Search NASA⌕ Search

SEARCH · Search NASA

Results for “Michael Addition”

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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Numerical Investigation of Multi-Rotor Configurations

The development of a conservative, parallel blade-element rotor model and its implementation into an adaptive Cartesian method is briefly reviewed. The model is first validated and then applied in edgewise forward flight for a variety of configurations. Validation studies comparing torque required for an isolated rotor match XV-15 wind tunnel trends well. Simulations of a simple quadrotor in “×” and “+” configurations are performed, demonstrating how the trailing rotor(s) lose thrust because of unfavorable aerodynamic interactions with the forward rotor(s), while power required remains near constant. A modified “+1” configuration is also examined, where the lateral rotors are brought inboard by 25%. This configuration showed a further 2% performance improvement over the “+” arrangement. The final paper will also include validation of a tandem rotor configuration with wind tunnel data as well as results and analysis of unsteady quadcopter simulations. In addition, a 4th configuration (× will be analyzed.

Multirotor↗
NASA NTRSDate not supplied

Initial Evaluation of CropManage Decision-Support Model for Vineyard ET Estimation

The CropManage(CM) decision-support web application was originally developed by U.C. Cooperative Extension to support evapotranspiration (ET) based irrigation scheduling and nutrient management of cool-season vegetables. The model uses prescribed crop phenology curves to develop daily estimates of fractional green canopy cover (Fc) within the field. Periodic Fc observations acquired by ground-based methods or imported from NASA’s Satellite Irrigation Management Support (SIMS) can be used to adjust the prescribed timeseries for such factors as weather anomalies or non-standard agronomic practice, as needed. Fc is then converted to daily crop coefficient (fraction of reference ET) values. The crop coefficient is combined with reference evapotranspiration, collected by the California Irrigation Management Information System, to derive daily ET estimates for the given field. Irrigation runtime recommendations are issued for a given date based on total ET since the last irrigation event (less any rainfall), and corrected for distribution uniformity of the water delivery system. In this project, CM was adapted to vineyards by adding sub-models to account for early-season depletion of stored soil moisture, cover crop presence, and intentional water stress. An initial trial was performed on a Central Coast winegrape vineyard, where an eddy covariance tower measured daily ET during from May-Dec 2020. Total ET for the period showed strong agreement between the tower-based measurements (443 mm) and the CM model (431 mm). The model tended to overestimate cumulative ET during mid-June by up to 30 mm (about 15%) and later underestimated cumulative ET by as much as 65 mm (about 23%) in late September, suggesting that additional model calibration is needed to improve simulation of within-season variability. Results will be reported for trials on additional vineyard sites conducted during the 2021 season.

Evaluation↗
DOE OSTI2026

Longitudinal Phase Space Tomography for the Booster Synchrotron

Efforts in the study of the longitudinal behavior of charged particles in the Fermilab Booster can be catalyzed with an image of the two-dimensional phase space distribution. In the past, tomography had been employed in the reconstruction of the phase space in accelerators such as the Recycler at Fermilab and the Proton Synchrotron Booster at CERN. However, such a capability had yet to realize for the Fermilab Booster synchrotron. In this work, the first successful tomographic phase space reconstruction of a low-energy Booster bunch is presented along with validation metrics. A numerical turn-by-turn model of the longitudinal particle dynamics in the Booster has been implemented, which utilizes a fast, map-based particle transport algorithm. Using a sinogram generated from the Wall Current Monitor signal, the iterative reconstruction algorithm recovers a discretized image of the original phase space distribution at variable resolution. The reconstruction result shows low root-mean-square error and a rapid convergence toward the solution, providing strong evidence of accuracy. Future and ongoing work includes modeling high-energy bunches above transition and using tomography to infer certain machine parameters such as synchronous phase, peak gap voltage, and synchronous energy in addition to the phase space distribution.

