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NASA NTRSDate not supplied

Microwave Structure Construction Capability Year One Accomplishments

The Microwave Structure Construction Capability (MSCC) element, part of the Moon to Mars Planetary Autonomous Construction Project (MMPACT) was initiated in 2020. MSCC is responsible for creating horizontal and vertical infrastructure on the moon using microwave energy. Microwave energy was selected since it is the only method to volumetrically heat the regolith. All other sintering/melting methods rely on thermal conduction through the very low conductivity surface, resulting in an inefficient process. Advances were achieved in materials characterization and understanding, microwave sintering in vacuum, and microwave design and analyses. Two dielectric property testing systems have been developed at Radiance Technologies and JPL. These will examine dielectric properties at cryogenic temperatures and over a broad frequency range. Permittivity and permeability testing at -60 ̊C in vacuum from 0.05 to 3 GHz has been generated at JPL. Additional modifications will be made to go to -190 ̊C (LN2). Radiance Technologies created a test system to measure dielectric properties at greater than 10 GHz and initiated work on developing a vacuum capable, portable test system to measure dielectric properties of Apollo regolith and simulants from 100 MHz to 18 GHz. These tests are to identify optimal heating frequencies and protocols. During microwave sintering at about 1100 ̊C, volatiles were creating difficulty in achieving a reasonably dense specimen. Due to processing in vacuum and the nature of the lunar regolith, some volatiles and porosity are expected. However, the Earth produced simulants have non-lunar materials in them that create volatiles that aren’t representative of lunar regolith. Therefore, a five month effort was conducted to establish a heat treat method to remove these non-lunar materials. Tests were conducted using TGA mass spectrometry, heating in vacuum and conducting mass spectrometry, dielectric and DTA, Raman, BET, particle size analysis, morphological analysis, carbon and sulfur chemical content determination and microscopy. The process has been scaled-up to 6 kg batch size and undergoing evaluation. A 36 kg batch size is the target for JSC-1A and other limited availability simulants. These calcining protocols will be standard for NASA and beyond. MSCC has also created scalable processes for fabricating synthetic lunar materials. Processes to fabricate Anorthite (plagioclase CaAl2Si2O8), Diopside (pyroxene CaMgSi₂O₆), and Enstatite (pyroxene Mg2Si2O6) have been generated. These materials will enable generation of microwave sintering models to bound various composition ranges anticipated on the Moon, therefore mitigating the need for a precise simulant with respect to location on the Moon. Successful microwave sintering in air using a horn applicator was demonstrated. All previous microwave vacuum sintering in the literature was at small scale and in a contained enclosure thus taking advantage of reflections. This is the first to use a lunar like microwave applicator to sinter ceramic in a bed as it would be done on the Moon. Small scale and inert sintering were conducted to assist in developing protocols with quicker turnaround times than larger scale testing. Testing has anchored thermal analysis predicting heat flow in vacuum during microwave sintering. Thermal conductivity testing was also initiated. Microwave coupling to the regolith has been modeled by multiple organizations and with different software packages. At least six horn designs and applicator configurations for both magnetron and solid state sources are being examined. Optimal simulant container designs for microwaves have also been generated. The power and electronics design for the solid state microwave system has been initiated. Concept designs for a lander based microwave sintering have been evaluated.

