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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

SARS-CoV2 billion-compound docking

Abstract This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗
NASA NTRS2020

Disruptive Technologies and Their Putative Impacts Upon Society and Aerospace- Entering The Virtual Age

Developments in technology over the recent decades have been extraordinary. They include the IT, bio, nano, and now quantum and energetics technology arenas and their many combinatorial interactions and impacts. In the main, these are at the frontiers of the small and in a combinational, synergistic feeding frenzy with each other. They fall under the broad category of Disruptive Technologies and have greatly altered society. The outlook for the runout of these and other technology developments augers mid-term to later alterations in components of the human existence theorem, including the requirement to work for our living and our physiological makeup and longevity (Ref 1). The IT revolution began in the 1950s with the development of solid-state electronics. The biologics revolution began later in the 1960s and 1970s with DNA and genomics, and the nano revolution in the 1990s with self-forming nano systems and carbon nanotubes. Quantum technology is now developing rapidly, aided by enabling nano systems, and the energetics revolution is providing ever more efficient and less expensive renewable energy sources. The IT revolution has produced improvements of an astounding eleven orders of magnitude in computing speed since the late 1950s. As we shift from silicon to biological, optical, nano, molecular, and atomic computing, improvements of some 4 orders of magnitude are evidently possible from either optical or DNA computing [Refs 2and 3], then there are combinatorials. Then there is quantum computing, under development worldwide for an increasing number of applications and proffering phenomenal capabilities. The current fastest computers are considerably beyond human brain speed. Machine intelligence is developing well after decades of inadequate machine capability, now no longer the case, and a detour into expert systems. Researchers in machine intelligence are now pursuing deep learning approaches using neural nets, which are proving to be extremely useful. Some believe the frontier of potential human-level machine intelligence may be found in biomimetics and brain-emulation approaches. There is even a possibility of “emergence”—i.e., when the machine intelligence is complex enough that it “wakes up,” as when human intelligence emerged via evolution during the million-plus years of the hunter-gatherer epoch [ Ref 4]. In fact, some posit that human intelligence can be improved upon and is only a cul-de-sac of what is conceivable. The IT revolution has produced massive changes in human society and economics—from the Internet, enabling the rapid expansion of knowledgeability (and even what is knowable), to an increasingly pervasive trend of “tele-everything.” The extraordinary compilation, storage, and availability of truly massive amounts of information could, when combined with AI and under the mantra of “big data,” greatly improve many of our technical and commercial processes and their content including elucidating new heuristic governing laws.

Dennis M. Bushnell↗
DOE OSTI2023

Efficient computational design of two-dimensional van der Waals heterostructures: Band alignment, lattice mismatch, and machine learning

Here, we develop a computational database, website applications (web-apps), and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D) heterostructures. Using density functional theory (DFT) based lattice parameters and electronic band energies for 674 nonmetallic exfoliable 2D materials, we generate 226 779 possible bilayer heterostructures. We classify these heterostructures into type-I, -II, and -III systems according to Anderson’s rule, which is based on the relative band alignments of the noninteracting monolayers. We find that type II is the most common and type III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the Periodic Table of constituent elements. The band alignment data can also be used for identifying photocatalysts and high-work-function 2D metals for contacts. We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS 2 /WSe 2 , MoS 2 /h-BN, and MoSe 2 /CrI 3 ), to compare the band-alignment description with the predictions from Anderson’s rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we use ML tools to predict band-alignment information for 2D materials. The web-apps, tools, and associated data will be distributed through the JARVIS-HETEROSTRUCTURE website. Our analysis, results, and the developed web-apps can be applied to the screening and design applications, such as finding alternative photocatalysts, photodetectors, and high-work-function (WF) 2D-metal contacts.

