Search NASA⌕ Search

SEARCH · Search NASA

Results for “Process settings”

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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Relating microstructure, temperature, and chemistry to explosive ignition and shock sensitivity

An analysis is put forth that relates explosive properties including microstructure, temperature, and chemistry to explosive ignition and sensitivity. Unlike approaches that focus only on elucidating mesoscopic mechanisms important to ignition, the present analysis seeks a methodology directly applicable to continuum explosive burn models. Because the Scaled Uniform Reactive Front (SURF) burn model has a microstructural basis, it is chosen as the starting point of the present analysis. To build upon the SURF framework, a literal translation is performed of the high-level conceptual notions for which SURF is based, to concrete ignition–combustion parameters, a statistical description of the microstructure, and other thermo-chemical data acquired by non-shock experiments. The analysis requires a void volume fraction distribution acquired from ultra-small angle neutron scattering (USANS). The shock response of PBX 9502 is used to illustrate the theory. While the analysis requires calibration to a shock experiment (the pop plot, for example), the results are a self-consistent set of realistic physical quantities that contribute to the shock initiation process including, initial temperature, chemical kinetics, a statistical description of the microstructure, and the hot spot size, spacing, and temperature. The utility of having a mesoscale based theory is that once the critical hot spot temperature, as a function of shock pressure, is known for a given explosive and initial porosity distribution, the entire set of meso- and macro-scale results, including the pop plot can be calculated for any other porosity. Thus, one can understand changes in sensitivity at different densities (void size distributions), relying only on the assumption that the hot spot temperature curve would not differ significantly if the morphology of the hots spots were similar. Another important utility of the present analysis is to address the question of the role of initial temperature on the observed shock sensitivity of PBX 9502 and other explosives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Evaluating Variable‐Resolution CESM Over China and Western United States for Use in Water‐Energy Nexus and Impacts Modeling

Abstract Climate models are critical tools to study earth system processes and are often further downscaled to provide refined‐resolution data sets for regional water‐energy impact analyses. In this study, the Variable‐Resolution Community Earth System Model (VR‐CESM), a new dynamical downscaling method, is employed to generate 1/8° simulations for China and the western United States over 1970–2006. Precipitation, temperature, snowpack, radiation, and wind speed are compared with two 1° global climate models, regional climate model ensemble, and reference data sets to evaluate their fidelity. Simulated precipitation skill is dependent upon the reference data sets used, a slight (<+10%) and spatially consistent wet bias in China during summer and the western United States during winter. Precipitation bias in VR‐CESM is the smallest among models and demonstrates the added value provided by resolution refinement. However, temperature biases are generally greater than precipitation due to more complicated interactions among processes. VR‐CESM simulates +2°C warm bias in the low‐elevation regions in China but −2°C cold bias in mountains. However, DJF cold bias (−2.23°C to−3.37°C) and JJA warm bias (+1.13°C to +1.87°C) exist in the western United States. The modeling skills of precipitation and temperature at various topographies are similar in the two evaluated regions. The cold bias was likely affected by downward shortwave radiation deficit (−15%) and slow bias of surface wind speeds (−16% to −29%). Despite the wet and cold bias, snow water equivalent and snow cover are underestimated in mountains. We conclude that VR‐CESM outperforms other models evaluated and provides the best estimation of downscaled climate model data for water‐energy studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Solar Influencer Next Door: Predicting Low-Income Solar Referrals and Leads

Increasing the adoption of solar among low-to-moderate income (LMI) households remains an important policy goal because of its promise to simultaneously reduce energy burden and support the just distribution of benefits of renewable energy. However, scaling LMI solar remains challenging due to affordability and access issues. Most existing LMI adoption has occurred under public-funded programs, highlighting the importance of increasing the cost-effectiveness of these programs at scale. We develop a new household-level data set on LMI solar lead acquisition, referrals, and adoption to understand the processes through which LMI solar uptake has occurred in California. Then, we develop models to predict two sub-mechanisms in the solar adoption process: whether an otherwise qualified lead becomes "lost" i.e. non-responsive to outreach and, for existing clients, whether they refer solar to others. For the program analyzed, participants received their solar system at no cost, which deemphasizes economic drivers of solar adoption and could differ from other program experiences. Both models substantially improved the accuracy of prediction relative to a baseline. Overall, we find that peer effects and solar economics are important to predicting referrals, and household demographic factors in lead loss prediction. Finally, we find that referrals are both the highest quality and largest source of LMI solar leads, providing a promising mechanism to expand LMI programs further.