microwave↗
DOE OSTI2021

Robust Molecular Predictive Methods for Novel Polymer Discovery and Applications

Polymeric materials are ubiquitous in modern society and they play an instrumental role in almost all industries, undoubtedly including the energy and environment sectors. Increased demand of energy and awareness to sustainability both necessitates the development of novel polymers with enhanced properties. Unfortunately, their structural and behavioral complexity render such discovery challenging and impeded. To address this problem, scientists are developing various computational modeling techniques and leveraging their power to depict the relationship between structural characteristics of polymers and their properties (such as rheological behaviors), and use such prediction to guide the design and syntheses of novel polymeric materials with enhanced performances. Unfortunately, predicting the relationships between polymer structure and composition with rheological properties via atomistic modeling is still a major challenge because of the extended time and length scales involved. Studying dynamic shear viscosity and linear viscoelasticity using molecular models requires capabilities that have been elusive, including representation of large molecular weight chains with an effective internal scale capable of describing entanglement, shear-rates that are in the s-1 scale with accurate quantitative stresses, and chemically-realistic combinations of both homogeneous and heterogeneous systems. Motivated by these unmet challenges, the overall technical objective of this DOE-STTR Phase II project is to develop robust molecular predictive methods for advanced polymer discovery and applications and especially for designing and demonstrating the “smart” polymer-based waterflooding enhanced oil recovery (EOR) process. In particular, we apply state-of-the-art molecular modeling methods developed by our academic partner, Materials Stimulation Center (MSC) at California Institute of Technology (Caltech), to facilitate and accelerate the experimental discovery processes. During the Phase I of this project, we had focused on development and demonstration of the molecular modeling methods to describe rheological properties of non-Newtonian polymer fluids, and to improve our fundamental understandings of shear-thickening mechanism and kinetics. In Phase II, we further apply the theoretical models to guide our experimental programs to improve our design of smart rheology modifier (SRM) polymers and their optimization for EOR. Specifically, we have three objectives in the Phase II study: (1) to further improve out computational modeling methods, coupling with the advanced machine learning algorithms; (2) to develop cost-effective and efficient SRM-flooding process suitable for EOR applications under typical reservoir conditions; and (3) to further explore the application of our molecular predictive models for innovative material discovery in other industrial applications. The recent development of our multiscale predictive framework allows the successful prediction of rheological properties from the chemical structure for polymers of experimentally relevant molecular weights, and provides an in-silico machine learning engine for screening novel compositions and structures with optimized non-Newtonian response, required for both shear-thinning and shear-thickening applications. Our framework provides: (1) procedures and tools for systematic coarsening from atomistic models and reverse mapping of coarse-grain models to atomistic, (2) unique ab initio methods to characterize the atomistic origin of colloidal and interfacial interactions and phenomena, (3) systematic structure and composition builders based on practical descriptors that drive rheological changes in polymer melts and diluted polymer mixtures, (4) a rheological properties engine capable of predicting viscosity in the zero-shear limit and under realistic dynamic conditions (for shear-rates commensurate with experiments) for large heterogeneous systems, (5) coarse-grain force fields with improved non-bond descriptions based on accurate quantum mechanics, (6) an in-silico screening machine learning engine that feeds from the systematic model builders to cover the descriptors search space, computes the rheological properties from converged trajectories spanning sub-milliseconds and ranks them for each structure/composition using an automated viscosity-vs-shear rate fitness function that can be tuned for shear-thickening, shear-thinning and other rheological responses.

02 PETROLEUM↗
NASA NTRS1994

Atomic Oxygen (ATOX) simulation of Teflon FEP and Kapton H surfaces using a high intensity, low energy, mass selected, ion beam facility

A high intensity (greater than 10(exp 15) ions/sq cm) low energy (down to 5 eV) mass selected ion beam (MSIB) facility was used to study the effects of ATOX on two polymers commonly used for space applications (Kapton H and Teflon FEP). The polymers were exposed to O(+) and Ne(+) fluences on 10(exp 15) - 10(exp 19) ions/sq cm, using 30eV ions. A variety of analytical methods were used to analyze the eroded surfaces including: (1) atomic force microscopy (AFM) for morphology measurements; (2) total mass loss measurements using a microbalance; (3) surface chemical composition using x-ray photoelectron spectroscopy (XPS), and (4) residual gas analysis (RGA) of the released gases during bombardment. The relative significance of the collisional and chemical degradation processes was evaluated by comparing the effects of Ne(+) and O(+) bombardment. For 30 eV ions it was found that the Kapton is eroded via chemical mechanisms while Teflon FEP is eroded via collisional mechanisms. AFM analysis was found very powerful in revealing the evolution of the damage from its initial atomic scale (roughness of approx. 1 nm) to its final microscopic scale (roughness greater than 1 micron). Both the surface morphology and the average roughness of the bombarded surfaces (averaged over 1 micron x 1 micron images by the system's computer) were determined for each sample. For 30 eV a non linear increase of the Kapton roughness with the O(+) fluence was discovered (a slow increase rate for fluences phi less than 5 x 10(exp 17) O(+)/sq cm, and a rapid increase rate for phi greater than 5 x 10(exp 17) O(+)/sq cm). Comparative studies on the same materials exposed to RF and DC oxygen plasmas indicate that the specific details of the erosion depend on the simulation facility emphasizing the advantages of the ion beam facility.

Vered, R.↗
NASA NTRS2011

Simulation and Characterization of a Miniaturized Scanning Electron Microscope

A miniaturized Scanning Electron Microscope (mSEM) for in-situ lunar investigations is being developed at NASA Marshall Space Flight Center with colleagues from the University of Alabama in Huntsville (UAH), Advanced Research Systems (ARS), the University of Tennessee in Knoxville (UTK) and Case Western Reserve University (CWRU). This effort focuses on the characterization of individual components of the mSEM and simulation of the complete system. SEMs can provide information on the size, shape, morphology and chemical composition of lunar regolith. Understanding these basic properties will allow us to better estimate the challenges associated with In-Situ Resource Utilization and to improve our basic science knowledge of the lunar surface (either precluding the need for sample return or allowing differentiation of unique samples to be returned to Earth.) The main components of the mSEM prototype includes: a cold field emission electron gun (CFEG), focusing lens, deflection/scanning system and backscatter electron detector. Of these, the electron gun development is of particular importance as it dictates much of the design of the remaining components. A CFEG was chosen for use with the lunar mSEM as its emission does not depend on heating of the tungsten emitter (lower power), it offers a long operation lifetime, is orders of magnitude brighter than tungsten hairpin guns, has a small source size and exhibits low beam energy spread.