2-dimensional systems↗
DOE OSTI2018

The 'Site of Things': Remediation in the Information Age - 18088

More than just a payload, machines now haul data as well. Wood Environment and Infrastructure's maiden voyage into civil engineering automation at the Port Granby Project Long-Term Waste Management Facility demonstrated enormous potential for the remediation industry. The Port Granby Project will relocate approximately 450,000 cubic meters of historic low-level radioactive waste and marginally contaminated soils, located at an existing waste management facility on the shoreline of Lake Ontario, to a new, engineered aboveground mound to be built about a kilometer north of the current site. Sounds simple, right? Just move a bunch of dirt up the road? Hardly, this is marginally contaminated soil situated near Lake Ontario in a beautiful little village an hour east of Toronto. We must relocate safely, meaning limiting the number of staff in the field and efficiently to deliver according to our agreed upon budget and schedule. As if that wasn't enough we were asked to keep track of where each truckload of dirt was excavated from (with a high degree of accuracy), where it was placed in the new engineered disposal facility, how much it weighed and the amount of radioactivity in each load... a virtual chain of custody for each load. Presented is an overview of the Information Technology that supports the Port Granby Project and how the Data Management System (DMS) is being leveraged to make the project safer, more effective and efficient. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

GPU Direct I/O with HDF5

Exascale HPC systems are being designed with accelerators, such as GPUs, to accelerate parts of applications. In machine learning workloads as well as large-scale simulations that use GPUs as accelerators, the CPU (or host) memory is currently used as a buffer for data transfers between GPU (or device) memory and the file system. If the CPU does not need to operate on the data, then this is sub-optimal because it wastes host memory by reserving space for duplicated data. Furthermore, this “bounce buffer” approach wastes CPU cycles spent on transferring data. A new technique, NVIDIA GPUDirect Storage (GDS), can eliminate the need to use the host memory as a bounce buffer. Thereby, it becomes possible to transfer data directly between the device memory and the file system. This direct data path shortens latency by omitting the extra copy and enables higher-bandwidth. To take full advantage of GDS in existing applications, it is necessary to provide support with existing I/O libraries, such as HDF5 and MPI-IO, which are heavily used in applications. In this paper, we describe our effort of integrating GDS with HDF5, the top I/O library at NERSC and at DOE leadership computing facilities. We design and implement this integration using a HDF5 Virtual File Driver (VFD). The GDS VFD provides a file system abstraction to the application that allows HDF5 applications to perform I/O without the need to move data between CPUs and GPUs explicitly. We compare performance of the HDF5 GDS VFD with explicit data movement approaches and demonstrate superior performance with the GDS method.

Ravi, J↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2024

Toward machine-learning-assisted PW-class high-repetition-rate experiments with solid targets

We present progress in utilizing a machine learning (ML) assisted optimization framework to study the trends in a parameter space defined by spectrally shaped, high-intensity, petawatt-class (8 J, 45 fs) laser pulses interacting with solid targets and give the first simulation-based overview of predicted trends. A neural network (NN) incorporating uncertainty quantification is trained to predict the number of hot electrons generated by the laser–target interaction as a function of pulse shaping parameters. The predictions of this NN serve as the basis function for a Bayesian optimization framework to navigate this space. For post-experimental evaluation, we compare two separate neural network (NN) models. One is based solely on data from experiments, and the other is trained only on ensemble particle-in-cell simulations. Reviewing the predicted and observed trends across the experiment-capable laser parameter search space, we find that both ML models predict a maximal increase in hot electron generation at a level of approximately 12%–18%; however, no statistically significant enhancement was observed in experiments. On direct comparison of the NN models, the average discrepancy is 8.5%, with a maximum of 30%. Since shot-to-shot fluctuations in experiments affect the observations, we evaluate the behavior of our optimization framework by performing virtual experiments that vary the number of repeated observations and the noise levels. Here, we discuss the implications of such a framework for future autonomous exploration platforms in high-repetition-rate experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1999

New frontiers in design synthesis

The Intelligent Synthesis Environment (ISE), which is one of the major strategic technologies under development at NASA centers and the University of Virginia, is described. One of the major objectives of ISE is to significantly enhance the rapid creation of innovative affordable products and missions. ISE uses a synergistic combination of leading-edge technologies, including high performance computing, high capacity communications and networking, human-centered computing, knowledge-based engineering, computational intelligence, virtual product development, and product information management. The environment will link scientists, design teams, manufacturers, suppliers, and consultants who participate in the mission synthesis as well as in the creation and operation of the aerospace system. It will radically advance the process by which complex science missions are synthesized, and high-tech engineering Systems are designed, manufactured and operated. The five major components critical to ISE are human-centered computing, infrastructure for distributed collaboration, rapid synthesis and simulation tools, life cycle integration and validation, and cultural change in both the engineering and science creative process. The five components and their subelements are described. Related U.S. government programs are outlined and the future impact of ISE on engineering research and education is discussed.

