SEARCH · Engineering Papers

Results for “process design”

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

SourceResearch connectionFrom yearThrough year

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

At least 757 records · Page 42

All sources NASA NTRS DOE OSTI

DOE OSTI2024

From ETROC to VTROC

The Endcap Timing ReadOut Chip (ETROC) is designed to process LGAD signals with time resolution down to about 40-50ps per hit, to reach 30-35ps per track with two detector layers. The ETROC2 is the first full size (16x16) prototype design and is fully compatible with the final chip specifications in terms of functionality. The ETROC2 has been extensively tested recently. In this talk, the testing results of the ETROC2 will be presented first, including performance study using charge injection, laser and beam, as well as TID testing and wafer probe testing to study the yield. In addition, future challenges of precision position and timing ASIC design will be discussed, based on what we have learned from the ETROC development, and how the challenges could be addressed in the VTROC, Vertically integrated Timing ReadOut chip (funded as SBIR phase 2) for precision position and timing detector R&D.

Liu, Ted↗

DOE OSTI2024

ATom (Acoustic Tomography Processing Suite) [SWR-24-120]

Acoustic tomography seeks the best-fit fluctuating velocity and temperature fields that explain a collection of signal travel times in a region of interest. This codebase defines an end-to-end framework for executing turbulent field retrievals from acoustic signals, acoustic signal design and processing tools for the physical array, and analysis tool that leverage virtual acoustic tomography arrays based on large-eddy simulations of the atmospheric boundary layer.

Hamilton, Nicholas↗

DOE OSTI2026

RU Net for Automatic Characterization of TRISO Fuel Cross Sections

TRistructural ISOtropic (TRISO) particle fuel is a type of nuclear fuel known for its high-temperature and high-burnup performance. Each sub-millimeter diameter TRISO particle consists of uranium-oxycarbide (UCO) or UO2 fuel kernel, coated with buffer, inner pyrolytic carbon (IPyC), silicon carbide (SiC), and outer pyrolytic carbon (OPyC) layers. The SiC layer acts as the main containment barrier for the TRISO particle to retain the fission products, while the IPyC and OPyC layers provide additional barriers to the release of fission products, especially fission gases. During irradiation, phenomena like kernel swelling, buffer densification, and IPyC fracture may impact fuel performance. Post-irradiation microscopy on entire compact cross sections or samples of individual particles deconsolidated from compacts is often used to identify these irradiation-induced changes in morphology. However, each fuel compact generally contains thousands of TRISO particles. To get statistical information on these phenomena, it is cumbersome work if done manually. For example, to get information about swelling/densification behaviors of different layers or kernels after irradiation, researchers previously manually measured the perimeter of each TRISO layer in hundreds of particles after four rounds of iterative grinding and polishing encompassing more than 2000 cross-section images for a total of four fuel compacts. To attempt to reduce the subjectivity inherent in that process and accelerate data analysis, we conducted a study on the automatic TRISO layer segmentation on cross-sectional microscopic images using Convolutional Neural Networks (CNNs). CNNs are a class of machine learning algorithms specifically designed for processing structured grid data that have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we have generated the large irradiated TRISO layer dataset with more than 2000 cross-section TRISO microscopic images and the corresponding annotated images. Based on these annotated images, we have employed different CNNs for automatic segmentation of different TRISO layers. These include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net has the best performance in terms of intersection-over-union (IoU). Through the aid of these CNN models, we can expedite the analysis of TRISO particle cross-sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

Convolutional Neural Networks↗

DOE OSTI2024

Beam test results for the Endcap Timing ReadOut Chip (ETROC2) for the CMS ETL upgrade

The Endcap Timing ReadOut Chip (ETROC) for the Compact Muon Solenoid (CMS) Minimum ionizing particle Timing Detector (MTD) is designed to process Low Gain Avalanche Diode (LGAD) signals with a time resolution of 40-50ps per hit, with 30-35 ps per charged particle using two layer detector. The ETROC2, is the first full size (16x16 pixels) and full functionally design that has been tested in hadron and electron beams at CERN and DESY (Sep 2023 - June 2024). Precision timing studies at these test beams are done using a self-referential beam telescope composed of up to 4 layers/chips. This talk describes the ETROC2 beam telescope and summarizes the performance of the ETROC2 chips bump-bonded to (16x16) Fondazione Bruno Kessler (FBK) and Hamamatsu Photonics K.K. (HPK) LGAD sensors using results from recent test beams. The experiences and lessons learned during the course of these test beams are also detailed in this talk.