Gaskin, Jessica A.↗
NASA NTRS2013

Nuclear Thermal Rocket Simulation in NPSS

Four nuclear thermal rocket (NTR) models have been created in the Numerical Propulsion System Simulation (NPSS) framework. The models are divided into two categories. One set is based upon the ZrC-graphite composite fuel element and tie tube-style reactor developed during the Nuclear Engine for Rocket Vehicle Application (NERVA) project in the late 1960s and early 1970s. The other reactor set is based upon a W-UO2 ceramic-metallic (CERMET) fuel element. Within each category, a small and a large thrust engine are modeled. The small engine models utilize RL-10 turbomachinery performance maps and have a thrust of approximately 33.4 kN (7,500 lbf ). The large engine models utilize scaled RL-60 turbomachinery performance maps and have a thrust of approximately 111.2 kN (25,000 lbf ). Power deposition profiles for each reactor were obtained from a detailed Monte Carlo N-Particle (MCNP5) model of the reactor cores. Performance factors such as thermodynamic state points, thrust, specific impulse, reactor power level, and maximum fuel temperature are analyzed for each engine design.

Belair, Michael L.↗
NASA NTRS2019

Safe and Optimal Techniques Enabling Recovery, Integrity, and Assurance

There is a trend in the aviation industry to go from federated to integrated computing systems. Combining a number of traditional stand-alone federated systems into an integrated common platform (called Integrated Modular Avionics, IMA) has the benefit of increased power efficiency, reduced support hardware, and reduced cabling. However, changing from federated to integrated has a significant impact on the system architecture and hence the process of how avionic systems are to be analyzed. Traditional approaches to safety analysis become inefficient when functional boundaries can no longer be assumed for failure independence and fault isolation. In this report, we describe a tool that we developed to accelerate the safety engineer's ability to perform safety analysis of IMA systems through modeling, as well as optimize the system engineer's ability to develop a system through architecture synthesis. This work was the result of a three-year research effort called SOTERIA (Safe and Optimal Techniques Enabling Recovery, Integrity, and Assurance). We developed a compositional modeling language that supports rapid development, modification, and evaluation of architectures. The modeling language is structured such that the end-user defines a library of components with information on component reliability, connectivity, and fault propagation logic. The system model is built by instantiating the components from the library, connecting the components, and identifying the top-level faults of interest. Our tool is compositional in that the end-user only needs to define safety aspects at the component level. The tool takes the model and automatically synthesizes both the qualitative and quantitative safety analyses. We go further by allowing users to describe system information such as components to use in an architecture and their connection compatibility and automatically synthesize an architecture that meets the top-level probability target adhering to end-user specified constraints. This capability allows users to rapidly explore a design space..

Siu, Kit Y.↗
DOE OSTI2024

Generating Emissions Inventory for Carbon Capture and Storage Analysis for Carbon-Intensive Industrial Sectors

Decarbonizing the industrial sector is critical to achieve carbon dioxide (CO2) emissions reductions goals of the Biden Administration. Currently available decarbonization options include electrification, fuel switching to zero carbon fuels like green hydrogen (H2) and carbon capture and storage (CCS). Application of post-combustion carbon capture (PCCC) technology in the power sector, as well as research at the U.S. Department of Energy's Fossil Energy and Carbon Management (FECM) Office has shown that its application in the industrial sector could have co-benefits in the form of emissions reductions of non-CO2 regulated pollutants. For example, solvent based PCCC systems require pre-conditioning of flue gas to remove sulfur and particulate matter (PM) upstream of the CO2 absorber. However, there is a lack of understanding about the type of non-CO2 pollutants which can be captured and the amount of reduction possible. PCCC application differs across industrial sectors as it depends on the availability of decarbonization options, characteristics of industrial processes and the amount and composition of pollutant flows. Certain facilities can also have multiple effluent flows with or without a CO2 stream. As such, understanding industrial processes and their effluent flows in detail is required to quantify the co-benefits opportunities presented by PCCC. Considering this requirement, the goal of this analysis is to develop a high-resolution inventory of effluent flows from facilities of 8 industrial sectors in the U.S. These industrial sectors - ethanol, ammonia, cement, steel, natural gas processing, hydrogen, petroleum refining and wood and pulp products - have carbon-intensive effluent flows, and thus are prime candidates for PCCC applications. In this study, we map the composition of pollutant flow from flue stacks across the identified facilities. Using data available in three Environmental Protection Agency (EPA) databases - the Green House Gas Reporting Program (GHGRP), the National Emissions Inventory (NEI) and the Toxic Release Inventory (TRI), we create a combined inventory which lists the type, amount, and concentration of pollutant flows. Using total weight of the pollutant flow back calculated from observed data for CO2 concentrations in flue gas for individual sectors, we calculate the concentration of each pollutant in the flue gas stream. Thus, the resultant emissions inventory includes the following details for each facility in the sector: facility-level and if possible, process-level pollutant flows, concentrations of pollutants in the flue gas, and geographical coordinates of the facilities. A detailed statistical analysis and summary allows us to search for erroneous data and remove them from the final inventory. The generation of the inventory is achieved using a python-based framework which can recreate this inventory for other industrial sectors as well as using newer releases of emission inventories from EPA. The statistical analysis performed on the inventory is also calibrated and automated to identify outliers efficiently.