User-Computer Interface↗
NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗
DOE OSTI2020

Approximating Trajectory Constraints With Machine Learning – Microgrid Islanding With Frequency Constraints

Here, we introduce deep earning aided constraint encoding to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2004

Applications of Intelligent Tutoring Systems to Human-Robotic Exploration of Mars

Space missions with small crews extending over several years with time-delay preventing normal conversations with people on earth will raise many challenges for training. Of special interest are possible three-year missions to Mars, requiring refresher instruction and learning new skills based on unexpected problems with machines and environmental conditions. For example, the crew will be required to monitor and repair more complex life support systems for air and water recycling than we even know how to build today. Highly educated astronauts, often with several doctorate degrees, require a very different mode of interaction than we have developed for school children or even typical college students. Explanation methods may need to differ-using analogies and techniques from different domains-depending on whether the astronaut is an astrophysicist, a pilot, or a geologist.Virtual reality (e.g., for Hubble repair missions) and "integrated" simulations (involving role-playing and emphasizing failure scenarios) are the most common advanced forms of instruction used in space flight today. The emphasis is on collaborative, embodied interaction with the same workstations and tools used in practice (e.g., a cockpit simulator). Otherwise, computerized instructional technology used by NASA is not model-based or tutorial in nature. This discussion will review some of the key instructional methods used at NASA over the past two decades and consider why ITS methods have not been exploited. Some of the problems and opportunities for training for Mars missions are examined, including how using robots in exploration activities will help but raise new training problems. These ideas will be illustrated with examples from the BrahmsVE system in which a browser- based virtual reality display with avatars allows interacting with a distributed multiagent system, in which agents can be people, robots, or software programs. Using BrahmsVE may provide a way for astronauts to interact with proxies of people who serve as instructional coaches on Mars.

Clancey, William J.↗
NASA NTRS2016

Additive Manufacturing, Design, Testing, and Fabrication: A Full Engineering Experience at JSC

I worked on several projects this term. While most projects involved additive manufacturing, I was also involved with two design projects, two testing projects, and a fabrication project. The primary mentor for these was Richard Hagen. Secondary mentors were Hai Nguyen, Khadijah Shariff, and fabrication training from James Brown. Overall, my experience at JSC has been successful and what I have learned will continue to help me in my engineering education and profession long after I leave. My 3D printing projects ranged from less than a 1 cubic centimeter to about 1 cubic foot and involved several printers using different printing technologies. It was exciting to become familiar with printing technologies such as industrial grade FDM (Fused Deposition Modeling), the relatively new SLA (Stereolithography), and PolyJet. My primary duty with the FDM printers was to model parts that came in from various sources to print effectively and efficiently. Using methods my mentor taught me and the Stratasys Insight software, I was able to minimize imperfections, hasten build time, improve strength for specific forces (tensile, shear, etc...), and reduce likelihood of a print-failure. Also using FDM, I learned how to repair a part after it was printed. This is done by using a special kind of glue that chemically melts the two faces of plastic parts together to form a fused interface. My first goal with SLA technology was to bring the printer back to operational readiness. In becoming familiar with the Pegasus SLA printer, I researched the leveling, laser settings, and different vats to hold liquid material. With this research, I was successfully able to bring the Pegasus back online and have successfully printed multiple sample parts as well as functional parts. My experience with PolyJet technology has been focused on an understanding of the abilities/limits, costs, and the maintenance for daily use. Still upcoming will be experience with using a composite printer that uses FDM technology to print plastic while laying an internal filament of Kevlar or carbon-fiber inside the printed material. It has been incredible being exposed to this range of technologies and I feel very fortunate to be ready for virtually any kind of printing technology I come across in the future. Design work played a part in my internship this term as well. Working with Hai Nguyen, I was able to design a set of testing tips and a test frame for use with an Arcjet. The testing tips will be made of several different materials that will possibly be used in the heat shield of the Orion space craft. These designs included technical drawings that were presented to the fabrication shop. The frame design was created from 80/20 (a popular brand of frame construction equipment) and included an order form with pricing for fabrication. An independent design was also done for the virtual reality lab. This design was to create a hand-controller based on a previous design. This final design was sent directly to a 3D printer without technical drawings. Overall, my design work has given me experience with using 80/20, helped improve my CAD (Computer Aided Design) proficiency, and increased my knowledge of how to set up technical drawings for fabrication. The final role I have played in this internship has been to assist with testing of the inflatable technology materials working Khadijah Shariff. I began the internship assisting with permeability testing with the initial plan to continue the testing independently after training. Unfortunately, the testing apparatus suffered a technical failure and had funding pulled which cancelled that portion of the project. Further testing with inflatable technology continued with tensile testing of various stitching methods. This testing took place over a two-days and concluded successfully. Final testing was to be more tensile testing but of straps used to connect various inflatable sections. Unfortunately, the needed grips for the tensile tests could not be located and put the testing on hold. It is possible this round of testing will still take place by the end of the internship if the grips can be found. Overall, this portion of the internship has helped me become familiar with one kind of permeability test as well as a popular tensile/compression testing machine. Finally, I also had the chance to be trained using a metal lathe for making very small tips for a soldering iron. These tips will be used to melt threaded brass inserts into 3D printed plastic. Working with James Brown, I was able to successfully machine a brass rod down to as little as 0.064 inches plus or minus 0.001 inches. It was very rewarding to learn how to best use the machine and become familiar with a skill that will undoubtedly be used again in the future. I have been told by several professional engineers that learning to use a lathe and mill will be invaluable skills in the field. This knocks 50 percent of that goal off and I look forward to learning the mill at some point in the future. As is apparent with this list of projects, my internship was not focused on a single over-arching goal. Instead, I was able to gain experience in a myriad of very different areas. I feel like my time here was spent bouncing from one project to the next. Though sometimes difficult to switch gears, it was very rewarding to be a part of so much in so little time. My career and education will be positively impacted by what I have learned at JSC. My experience with 3D printing has improved my ability to handle many issues that may come up in the future with multiple different technologies. The design work I took part in, especially creating technical drawings, will help me better present designs to any engineer or shop I will encounter. My testing experience has helped me become familiar with a popular kind of tensile test machine that will likely be similar to the kinds I will encounter in the future. Finally, my experience with fabrication has given me a rare opportunity, as an engineer, to take part in the fabrication of a part. This experience will help me better tailor my future designs for the manufacturing process and has given me an appreciation for detailed/delicate machining work. My experience at JSC has been successful and will continue to assist me for a long time within the engineering field.

