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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data for A Generalized Platform for Artificial Intelligence-powered Autonomous Protein Engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-foldimprovement in substrate preference and 16-fold improvement in ethyl-transferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

AI/ML↗
DOE OSTI2025

Comprehensive Database of Environmental Mitigations Extracted from FERC-Licensed Hydropower Projects Using Artificial Intelligence Techniques, 1998-2023

This dataset provides a comprehensive inventory of environmental mitigation measures required by Federal Energy Regulatory Commission (FERC) licensed hydropower facilities from 461 licenses that were issued from 1998 to 2023. These licenses constitute 446 of the 1015 FERC projects that were active at the end of 2023. 17,612 mentions of environmental mitigations were identified and categorized in 128 unique categories. Mitigations were identified using a Natural Language Processing (NLP) approach, specifically with a Bidirectional Encoder Representations from Transformer (BERT) model. Model-derived results were then reviewed and updated by a subject matter expert as needed. This dataset introduces important enhancements to previous efforts to inventory environmental mitigations, such as including associated license text for each mitigation, tracking the number of instances a mitigation was identified within a license, and providing improved location information. These enhancements significantly expand the dataset's utility, offering greater analytical capabilities and ensuring reproducibility. The dataset is downloadable as a zip file containing the metadata and dataset files.

Ruggles, Thomas [Oak Ridge National Laboratory (OR↗
NASA NTRS1994

Artificial gravity in space and in medical research

The history of manned space flight has repeatedly documented the fact that prolonged sojourn in space causes physiological deconditioning. Physiological deterioration has raised a legitimate concern about man's ability to adequately perform in the course of long missions and even the possibility of leading to circumstances threatening survival. One of the possible countermeasures of physiological deconditioning, theoretically more complete than others presently used since it affects all bodily systems, is artificial gravity. Space stations and spacecrafts can be equipped with artificial gravity, but is artificial gravity necessary? The term "necessary" must be qualified because a meaningful answer to the question depends entirely on further defining the purpose of space travel. If man intends to stay only temporarily in space, then he must keep himself in good physical condition so as to be able to return to earth or to land on any other planetary surface without undue exposure to major physiological problems resulting from transition through variable gravitational fields. Such a situation makes artificial gravity highly desirable, although perhaps not absolutely necessary in the case of relative short exposure to microgravity, but certainly necessary in interplanetary flight and planetary landings. If the intent is to remain indefinitely in space, to colonize space, then artificial gravity may not be necessary, but in this case the consequences of long term effects of adaptation to weightlessness will have to be weighed against the biological evolutionary outcomes that are to be expected. At the moment, plans for establishing permanent colonies in space seem still remote. More likely, the initial phase of exploration of the uncharted solar system will take place through successive, scope limited, research ventures ending with return to earth. This will require man to be ready to operate in gravitational fields of variable intensity. Equipping spacecrafts or space stations with some means of artificial gravity in this initial phase is, therefore, necessary without question. In a strict sense artificial gravity is conceived as a means of replacing natural gravity in space by the centripetal acceleration generated by some sort of rotating device. Rotating devices create an inertial force which has effects on bodies similar to those caused by terrestrial gravity, but artificial gravity by a rotation device is not the same as terrestrial gravity, as we shall see. Present research in artificial gravity for space exploration is projected in two main directions: artificial gravity for whole space stations and artificial gravity produced by short arm centrifuges designed for human use in space.