air pollutants↗
DOE OSTI2023

Systems Engineering of Rhodococcus opacus to Enable Production of Drop-in Fuels from Lignocellulose

Production of drop-in fuels from lignocellulose using Rhodococcus opacus PD630 (hereafter R. opacus) is a challenging goal. During the grant period we have pushed the field forward significantly in several areas of research. Towards the end goal of accelerating the adoption of R. opacus in biofuel production, during the grant period we have expanded the phenotypic characterization of R. opacus grown in single aromatic (model lignocellulosic) compounds or their mixtures, modeling the growth conditions in lignocellulosic biomass. Harnessing the power of adaptive evolution, we produced evolved R. opacus isolates with superior lignin valorization capabilities and identified differentially expressed genes and pathways after adaptation. We used next generation multi-omic techniques such as genomic, transcriptomic, and metabolomic analyses, to identify the catabolic pathways used by R. opacus to degrade aromatic compounds and funnel these degradation products into central metabolism, as well as the aromatic transport genes required for increased tolerance and utilization. Taking this information one step further, we identified endogenous transcription factors and regulatory mechanisms important for degradation of five model aromatic compounds. To accurately estimate R. opacus growth and consumption on model lignin compounds we pioneered the use of novel extraction procedures prior to GC-MS analysis. Alongside 13 C-metabolic flux analysis, we have elucidated the metabolic routes preferred by Rhodococcus opacus during aromatic compound degradation. Finally, we used in tandem lipidomics and high-resolution mass spectrometry to identify the modulation of mycolic acids and phospholipid membrane composition modification as a strategy for aromatic tolerance in R. opacus. Being a non-model organism, R. opacus lacks the breadth of tools and technical foundation which drive biofuel research in more well-understood microbes such as Escherichia coli. To reduce this burden for use, we designed and produced new tools for genomic manipulation and engineering in R. opacus. These engineering breakthroughs support efficient genomic editing, enabling gene overexpression, repression, and genetic alteration. Using these tools, we have generated synthetically engineered strains with increased lipogenesis and growth, both positive traits required for increased lignin valorization. Optimizing engineered strains for biofuel production from lignocellulose requires extremely sophisticated synthetic rewiring of metabolism. To facilitate systems-level reorganization of metabolism in R. opacus, we created a genome-scale model that accurately predicts metabolic flux and growth rates on the aromatic compound phenol. Lignin requires extensive pre-treatment before biological degradation by R. opacus. Towards an eventual goal of degrading real-world lignin, we developed new depolymerization processes to generate lignin breakdown products (LBP). We optimized LBP storage and composition analysis techniques, enabling accurate prediction of specific LBP compound integration into cell wall components. Overall, through the work funded by this grant we generated 20 manuscripts (17 published, 3 in review/preparation), methods for increased accuracy in metabolomics of aromatic compounds, multiple genetic tools for altering the R. opacus genome, genome scale models for predicting flux through metabolic pathways, as well as multi-omic data for community use. The work funded by this grant has increased the knowledge of aromatic degradation in bacteria and advanced our efforts to optimize R. opacus for lignin valorization.

09 BIOMASS FUELS↗
NASA NTRS2003

Analysis of Moessbauer Data from Mars: A Database and Artificial Neural Network for Identification of Iron-bearing Phases