Zusack, Steven↗
NASA NTRS2023

Automated Fiber Placement Through Thickness Defect Stacking Optimization

In its 2022 commercial market outlook, Boeing forecasted an 80% increase in the global fleet through 2041 compared to 2019 pre-pandemic levels. This sharp rise in demand will drive pressure onto airframe manufacturers to ramp up production and find more efficient ways to design and manufacture airplanes. Complicating this challenge is the industry’s recent transformation from traditional metal-based airframes towards hybrid composite-metal aircraft. While composites have been used in aviation for decades, aircraft manufacturers are still struggling to design and manufacture quality parts at a high rate. Automated Fiber Placement (AFP) is one of the main manufacturing techniques used to produce large-scale composite parts. After a design has been created, a manufacturing strategy has to be developed based on the working material, part geometry, and machine capabilities. This process planning stage is essential to the AFP workflow and currently requires a high level of manual input from an experienced process planner. In an effort to automate and optimize this stage, the Computer Aided Process Planning (CAPP) module was developed. CAPP assists process planners in identifying optimal starting point location and layup strategy for each ply of a laminate. This Ply-Level Optimization (PLO) phase operates on the quantification of ply quality through predictable geometry-based defects such as gaps, overlaps, angle deviation, and steering. As you move from PLO to Laminate-Level Optimization (LLO) the design space grows exponentially, emphasizing the need for automated optimization. The work presented in this thesis expands CAPP’s functionality by comparing the planned fiber paths through the thickness of the laminate to mitigate stacked area defects and achieve an optimal laminate-level manufacturing strategy. Within CAPP, predicted gap and overlap defects are imported from Vericut Composites Programming (VCP) and then discretized to streamline the through-thickness comparison. Two objective functions are used to score different combinations of ply layup strategies based on defect stacking both globally and locally. Four combinatorial optimization algorithms were coupled with these objective functions to investigate the laminate-level manufacturing strategy design space and converge on the optimal plan. These algorithms were evaluated based on accuracy and efficiency through virtual testing on a complex tool surface. A separate LLO approach was developed to achieve near-optimal laminates in significantly less time. The end result is a software package which greatly reduces the required input from process planners, shortening the design-build cycle time and improving part quality.