customer acquisition costs↗
DOE OSTI2021

Rapid Identification of Synthetic Routes to Functional Metastable Phases Using X-ray Probed Laser Anneal Mapping (XPLAM) Time-Temperature Quench Maps

Many material systems have known or predicted functional phases that are metastable at standard temperature and pressure. While substantial advances have been made in the high-throughput and combinatorial synthesis of materials with a range of stoichiometries, investigation of thermal processing remains largely the domain of iterative uniform anneals or static gradients. Here we develop X-ray probed laser anneal mapping (XPLAM), a high throughput technique coupling spatially resolved X-ray diffraction with microsecond to millisecond laser gradient anneals to produce temperature–dwell–transformation (TDT) diagrams of the phase as a function of quench time and temperature. In addition to showing regimes where specific metastable phases form preferentially, TDT diagrams provide insight into the submillisecond kinetics of solid–solid phase transitions. This is a unique tool for mapping reaction pathways for metastable phases. As a first demonstration of XPLAM, we study Bi2O3, which has a rich set of polytypes, including the d-phase with an exceptionally high oxygen ion conductivity. We demonstrate the first annealing-driven synthesis of room temperature d-Bi2O3. We expect XPLAM to prove a powerful technique for rapid identification of synthetic routes to metastable phases and to generate the exhaustive data sets required for machine learning-guided exploration of materials processing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Visualizing Charge Transport and Nanoscale Electrochemistry by Hyperspectral Kelvin Probe Force Microscopy

Charge-transport and electrochemical processes are heavily influenced by the local microstructure. Kelvin probe force microscopy (KPFM) is a widely used technique to map electrochemical potentials at the nanometer scale; however, it offers little information on local charge dynamics. In this study, we implement a hyperspectral KPFM approach for spatially mapping bias-dependent charge dynamics in timescales ranging from the sub-millisecond to the second regime. As a proof of principle, we investigate the role mobile surface charges play in a three-unit-cell LaAlO 3 /SrTiO 3 oxide heterostructure. We explore machine learning approaches to assist with visualization, pattern recognition, and interpretation of the information-rich data sets. Linear unmixing methods reveal hidden bias-dependent interfacial processes, most likely water splitting, which are essentially unnoticed by functional fitting of the dynamic response alone. Hyperspectral KPFM will be beneficial for investigating nanoscale charge transport and local reactivity in systems involving a possible combination of electronic, ionic, and electrochemical phenomena.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Lignin valorization process control under feedstock uncertainty through a dynamic stochastic programming approach

The randomness introduced by reactants is an issue when processing renewable bioresources. In this paper, we apply tools from dynamic stochastic programming theory to the biochemical process. Instead of introducing extra preprocessing units, we consider the inherent randomness of the process and optimize in expectation the performance of the system. In a general setting, this is a multistage stochastic optimization problem and we investigate its approximate solution via two approaches, namely stochastic dual dynamic programming (SDDP) and the finite state, finite action Markov decision process (MDP) framework. These two methods are implemented to a case study of lignin valorization that is crucial to a cost-effective biorefinery process using biomass as the feedstock.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Facility-Level Industry Representation for Decarbonization Modeling [Slides]

The largest facilities of energy-intensive materials processing industries are disproportionate contributors to U.S. greenhouse gas (GHG) emissions. In general, industrial energy system modeling for the United States currently represents industrial demands at a much lower resolution than other end-use sector modeling. Therefore, characterizing even a subset of energy-intensive materials processing facilities will capture a significant portion of industrial GHG emissions. In order to further the development of publicly-available data to support modeling of industrial decarbonization, we summarize a set of approaches and results for characterizing the location, energy intensity and mix, process emissions intensity, and general production technology of existing clinker, ammonia, and iron and steel facilities in the United States. We also characterize facilities that represent options for reducing GHG emissions from each industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Conditions for the Solvability of the Linear Programming Formulation for Constrained Discounted Markov Decision Processes