The exploration of the planet Mars is one of the major goals within the Solar system exploration programs of the US-American space agency NASA and the European Space Agency ESA. In particular the search for water and life and understanding of the history of the surface and atmosphere will be the major tasks of the upcoming space missions to Mars. The miniaturized Moessbauer spectrometer MIMOS II has been selected for the NASA Mars-Exploration-Rover twin-mission to Mars in 2003 and the ESA 2003 Mars-Express Beagle 2 mission. Reduced in size and weight, in comparison to ordinary laboratory setup, the sensor head just weights approximately 400 g, with a volume of (50x50x90) cu mm, and holds two gamma-ray sources: the stronger for experiments and the weaker for calibrations. The collimator (in sample direction) also shields the primary radiation off the detectors. Around the drive four detectors are mounted. The detectors are made of Si-PIN-photodiodes in chip form (100 sq mm, thickness of 0.5 mm). The control unit is located in a separate electronics board. This board is responsible for the power supply, generation of the drive's velocity reference signal, read of the detector pulses to record the spectrum, data storage and communication with the host computer. After more than four decades from the discovery of the Moessbauer effect, more than 400 minerals were studied at different temperatures. Their Moessbauer parameters were reported in the literature, and have been recently collected in a data bank. Previous Mars-missions, namely Viking and Mars Pathfinder, revealed Si, Al, Fe, Mg, Ca, K, Ti, S and Cl to be the major constituents in soil and rock elemental composition of the red planet. More than 200 minerals already studied by Moessbauer spectroscopy contain significant amounts of these elements. A considerable number of Moessbauer studies were also carried out on meteorites and on Moon samples. Looking backward in the studies of the whole Moessbauer community, we have built a specific library containing Moessbauer parameters of those possible Mars minerals. The selected minerals, their Moessbauer parameter values (min. max. s.d and number of available data), main site substitution, behavior as a function of temperature and a ranking as expected to be found on Mars were organized. Mars-analogue Fe-bearing minerals not studied by Moessbauer spectroscopy are being collected and investigated. In addition, it an identification system based on Artificial Neural Networks (ANN) was implemented which enables fast and precise mineral identification from the experimental Moessbauer parameters at a given temperature.

P A de Souza Jr.↗
DOE OSTI2025

Thermal and electric multidomain dynamic model for integration of power grid distribution with behind-the-meter devices

As renewable energy sources like solar and wind power become more integrated into the grid, coordinated control of behind-the-meter devices is crucial for enhancing grid flexibility and reliability and for meeting cost targets, with standardized models being developed to support this transition. The increasing flexibility and uncertainty of integrated renewable energy grids, along with interactions between various subsystems, make traditional steady-state modeling insufficient to capture transient and dynamic behaviors. Current models (e.g., composite load and battery equivalent models) focus on thermodynamic or electrical characteristics but overlook critical electromechanical interactions. This limits the ability to share performance information for grid services and hampers fast dynamic simulations. In addition, motor stalling is usually triggered by a fault event and attributed to the characteristics of the mechanical torque of the motor, resulting in absorption of a large amount of reactive power during the stalling period. Further, this significant withdrawal of reactive power will deteriorate the dynamic voltage stability of power grids and cause delayed voltage recovery. Therefore, an in-depth modeling of the thermodynamics or mechanical torque is essential to study the impacts of the realistic torque characteristics of those behind-the-meter devices on power system voltage stability. This study developed a dynamic multidomain model for building HVAC systems, such as air-source heat pumps, to simulate their thermal and electrical responses to grid transients. The model can accurately predict power metrics with a mean absolute percentage error of 10%, by validating against with power system computer-aided design performance data. Case studies demonstrate the model capability of capturing the transient response to sudden voltage changes, rapid load fluctuations, and system shutdowns respectively. During a sudden voltage drop (30% for 0.1s), a fully loaded heat pump’s motor speed dropped, continued declining, and shut down after 3.6s, with severe power oscillations and a torque spike. A partially loaded unit experienced temporary oscillations but stabilized. Under higher building loads, compressor speed increased from 64% to 100%, with power and torque rising before stabilizing. In safety-triggered shutdowns, power decreased after minor fluctuations, and torque briefly spiked before dropping to zero.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Development of High Bs Fe-Ni Based Metal Amorphous Nanocomposite by Optimization of Glass Forming Ability