AFP↗
NASA NTRS2022

Predicting Unreinforced Fabric Mechanical Behavior with Recurrent Neural Networks

Unreinforced woven fabrics are widely employed in various high-performance applications, including parachute deployment systems, airbags, and ballistic armor. The analysis of such materials is inherently complex due to the multiscale structure of these materials, and the dependence of macroscale behavior on changes that occur at lower scales. Previously, NASA’s Multiscale Analysis Tool (NASMAT) showed its capability in predicting unreinforced fabric behavior at the macroscale by capturing finite rotations that occur at the mesoscale. Though effective, the tool can face high computational cost for large, complex problems, motivating the need for the development of a surrogate model that can capture the same behavior. A recurrent neural network (RNN) was developed and trained on virtual NASMAT data to mimic the physics-based solutions while improving the computational runtime. The architecture of the RNN to best simulate the fabric behavior was carefully crafted based on heuristic knowledge of predicting physics-based temporal data, manual hyperparameter case studies, and Hyperband optimization.. The resultant model was able to predict a variety of stress-strain curves for fabrics with different mesoscale geometries, and was further validated by comparing to experimental data for the K706 style Kevlar plain-weave fabric, demonstrating the ability of the model to effectively capture the geometric changes in the fabric without explicitly calculating them, as is done in NASMAT. Furthermore, the tool showed its ability to improve on the runtime by a factor of 10 for fabric solutions compared to the multiscale tool, which would further enable the simulation of complex loading scenarios on unreinforced fabrics.

Fabric↗
NASA NTRS2004

Research Capabilities for Oil-Free Turbomachinery Expanded by New Rotordynamic Simulator Facility

A new test rig has been developed for simulating high-speed turbomachinery shafting using Oil-Free foil air bearing technology. Foil air journal bearings are self-acting hydrodynamic bearings with a flexible inner sleeve surface using air as the lubricant. These bearings have been used in turbomachinery, primarily air cycle machines, for the past four decades to eliminate the need for oil lubrication. More recently, interest has been growing in applying foil bearings to aircraft gas turbine engines. They offer potential improvements in efficiency and power density, decreased maintenance costs, and other secondary benefits. The goal of applying foil air bearings to aircraft gas turbine engines prompted the fabrication of this test rig. The facility enables bearing designers to test potential bearing designs with shafts that simulate the rotating components of a target engine without the high cost of building actual flight hardware. The data collected from this rig can be used to make changes to the shaft and bearings in subsequent design iterations. The rest of this article describes the new test rig and demonstrates some of its capabilities with an initial simulated shaft system. The test rig has two support structures, each housing a foil air journal bearing. The structures are designed to accept any size foil journal bearing smaller than 63 mm (2.5 in.) in diameter. The bearing support structures are mounted to a 91- by 152-cm (3- by 5-ft) table and can be separated by as much as 122 cm (4 ft) and as little as 20 cm (8 in.) to accommodate a wide range of shaft sizes. In the initial configuration, a 9.5-cm (3.75-in.) impulse air turbine drives the test shaft. The impulse turbine, as well as virtually any number of "dummy" compressor and turbine disks, can be mounted on the shaft inboard or outboard of the bearings. This flexibility allows researchers to simulate various engine shaft configurations. The bearing support structures include a unique bearing mounting fixture that rotates to accommodate a laserbased alignment system. This can measure the misalignment of the bearing centers in each of 2 translational degrees of freedom and 2 rotational degrees of freedom. In the initial configuration, with roughly a 30.5-cm- (12-in.-) long shaft, two simulated aerocomponent disks, and two 50.8-cm (2-in.) foil journal bearings, the rig can operate at 65,000 rpm at room temperature. The test facility can measure shaft displacements in both the vertical and horizontal directions at each bearing location. Horizontal and vertical structural vibrations are monitored using accelerometers mounted on the bearing support structures. This information is used to determine system rotordynamic response, including critical speeds, mode shapes, orbit size and shape, and potentially the onset of instabilities. Bearing torque can be monitored as well to predict the power loss in the foil bearings. All of this information is fed back and forth between NASA and the foil bearing designers in an iterative fashion to converge on a final bearing and shaft design for a given engine application. In addition to its application development capabilities, the test rig offers several unique capabilities for basic bearing research. Using the laser alignment system mentioned earlier, the facility will be used to map foil air journal bearing performance. A known misalignment of increasing severity will be induced to determine the sensitivity of foil bearings to misalignment. Other future plans include oil-free integral starter generator testing and development, and dynamic load testing of foil journal bearings.