We consider a discrete-time constrained discounted Markov decision process (MDP) with Borel state and action spaces, compact action sets, and lower semi-continuous cost functions. We introduce a set of hypotheses related to a positive weight function which allow us to consider cost functions that might not be bounded below by a constant, and which imply the solvability of the linear programming formulation of the constrained MDP. In particular, we establish the existence of a constrained optimal stationary policy. Our results are illustrated with an application to a fishery management problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

SU-E-J-116: Uncertainties Associated with Dose Summation of High-Dose Rate Brachytherapy and Intensity Modulated Radiotherapy for Gynecological Cases

Purpose: Determining the cumulative dose distribution (CDD) for gynecological patients treated with both high-dose rate (HDR) brachytherapy and intensity-modulated radiotherapy (IMRT) is challenging. The purpose of this work is to study the uncertainty of performing this with a structure-guided deformable (SGD) approach in Velocity. Methods: For SGD, the Hounsfield units inside specified contours are overridden to set uniform values. Deformable image registration (DIR) is the run on these process images, which forces the DIR to focus on these contour boundaries. 18 gynecological cancer patients were used in this study. The original bladder and rectum planning contours for these patients were used to drive the SGD. A second set of contours were made of the bladder by the same person with the intent of carefully making them completely consistent with each other. This second set was utilized to evaluate the spatial accuracy of the SGD. The determined spatial accuracy was then multiplied by the local dose gradient to determine a dose uncertainty associated with the SGD dose warping. The normal tissue complication probability (NTCP) was then calculated for each dose volume histogram (DVH) that included four different probabilistic uncertainties associated with the spatial errors (e.g., 68.3% and 95.4%). Results: The NTCPs for each DVH (e.g., NTCP-−95.4%, NTCP-−68.3%, NTCP-68.3%, NTCP-95.4%) differed amongst patients. All patients had an NTCP-−95.4% close to 0%, while NTCP-95.4% ranged from 0.67% to 100%. Nine patients had an NTCP-−95.4% less than 50% while the remaining nine patients had NTCP-95.4% greater than 50%. Conclusion: The uncertainty associated with this CDD technique renders a large NTCP uncertainty. Thus, it is currently not practical for clinical use. The two ways to improve this would be to use more precise contours to drive the SGD and to use a more accurate DIR algorithm.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis

We introduce new dictionary learning methods for tensor-variate data of any order. We represent each data item as a sum of Kruskal decomposed dictionary atoms within the framework of beta-process factor analysis (BPFA). Our model is nonparametric and can infer the tensor-rank of each dictionary atom. This Kruskal-Factor Analysis (KFA) is a natural generalization of BPFA. We also extend KFA to a deep convolutional setting and develop online learning methods. We test our approach on image processing and classification tasks achieving state of the art results for 2D & 3D inpainting and Caltech 101. The experiments also show that atom-rank impacts both overcompleteness and sparsity.

Stevens, Andrew J.↗
DOE OSTI2022

Requirements for Cataloging Hanford Geophysical Datasets

Environmental management activities at the Hanford Site produce extensive data about site conditions, contaminants, cleanup, and more. Managing and archiving that data requires a high degree of collaboration among site contractors and a high level of awareness by project managers and staff. Part of that effort is developing a Hanford Environmental Information and Data Index (HEIDI) to organize the data and maximize its value by making it findable and available for reuse. The objective is to catalog the disparate data sets collected to address the evolving needs of planning, executing, and documenting cleanup over several decades up to the present day, including links to active data sources when available. A properly implemented data catalog makes finding environmental datasets related to an area or theme a routine, reliable process, without requiring the searcher to have special knowledge that a data set exists and where it may be stored. In this project, a working group, including the U.S. Department of Energy, the Hanford Site contractors, and Pacific Northwest National Laboratory staff, identified needs and requirements for handling complex site data. Geophysical data was chosen as a test case because it can be large and complex and often involves multiple processing steps to extract the information incorporated into deliverables. The ability to document those steps was one of the requirements identified for the catalog. In addition to developing requirements, other activities included selecting a metadata schema and initial testing with the objective of determining whether the workflow and capabilities of selected data catalog software platforms were sufficient to implement and impose the identified requirements. This initial testing involved running the default catalog instance using the software platform of interest and altering the configuration to achieve each requirement, if possible. Where configuration alone was insufficient, the possibility of modifying the software by changing the code was examined, but not implemented. A follow-on task is planned to reprogram the code as necessary to implement requirements in a prototype catalog.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