Ebeid, Safi [Unlisted; Fermilab]↗
DOE OSTI2022

Experimental measurements and mathematical modeling of cold plate for aviation thermal management

Herein, this study, which has been motivated by the recent applications of the cold plate device in aviation thermal management, reports on physics-based mathematical models derived from the conservation laws of mass, momentum, and energy, and empiricism-based models. One of the objectives of the present work is to report on an elaborate and successful experimental work carried out on an additively manufactured device for the purpose of rigorously validating the numerical predictions. The excellent agreement between the numerical predictions and measured performance provides much needed confidence in the implementation, in the software package, of the offset-strip fin passage correlations, as well as in the software implementation of user-defined wavy fin correlations for aerospace heat exchangers and cold plates operating with ram air at Reynolds numbers below 8000. The contributions of this work can also be found in the development of a new and accurate thermal-hydraulic analysis procedure, referred to in this paper as plate-fin analogy. Results from this procedure are compared with those from thermal resistance network. The comparative study in this paper of the bulk and discrete enthalpy flux method is also new, as is the relative assessment of four off-set strip fin thermal-hydraulic models.

42 ENGINEERING↗
DOE OSTI2020

A Digital Twin Approach to Study Additive Manufacturing Processing Using Embedded Optical Fiber Sensors and Numerical Modeling

One of the major challenges for metal-powder-based additive manufacturing is measuring and mitigating residual strain induced during the manufacturing processes. This article reports distributed fiber optic sensors embedded in Inconel alloy components as experimental means to validate numerical models of additive manufacturing process. Electroplating was used to deposit a metal protective jacket onto standard telecom single-mode fibers for strain measurements, Fiber sensors were embedded in an Inconel alloy substrate using the laser engineered net shaping (LENS) process. Here by using a Rayleigh-scattering optical frequency domain reflectometer (OFDR), temperature changes, and residual strain in the metal substrate were monitored with 5 mm spatial resolution during the LENS process. Using finite element analysis, temperature and strain profiles induced by the LENS deposition processes were also numerically studied. Discrepancies between the simulated temperature and strain profiles and those measured directly were less than 10%. Results presented in this article demonstrates a digital twin approach to fuse modeling results with distributed fiber sensor measurement data to study additive manufacturing process toward design and fabrication process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Wire arc additive manufactured A36 steel performance for marine renewable energy systems

Additive manufacturing has established itself to be advantageous beyond small-scale prototyping, now supporting full-scale production of components for a variety of applications. Despite its integration across industries, marine renewable energy technology is one largely untapped application with potential to bolster clean energy production on the global scale. Wave energy converters (WEC) are one specific facet within this realm that could benefit from AM. As such, wire arc additive manufacturing (WAAM) has been identified as a practical method to produce larger scale marine energy components by leveraging cost-effective and readily available A36 steel feedstock material. The flexibility associated with WAAM can benefit production of WEC by producing more complex structural geometries that are challenging to produce traditionally. Additionally, for large components where fine details are less critical, the high deposition rate of WAAM in comparison to traditional wrought techniques could reduce build times by an order of magnitude. In this context of building and supporting WEC, which experience harsh marine environments, an understanding of performance under large loads and corrosive environments must be understood. Hence, WAAM and wrought A36 steel tensile samples were manufactured, and mechanical properties compared under both dry and corroded conditions. Here, the unique microstructure created via the WAAM process was found to directly correlate to the increased ultimate tensile and yield strength compared to the wrought condition. Static corrosion testing in a simulated saltwater environment in parallel with electrochemical testing highlighted an outperformance of corroded WAAM A36 steel than wrought, despite having a slighter higher corrosion rate. Ultimately, this study shows how marine energy systems may benefit from additive manufacturing components and provides a foundation for future applications of WAAM A36 steel.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Application of printed strain gauges in prototypical nuclear reactor conditions

This report discusses the current development and testing of additively manufactured temperature sensors and resistive/ capacitive strain gauges. This has potential to improve the sensor design and manufacturing techniques to meet the requirements of in-pile monitoring of mechanical properties and structural health of materials and specimens in current and advanced nuclear test reactors (i.e., in terms of environment conditions, sample geometry, and materials compatibility). The developmental additively manufactured strain gauges are exposed to separate effects testing (i.e., mechanical strain (up to 1000 µe), high temperature (up to 700 °C)) to determine environmental factors that affect the performance of the strain gauge. The robustness and integrity of a printed strain gauge layer exposed to a molten salt environment for up to 500 hours was evaluated, with the goal of assessing material compatibility of printed strain sensors for instrumented surveillance test articles in molten salt reactors. In addition, sensor qualification methodologies are further developed for determining the reliability and robustness at the interface of the additively manufactured strain gauge materials.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Material Extrusion Printing of Poly(Acrylonitrile‐Styrene‐Acrylate) and Poly(Acrylonitrile‐Butadiene‐Styrene) Structures Reinforced with Poly(Phenylene Oxide) Additives for Improved Thermomechanical Properties and their Surface Analysis by Time‐of‐Flight Secondary Ion Mass Spectrometry

