Search NASA⌕ Search

SEARCH · Search NASA

Results for “Optimal sizing method”

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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Modeling Evolvable Water Recovery Systems for Short and Long-Duration Missions in Partial Gravity

Water recovery technologies on the International Space Station (ISS) are designed and optimized for microgravity environments, thus creating a need for innovative systems optimized for mission operations in the presence of gravity. Alternative water recovery technologies under research by the Life Support Systems division were compiled into a series of Partial Gravity Water Recovery System (PGWRS) architectures. Individual unit processes were modeled via fundamental physical-chemical process equations to simulate their method of treatment, including chemical or biological oxidation, flash evaporation, filtration, or adsorption and ion exchange. Dimension and sizing data were then utilized to scale each architecture. Modeling was completed on an assortment of architectures assuming three scenarios in which treatment methods could take advantage of partial gravity: a short-duration mission with a temporary surface habitat, a longer duration mission with a more permanent habitat, and a long duration mission with minimal resupply. Total system mass was estimated to quantitatively compare architectures within scenarios, but qualitative observations were also made regarding system robustness, flexibility, and capacity to meet more stringent demands. Technologies that traded most favorably between the scenarios were those that included a pre-oxidation stage to minimize mass loading to downstream absorption and ion exchange beds, exploited gravity in liquid-vapor separation processes, and reconstituted wastewater components into recyclable material streams.

Avery Carlson↗
DOE OSTI2019

Towards optimized viral metagenomes for double-stranded and single-stranded DNA viruses from challenging soils

Soils impact global carbon cycling and their resident microbes are critical to their biogeochemical processing and ecosystem outputs. Based on studies in marine systems, viruses infecting soil microbes likely modulate host activities via mortality, horizontal gene transfer, and metabolic control. However, their roles remain largely unexplored due to technical challenges with separating, isolating, and extracting DNA from viruses in soils. Some of these challenges have been overcome by using whole genome amplification methods and while these have allowed insights into the identities of soil viruses and their genomes, their inherit biases have prevented meaningful ecological interpretations. Here we experimentally optimized steps for generating quantitatively-amplified viral metagenomes to better capture both ssDNA and dsDNA viruses across three distinct soil habitats along a permafrost thaw gradient. First, we assessed differing DNA extraction methods (PowerSoil, Wizard mini columns, and cetyl trimethylammonium bromide) for quantity and quality of viral DNA. This established PowerSoil as best for yield and quality of DNA from our samples, though ~1/3 of the viral populations captured by each extraction kit were unique, suggesting appreciable differential biases among DNA extraction kits. Second, we evaluated the impact of purifying viral particles after resuspension (by cesium chloride gradients; CsCl) and of viral lysis method (heat vs bead-beating) on the resultant viromes. DNA yields after CsCl particle-purification were largely non-detectable, while unpurified samples yielded 1–2-fold more DNA after lysis by heat than by bead-beating. Virome quality was assessed by the number and size of metagenome-assembled viral contigs, which showed no increase after CsCl-purification, but did from heat lysis relative to bead-beating. We also evaluated sample preparation protocols for ssDNA virus recovery. In both CsCl-purified and non-purified samples, ssDNA viruses were successfully recovered by using the Accel-NGS 1S Plus Library Kit. While ssDNA viruses were identified in all three soil types, none were identified in the samples that used bead-beating, suggesting this lysis method may impact recovery. Further, 13 ssDNA vOTUs were identified compared to 582 dsDNA vOTUs, and the ssDNA vOTUs only accounted for ~4% of the assembled reads, implying dsDNA viruses were dominant in these samples. This optimized approach was combined with the previously published viral resuspension protocol into a sample-to-virome protocol for soils now available at protocols.io, where community feedback creates ‘living’ protocols. This collective approach will be particularly valuable given the high physicochemical variability of soils, which will may require considerable soil type-specific optimization. This optimized protocol provides a starting place for developing quantitatively-amplified viromic datasets and will help enable viral ecogenomic studies on organic-rich soils.

Trubl, Gareth↗
DOE OSTI2019

Towards optimized viral metagenomes for double-stranded and single-stranded DNA viruses from challenging soils