A growing interest in electric vehicles challenges soft magnetic materials to improve efficiency and power density. Metal amorphous nanocomposites (MANCs) have lower coercivities and eddy current losses, allowing for greater efficiency and higher switching frequency. The later allows for high motor speeds and improved power density. However, commercially available and laboratory demonstrated MANCs have shortcomings. Fe-based MANCs, such as FINEMET have excellent magnetic properties, but mechanical properties limit their application in high speed electric motors (HSMs). Fe-Co based MANCs, such as HITPERM, have high saturation inductions and good mechanical properties, but elevated magnetostrictive losses. Co-based MANCs and more recent Fe-Ni based MANCs have low losses and good mechanical properties, but lower saturation inductions (1-1.2 T), that limit power density in HSMs. Recently, much work has explored improving saturation induction in MANCs by increasing the content of magnetic elements, which comes at the cost of glass-forming ability (GFA). These efforts where generally limited to trial and error testing. Additionally, all work to date has focused on Fe-based alloys. In this work, a method of using Thermocalc simulation to locate near-eutectic compositions has been applied to Fe-Ni based alloys. Minima in liquidus temperature and solidification range were found for a ternary composition range and used to identify compositions that retain good GFA as the percentage of magnetic elements is increased. The (Fe 70 Ni 30 ) x (B-Si-Nb) 100-x alloy system for x=82% and x=85% was explored by Thermocalc simulations. This is an increase in magnetic element content compared to previously developed x=80% alloys. 3 compositions in the x=82% system, and 1 alloy in the x=85% system where identified and successfully cast as amorphous ribbon. The amorphous nature of the ribbon was confirmed by a bend test and XRD. Magnetic testing was performed by PPMS to measure saturation induction and Curie temperature of the amorphous material. The Curie temperatures increased to 407-438 °C for the x=82% alloys, and 462 °C for the x=85% alloy. Saturation induction increased to 1.28-1.36 T for the x=82% alloy and 1.48 T for the x=85% alloy, which is a significant improvement over the ~370 °C Curie temperature and 1.2 T saturation of previous Fe-Ni alloy. Crystallization behavior was studied by XRD for the x=85% alloy, showing that crystallization process follows a 2-step process of Amorphous → BCC+FCC+Amorphous → BCC+FCC+Fe 3 B+Fe 23 B 6 . Post annealing magnetic properties were studied by strip testing, and saturation induction as high as 1.48 T was seen after optimal annealing. TEM was used to study structure of as cast and annealed material. 2 alloys with x=82% compositions were identified with an optimal crystalline size of 10-20 nm, while the x=85% alloy had this structure in the as cast state. This opens the possibility of using the alloy in the as cast state, without requiring annealing. In conclusion, alloys with good GFA and improved magnetic properties were identified by Thermocalc simulation.

Krimer, Yuval↗
DOE OSTI2020

Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides

The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity, and thus the semiconductor's performance in solar cells, photodiodes, and optoelectronics. The difficulty and expense of experimental and computational determination of impurity levels makes a data-driven machine learning approach appropriate. In this work, we show that a density functional theory-generated dataset of impurities in Cd-based chalcogenides CdTe, CdSe, and CdS can lead to accurate and generalizable predictive models of defect properties. By converting any semiconductor + impurity system into a set of numerical descriptors, regression models are developed for the impurity formation enthalpy and charge transition levels. These regression models can subsequently predict impurity properties in mixed anion CdX compounds (where X is a combination of Te, Se and S) fairly accurately, proving that although trained only on the end points, they are applicable to intermediate compositions. We make machine-learned predictions of the Fermi-level-dependent formation energies of hundreds of possible impurities in 5 chalcogenide compounds, and we suggest a list of impurities which can shift the equilibrium Fermi level in the semiconductor as determined by the dominant intrinsic defects. Machine learning predictions for the dominating impurities compare well with DFT predictions, revealing the power of machine-learned models in the quick screening of impurities likely to affect the optoelectronic behavior of semiconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

IACMI Project 4.10: Blade Finishing Automation (Final Report)

IACMI Project 4.10, the Innovative Wind Turbine Blade Finishing with Advanced Automated Technology project, performed research addressing the challenges and opportunities associated with automating wind turbine blade finishing operations, including blade trimming and grinding. These activities, developed and conducted in close cooperation with wind industry partners, included: • The development of new automation technology and systems; • The integration of automation technology and components; • The improvements of sensor technology and their deployment in automation systems • The elimination of extensive manual programing for the automation of wind blade finishing; • The rethinking of the approach to specific work tasks for automation systems; • The demonstration at scale of innovative wind turbine blade finishing automations systems. The IACMI Wind TA team worked with GE Renewable Energy and LM Wind Power to establish the metrics of the automated blade finishing system developed in the project. In addition, the project team helped to integrate the advanced automated systems into wind turbine blade production facilities. This project was divided into two phases. Phase I provided project management, preliminary techno-economic model development, early US wind turbine industry outreach, and early research into robotic solutions for automated wind blade finishing. This second phase continued with project management, as well as final techno-economic model development, advanced research into robotic solutions for automated wind blade finishing, as well as specifications and procurement of a robotic system for automated wind blade finishing. The 4.10 project leveraged the capabilities and facilities established through the Institute for Advanced Composites Manufacturing Innovation (IACMI), the Colorado Office of Economic Development and International Trade (OEDIT) and the National Renewable Energy Laboratory (NREL). The IACMI Wind Technology Area (TA) designed and prepared for deployment a robotic automated wind turbine blade finishing system at the NREL/IACMI Composites Manufacturing Education and Technology (CoMET) facility. The results of this project led to follow-on funding to build upon and expand research in automated wind turbine blade finishing.

