experimental study on the O( 3 P) + 2,5-dimethylfuran reaction in the gas phase

In this work we report a joint experimental and computational study on the 2,5-dimethylfuran oxidation reaction in the gas phase initiated by atomic oxygen O( 3 P). The experiments have been performed by using vacuum-ultraviolet synchrotron radiation at the Advanced Light Source (ALS) of the Lawrence Berkeley National Laboratory (LBNL), at a temperature of 550 K and a pressure of 8 Torr. The experimental data were supported by quantum-chemical calculations along with a kinetic model, also taking into account the possible involvement of different magnetic states, performed in the framework of the RRKM theory. Propyne, acetaldehyde, methylglyoxal, dimethylglyoxal, 3-penten-2-one, 2,5-dimethylfuran-3(2 H )-one, and 1,2-diacetyl ethylene have been identified as the main primary products arising under the conditions of the experiment. Our computational model suggests that these species can be formed at the concentration and branching ratio experimentally observed only in the presence of a non-negligible fraction of non-thermalized intermediates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1991

Consort 3 flight experiments

The third sounding rocket payload in the Consort program was launched from the White Sands Missile Range on May 16, 1990. It carried 12 experiments designed to investigate materials processes in low gravity. All of the experiments were reflights from the Consort 2 payload that was launched on November 1989 but failed to achieve microgravity because of a malfunction in the launch vehicle. Four national Centers for the Commercial Development of Space participated in the mission. The payload included five experiments and two accelerometer systems that flew on Consort 1 and seven new experiments, designed and developed since Consort 1. Experiments from Consort 1 incorporated hardware modifications and changes in experimental parameters based on mission results. The new experiments covered a variety of polymeric and biological investigations. A new power distribution and control system designed to provide discrete, computer-supervised, experiment power monitoring and control was flight qualified on Consort 3. Consort 3 featured very late access (3-5 h before launch vs 27-30 h for Consort 1) for installation of sensitive biological specimens. The integrated payload and mission sequence of events are described. Changes in the Consort 1 experiments are defined and the objectives, methods, and expectations for new experiments are discussed.

Wessling, Francis C.↗

NASA NTRS1998

Fullerene Machines

Recent computational efforts at NASA Ames Research Center and computation and experiment elsewhere suggest that a nanotechnology of machine phase functionalized fullerenes may be synthetically accessible and of great interest. We have computationally demonstrated that molecular gears fashioned from (14,0) single-walled carbon nanotubes and benzyne teeth should operate well at 50-100 gigahertz. Preliminary results suggest that these gears can be cooled by a helium atmosphere and a laser motor can power fullerene gears if a positive and negative charge have been added to form a dipole. In addition, we have unproven concepts based on experimental and computational evidence for support structures, computer control, a system architecture, a variety of components, and manufacture. Combining fullerene machines with the remarkable mechanical properties of carbon nanotubes, there is some reason to believe that a focused effort to develop fullerene nanotechnology could yield materials with tremendous properties.

Globus, Al↗

NASA NTRSDate not supplied

Rational Design of Nanoplasmonic Array Geometries for Biosensing

Background: Molecular diagnostics provide early and accurate diagnosis, which is essential for the prevention and treatment of infectious as well as chronic diseases. These tests are designed to detect disease-specific bioanalytes such as nucleic acid (DNA or RNA) or protein (antigens, antibodies) biomarkers. In the context of infectious disease diagnosis, nucleic acid-based detection methods are known to provide more specific and sensitive results. Here, the presence of a unique sequence belonging to the pathogenic genomic material is targeted to identify species, organism, genera and/or antimicrobial resistant gene markers. The majority of the common nucleic acid based diagnostic techniques require amplification (polymerase chain reaction, isothermal amplification etc.) of the pathogenic genetic material prior to detection impacting diagnostic speed, complexity, and cost thereby limiting ease of use. Thus, the development of simplified nucleic acid-based diagnostics that can be even used in resource-poor settings may hugely benefit patients across the globe. Nanopath is a molecular diagnostics company utilizing a solid-state nanosensor to enable sequence-specific detection of target nucleic acids without the need of amplification. These nanostructures enable ultra-sensitive biomarker detection using geometric, feature-dependent properties highly dependent on the local dielectric environment, allowing them to be sensitive to low concentration binding events. This paper describes an application of this approach to provide highly relevant clinical information within a single doctor’s office visit. Intro: The Nanopath team is in collaboration with NASA (National Aeronautics and Space Administration) and NIST (National Institute of Standards and Technology) to push the bounds of the fundamental physics associated with their biosensing platform. The ability of metals to support electromagnetic surface waves gives rise to surface plasmons when optically illuminated. This property, and its strong sensitivity to changes in the local refractive index, allows for the use of metal nanoparticles as ultra-sensitive transducers. In prior work by members of this team, ensembles of randomly oriented nanoparticles (i.e., colloidal nanorods dispersed on chip) were employed for sequence-specific nucleic acid sensing (1-3). While these particle sensors have the advantage of rapid fabrication, they suffer from low sensitivity and quality factor due to the random particle dispersity. In contrast, in this study we employ ordered array nanoparticle ensembles which can be used to improve sensor sensitivity and figure-of-merit. Study Methods Overview: In this talk, we detail the results of sensing experiments and computational simulations to outline a rational design of the structure of these plasmonic nanoparticle arrays for biomolecular sensing. Through simulation and experiment, we iteratively tailor nanostructure dimension to provide high quality signal and large resonance shifts upon modeled nucleic acid binding. In particular, full-wave electromagnetic simulations were conducted using Lumerical photonic simulation software in which periodic boundary conditions were applied in the x- and y- dimensions for each of the nanoplasmonic sensor geometries. To simulate the resonance response to changes in the bulk solution in contact with the sensor surface, the refractive index of the surrounding media was changed appropriately. Nucleic acid hybridization events were modeled using either using spherical structures approximating the relevant radius of genomic material as estimated by polymer models, or as conformal layers with the known refractive indices for nucleic acids. On the basis of initial simulations, nanosensors were fabricated using traditional electron-beam lithography protocols at NIST. To evaluate consensus between simulations and experiments, bulk sensing experiments were carried out in which the resonance peaks were obtained by submerging the sensors in refractive index standards. Key nanosensor characteristics including resonance peak locations, resonance peak shifts as a function of refractive index, and figure of merit (FOM) of extinction curves were examined between the experimental and simulation results prior to proceeding with simulations on additional geometries and more complex solution conditions, and further device fabrication. This iterative process is repeated toward a rational design of nanoplasmonic array geometries for biosensing optimizing response for targeted disease detection. In summary, this study puts forth a methodology for rational design and characterization of regularly spaced nanoparticle arrays for optics-based biosensing. The results of this study will allow for more informed design of nanostructure geometries towards sequence-specific nucleic acid detection. These improved designs have the potential to improve clinical sensitivity and limit-of-detection across disease indication.