Case Reports↗
DOE OSTI2023

Explainable Artificial Intelligence Technology for Predictive Maintenance

The domestic nuclear power plant fleet has relied on labor-intensive and time-consuming preventive maintenance programs, thus driving up operation and maintenance costs to achieve high-capacity factors. Artificial intelligence and machine learning can help simplify complex problems, such as diagnosing equipment degradation, to enable more effective decision-making. Benefits will be felt not only within existing analog and digital instrumentation and control, but also work processes, the integration of people with technology, and most importantly, the business case. Together, these hold promise to make nuclear power more efficient and reduce costs associated with operation and maintenance. While the artificial intelligence and machine learning technologies hold significant promise in the nuclear industry, there are challenges or barriers to their adoption. This report outlines the those different machine learning adoption barriers (categorized as historical, technical, economic, regulatory, and user) that the industry must overcome to realize the full benefits of artificial intelligence and machine learning capabilities for long-term economic sustainability. This report also provides solutions for some of these barriers by focusing on improving the explainability of machine learning to encourage trust from the end-user. Trust and explainability are essential to machine learning adoption. This report focuses on research-developed solutions to some of these barriers while analyzing a non-safety-related system, namely the circulating water system. This system frequently experiences waterbox fouling which our models preemptively diagnoses then explains to the operator how those conclusions were reached. This report presents and discusses the inherent trade-off between machine learning performance (in terms of accuracy) and explainability, where highly accurate machine learning methods (such as deep-learning) are the least explainable, and the most explainable methods (such as decision trees) are the least accurate. In addition, explainability of artificial intelligence techniques in terms of transparency and post-hoc metrics are discussed. This report outlines the importance of data novelty and value of new information in evaluating both the explainability and trustworthiness. Novelty detection helps to establish consistency or inconsistency of the new data with respect to the training data. On the other hand, value of information could be a part of the user-centric visualization recommendation system that request additional information to be collected, thereby strengthening the machine learning outcomes. During this project, a copyrighted user-centric visualization that aligns with a human-in-the-loop approach was developed. The user-centric visualization presents different levels of information and can be tailored as per user credentials to gain user confidence. One of the salient features of the user-centric visualization is it presents machine learning methods with explainability metrics. A simplified version of the user-centric visualization was presented to 32 users with varying levels of machine learning expertise. Feedback was solicited to test the hypothesis that the app contained sufficient explainability and that the users would trust the algorithm. Overall, the app was positively received, and the hypothesis was supported. This report discusses the trust-but-verify framework – a potential approach to build user trust artificial intelligence. The framework discusses trust from the human level to artificial intelligence level. The fundamental premise of the trust but verify framework is derived from an observation of nuclear safety culture (i.e., nuclear power plant personnel do not rely on a singular source of data to make a decision). This also ties back to the user-centric visualization that presents different levels of information to achieve both explainability and trustworthiness of artificial intelligence. Even so, the adoption of artificial intelligence and machine learning in the nuclear industry faces additional barriers, namely regulatory and stakeholder readiness. To overcome these challenges, new solutions must gain regulatory approval and cater to stakeholder needs. The Nuclear Regulatory Committee has a 5-year strategic plan which prepares them for reviewing artificial intelligence technologies in licensee submissions. Early and frequent engagement with the regulator is encouraged. Additionally, artificial intelligence solutions should incorporate human-in-the-loop considerations and offer explainability. Stakeholders must prepare by hiring or training staff to adapt to advancing technology in everyday plant tasks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

The effect of artificial bulk viscosity in simulations of forced compressible turbulence

The use of an artificial bulk viscosity for shock stabilization is a common approach employed in turbulence simulations with high-order numerics. The effect of the artificial bulk viscosity is analyzed in the context of large eddy simulations by using as a test case simulations of linearly-forced compressible homogeneous turbulence (Petersen and Livescu, 2010 [12]). This case is unique in that it allows for the specification of a priori target values for total dissipation and ratio of solenoidal to dilatational dissipation. A comparison between these target values and the true predicted levels of dissipation is thus used to investigate the performance of the artificial bulk viscosity. Results show that the artificial bulk viscosity is effective at achieving stable solutions, but also leads to large values of artificial dissipation that outweigh the physical dissipation caused by fluid viscosity. An alternate approach, which employs the artificial thermal conductivity only, shows that the dissipation of dilatational modes is entirely due to the fluid viscosity. However, this method leads to unwanted Gibbs oscillations around the shocklets. The use of shock sensors that further localize the artificial bulk viscosity did not reduce the amount of artificial dissipation introduced by the artificial bulk viscosity. Finally, an improved forcing function that explicitly accounts for the role of the artificial bulk viscosity in the budget of turbulent kinetic energy was explored.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2020