Safdari, Murtaza↗

DOE OSTI2024

Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

Abstract This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one‐to‐many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root‐mean‐squared‐error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser‐matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2022

M2CT-22IN1202096 Design for Carbon Conversion Product Pathways with Nuclear Power Plant Integration

Coal is a globally abundant resource that historically has been used for power generation via combustion. As the power industry replaces coal with cleaner methods of generation, energy-rich coal could be used in other chemical and fuel applications. This study presents a coal utilization option in which coal combustion is replaced with a carbon-free nuclear power plant and the coal is upgraded to valuable products for a variety of markets. Coal is prepared for conversion first by the pyrolysis process, which will optimize solid, liquid, and gaseous products based on the market size and potential product value, maximizing the monetary value of coal. This process is designed using bituminous coal from the Appalachian region as a basis to provide a pathway to preserve or transition coal-related jobs and create new jobs associated with the clean energy transition. Process modeling in the AspenOne Suite will be used to determine each component’s sensitivities, costs, inputs, and outputs. Dispatch modeling in the FORCE toolset will optimize the entire system and calculate the NPV for the refinery lifetime. Advanced and light-water reactors are considerations to supply the heat, steam, and electricity to the process. This paper focuses on the technical and market analysis used to determine the optimal processes and product pathways for the carbon refinery. Product pathways are on activated carbon, formic acid synthesis, and methanol synthesis for further upgrading to marketable chemical and polymer products.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

M2CT-22IN1202096 Design for Carbon Conversion Product Pathways with Nuclear Power Plant Integration

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

Design for Carbon Conversion Product Pathways with Nuclear Power Plant Integration (PCC)

Coal is a globally abundant resource that historically has been used for power generation via combustion. As the power industry replaces coal with cleaner methods of generation, energy rich coal could be used in other chemical and fuel applications. This study presents a coal utilization option in which coal combustion is replaced with a carbon-free nuclear power plant and the coal is upgraded to valuable products for a variety of markets. Coal is prepared for conversion first by the pyrolysis process, which will optimize solid, liquid, and gaseous products based on the market size and potential product value, maximizing the monetary value of coal. This process is designed using bituminous coal from the Appalachian region as a basis to provide a pathway to preserve or transition coal-related jobs and create new jobs associated with the clean energy transition. Process modeling will be used to determine each component’s sensitivities, costs, inputs, and outputs Advanced and light-water reactors are considerations to supply the heat, steam, and electricity to the process. This paper focuses on the technical and market analysis used to determine the optimal processes and product pathways for the carbon refinery. Product pathways are on activated carbon, formic acid synthesis, and methanol synthesis for further upgrading to marketable chemical and polymer products.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Mark-18A Cold Runs

The Savannah River National Laboratory (SRNL) is tasked by the National Nuclear Security Administration (NNSA) to recover highly valued isotopes from irradiated Mark-18A (Mk-18A) targets. The Savannah River Site (SRS) has sixty-five Mk-18A targets available for the recovery of the high valued materials. The sixty-five Mk-18A targets are currently stored in the L-Area Basin and will be removed one at a time and individually transported to SRNL. Upon receipt at SRNL, the Mk-18A target material will be removed from the confinement, dissolved, chemically separated, and calcined to a stable oxide. The flowsheet is designed to recover the plutonium as well as the trivalent actinides. The remaining unrecovered material will be discarded to the high activity drain (HAD) system in SRNL. A specially designed cask was procured for transport of the targets from L-Area to SRNL. Once received at SRNL, the targets will be loaded into the back of Cell 7 and resized as they enter the cell. The resized targets (1/4 lengths) will then be processed one at a time through the following processes: caustic dissolution and filtration; acidic dissolution and filtration, Reillex anion exchange, diglycolamide (DGA) cation exchange; and DGA calcination. This processing will result in two product streams. The first is an aqueous plutonium solution which will be removed from the shielded cells and taken to a glovebox for further purification and conversion to an oxide. The second is a calcined oxide containing the Am and Cm as well as other lanthanide fission products which will be removed from the shielded cells using a bagless transfer system. Both materials will be packaged for shipment to Oak Ridge National Laboratory (ORNL). All equipment to carry out this process was designed, procured or fabricated, and installed in a mock-up facility (716-4A) at SRS to allow for simulation testing in a non-radioactive area. This equipment was then dismantled and transferred from 716-4A to 773-A and installed in the SRNL Shielded Cells. After installation in the shielded cells facility testing was performed using water followed by surrogates and cold chemicals. Issues were identified during these evaluations, including equipment issues as well as technical challenges. Many of the issues were rectified during performance of the cold runs, and the remaining have a resolution identified. Table ES-1 provides a summary of all issues identified during the cold run operations, as well as the status and identified resolutions to outstanding issues.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2021

