Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Generators”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Development and Implementation of a New AI-Based Tool to Support Fast Reactor Software Model Generation and Validation

This report summarizes FY26 work to develop Maggie, an artificial intelligence-based assistant designed to support software model generation and validation activities for fast reactor analysis codes. The project established a modular, code-agnostic software architecture that separates reusable agent capabilities from code-specific knowledge and tools, with initial implementation focused on the FRP-supported fast reactor safety analysis code SAS4A/SASSYS1 (SAS). A curated SAS-specific knowledge base was assembled from the code manual, training materials, historical analysis reports, and representative input files, and was integrated through retrieval-augmented generation to ground Maggie’s responses in authoritative sources. Maggie was deployed on the internal Argonne network, where it demonstrated practical user-facing capability as a chatbot for answering natural language questions about SAS and retrieving relevant technical information. Demonstration cases also showed that Maggie can generate useful snippets of SAS input for selected modeling tasks, while highlighting current limitations in reliability and consistency for more complex input generation tasks. Overall, the FY26 effort established the technical foundation for an AI-assisted capability intended to improve the efficiency, consistency, and accessibility of fast reactor software model development at Argonne and, with further improvements, to support eventual use by the broader fast reactor community, including industry users of FRP-supported analysis tools.

Thomas, Rachel [Argonne National Laboratory (ANL),↗
DOE OSTI2021

Simulation of Electron-Proton Scattering Events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)

We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of efficiently training a GAN event simulator lies in learning the complicated patterns of the distributions of the particles physical properties. We develop a GAN that selects a set of transformed features from particle momenta that can be generated easily by the generator, and uses these to produce a set of augmented features that improve the sensitivity of the discriminator. The new Feature-Augmented and Transformed GAN (FAT-GAN) is able to faithfully reproduce the distribution of final state electron momenta in inclusive electron scattering, without the need for input derived from domain-based theoretical assumptions. The developed technology can play a significant role in boosting the science of existing and future accelerator facilities, such as the Electron-Ion Collider.

Alanazi, Yasir↗
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗
DOE OSTI2020

Modeling and Investigations on Surface Colors of Wings on the Performance of Albatross-Inspired Mars Drones and Thermoelectric Generation Capabilities

Thermal effects of wing color for Albatross-inspired drones performing in the Martian atmosphere are investigated during the summer and winter seasons. This study focuses on two useful consequences of the thermal effects of wing color: the drag reduction and the thermoelectric generation of power. According to its color, each wing side has a certain temperature affecting the drag. Investigations of various configurations have shown that the thermal effect on the wing boundary layer skin drag is insignificant because of the low atmospheric pressure. However, the total drag varies as much as 12.8% between the highest performing wing color configuration and the lowest performing configuration. Additionally, the large temperature differences between the top and the bottom wing surfaces show great potential for thermoelectric power generation. The maximum temperature differences between the top and bottom surfaces for the summer and winter seasons are, respectively, 65 K and 30 K. The drag reduction and the power generation via thermoelectric generators both contribute to enhancing the endurance of drones. Future drone designs will benefit from increased endurance through optimizing the wing color configuration.

Rice, Devyn↗
DOE OSTI2023

Assessing and Improving Biooxidation for Acid Generation and Rare Earth Element Extraction

Microorganisms (Acidithiobacillus ferrooxidanns) are effective in oxidizing ferrous ions that can be used to oxidize pyrite and produce sulfuric acid. Many coal waste resources contain significant concentrations of rare earth elements (REE) and critical materials (CM) that can be extracted using sulfuric acid. These coal waste resources often contain significant concentrations of pyrite, which if not utilized or removed present a future environmental liability for potential acid mine drainage. Thus, the combination of pyrite and REE/CM in coal waste provides a significant resource opportunity for sulfuric acid generation that can be utilized using biooxidation. In addition, the pyrite concentrate used for acid generation also contains REE/CM content that is released during biooxidation of the pyrite concentrate that augments the REE/CM release from the main ore being leached with the acid generated from the pyrite. Thus, this approach provides a very significant environmental advantage as well as augmented REE/CM recovery. Although there are many studies associated with biooxidation in relation to mineral oxidation, there is a lack of information regarding the effects of operating parameters on biooxidation performance and optimization for practical applications. In this study, findings from research in assessing and improving biooxidation for acid generation for REE/CM extraction are presented. Results show that bacteria can very effectively and efficiently oxidize ferrous ions to ferric ions, which oxidize pyrite to produce acid for REE/CM extraction. The factors that showed significant impact on biooxidation performance include air flow rate, stirring speed, residence time, solids concentration, and temperature. The dominance of Leptospirillum ferriphilum species was noted in the bioreactor after a prolonged period of operation, although Acidithiobacillus ferrooxidanns was used in the beginning.