The Mitigating Effects of Artificial gravity on Microgravity

The effects of microgravity on the human body can be debilitating however, the use of artificial gravity has the potential to completely mitigate these effects. Developing an artificial gravity habitation spacecraft concept could have positive effects on the astronauts spending long durations in space. Researching the past and upcoming artificial gravity concepts was accomplished using resources provided by my mentors, i.e. NASA Technical Reports Server (NTRS), human research roadmap, and the Aerospace Research Central (ARC) in coordination with other academic sources and research papers. This research has been accomplished over a several week period that looks at past artificial gravity concepts and the implications of instituting them or new designs in the future. Also, the research examines how to improve future artificial gravity concepts by applying what we know about past designs and what we are creating today. The expected outcomes of artificial gravity are that the health risks imposed on astronauts due to not being in Earth’s gravity environment for long duration space missions will decrease. No other microgravity mitigation technique addresses neurological problems such as fluid shift and VIIP syndrome as they only address physical conditions with exercise and supplements Another outcome is that using artificial gravity should reduce the long-term health effects that happen after the astronauts return to Earth because they will not be exposed to microgravity for as long. The real outcomes should be similar, if not the same, as the expected outcomes mentioned above. This process of creating artificial gravity aboard spacecrafts has potential to create a plethora of positive outcomes. The research completed will contribute to NASA’s missions and goals because the information gathered directly relates to the goal of sending humans to Mars and beyond and the research looks at how to solve one of the major problems associated with achieving that goal soon: microgravity. This research will contribute to NASA’s mission of sending astronauts to Mars, with a lower risk assessment, because artificial gravity will potentially mitigate the health risks (VIIP syndrome) associated with long duration in space flight.

Elizabeth Anne Westfall↗
DOE OSTI2023

Toward a Diverse Next-Generation Energy Workforce: Teaching Artificial Photosynthesis and Electrochemistry in Elementary Schools through Active Learning

Artificial photosynthesis is a promising approach to generate commodity chemicals using abundant chemical feedstocks and renewable energy sources. Despite its importance, affordable and effective hands-on classroom activities that demonstrate artificial photosynthesis and teach key concepts, especially for primary school students, are lacking. Educating young students on this topic is a critical step in the development of the next-generation energy workforce, especially one that is diverse in race and gender. Here, we hypothesize that an effective approach to educate a broad range of young students on the topic of artificial photosynthesis is through the use of an active learning-based lesson plan that employs cheap and accessible materials. This hypothesis is confirmed by evaluating the understanding of fifth grade students with a survey before and after a lesson plan on artificial photosynthesis that uses active-learning techniques and uses safe and highly accessible materials (baking soda, tap water, plastic jars, Ni coil, alligator clips, and a solar cell) to perform solar-powered water splitting. The lesson plan and survey questions are designed to align with the educational outcomes for fifth grade classrooms in California and to address four general learning objectives: (1) Motivations of Artificial Photosynthesis, (2) Applications of Artificial Photosynthesis, (3) Inputs and Outputs of Artificial Photosynthesis, and (4) Engineering Design for Artificial Photosynthesis. The survey data demonstrate a statistically significant improvement in overall student understanding from the lesson plan. Importantly, the data show that the lesson plan presented here is effective at narrowing the performance gap between minority students and overly represented groups.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Characterizing precipitate evolution of an Al–Zn–Mg–Cu-based commercial alloy during artificial aging and non-isothermal heat treatments by in situ electrical resistivity monitoring

In situ electrical resistivity monitoring technique was employed to continuously evaluate the precipitate evolution of an Al–Zn–Mg–Cu-based commercial alloy during typical artificial aging treatments. The effects of artificial aging on the precipitates stability during non-isothermal heat treatments were also explored. Conventional hardness test, transmission electron microscopy and differential scanning calorimetry were also adopted to verify the electrical resistivity results. The results indicated that both the precipitation process and its timely rate could be followed by the monitored electrical resistivity during artificial aging treatments. The electrical resistivity results gave overall information on continuous precipitation and dissolution processes, especially under high heating rates. Samples artificial aging heat treated at 120 °C for 24 h followed by aging at 150 °C for 24 h presented more stable state and coarser precipitates than the samples only artificial aging heat treated at 120 °C for 24 h or triple artificial aging heat treated at 120 °C/24 h + 195 °C/15 min + 120 °/24 h. While the incoherent η precipitates in the samples artificial aging heat treated at 120 °C for 24 h followed by aging at 150 °C for 24 h were more easiness to coarsening and dissolve during non-isothermal heat treatments as well. - Highlights: • In situ electrical resistivity monitoring technique was employed on an Al-Zn-Mg-Cu alloy. • The precipitate evolution during typical artificial aging treatments was studied. • The precipitate stability during non-isothermal heat treatments was explored. • The electrical resistivity wonderfully monitored continuous precipitation and dissolution. • The alloy submitted to a T7 treatment presents a more stable state during heating due to incoherent η precipitates.