CO 2 Sequestration and Enhanced Oil Recovery at Depleted Oil/Gas Reservoirs

This study presents a quantitative evaluation of the operational and technical risks of an active CO 2 -EOR project. A set of risk factor metrics is defined to post-process the Monte Carlo (MC) simulations for statistical analysis. The risk factors are expressed as measurable quantities that can be used to gain insight into project risk (e.g. environmental and economic risks) without the need to generate a rigorous consequence structure, which include (a) CO 2 injection rate, (b) net CO 2 injection rate, (c) cumulative CO 2 storage, (d) cumulative water injection, (e) oil production rate, (f) cumulative oil production, (g) cumulative CH 4 production, and (h) CO 2 breakthrough time. The Morrow reservoir at the Farnsworth Unit (FWU) site, Texas, is used as an example for studying the multi-scale statistical approach for CO 2 accounting and risk analysis. A set of geostatistical-based MC simulations of CO 2 -oil/gas-water flow and transport in the Morrow formation are conducted for evaluating the risk metrics. A response-surface-based economic model has been derived to calculate the CO 2 -EOR profitability for the FWU site with a current oil price, which suggests that approximately 31% of the 1000 realizations can be profitable. If government carbon-tax credits are available, or the oil price goes up or CO 2 capture and operating expenses reduce, more realizations would be profitable.

02 PETROLEUM↗
DOE OSTI2024

Bayesian stability and force modeling for uncertain machining processes

Accurately simulating machining operations requires knowledge of the cutting force model and system frequency response. However, this data is collected using specialized instruments in an ex-situ manner. Bayesian statistical methods instead learn the system parameters using cutting test data, but to date, these approaches have only considered milling stability. This paper presents a physics-based Bayesian framework which incorporates both spindle power and milling stability. Initial probabilistic descriptions of the system parameters are propagated through a set of physics functions to form probabilistic predictions about the milling process. The system parameters are then updated using automatically selected cutting tests to reduce parameter uncertainty and identify more productive cutting conditions, where spindle power measurements are used to learn the cutting force model. The framework is demonstrated through both numerical and experimental case studies. Results show that the approach accurately identifies both the system natural frequency and cutting force model.

42 ENGINEERING↗
DOE OSTI2017

A Study Of Geological Disposal Facility (GDF) Ageing Phenomena In Bentonite - 17605