Free, Michael L.↗
DOE OSTI2020

A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing

Data-driven algorithms have been widely used as effective tools to mimic hydrologic systems. Unlike black-box models, decision tree algorithms offer transparent representations of systems and reveal useful information about the underlying process. A popular class of decision tree models is model tree (MT), which is designed for predicting continuous variables. Most MT algorithms employ an exhaustive search mechanism and a pre-defined splitting criterion to generate a piecewise linear model. However, this approach is computationally intensive, and the selection of the splitting criterion can significantly affect the performance of the generated model. These drawbacks can limit the application of MTs to large datasets. To overcome these shortcomings, a new flexible Model Tree Generator (MTG) framework is introduced here. MTG is equipped with several modules to provide a flexible, efficient, and effective tool for generating MTs. The application of the algorithm is demonstrated through simulation of controlled discharge from several reservoirs across the Contiguous United States (CONUS).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

HYPHY: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics

Generating large-volume hydrodynamical simulations for cosmological observables is a computationally demanding task necessary for next-generation observations. In this work, we construct a novel fully convolutional variational autoencoder (VAE) to synthesize hydrodynamic fields conditioned on dark matter fields from N -body simulations. After training the model on a single hydrodynamical simulation, we are able to probabilistically map new dark-matter-only simulations to corresponding full hydrodynamical outputs. By sampling over the latent space of our VAE, we can generate posterior samples and study the variance of the mapping. We find that our reconstructed field provides an accurate representation of the target hydrodynamical fields as well as reasonable variance estimates. This approach has promise for the rapid generation of mocks as well as for implementation in a full inverse model of observed data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Virtual Refrigerant Charge Sensing Method for Next-Generation Refrigerant in Residential Heat Pumps

The charge level of refrigerant in heat pump systems significantly affects their operational performance. Virtual refrigerant charge (VRC) sensing technology has been well-established for traditional refrigerants (HFCs and HCFCs) for its low cost compared to physical sensors. However, other than traditional refrigerants, HFOs are increasingly used in next-generation heat pumps; whether these conventional VRC sensing methods remain applicable for heat pump systems utilizing next-generation refrigerants requires further investigation. To address these issues, this study develops a low-cost VRC sensing method for next-generation refrigerant heat pumps used in residential buildings. The developed algorithm is evaluated by using simulation models to evaluate the accuracy, considering an R454B heat pump with a nominal heating capacity of 51K Btu/hr (14.95 kW) as an example, and compared with those of the two reference VRC sensing algorithms. Though the developed VRC sensing algorithm and the two reference methods can accurately predict the charge level for the R454B heat pump system (with mean absolute percentage error for various cooling and heating conditions less than 7%), the developed VRC sensing algorithm uses fewer sensors and improves the overall accuracy for heating conditions by 7.1%, and the accuracy for undercharge cooling conditions 14.2%, compared with a mainstream algorithm. This technology will complement physical leakage detectors, and promote the adoption of next-generation heat pump systems, along with reducing wasted energy and maintenance costs.

Liang, Chenjiyu↗
DOE OSTI2021

Apprentice for Event Generator Tuning

Apprentice is a tool developed for event generator tuning. It contains a range of conceptual improvements and extensions over the tuning tool Professor. Its core functionality remains the construction of a multivariate analytic surrogate model to computationally expensive Monte-Carlo event generator predictions. The surrogate model is used for numerical optimization in chi-square minimization and likelihood evaluation. Apprentice also introduces algorithms to automate the selection of observable weights to minimize the effect of mis-modeling in the event generators. We illustrate our improvements for the task of MC-generator tuning and limit setting.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Generating clock signals for a cycle accurate, cycle reproducible FPGA based hardware accelerator