17 WIND ENERGY↗
NASA NTRS2014

The Near Earth Object Scout Spacecraft: A Low Cost Approach to in-situ Characterization of the NEO Population

In this paper we describe a micro/nano satellite spacecraft and a supporting mission profile and architecture designed to enable preliminary in-situ characterization of a significant number of Near Earth Objects (NEOs) at reasonable cost. The spacecraft will be referred to as the NEO Scout. NEO Scout spacecraft are to be placed in GTO, GEO, or cis-lunar space as secondary payloads on launch vehicles headed for GTO or beyond and will begin their mission after deployment from the launcher. A distinguishing key feature of the NEO scout system is to design the mission timeline and spacecraft to rendezvous with and land on the target NEOs during close approach to the Earth-Moon system using low-thrust/high- impulse propulsion systems. Mission feasibility and preliminary design analysis are presented along with detailed trajectory calculations. The use of micro/nano satellites in low-cost interplanetary exploration is attracting increasing attention and is the subject of several annual workshops and published design studies (1-4). The NEO population consists of those asteroids and short period comets orbiting the Sun with a perihelion of 1.3 astronomical units or less (5-8). As of July 30, 2013 10065 Near-Earth objects have been discovered. The spin rate, mass, density, surface physical (especially mechanical) properties, composition, and mineralogy of the vast majority of these objects are highly uncertain and the limited available telescopic remote sensing data imply a very diverse population (5-8). In-situ measurements by robotic spacecraft are urgently needed to provide the characterization data needed to support hardware and mission design for more ambitious human and robotic NEO operations. Large numbers of NEOs move into close proximity with the Earth-Moon system every year (9). The JPL Near-Earth Object Human Space Flight Accessible Targets Study (NHATS) (10) has produced detailed mission profile and delta V requirements for various NEO missions ranging from 30 to 420 days in duration and assuming chemical propulsion. Similar studies have been reported assuming high power electric propulsion for manned NEO rendezvous missions (11). The delta V requirement breakdown and mission profile data from references 10 and 11 are used as a basis for sizing the NEO Scout spacecraft and for conducting preliminary feasibility assessments using the Tsiokolvsky rocket equation, a (worst-case) delta V requirement of 10 km/sec, and a maximum spacecraft dry mass of 20 kg. Using chemical propellant for a 10 km/sec delta V drives spacecraft wet mass well above 300 kg so that chemical propulsion is a non-starter for the proposed mission profile and spacecraft wet mass limits. In contrast, a solar electric propulsion system needs only 8 kg of Xe propellant to accelerate the spacecraft to 10 km/sec in 163 days with 0.02 N of thrust and 500 W of power from1.6 sq m of 29% efficient solar panels. In a second example, accelerating a 4 kg payload to 7 km/sec over 180 days requires about 6.7 kg of propellant and 1.2 kg of solar panels (12 kg total spacecraft wet mass).

Koontz, Steven L.↗
NASA NTRS1991

ASPEC: Solar power satellite

The solar power satellite (SPS) will provide a clean, reliable source of energy for large-scale consumption. The system will use satellites in geostationary orbits around the Earth to capture the Sun's energy. The intercepted sunlight will be converted to laser beam energy that can be transmitted to the Earth's surface. Ground systems on the Earth will convert the transmissions from space into electric power. The preliminary design for the SPS consists of one satellite in orbit around the Earth transmitting energy to a single ground station. The SPS design uses multilayer solar cell technology arranged on a 20 km squared planar array to intercept sunlight and convert it to an electric voltage. Power conditioning devices then send the electricity to a laser, which transmits the power to the surface of the Earth. A ground station will convert the beam into electricity. Typically, a single SPS will supply 5 GW of power to the ground station. Due to the large mass of the SPS, about 41 million kg, construction in space is needed in order to keep the structural mass low. The orbit configuration for this design is to operate a single satellite in geosynchronous orbit (GEO). The GEO allows the system to be positioned above a single receiving station and remain in sunlight 99 percent of the time. Construction will take place in low Earth orbit (LEO); array sections, 20 in total, will be sailed on solar wind out to the GEO location in 150 days. These individual transportation sections are referred to as solar sailing array panels (SSAP's). The primary truss elements used to support the array are composed of composite tubular members in a pentahedral arrangement. Smart segments consisting of passive and active damping devices will increase the control of dynamic SPS modes.