Fused filament fabrication offers the ability to 3D print complex geometries made from plastic filament materials; however, these parts are mechanically outperformed by parts created by traditional fabrication methods. To overcome this challenge, a high-performance polymer poly(2,6-dimethyl-1,4-phenylene oxide) (PPO) is incorporated as an additive into two common engineering thermoplastics, poly(acrylonitrile-styrene-acrylate) (ASA) and poly(acrylonitrile-butadiene-styrene) (ABS). Structures printed from these polymer blends are more mechanically robust compared to those prepared from the parent polymers, with low loading levels (1–5 wt%) of PPO improving the elastic strength by up to ≈30% relative to the parent terpolymers. Even at higher loading levels (10 and 20 wt% PPO), there is no evidence of additive aggregation in the model thin films, which is supported by compositional analysis of the copolymers and chemical analysis via time-of-flight secondary ion mass spectrometry. The enhancements in mechanical properties of ASA and ABS blends appear to be a consequence of homogeneous incorporation of the PPO additive. In conclusion, this work explores expanding materials-property space using miscible blends of engineering thermoplastics to improve mechanical performance as a general approach to overcoming challenges with parts created by melt-based material extrusion printing.

additive manufacturing↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
NASA NTRS2023

Competition of Failure Modes in an Additively Manufactured Disk Superalloy

Additive manufacturing of powder metallurgy (PM) disk superalloys can produce unique microstructures that differ from those usually encountered in traditional processing as the result of consolidation, forging, and heat treatments. Unusual variations in grain size, major and minor phase precipitate sizes, and defects can occur. The failure modes associated with these unique microstructures are of high interest. The objective of this study was to compare the failure modes for a low solvus, high refractory (LSHR) PM disk superalloy produced by electron-beam-melting additive manufacturing. Specimens were subsequently given different solution heat treatments and a fixed aging heat treatment. Tensile, creep, and fatigue failure modes were screened in tests at elevated temperatures. Failure modes were considered with respect to these unique microstructures.

additive manufacturing↗
NASA NTRSDate not supplied

Design and Analysis of Battery Thermal Management Systems

Thermal management of battery cell packs is a critically needed technology. The purpose of this work is to design new and improved Battery Thermal Management Systems (BTMS) for use in electric airplanes. The BTMS should be 3D printable and hold twelve to sixteen 18650 batteries. To minimize aircraft’s weight, an actively air-cooled battery pack was chosen to avoid the excess weight of water-cooled and phase change material (PCM) cooled packs. As a result, significant changes were made to remove all metal in the pack and replace it with lower density polymer matrix composites (PMCs) which can be additively manufactured. Two air cooled battery pack configurations (traditional propeller fans & bladeless fans) were designed, modeled, and compared. These packs were first modeled in SolidWorks 2021 3D CAD, then imported into COMSOL MultiPhysics to be studied using the “Heat Transfer in Solids and Fluids” module. The design with the bladeless fans eliminated the need to use high conductivity heavy metal to remove unwanted thermal energy. These bladeless fans were designed entirely out of PMCs. This thermal pack design weighs 0.04 kg less than that of the traditional propeller fans design and has increased the battery pack energy density by 8.25 Wh/kg.

Thermal Management System for Battery Packs↗
DOE OSTI2020

DICE-gas turbine compound reheat combined cycle

Coal-fired Direct Injection Carbon Engine – Gas Turbine (DICE-GT) Compound-Reheat Combined Cycle (CRCC), is a combined cycle power plant comprising a multiplicity of coal-fired reciprocating internal combustion engines (RICE), which is commonly referred to by the acronym DICE (Direct Injection Carbon Engine); a natural gas-fired gas turbine (turbine and combustor); a heat recovery steam generator (HRSG); a steam turbine generator; and an integrally geared and intercooled centrifugal air compressor. Finally, the concept uses coal-water slurry/fluid in the DICE and natural gas (or hydrogen) in the gas turbine for unmatched efficiency (well above 50% net LHV) and modularity (block sizes of 120, 240 and 360 MW) with easy adaptability to carbon capture and sequestration with minimal additional cost, complexity and performance hit.

42 ENGINEERING↗