A method, system and computer program product are disclosed for generating clock signals for a cycle accurate FPGA based hardware accelerator used to simulate operations of a device-under-test (DUT). In one embodiment, the DUT includes multiple device clocks generating multiple device clock signals at multiple frequencies and at a defined frequency ratio; and the FPG hardware accelerator includes multiple accelerator clocks generating multiple accelerator clock signals to operate the FPGA hardware accelerator to simulate the operations of the DUT. In one embodiment, operations of the DUT are mapped to the FPGA hardware accelerator, and the accelerator clock signals are generated at multiple frequencies and at the defined frequency ratio of the frequencies of the multiple device clocks, to maintain cycle accuracy between the DUT and the FPGA hardware accelerator. In an embodiment, the FPGA hardware accelerator may be used to control the frequencies of the multiple device clocks.

Asaad, Sameh W.↗
DOE OSTI2021

Apparatuses and methods for generating carbon particles and exhaust gas used by gas turbine systems

Apparatuses for generating carbon particles and exhaust gas used by gas turbine systems are disclosed. One apparatus may include a decarbonization component combusting or reacting a mixture of a fuel and a mixing gas to generate the carbon particles and the exhaust gas and an exhaust conduit to receive the exhaust gas generated by the decarbonization component. The apparatus may also include a mixing duct in fluid communication with the exhaust conduit and the gas turbine system. The mixing duct may receive the exhaust gas and provide the exhaust gas to the gas turbine system to be used to produce a working fluid within the gas turbine system. The apparatus may further include a carbon particle collection component for receiving and storing the generated carbon particles.

Berry, Jonathan Dwight↗
DOE OSTI2021

Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks : *Full/Regular Research Paper submission for the symposium CSCI-ISAI: Artificial Intelligence

We use a stable parallel approach to train Wasserstein Conditional Generative Adversarial Neural Networks (W-CGANs). The parallel training reduces the risk of mode collapse and enhances scalability by using multiple generators that are concurrently trained, each one of them focusing on a single data label. The use of the Wasserstein metric reduces the risk of cycling by stabilizing the training of each generator. We apply the approach on the CIFAR10 and the CIFAR100 datasets, two standard benchmark datasets with images of the same resolution, but different number of classes. Performance is assessed using the inception score, the Fréchet inception distance, and image quality. An improvement in inception score and Fréchet inception distance is shown in comparison to previous results obtained by performing the parallel approach on deep convolutional conditional generative adversarial neural networks (DC-CGANs). Weak scaling is attained on both datasets using up to 100 NVIDIA V100 GPUs on the OLCF supercomputer Summit.

Lupo Pasini, Massimiliano↗
DOE OSTI2022

Ozone generator system

An ozone generator system utilizes an electrochemical cell to produce and control ozone concentrations within an enclosure or to supply ozone to a flow conduit. The enclosure may he coupled with a flow conduit that carries the produced ozone to a desired location. An enclosure may be a sterilization chamber and the concentration of ozone produced by the ozone generating system may be sufficient to sterilize articles within the enclosure. An oxygen control electrolyzer cell and/or humidity control electrolyzer cell may be coupled with the enclosure to further control the environment of the enclosure. A humidity control electrolyzer cell may be fluidly coupled with the ozone generator to supply humidity for reaction on the anode of the ozone generator.

Bahar, Bamdad↗
DOE OSTI2022

Reduced Order Models Generation for HTGRs Pebble Shuffling Procedure Optimization Studies

This report provides an initial study for producing reduced-order models (ROMs) of pebble-bed high temperature gas reactor (HTGR) models for the purposes of design optimization. As an initial study, this work is meant to be exploratory---identifying useful workflows and methods for ROM generation---and not meant to be a catch-all analysis of HTGR ROM generation and usage for optimization. This report summarizes three tasks performed in Fiscal Year 2022: 1) the creation of HTGR model, 2) the sensitivity analysis of model design parameters, and 3) an introduction to ROM generation techniques. The representative HTGR model created in this work is a multiphysics equilibrium-core using the BlueCRAB (comprehensive reactor analysis bundle) reactor analysis application, coupling four physical phenomena: neutronics, streamline depletion, porous flow thermal hydraulics, and pebble heat conduction. Part of the model creation was identifying some design parameters and quantities of interest that are relevant in an optimization analysis and adjustable in the model. The sensitivity analysis utilized a polynomial chaos expansion methodology to compute global sensitivity metrics. This analysis showed that thermal hydraulics parameters and quantities of interest had a relatively small impact on simulation results. Finally, the ROM generation work involved exploring three different ROM methodologies: polynomial regression, a Gaussian process, and artificial neural networks. Using a cross-validation technique to characterize ROM performance, the Gaussian process and single-layer artificial neural networks showed the most promising results. Overall, this study was insightful and the lessons learned will be invaluable for the eventual development of an HTGR design optimization workflow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Programmable pseudo-random sequence generator for use with universal lidar and its associated method of operation