Since the 1950's, the UK's nuclear activity in energy, defence and medicine has led to the accumulation of a large amount of higher activity radioactive waste (HAW). Most of nuclear waste arises in a wide variety of physical and chemical forms with different radioisotopes and loadings. Due to complexity in HAW, it is imperative that there be a rigorous management system that ensures population and environmental safety over extended periods. The concept of Deep Geological Disposal of waste in a stable geological formation is, nowadays, an internationally accepted solution as the surest and the most viable long term method for management. Any future Geological Disposal Facility (GDF) will comprise, among other artificial and natural engineered barriers, a barrier immediately around the waste containers compounded by a sealing material. The long-term stability of a GDF will be determined by the design and construction of the engineered barriers, and the mechanical, hydraulic and geochemical interactions that may happen between the different engineered components as a result of combined processes such as the heat produced by radioactive decay and the ion water composition of the surrounding host rock. Bentonite clay has been selected as the most promising buffer and backfill material in many disposal programmes because of its swelling capacity, low permeability, plasticity and cation exchange capacity. In the nuclear scope, bentonite testing at different scale-lengths (laboratory and field scale) has been performed and implemented in order to examine the thermo-hydro-mechanical (THM) and thermo-hydro-geochemical (THG) processes that may happen in the engineered barriers and geological settings associated with a GDF. Two major European field scale tests have been performed to better understand bentonite performance and behaviour immediately after HAW emplacements. The Full-Scale Engineered Barriers Experiment (FEBEX) at the Grimsel Test Sit (Switzerland) and the Alternative Buffer Material (ABM) Project at the Aespoe Hard Rock Laboratory (Sweden). The Febex Experiment was a pilot project designed for 'field research' of HAW repository hosted in crystalline rock where surrogate HAW canisters were placed horizontally in drifts, surrounded by compacted blocks of bentonite, sealed by a concrete plug and maintaining a constant canister surface temperature of 100 deg. C. After five years of operation, a partial dismantling was carried out in 2002. Heater processes occurring in the surrounding bentonite. The dismantling of the heater no. 2 (after 18 years of operation) started in late 2014 and is planned to be finished at the end of 2016 (termed FEBEX-DP). The FEBEX Project utilised bentonite from the Cortijo de Archidona deposit (Almeria, Spain). This 'Febex' bentonite is sourced from calcium-magnesium montmorillonite with good properties in relation to swelling capacity, permeability, adsorption capacity, and thermal conductivity. The ABM Project is a large-scale programme that has been running since 2006. The programme is designed to study a suite of possible buffer materials and correlate the physical and chemical properties of eleven different types of bentonites to their fundamental mineralogical properties. The ABM test consists in three 'packages' containing a central heater tube surrounded by different pre-compacted bentonite blocks and instrumentation. Each 'package' was installed in separate boreholes. The target temperature for the surface of the heated tubes in contact with the bentonite was 130 deg. C. Both FEBEX and ABM test the bentonite was saturated with water percolating from surrounding host rock (FEBEX and ABM) and also via artificial supply systems (ABM), being the water itself acting as a long term corrosion agent. For both experiments and emerging interest is in the interaction of bentonite with metallic components, under geological disposal conditions are not considered to be thermodynamically stable and hence arising corrosion products could interact with the buffer. These interactions depend on various factors such as the redox potential, temperature, pH, solute concentration and buffer material characteristics. During the operational phases of the FEBEX and ABM and after its 'closure' a significant volume of air was found to be retained in the bentonite. Under these conditions a period of initial aerobic corrosion by the air in the bentonite barrier would have occurred followed by establishment of anaerobic corrosion would be the dominant process ongoing. The anaerobic corrosion began when the bentonite became water saturated. Under these conditions, the water acted as the major oxidant. The present project focuses on the characterization of samples arising from the FEBEX and ABM Projects as well as performing additional independent experiments designed to complement these prior studies. Characterization will focus mainly on corrosion related alteration combined with thermal effects. Through detailed sample characterization, the intention is to achieve a greater knowledge regarding the changes to the bentonite under the experimental conditions to which it was exposed, and plausible alterations in its THM and THG properties occurring during the experiment due to blended effects of temperature,water content, engineering structural components and solutes are also to be measured. The fulfilment of a large-scale 'in-situ' test is intricate, time-consuming and with an abundance of external variables that are not always well controlled. For this reason, the University of Bristol is performing a series of well constrained time-resolved laboratory tests which bentonite is being subjected to different possible scenarios that may happen whilst in a HAW repository. In virtue of this experiment, the UoB aims to understand the illitisation and cementation process and their effects on bentonite safety properties. (authors)

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

Deep Learning Emulation of Subgrid-Scale Processes in Turbulent Shear Flows

Deep neural networks (DNNs) are developed from a data set obtained from the dynamic Smagorinsky model to emulate the subgrid-scale (SGS) viscosity (ν sgs ) and diffusivity (κ sgs ) for turbulent stratified shear flows encountered in the oceans and the atmosphere. These DNNs predict ν sgs and κ sgs from velocities, strain rates, and density gradients such that the evolution of the kinetic energy budget and density variance budget terms is similar to the corresponding values obtained from the original dynamic Smagorinsky model. These DNNs also compute ν sgs and κ sgs ~2–4 times quicker than the dynamic Smagorinsky model resulting in a ~2–2.5 times acceleration of the entire simulation. This study demonstrates the feasibility of deep learning in emulating the subgrid-scale (SGS) phenomenon in geophysical flows accurately in a cost-effective manner. In a broader perspective, deep learning-based surrogate models can present a promising alternative to the traditional parameterizations of the subgrid-scale processes in climate models.