sensor↗

DOE OSTI2019

Parameter inference with deep jointly informed neural networks

Abstract A common challenge in modeling inertial confinement fusion (ICF) experiments with computer simulations is that many of the simulation inputs are unknown and cannot be directly measured. Often, parameters that are measured in the experiment are used to infer the unknown inputs by solving the inverse problem: finding the set of simulation inputs that result in outputs consistent with the experimental observations. In ICF, this process is often referred to as a “post‐shot analysis.” Post‐shot analyses are challenging as the inverse problem is often highly degenerate, the input parameter space is vast, and simulations are computationally expensive. In this work, deep neural network models equipped with model uncertainty estimates are used to train inverse models, which map directly from output to input space, to find the distribution of post‐shot simulations that are consistent with experimental observations. The inverse model approach is compared to Markov chain Monte Carlo (MCMC) sampling of the forward model, which maps from input to output space, for parameter inference tasks of varying complexity. The inverse models perform best when searching vast parameter spaces for post‐shot simulations that are consistent with a large number of observables, where MCMC sampling can be prohibitively expensive. We demonstrate how augmenting inverse models with autoencoders enable the inclusion of several dozen observables in the inverse mapping, reducing the degeneracy of the model and improving the accuracy of the post‐shot analysis.

Humbird, Kelli D.↗

NASA NTRS1986

HOST combustion R and T overview

The overall objective of the Turbine Engine Hot Section Technology Combustion Project was to develop and verify improved and more accurate numerical analysis methods for increasing the ability to design with confidence combustion systems for advanced aircraft gas turbine engines. The objective was approached from two directions: computational and experimental. On the computational side, the approach was to first assess and evaluate existing combustor aerothermal analysis models. On the experimental side, three types of experiments are identified; first, fundamental experiments directed toward improved understanding of the flow physics and chemistry; second, experiments run to provide data for the empirical modeling of complex phenomena; and third, benchmark experiments for computer code validation.

Gaugler, Raymond E.↗

DOE OSTI2016

Analysis and Simulation of a Blue Energy Cycle

The mixing process of fresh water and seawater releases a significant amount of energy and is a potential source of renewable energy. The so called ‘blue energy’ or salinity-gradient energy can be harvested by a device consisting of carbon electrodes immersed in an electrolyte solution, based on the principle of capacitive double layer expansion (CDLE). In this study, we have investigated the feasibility of energy production based on the CDLE principle. Experiments and computer simulations were used to study the process. Mesoporous carbon materials, synthesized at the Oak Ridge National Laboratory, were used as electrode materials in the experiments. Neutron imaging of the blue energy cycle was conducted with cylindrical mesoporous carbon electrodes and 0.5 M lithium chloride as the electrolyte solution. For experiments conducted at 0.6 V and 0.9 V applied potential, a voltage increase of 0.061 V and 0.054 V was observed, respectively. From sequences of neutron images obtained for each step of the blue energy cycle, information on the direction and magnitude of lithium ion transport was obtained. A computer code was developed to simulate the process. Experimental data and computer simulations allowed us to predict energy production.

30 DIRECT ENERGY CONVERSION↗

NASA NTRS1992

Proposed radiometric measurement of the wake of a blunt aerobrake

This paper describes the aerothermal environment in the afterbody region of a blunt entry body. Recent ground-based experiments and computational predictions of the afterbody flow structure and radiation are presented. The similarity between the flowfield structures observed in the ground-based experiments and that obtained by calculation is encouraging. Approximate calculations of the radiative heating rate to the base are presented. Many of the phenomena associated with the expanding flow at the corner and the formation of the wake neck, however, are not well understood and require further study. A flight experiment is described that would use spectral and total measurements of the wake radiation as a nonintrusive diagnostic method to provide insight into the thermodynamic state of the wake gas.

Strawa, A. W.↗

NASA NTRS2000

Potential pitfalls of strain rate imaging: angle dependency

Strain Rate Imaging (SRI) is a new echocardiographic technique that allows for the real-time determination of myocardial SR, which may be used for the early and accurate detection of coronary artery disease. We sought to study whether SR is affected by scan line alignment in a computer simulation and an in vivo experiment. Through the computer simulation and the in vivo experiment we generated and validated safe scanning sectors within the ultrasound scan sector and showed that while SRI will be an extremely valuable tool in detecting coronary artery disease there are potential pitfalls for the unwary clinician. Only after accounting for these affects due to angle dependency, can clinicians utilize SRI's potential as a valuable tool in detecting coronary artery disease.

NASA Discipline Cardiopulmonary↗