Printing thermoelectric inks toward next-generation energy and thermal devices

The ability of thermoelectric (TE) materials to convert thermal energy to electricity and vice versa highlights them as a promising candidate for sustainable energy applications. Despite considerable increases in the figure of merit zT of thermoelectric materials in the past two decades, there is still a prominent need to develop scalable synthesis and flexible manufacturing processes to convert high-efficiency materials into high-performance devices. Scalable printing techniques provide a versatile solution to not only fabricate both inorganic and organic TE materials with fine control over the compositions and microstructures, but also manufacture thermoelectric devices with optimized geometric and structural designs that lead to improved efficiency and system-level performances. In this review, we aim to provide a comprehensive framework of printing thermoelectric materials and devices by including recent breakthroughs and relevant discussions on TE materials chemistry, ink formulation, flexible or conformable device design, and processing strategies, with an emphasis on additive manufacturing techniques. Additionally, we review recent innovations in the flexible, conformal, and stretchable device architectures and highlight state-of-the-art applications of these TE devices in energy harvesting and thermal management. Perspectives of emerging research opportunities and future directions are also discussed. While this review centers on thermoelectrics, the fundamental ink chemistry and printing processes possess the potential for applications to a broad range of energy, thermal and electronic devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Development of an Optimal Variable-Pitch Controller for Floating Axial-Flow Marine Hydrokinetic Turbines: Preprint

This article discusses the development of an optimal variable-pitch controller for floating, axial-flow marine turbines. Recently, OpenFAST, an open-source wind turbine modeling tool, has been extended to model marine turbines. A controller is necessary to simulate marine turbines for different load cases using OpenFAST, which greatly impacts the performance of the energy system. Previous studies have designed controllers using a linearized model of the marine turbine, which can be timeconsuming and require the expertise of a control engineer. In this study, we use an automated approach that uses generic models of the marine turbine to identify the controller gains, which can expedite the process of designing a controller. Using an optimizer to identify the control system parameters can additionally improve the controller's performance. The optimal controller tuned using such an approach results in a 20% reduction in the towerbase damage equivalent loading and better tracking of the rated generator speed and power.

closed-loop control↗

DOE OSTI2024

Development of an Optimal Variable-Pitch Controller for Floating Axial-Flow Marine Hydrokinetic Turbines

This article discusses the development of an optimal variable-pitch controller for floating, axial-flow marine turbines. Recently, OpenFAST, an open-source wind turbine modeling tool, has been extended to model marine turbines. A controller is necessary to simulate marine turbines for different load cases using OpenFAST, which greatly impacts the performance of the energy system. Previous studies have designed controllers using a linearized model of the marine turbine, which can be time-consuming and require the expertise of a control engineer. In this study, we use an automated approach that uses generic models of the marine turbine to identify the controller gains, which can expedite the process of designing a controller. Using an optimizer to identify the control system parameters can additionally improve the controller's performance. The optimal controller tuned using such an approach results in a 20% reduction in the tower-base damage equivalent loading and better tracking of the rated generator speed and power.

axial flow↗

DOE OSTI2020

Modeling and simulation of gas separations with spiral–wound membranes

Models for gas separations with spiral–wound membranes are developed and found to exhibit good agreement with experiments performed on N 2 /O 2 mixtures. The two–dimensional (2D) model can be accurately approximated by a one–dimensional (1D) surrogate model when the spacer widths are chosen to make the channel pressure drops small. Subsequently, the separation of propane/propylene mixtures from the recycle purge stream of a polypropylene reactor is investigated. Assuming ideal gas is found to lead to significant overestimations in membrane stage cuts (sometimes more than 10%), an extent comparable to that associated with extrapolating constant olefin permeance from a low–pressure condition. While olefin permeance can change significantly with pressure, using a constant–permeance formulation can result in a small (< 2.5%) underprediction in stage cut if the value for the permeance is taken from the feed condition. Finally, membrane properties and costs necessary for a viable separation process are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Solvent-mediated contaminant removal from plastic waste using thermodynamic modeling

Plastics recycling is hindered by the compositional complexity of plastic waste, which can include numerous polymer components as well as low concentrations of additives and non-intentionally added substances. These latter small-molecule species, which we collectively refer to as contaminants, can harm human health and will build up in recycled plastic causing environmental and downstream processing challenges if not removed. In this work, we present molecular modeling approaches using the COnductor-like Screening MOdel for Real Solvents (COSMO-RS) to guide the selection of solvents that are capable of removing targeted contaminants from plastic waste. By considering the thermodynamic partitioning of contaminant species between a solvent phase and polymer phase, we identify guidelines for solvent selection to promote either the low-temperature extraction of contaminants from plastic waste or the removal of contaminants as part of a dissolution-based plastics recycling process. We present four case studies to illustrate the application of the computational approach to the removal of brominated flame retardants, phthalates, and selected perfluoroalkyl substances, and compare to both literature and newly collected experimental data to illustrate model prediction accuracy. Furthermore, the case studies highlight the capability of the modeling approach to help design recycling processes that explicitly account for contaminant removal, thereby increasing product purity during dissolution-based recycling or facilitating chemical recycling of contaminant-free plastics.