Soils impact global carbon cycling and their resident microbes are critical to their biogeochemical processing and ecosystem outputs. Based on studies in marine systems, viruses infecting soil microbes likely modulate host activities via mortality, horizontal gene transfer, and metabolic control. However, their roles remain largely unexplored due to technical challenges with separating, isolating, and extracting DNA from viruses in soils. Some of these challenges have been overcome by using whole genome amplification methods and while these have allowed insights into the identities of soil viruses and their genomes, their inherit biases have prevented meaningful ecological interpretations. Here we experimentally optimized steps for generating quantitatively-amplified viral metagenomes to better capture both ssDNA and dsDNA viruses across three distinct soil habitats along a permafrost thaw gradient. First, we assessed differing DNA extraction methods (PowerSoil, Wizard mini columns, and cetyl trimethylammonium bromide) for quantity and quality of viral DNA. This established PowerSoil as best for yield and quality of DNA from our samples, though ~1/3 of the viral populations captured by each extraction kit were unique, suggesting appreciable differential biases among DNA extraction kits. Second, we evaluated the impact of purifying viral particles after resuspension (by cesium chloride gradients; CsCl) and of viral lysis method (heat vs bead-beating) on the resultant viromes. DNA yields after CsCl particle-purification were largely non-detectable, while unpurified samples yielded 1–2-fold more DNA after lysis by heat than by bead-beating. Virome quality was assessed by the number and size of metagenome-assembled viral contigs, which showed no increase after CsCl-purification, but did from heat lysis relative to bead-beating. We also evaluated sample preparation protocols for ssDNA virus recovery. In both CsCl-purified and non-purified samples, ssDNA viruses were successfully recovered by using the Accel-NGS 1S Plus Library Kit. While ssDNA viruses were identified in all three soil types, none were identified in the samples that used bead-beating, suggesting this lysis method may impact recovery. Further, 13 ssDNA vOTUs were identified compared to 582 dsDNA vOTUs, and the ssDNA vOTUs only accounted for ~4% of the assembled reads, implying dsDNA viruses were dominant in these samples. This optimized approach was combined with the previously published viral resuspension protocol into a sample-to-virome protocol for soils now available at protocols.io, where community feedback creates ‘living’ protocols. This collective approach will be particularly valuable given the high physicochemical variability of soils, which will may require considerable soil type-specific optimization. This optimized protocol provides a starting place for developing quantitatively-amplified viromic datasets and will help enable viral ecogenomic studies on organic-rich soils.

Trubl, Gareth↗
DOE OSTI2019

Towards optimized viral metagenomes for double-stranded and single-stranded DNA viruses from challenging soils

Soils impact global carbon cycling and their resident microbes are critical to their biogeochemical processing and ecosystem outputs. Based on studies in marine systems, viruses infecting soil microbes likely modulate host activities via mortality, horizontal gene transfer, and metabolic control. However, their roles remain largely unexplored due to technical challenges with separating, isolating, and extracting DNA from viruses in soils. Some of these challenges have been overcome by using whole genome amplification methods and while these have allowed insights into the identities of soil viruses and their genomes, their inherit biases have prevented meaningful ecological interpretations. Here we experimentally optimized steps for generating quantitatively-amplified viral metagenomes to better capture both ssDNA and dsDNA viruses across three distinct soil habitats along a permafrost thaw gradient. First, we assessed differing DNA extraction methods (PowerSoil, Wizard mini columns, and cetyl trimethylammonium bromide) for quantity and quality of viral DNA. This established PowerSoil as best for yield and quality of DNA from our samples, though ∼1/3 of the viral populations captured by each extraction kit were unique, suggesting appreciable differential biases among DNA extraction kits. Second, we evaluated the impact of purifying viral particles after resuspension (by cesium chloride gradients; CsCl) and of viral lysis method (heat vs bead-beating) on the resultant viromes. DNA yields after CsCl particle-purification were largely non-detectable, while unpurified samples yielded 1–2-fold more DNA after lysis by heat than by bead-beating. Virome quality was assessed by the number and size of metagenome-assembled viral contigs, which showed no increase after CsCl-purification, but did from heat lysis relative to bead-beating. We also evaluated sample preparation protocols for ssDNA virus recovery. In both CsCl-purified and non-purified samples, ssDNA viruses were successfully recovered by using the Accel-NGS 1S Plus Library Kit. While ssDNA viruses were identified in all three soil types, none were identified in the samples that used bead-beating, suggesting this lysis method may impact recovery. Further, 13 ssDNA vOTUs were identified compared to 582 dsDNA vOTUs, and the ssDNA vOTUs only accounted for ∼4% of the assembled reads, implying dsDNA viruses were dominant in these samples. This optimized approach was combined with the previously published viral resuspension protocol into a sample-to-virome protocol for soils now available at protocols.io , where community feedback creates ‘living’ protocols. This collective approach will be particularly valuable given the high physicochemical variability of soils, which will may require considerable soil type-specific optimization. This optimized protocol provides a starting place for developing quantitatively-amplified viromic datasets and will help enable viral ecogenomic studies on organic-rich soils.

dsDNA viruses↗
DOE OSTI2021

A solution framework for linear PDE-constrained mixed-integer problems

Abstract We present a general numerical solution method for control problems with state variables defined by a linear PDE over a finite set of binary or continuous control variables. We show empirically that a naive approach that applies a numerical discretization scheme to the PDEs to derive constraints for a mixed-integer linear program (MILP) leads to systems that are too large to be solved with state-of-the-art solvers for MILPs, especially if we desire an accurate approximation of the state variables. Our framework comprises two techniques to mitigate the rise of computation times with increasing discretization level: First, the linear system is solved for a basis of the control space in a preprocessing step. Second, certain constraints are just imposed on demand via the IBM ILOG CPLEX feature of a lazy constraint callback. These techniques are compared with an approach where the relations obtained by the discretization of the continuous constraints are directly included in the MILP. We demonstrate our approach on two examples: modeling of the spread of wildfire and the mitigation of water contamination. In both examples the computational results demonstrate that the solution time is significantly reduced by our methods. In particular, the dependence of the computation time on the size of the spatial discretization of the PDE is significantly reduced.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Similarity Metric for Data Optimization and Efficient Training of Reactive Machine Learning Force Fields for Hydrocarbon Radiolysis

Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. In conclusion, our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer–radiation interactions can have a strong effect on the resulting products observed in experiments.