36 MATERIALS SCIENCE↗
NASA NTRS1988

A physically consistent model for artificial dissipation in transonic potential flow computations

The effect that artificial dissipation has on numerical solutions of the transonic Full Potential Equation (FPE) are investigated by comparing the artificially dissipative FPE to a Physically Dissipative Potential (PDP) equation. Analytic expressions were derived from the variables C and M sub c that are used in the artificial density formulation. It was shown that these new values generate artificial dissipation which is equivalent to the physical dissipation existing in the PDP equation. The new expression for the variables C and M sub c can easily be incorporated into the existing full potential codes which are based either on the artificial density or on the artificial viscosity formulation. A comparison of Physically Dissipative Potential (PDP), Artificial Density or Viscosity (ADV), Artificial Mass Flux (AMF), and ADV with variable C and M sub c formulation (MCC) is also presented.

Dulikravich, George S.↗
NASA NTRS1997

Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network development. The changes were to include evaluation tools that can be applied to neural networks at each phase of the software engineering life cycle. The result was a formal evaluation approach to increase the product quality of systems that use neural networks for their implementation.

Broderick, Ron↗
DOE OSTI2019

Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames

The “curse of dimensionality” has limited the applicability and expansion of tabulated combustion models. While the tabulated flamelet model and other multi-dimensional manifold approaches have shown predictive capability, the associated tabulation involves the storage of large lookup tables, requiring large memory as well as multi-dimensional interpolation subroutines, all implemented during runtime. As such, this work investigates the use of deep artificial neural networks to replace lookup tables in order to reduce the memory footprint and increase the computational speed of tabulated flamelets and related approaches. Specifically, different strategic approaches to training the artificial neural network models are explored and a grouped multi-target artificial neural network is introduced, which takes advantage of the ability of artificial neural networks to map an input space to multiple targets by classifying the species based on their correlation to one another. The grouped multi-target artificial neural network approach is validated by applying it to an n-dodecane spray flame using conditions of the Spray A flame from the Engine Combustion Network and comparing global flame characteristics for different ambient conditions using a well-established large-eddy simulation framework. The same framework is then extended to the simulations of methyl decanoate combustion in a compression ignition engine. The validation studies show that the grouped multi-target artificial neural networks are able to accurately capture flame liftoff, autoignition, two-stage heat release and other quantitative trends over a range of conditions. The use of neural networks in conjunction with the grouping mechanism as performed in the grouped multi-target artificial neural network produces a significant reduction in the memory footprint and computational costs for the code and, thus, widens the operating envelope for higher fidelity engine simulations with detailed mechanisms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Ionic conductive polymers as artificial solid electrolyte interphase films in Li metal batteries – A review

Lithium (Li) metal has been considered as the ultimate anode material for next-generation rechargeable batteries due to its ultra-high theoretical specific capacity (3860 mAh g -1 ) and the lowest reduction voltage (-3.04 V vs the standard hydrogen electrode). However, the dendritic Li formation, uncontrolled interfacial reactions, and huge volume variations lead to unstable solid electrolyte interphase (SEI) layer, low Coulombic efficiency and hence short cycling lifetime. Designing artificial solid electrolyte interphase (artificial SEI) films on the Li metal electrode exhibits great potential to solve the aforementioned problems and enable Li–metal batteries with prolonged lifetime. Polymer materials with good ionic conductivity, superior processability and high flexibility are considered as ideal artificial SEI film materials. In this review, according to the ionic conductive groups, recent advances in polymeric artificial SEI films are summarized to afford a deep understanding of Li ion plating/stripping behavior and present design principles of high-performance artificial SEI films in achieving stable Li metal electrodes. Perspectives regarding to the future research directions of polymeric artificial SEI films for Li–metal electrode are also discussed. The insights and design principles of polymeric artificial SEI films gained in the current review will be definitely useful in achieving the Li–metal batteries with improved energy density, high safety and long cycling lifetime toward next-generation energy storage devices.