Zhou, Panzheng [University of, Wisconsin, Madison,↗

DOE OSTI2022

Tunable quantum logic gate on photonic qubits with a ladder emitter

We describe how a ladder emitter can implement a tunable quantum logic gate on photonic qubits encoded in the frequency basis. The ground-to-first excited state of the ladder emitter interacts with the control photon, and the first-to-second excited state transition interacts with the target photon. By controlling the relative detuning between the target photon and the first-to-second excited state transition of the ladder emitter, we enable any controlled-phase operation from 0 to π. We derive analytical formulas for the performance of the gate through the S-matrix formalism as well as describe the mechanism intuitively. This gate is deterministic, does not utilize any active control, and needs only a single ladder emitter, enabling low-footprint and more efficient decomposition of quantum circuits, especially the quantum Fourier transform. We suggest multiple potential systems for physical realization of our proposal, such as lanthanide ions embedded in Purcell-enhanced cavities. We expect these results to motivate further interest in photonic quantum information processing with designer emitters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

One–Step Fabrication of Nanocrystalline Nanonetwork SnO 2 Gas Sensors by Integrated Multilaser Processing

An integrated multilaser process is developed to fabricate nanocrystalline nanonetwork SnO 2 gas sensors in one integrated procedure, which combines electrodes fabrication, nanomaterials deposition, and postannealing. Interdigit electrodes are fabricated on an Au-coated fused silica substrate using a picosecond (ps) laser, which ablates the Au coating from the back of the substrate to pattern the electrodes. A novel transmitted Ps laser deposition (TPLD) process is designed to deposit SnO 2 nanonetwork on the interdigit electrodes with precise deposition area control under a close target-to-substrate distance. The obtained SnO 2 nanonetwork is in situ postannealed by a CO 2 laser to improve the crystallinity, while the nano morphology and grain size keep intact. To investigate the morphology and formation process of the nanonetwork, the microstructure of the laser-deposited SnO 2 layer is characterized. As a result, the crystallization control of CO 2 laser annealing is investigated through analyzing the Raman spectrum, X-ray diffraction (XRD) patterns, and lattice structures of the samples. By exposed to H 2 atmosphere, the fabricated gas sensor is demonstrated for H 2 monitoring.

08 HYDROGEN↗

DOE OSTI2022

ClusterCAD 2.0: an updated computational platform for chimeric type I polyketide synthase and nonribosomal peptide synthetase design

Abstract Megasynthase enzymes such as type I modular polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs) play a central role in microbial chemical warfare because they can evolve rapidly by shuffling parts (catalytic domains) to produce novel chemicals. If we can understand the design rules to reshuffle these parts, PKSs and NRPSs will provide a systematic and modular way to synthesize millions of molecules including pharmaceuticals, biomaterials, and biofuels. However, PKS and NRPS engineering remains difficult due to a limited understanding of the determinants of PKS and NRPS fold and function. We developed ClusterCAD to streamline and simplify the process of designing and testing engineered PKS variants. Here, we present the highly improved ClusterCAD 2.0 release, available at https://clustercad.jbei.org. ClusterCAD 2.0 boasts support for PKS-NRPS hybrid and NRPS clusters in addition to PKS clusters; a vastly enlarged database of curated PKS, PKS-NRPS hybrid, and NRPS clusters; a diverse set of chemical ‘starters’ and loading modules; the new Domain Architecture Cluster Search Tool; and an offline Jupyter Notebook workspace, among other improvements. Together these features massively expand the chemical space that can be accessed by enzymes engineered with ClusterCAD.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Designing accurate emulators for scientific processes using calibration-driven deep models

Abstract Predictive models that accurately emulate complex scientific processes can achieve speed-ups over numerical simulators or experiments and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning methods to build data-driven emulators. In this work, we study an often overlooked, yet important, problem of choosing loss functions while designing such emulators. Popular choices such as the mean squared error or the mean absolute error are based on a symmetric noise assumption and can be unsuitable for heterogeneous data or asymmetric noise distributions. We propose Learn-by-Calibrating, a novel deep learning approach based on interval calibration for designing emulators that can effectively recover the inherent noise structure without any explicit priors. Using a large suite of use-cases, we demonstrate the efficacy of our approach in providing high-quality emulators, when compared to widely-adopted loss function choices, even in small-data regimes.

97 MATHEMATICS AND COMPUTING↗