Howard, Samuel A.↗
DOE OSTI2018

Industrial Scale Demonstration of Smart Manufacturing, Achieving Transformational Energy Productivity Gains

While many U.S. manufacturing operations utilize optimization for individual unit processes, smart manufacturing (SM) systems that integrate manufacturing intelligence in real time across an entire production operation are rare in large companies and virtually nonexistent in smaller organizations. One example of an area where SM systems can be applied is in the management of waste heat. A smart system that not only seeks to recover waste heat, but also to use energy more efficiently, is a more cost-effective approach because energy use, waste energy loss and product output are optimized together. The overall objective of this project was to develop an industry-accepted SM Platform that enables smart systems by accelerating and lowering the cost of development and implementation scales for use in diverse sets of manufacturing industry sectors, manufacturing operations and company sizes. Specifically, the project (1) designed and demonstrated the application of a prototype SM Platform for two diverse commercial test beds, (2) demonstrated at one and estimated for the other test bed the potential reduction in waste heat generation, and (3) worked with leading automation vendors to catalyze low-cost commercialization of the technology developed. The Smart Manufacturing Application and Data Platform (SM Platform) developed during this project is an innovative approach that marries IT virtualization technologies with operational data and modeling system requirements to construct a ready-to-go platform for enabling sensor – data – modeling – actuation systems, driven by real-time plant data and performance metrics. This SM Platform allows manufacturing organizations, regardless of their industry or size, to assemble new operational systems at a much lower cost, optimizing process knowledge and improving energy productivity by integrating with existing process control and automation systems. Also developed during this project is a methodology for developing energy productivity metrics that use real-time process information and provide comparisons for potential, practical, and actual performance, as well as examples and the methodology for provisioning of an Energy Dashboard that displays alternatives to optimize energy use within specific business contexts aimed at helping improve energy productivity and reduce emissions in U.S. manufacturing. The SM Platform designed utilizes current manufacturing, operational and IT industry standards. The platform can be used to expedite sensor – data – model – actuation system development and deployment in a cloud-based environment using a novel end-to-end workflow as a service capability that is already integrated with data connection, ingestion, and contextualization services. In addition, the methodology for development of energy productivity metrics and reusable application templates called Toolkits have been defined so that they can be tailored to a specific business situation. The project’s two test beds, one in a hydrogen production plant and the other in a forging, heat treatment, and machining operation, utilize sensor-driven modeling, measurement, and simulation systems. In these two test beds, the implementation of these as a comprehensive system has made it possible for energy productivity to be managed in real-time throughout the plant and enterprise using an energy dashboard. The development pathway to direct machine actuation was articulated. During the first year, the project team developed the most highly instrumented steam-methane reformer (SMR) in the U.S. using infrared cameras and thermocouples. Real-time data are streamed to high fidelity and reduced-order models for analysis. A high fidelity CFD model, parallelized in the SM Platform cloud environment, was demonstrated for operational use. This is the first real-time operational productivity system of a steam methane reformer using both high fidelity and data-driven models together in a real time situation to balance natural gas flow to the reformer tubes. An infrared camera system was developed and shown to outperform thermocouples in coverage, accuracy, reliability, and cost effectiveness for use in a high temperature, harsh environment. A forging, heat-treating, machining line operation test bed includes new instrumentation for data capture analysis, modeling, and simulation and will be integrated into SM Platform workflows so that business performance can be managed in real-time. Composable metrics were developed using the SM Platform to drive improvements in energy productivity, environmental performance, safety, asset management, costs, and overall operations. A major focus of the SM systems developed was optimization of energy productivity at the enterprise level, which resulted in reductions in waste heat generation for the two project test beds. Reductions in the waste heat generated will result in greater energy savings than will waste heat recovery due to inefficiencies in the heat transfer process. For the SMR test bed an efficiency improvement of 1 – 2% with a corresponding waste heat reduction of 5% were demonstrated. It should be noted that the test bed SMR unit had an efficiency of more than 90% as compared to 70 to 80% efficiencies of most SMR units. So implementation of similar SM systems at typical SMR units could yield more than 15 to 25% waste heat reductions. Similarly the forging, heat-treating, machining line operation test bed is a DOE AMO Platinum Level Superior Energy Performance plant with a much higher than typical overall operating efficiency. For this test bed heat integration using recuperators is estimated to result in fuel savings of 15.9% due to waste heat recovery compared to the base case without recuperators with negligible changes in part exit conditions. This results in an 18.9% energy efficiency improvement. The project team estimates that broad multi-sector adoption of the developed technology could reduce waste heat generation by an amount equivalent to 1.3% of total U.S. energy use. This waste heat reduction is associated with 69 million tons reduction in annual carbon dioxide emissions. Other potential benefits include reducing process times, solid and liquid wastes, environmental impacts, and water use. It is estimated that this investment has an anticipated payback period of less than one year in energy-intensive applications. The SM Platform could also reduce the costs of deploying SM systems by 50% in manufacturing operations. Commercialization of the SM Platform will require significant cross-industry collaboration and technology provider market changes. Steps taken toward commercialization included 1) analyzing savings from deployment of the SM Platform and 2) holding SM Platform workshops, webinars, and conferences with targeted individual industry segments, with a focus on small and medium-sized organizations. An upgraded community website has been established as a portal for accessing and distributing information. Most importantly, the SM platform enabling technology developed during this project and its commercialization will continue, evolve and expand under the Clean Energy Smart Manufacturing Innovation Institute (CESMII).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Metal 3D Printing of Low-NOX Fuel Injectors with Integrated Temperature Sensors (Final Technical Report)