A pseudo-random sequence generator for use within a universal lidar system and its corresponding method of operation. The pseudo-random sequence generator uses synchronized shift registers that are in series Binary adders are provided. The signal output of each of the shift registers is tapped and directed to the binary adders. High-speed switches are provided between the shift registers and the binary adders. The switches are programmed to connect only two of the shift registers to the binary adders for each of the pseudo-random patterns being generated. The binary adders generate an output signal that is received by the first shift register. The signal propagates through all the shift registers to the last shift register. The last shift register outputs a pseudo-random sequence.

Datta, Shubhashish↗
DOE OSTI2020

Study of Waste Generated by Retrieval of Fuel Debris at Fukushima Daiichi Nuclear Power Station - 20178

As a member of International Research Institute for Nuclear Decommissioning (IRID), we organized information on waste generated from the fuel debris retrieval, and considered containment, transport and storage processes. An important safety function to consider for these wastes is not only the handling of ordinary high-dose waste, but also the prevention of diffusion of fuel component contamination and the countermeasures against hydrogen gas generation. In order to satisfy these safety functions, we considered guideline for waste generated from the fuel debris retrieval, and proposed an example of handling process that includes generation of waste, container storage, on-site transfer and storage in a storage building. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Towards Automated Generation of Chiplet-Based Systems

The Software Defined Architectures (SODA) Synthesizer is an open-source compiler-based tool able to automatically generate domain-specialized systems targeting Application- Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) starting from high-level programming. SODA is composed of a high-level frontend, SODA-OPT, which leverages the multilevel intermediate representation (MLIR) framework to interface with productive programming tools (e.g., machine learning frameworks), identify kernels suitable for acceleration, and perform high-level optimizations, and of a state-of-the-art high-level synthesis backend, Bambu from the PandA framework, to generate custom accelerators. One specific application of the SODA Synthesizer is the generation of accelerators to enable ultra-low latency inference and control on autonomous systems for scientific discovery (e.g., electron microscopes, sensors in particle accelerators, etc.). This talk will discuss ongoing work on the SODA synthesizer to enable no-human-in-the-loop generation and design space exploration of the chiplets for highly specialized artificial intelligence accelerators. Connecting these highly specialized chiplets to general-purpose cores or programmable accelerators will allow to quickly deploy autonomous systems for scientific discovery.

Limaye, Ankur M.↗
DOE OSTI2024

Impact of Glen Canyon Generation Loss

Colorado River Basin hydropower generation has faced challenges due to droughts and ecological and social water requirements. Specifically, Glen Canyon hydropower generation fluctuates substantially with recent extreme weather trends. Further, the western grid evolves with higher wind and solar share, and Glen Canyon hydropower's contribution to grid flexibility services is essential. The Western Area Power Administration (WAPA) markets and schedules electricity production at GCD, and the loss of this power could have significant financial consequences for the WAPA Colorado River Storage Project's (CRSP) Office because it may need to purchase relatively large amounts of energy to serve its firm electrical obligations. In addition, GCD provides grid reliability services for the WAPA Colorado-Missouri (WACM) balancing authority (BA). Both WAPA and DOE's Water & Power Technology Office (WPTO) are interested in researching how these drier hydrological conditions will impact federal electrical energy production, the Western Electricity Coordinating Council (WECC) power grid, and the value of hydropower in the face of lower production. We study multiple hydrologic and power grid scenarios to understand the grid impacts of the loss of Glen Canyon generation. The study uses a production cost model, water resources planning models, water-centric grid models, and various data analytic techniques. The study progress presentation discusses the selection of probable CRSP' hydropower scenarios and power grid scenarios to understand the impacts of Glen Canyon generation, which includes technologies that compensate the Glen Canyon energy and ancillary services contributions, transmission availability, and energy local marginal prices at interested grid locations of WAPA operation.

droughts↗