25 ENERGY STORAGE↗
DOE OSTI2018

Artificially Structured Semiconductors to Model Novel Quantum Phenomena

Award Period: September 1st, 2013 through February 15th, 2017 Submitted to the USDOE Office of Basic Energy Sciences By Aron Pinczuk and Shalom J. Wind Department of Applied Physics and Applied Mathematics Columbia University New York, NY 10027 January 2017 Award # DE-SC0010695 ABSTRACT Research in this project seeks to design, create and study a class of tunable artificial quantum structures in order to extend the range and scope of new and exciting physical phenomena and to explore the potential for new applications. Advanced nanofabrication was used to create an external potential landscape that acts as a lattice of confinement sites for electrons (and/or holes) in a two-dimensional electron gas in a high perfection semiconductor in such a manner that quantum interactions between different sites dictate the significant physics. Our current focus is on ‘artificial graphene’ (AG) in which a set of quantum dots (or sites) are patterned in a honeycomb lattice. The combination of leading edge nanofabrication with ultra-pure semiconductor materials in this project extends the frontier for small period, low-disorder AG systems, enabling the exploration of graphene physics in a semiconductor platform. TECHNICAL DESCRIPTION Contemporary condensed matter science has entered an era of discovery of new low-dimensional materials, such as graphene and other atomically thin materials, that exhibit exciting new physical phenomena that were previously inaccessible. Concurrent with the discovery and development of these new materials are impressive advancements in nanofabrication, which offer an ever-expanding toolbox for creating a myriad of high quality patterns at nanoscale dimensions. This project started about four years ago. Among its major achievements are the realizations of very small period artificial lattices with honeycomb topology in GaAs quantum wells. In our most recent work the periods of the ‘artificial graphene’ (AG) lattices extend down to 40 nm. These small periods are about three times smaller than previously reported in GaAs quantum wells. This milestone establishes a new state-of-the-art in fields of research and nanofabrication. In experiments using optical scattering methods we uncovered evidence that free electrons in the small period AG lattices display novel features that arise from the symmetry of the honeycomb lattice. These achievements create semiconductor platforms for explorations of novel states and effects that offer opportunities to create quasiparticles with tunable character. The quest for the discovery of novel quantum physics by nanofabrication of ‘artificial structures’ in semiconductor quantum structures overlaps with the development of quantum simulators. Nanopatterns were created at Columbia University by the group of co-PI Shalom Wind using a 100keV e-beam nanolithography instrument (along with associated processing) that is part of the Columbia Nano Initiative. Optical experiments were carried out in the group of PI Aron Pinczuk. GaAs/AlGaAs quantum wells(QWs) of world-class perfection that serve as electron hosts are the starting material grown by molecular beam epitaxy (MBE) by our partners Dr. Loren Pfeiffer (Princeton Univ.) and Prof. Michael Manfra (Purdue Univ.). The inductively coupled plasma reactive ion etching (ICP-RIE) was carried out at the PRISM Micro/Nano Fabrication Laboratory of Princeton University. Dr. Vittorio Pellegrini (Istituto Italiano di Tecnologia, Genoa, Italy) has contributed critical insight on this research. Two graduate students in this project Sheng Wang and Diego Scarabelli, graduated in the summer/fall of 2016. Dr. Yuliya Kuznetsova has been a postdoc in the group. The current work is led by Dr. Lingjie Du, a postdoctoral scientist that joined the group of the PI on October 15th, 2016. Since the start of this project we have focused primarily on developing protocols towards the fabrication of the artificial lattices and in the implementation of characterization methods to identify the new electron states that are created. A major milestone has been the realization of the Dirac cones that emerge in honeycomb lattices. In the next phases of this project, and as we achieve milestones, we wish to expand our experimental platform so that we will be better positioned to access emergent novel quantum behavior and phases of electrons in artificial lattices designed and fabricated with ‘exquisite’ precision. The AG lattices with greatly reduced disorder from nano-processing that we have already created demonstrate that we are poised for the next stage exploration of graphene physics and applications in semiconductor quantum structures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2015

Modular Extended-Stay HyperGravity Facility Design Concept: An Artificial-Gravity Space-Settlement Ground Analogue

This document defines the design concept for a ground-based, extended-stay hypergravity facility as a precursor for space-based artificial-gravity facilities that extend the permanent presence of both human and non-human life beyond Earth in artificial-gravity settlements. Since the Earth's current human population is stressing the environment and the resources off-Earth are relatively unlimited, by as soon as 2040 more than one thousand people could be living in Earthorbiting artificial-gravity habitats. Eventually, the majority of humanity may live in artificialgravity habitats throughout this solar system as well as others, but little is known about the longterm (multi-generational) effects of artificial-gravity habitats on people, animals, and plants. In order to extend life permanently beyond Earth, it would be useful to create an orbiting space facility that generates 1g as well as other gravity levels to rigorously address the numerous challenges of such an endeavor. Before doing so, developing a ground-based artificial-gravity facility is a reasonable next step. Just as the International Space Station is a microgravity research facility, at a small fraction of the cost and risk a ground-based artificial-gravity facility can begin to address a wide-variety of the artificial-gravity life-science questions and engineering challenges requiring long-term research to enable people, animals, and plants to live off-Earth indefinitely.