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NASA NTRS2007

Heat Rejection Systems Utilizing Composites and Heat Pipes: Design and Performance Testing

Polymer matrix composites offer the promise of reducing the mass and increasing the performance of future heat rejection systems. With lifetimes for heat rejection systems reaching a decade or more in a micrometeoroid environment, use of multiple heat pipes for fault tolerant design is compelling. The combination of polymer matrix composites and heat pipes is of particular interest for heat rejection systems operating on the lunar surface. A technology development effort is under way to study the performance of two radiator demonstration units manufactured with different polymer matrix composite face sheet resin and bonding adhesives, along with different titanium-water heat pipe designs. Common to the two radiator demonstration units is the use of high thermal conductivity fibers in the face sheets and high thermal conductivity graphite saddles within a light weight aluminum honeycomb core. Testing of the radiator demonstration units included thermal vacuum exposure and thermal vacuum exposure with a simulated heat pipe failure. Steady state performance data were obtained at different operating temperatures to identify heat transfer and thermal resistance characteristics. Heat pipe failure was simulated by removing the input power from an individual heat pipe in order to identify the diminished performance characteristics of the entire panel after a micrometeoroid strike. Freeze-thaw performance was also of interest. This paper presents a summary of the two radiator demonstration units manufactured to support this technology development effort along with the thermal performance characteristics obtained to date. Future work will also be discussed.

Jaworske, Donald A.↗
NASA NTRS2013

Thermal Infrared Sensor (TIRS) Instrument Thermal Subsystem Design and Lessons Learned

The Thermal Infrared Sensor (TIRS) is one of two instruments on the Landsat Data Continuity Mission (LDCM), which is scheduled to launch in February of 2013. The TIRS instrument was officially added to the mission later in the flow, which led to a highly aggressive schedule that became one of the main drivers during instrument development. The thermal subsystem design of the TIRS Sensor Unit is comprised of five thermal zones which range in temperature from less than 43 Kelvin to 330 Kelvin. Most zones are proportional heater controlled, and all are within a volume of 35 cu.ft. A two-stage cryocooler is used to cool the "cold stage" including three QWIP detectors to less than 43 Kelvin, and cool the "warm stage" to 105 Kelvin. The excess power dissipation from the cryocooler is rejected via ammonia transport heat pipes to a dedicated Cryocooler Radiator with embedded ammonia heat pipes. The cryogenic subsystem includes a series of shells used to radiatively and conductively isolate the cold stage from the warmer surroundings. The Optical System (telescope) is passively cooled to 180-190 Kelvin using a "thermal link" (comprised of a Flexible Conductive Thermal Strap and an APG Bar) which couples the telescope stage to a dedicated radiator with embedded ethane heat pipes. The Scene Select Mechanism, which is responsible for moving the Scene Select Mirror to three distinct positions (including Nadir, Space, and On-board Black Body Calibrator pointing), runs nominally at 278 Kelvin and is thermally isolated from the cryogenic thermal zones. The On-board Black Body Calibrator requires a dedicated radiator which allows for a temperature range of 260-330 Kelvin at the Source. The detectors are powered by the FPE Box, which is mounted to the nadir external surface of the composite honeycomb structure. There are two additional electronics boxes which are wet-mounted directly to the spacecraft shear panel, the Main Electronics Box and Cryocooler Electronics Box; thermal control of these boxes is the responsibility of Orbital Sciences Corporation, the spacecraft developer. The TIRS thermal subsystem design was successfully verified during months of testing campaign, from component & subsystem level to two instrument-level thermal vacuum tests. The Instrument, despite an aggressive schedule, was delivered to the spacecraft vendor in February of 2012 and is currently undergoing the final stages of spacecraft environmental testing in preparation for launch.

Otero, Veronica↗
DOE OSTI2022

Evaluating the Durability of Balance of Systems Components Using Combined-Accelerated Stress Testing

The degradation of photovoltaic (PV) balance of systems (BoS) components is not well-studied, but the consequences include: offline-modules, -strings, -inverters; system shutdown; arc-faults; and fires. A utility provider experienced a ~30% failure rate in their power transfer chain, attributed to branch connectors. Field-failed specimen assemblies were therefore examined, consisting of cable connector, branch connector and discrete fuse components. Unused field-vintage specimens are presently being examined using combined-accelerated stress testing (C-AST) to clarify the most influential environmental stressors as well as the effect of external mechanical perturbation. A benchtop prototype fixture was used to develop the perturbation capability for C-AST. The benchtop experiments were also used to develop the in-situ data acquisition of specimen: current, voltage, and temperature. A significant increase in operating temperature, ~100 deg C from ~40 deg C, and a different failure mode was observed immediately once periodic mechanical perturbation was applied. The current at failure was decreased from 35 A (with failure occurring in the fuses) to 15 A (failure at the male/female metal pin connection). After initial examination using X-ray computed tomography, the external plastic was machined away from failed specimens to allow failure analysis, including the extraction of the internal convolute springs for morphological examination (optical- and electron-microscopy). Chemical composition analysis included: energy-dispersive X-ray spectroscopy, differential scanning calorimetry, and Fourier transform infrared spectroscopy.

balance of systems↗