58 GEOSCIENCES↗
DOE OSTI2018

High-Temperature Sapphire Pressure Sensors for Harsh Environments

The project aimed to develop and characterize an optical pressure sensor capable of withstanding high temperatures, up to 1000 °C. Survivability in harsh environments was enabled using sapphire for both the substrate and the optical fibers, therefore mitigating thermal expansion mismatch. Such material cannot be machined through conventional microfabrication techniques, and unique additive and subtractive methods had to be studied and implemented. Subtractive methods included laser-machining the sapphire substrate using a picosecond laser machining tool. At first, the parameters of importance were theoretically and experimentally evaluated such that the fluence, the pulse spacing, and the number of passes were found to strongly influence the laser ablation process. A simulation tool was designed to predict the final pattern when inputting a set of parameters and a profile. This model was complemented with laser ablation material physics models and Bayesian uncertainty analysis for one dimensional and three dimensional ablation processes. An additive process called thermocompression bonding was studied in order to seal the diaphragm substrate to the back cavity. At first, bonding strength using this technique was examined using the chevron-notch method that allow to determine the fracture toughness of this bond. This parameter is independent of the geometry used and therefore can be extended to other geometries used in actual sensor bonding. A theoretical and experimental study of internal damage caused by the laser micromachining of the sapphire material was carried out. The research identified enhanced strength and fracture toughness along laser ablated surfaces. This was determined using bend bar specimens and Vicker’s microindentations. Softening of the plasticity behavior (as indicated by nanoindentation experiments) was also investigated using finite deformation plasticity modeling via a contact mechanics finite element simulation. Good correlations between experiments and the model were achieved to determine material parameters that can be used in future MEMS based sensor design and modeling to minimize sensor failure loads. The enhanced strength and fracture toughness was determined to be due to compressive residual stress near laser ablated surfaces. This was confirmed through x-ray spectra taken along different crystal structure planes in combination and Bayesian uncertainty analysis. The Bayesian analysis inferred the entire residual strain tensor (six components) and the residual stress tensor. The results illustrated compressive in-plane stress was the most likely cause of enhanced strength and toughness after laser machining. Due to major equipment failures, a parallel route for fabricating the sensor was taken, using commercially available sapphire removal method (subtractive) and ceramic adhesive bonding (additive). The fully-packaged sensor utilized laser micromachining for the sapphire substrate fabrication and was comprised only of high-temperature-compatible materials. Calibration tests of the sensor in both room-temperature and high-temperature plane-wave tubes showed good linearity and consistent frequency response, in the tested frequency range. The sensor also showed good thermal stability in tests up to 300 °C, proving the fiber-optic lever and its packaging process to be a promising approach for high-temperature measurements. The calibration from 23 °C to 300 °C gave a raw sensitivity of 3.9 × 10 -7 V/Pa and a normalized sensitivity of 9.8 × 10 -8 Pa -1 which were consistent within the temperature range. The sensor had a resonance at 16.3 kHz and a flat band (±3 dB) up to 10.3 kHz. The noise floor of the sensor at 1 kHz was 316 nV/Hz 1/2 , resulting in a MDP of 0.8 Pa. Previously, diaphragm delamination due to CTE mismatch was found at 500 °C, causing packaging failure. Unfortunately high temperature validation in hot jet environment could not be made in time. In the future, it is suggested that the two sapphire substrates be bonded by thermal compression bonding with an intermediate platinum layer instead of using an alumina-based adhesive, and therefore achieve higher temperature capacity up to 1000 °C.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

Monitoring for Additive Manufacturing Technologies: Report on Progress, Achievements, and Limitations of Monitoring Techniques

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to produce multiple nuclear components to be assembled into a fully functional microreactor core. AM was selected as the main manufacturing technology of choice for TCR because it has the potential to disrupt the nuclear industry on two fronts: (1) it enables the manufacturing of very complex geometries with optimized and tailored material properties for the intended use of the component, opening up new options for reactor designs, and (2) it allows for a better understanding of the manufacturing process through real-time in situ monitoring, data analytics, and artificial intelligence that can lead to a streamlined qualification and certification process. Within TCR, the development and deployment of a digital platform aims at addressing the latter opportunity. As part of this effort, the collection of pedigree data sets is vital, should these data be generated before, during, or after the manufacturing process. This report focuses on the collection of manufacturing process data using systems instrumentation and monitoring. It presents an inventory of the various monitoring modalities installed on the manufacturing systems by the machine manufacturer or specifically designed by our team to fill information gaps. It also provides details on other relevant data streams available and leverageable to evaluate the manufacturing process on all classes of AM systems investigated during the first year of this program. Through examples, the report gives an overview of the data collected and presents a path forward for the digital platform.

42 ENGINEERING↗