Gravity↗
DOE OSTI2026

Covalent Organic Framework Photocatalysts for Water Oxidation and Overall Artificial Photosynthesis

Artificial photosynthesis through proton reduction or CO 2 reduction to generate chemical fuels has gained increasing attention as an attractive strategy for solar-to-fuel conversion. In these systems, the oxygen evolution reaction (OER) provides the necessary electrons and protons for driving the overall reaction but represents the rate-limiting step due to its inherently sluggish kinetics. Therefore, efficient overall artificial photosynthesis requires photocatalysts that can drive both the oxidation and reduction half-reactions, which impose stringent demands on catalyst design. Covalent organic frameworks (COFs) offer a versatile platform for designing such photocatalysts, owing to their strong light-harvesting capabilities , periodic architectures, and highly tunable frameworks that allow programmable catalytic sites and adjustable electronic band structures. While notable progress has been made in developing COF photocatalysts for the OER and overall artificial photosynthesis, these advances remain scattered across the literature and existing reviews, and a dedicated, systematic overview of the OER and its central role in integrated artificial photosynthetic processes is still lacking. This Review systematically summarizes recent advances in COF-based photocatalysts for the OER half-reaction and overall artificial photosynthesis. Our aim is to offer a comprehensive roadmap that establishes fundamental design principles for next-generation COF-based photocatalysts toward efficient and sustainable artificial photosynthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1989

On the role of artificial viscosity in Navier-Stokes solvers

A method is proposed to determine directly the amount of artificial viscosity needed for stability using an eigenvalue analysis for a finite difference representation of the Navier-Stokes equations. The stability and growth of small perturbations about a steady flow over the airfoils are analyzed for various amounts of artificial viscosity. The eigenvalues were determined for a small perturbation about a steady inviscid flow over a NACA 0012 airfoil at a Mach number of 0.8 and angle of attack of 0 degrees. The movement of the eigenvalue constellation with respect to the amount of artificial viscosity is studied. The stability boundries as a function of the amount of artificial viscosity from both the eigenvalue analysis and the time marching scheme are also presented. This procedure not only allows for determining the effect of varying amounts of artificial viscosity, but also for the effects of different forms of terms for artificial viscosity.

Mahajan, Aparajit J.↗
NASA NTRS1991

Role of artificial viscosity in Euler and Navier-Stokes solvers

A method is proposed to determine directly the amount of artificial viscosity needed for stability using an eigenvalue analysis for a finite difference representation of the Navier-Stokes equations. The stability and growth of small perturbations about a steady flow over airfoils are analyzed for various amounts of artificial viscosity. The eigenvalues were determined for a small time-dependent perturbation about a steady inviscid flow over an NACA 0012 airfoil at a Mach number of 0.8 and angle of attack of 0 deg. The method has been applied to inviscid flows here, but as discussed is also applicable to viscous flows. The movement of the eigenvalue constellation with respect to the amount of artificial viscosity is studied. The stability boundaries as a function of the amount of artificial viscosity from both the eigenvalue analysis and the time-marching scheme are also presented. The eigenvalue procedure not only allows for determining the effect of varying amounts of artificial viscosity, but also for the effects of different forms of artificial viscosity.

Mahajan, Aparajit J.↗
NASA NTRS1995

A computational fluid dynamics simulation of the hypersonic flight of the Pegasus(TM) vehicle using an artificial viscosity model and a nonlinear filtering method

The computational fluid dynamics code, PARC3D, is tested to see if its use of non-physical artificial dissipation affects the accuracy of its results. This is accomplished by simulating a shock-laminar boundary layer interaction and several hypersonic flight conditions of the Pegasus(TM) launch vehicle using full artificial dissipation, low artificial dissipation, and the Engquist filter. Before the filter is applied to the PARC3D code, it is validated in one-dimensional and two-dimensional form in a MacCormack scheme against the Riemann and convergent duct problem. For this explicit scheme, the filter shows great improvements in accuracy and computational time as opposed to the nonfiltered solutions. However, for the implicit PARC3D code it is found that the best estimate of the Pegasus experimental heat fluxes and surface pressures is the simulation utilizing low artificial dissipation and no filter. The filter does improve accuracy over the artificially dissipative case but at a computational expense greater than that achieved by the low artificial dissipation case which has no computational time penalty and shows better results. For the shock-boundary layer simulation, the filter does well in terms of accuracy for a strong impingement shock but not as well for weaker shock strengths. Furthermore, for the latter problem the filter reduces the required computational time to convergence by 18.7 percent.

Mendoza, John Cadiz↗