This technical report presents the exploration of design and prototyping of an Oxy-fuel injector with integrated temperature sensing capabilities using Additive Manufacturing (AM) technology. The AM process has proven itself as a viable method to fabricate complex shapes for custom-designed metallic components rapidly. The lack of assembly requirements and the virtually unlimited geometrical complexity renders the AM process particularly attractive for fabricating complex energy system components. The unique layer-by-layer fabrication technique allows the embedding of sensors within complex components early in the design process. Sensors can be embedded (without post-production component modifications) in AM-fabricated components through two distinct processes: Stop and Go or Post-Integration. The Stop and Go fabrication process allows sensor placement within a cavity during fabrication; where the process is allowed to continue upon sensor placement. Post-integration process supports selective build of customized compartments for sensors within the part. The Stop and Go process requires an extremely accurate re-alignment of the powder-bed during the restart process. Additionally, metallization and shorting of sensors due to a considerably high temperature of the AM process creates significant fabrication challenges and limit the types of sensors that can be embedded. The Post-Integration of sensors is a practical alternative for components that can be effectively designed and fabricated with pre-built complex sensor compartments without the need for post-production component modifications. The proposed effort aimed at exploring the fabrication of an oxy-fuel injector designed for high-pressure Oxy-Combustion applications (Combustor for Directly Heated Supercritical Power Cycle) with integrated temperature measurement capabilities using the AM technique. Since the current design methodology of injectors is based on conventional fabrication techniques (e.g., multi-step machining and welding processes), a new paradigm of design methodology needs to be developed for their adaptation in the AM fabrication process. One of the most challenging issues addressed in powder bed fusion is the removal of powder from internal channels/cavities as the powder to be removed has been lightly sintered during the fabrication process. In this research, a major task has been assigned to developing and evaluating powder removal techniques that will ultimately be used when removing sintered powder from cavities/channels used for sensor placement. Achieving thorough powder removal will permit the incorporation of intricate cavities, integrated fasteners, or other novel features to incorporate sensors into parts directly post-fabrication – allowing for the novel, AM-based design practices to be developed and employ for sensor integration. The designed injector was initially tested at atmospheric conditions to review its successful operation. The tests were carried out for different firing inputs; with a minimum of 55 kW and a maximum of 275 kW. Later tests were carried out for pressurized conditions between 82 kW to 275 kW firing input. A pressure of 16.35bar was observed in the combustion chamber pressure during the pressurized test of 275 kW firing input; test duration was 15 seconds. Integrated thermocouples within the injector provided temperature data for test operations. The data revealed during the atmospheric condition the injector temperature is almost unaffected by the combustion. However, during the pressurized condition, the injector temperature rapidly up to 205°C within 15 seconds; at a 275 kW firing input.

42 ENGINEERING↗