Hydrocarbons↗
DOE OSTI2020

Analysis and improvement of the hot disk transient plane source method for low thermal conductivity materials

The hot disk transient plane source (TPS) method is a widely used standard technique (ISO 22007-2) for the characterization of thermal properties of materials, especially the thermal conductivity, k. Despite its well-established reliability for a wide variety of common materials, the hot disk TPS method is also known to suffer from a substantial systematic errors when applied to low-k thermal insulation materials. Here, we present a combined numerical and experimental study on the influence of the geometry of hot disk sensor on measured value of low-k materials. We demonstrate that the error is strongly affected by the finite thickness and thermal mass of the sensor's insulation layer was well as the corresponding increase of the effective heater size beyond the radius of the embedded metal heater itself. We also numerically investigate the dependence of the error on the sample thermal properties, confirming that the errors are worse in low-k samples. A simple correction function is also provided, which converts the apparent (erroneous) result from a standard hot disk TPS measurement to a more accurate value. A standard polyimide sensor was also optimized using both wet and dry etching to provide more accurate measurement directly. Experimentally corrected value of k for Airloy x56 aerogel and a commercial silica aerogel using the numerical correction factor derived based on the standard TPS sensor is in excellent agreement with the directly measured value from the TPS sensor using the optimized polyimide sensor. Finally, both of these methods can reduce the errors to less than 4% as compared to around 40% error of overestimation from raw values measured with the pristine sensor. Such results show that both the numerical correction to a pristine senor or an optimized sensor are capable of providing highly accurate value of thermal conductivity for such materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Rapid Bayesian High Entropy Alloy Designs Fabricated via Wire Arc Additive Manufacturing

Purpose: This project seeks to demonstrate a new high-throughput (rapid) alloy design technique applied to creating new high entropy alloys (HEAs) for extreme environments. High entropy alloys shift the design paradigm from being focused on a single principal element (e.g. nickel-based alloys) to target alloys that include high atomic fractions (X >10%) of multiple elements. These HEA materials can exhibit sluggish diffusion and enhanced corrosion resistance, ideal for potential applications in advanced ultra supercritical (A-USC) steam cycles for power generation. Scope: The addition of multiple elements in high atomic fractions creates an enormous design space that cannot easily be investigated by traditional material design strategies such as designed of experiments (DOE). This project utilizes a Bayesian machine learning algorithm that has been modified to work with calculation of phase diagrams (CALPHAD) software. This Bayesian algorithm reduces manual inputs and increase the likelihood of achieving an optimal solution. Compositional inputs to this algorithm will be assessed using existing material property models for high temperature strength and corrosion resistance. The target for alloy performance will be a 15% (~100 ⁰C) increase in allowable service temperature beyond heat-resistant stainless steels while maintaining or improving alloy cost and corrosion resistance. Haynes 230 was selected as a baseline, which is 57 wt% Ni with 22 wt% Cr 14 wt% W, and 2 wt% Mo as solid solution strengtheners. In addition to rapid design via Bayesian machine learning, the alloys were rapidly fabricated using a multi-wire arc additive manufacturing (mWAAM) technique which allows for precise control of alloy composition and assessing of alloy design “windows” to study composition effects. Build speeds for wire-arc additive processes are among the highest for additive technologies enabling rapid and reliable sample fabrication when compared to conventional methods such as arc button melting. The mWAAM samples will be rapidly characterized via instrumented indentation for room temperature modulus and strength and for elevated temperature strength via hot hardness tests. After being screened with hardness testing, potential alloys will be further evaluated with conventional microscopy techniques including scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to assess agreement with modeling results. The most promising compositions will also be evaluated by printing full sized tensile specimens for mechanical behavior tests at elevated temperatures. Results: Bayesian machine learning of a single performance function was initially used to optimize five performance metrics: 1) single phase stability, 2) yield strength, 3) creep resistance (low diffusion coefficient), 4) freezing range (weldability), and 5) material cost. The single performance function was suboptimal as assumptions had to be made about the results while formulating the optimization. A goal-oriented Bayesian optimization strategy (Hanaoka, 2021) was implemented with CALPHAD for use with the five metrics above. This multi-objective Bayesian optimization (MOBO) enabled the design of NiCrCoFe alloys with V and W additions. A base composition of NiCoCr was selected as Ni provides a stable FCC matrix, Cr aids corrosion/oxidation resistance, and Co is a solid-solutions strengthener that also improves creep by increasing the activation energy. Fe helps reduce diffusion coefficients and cost. Finally, V and W were selected for their reasonable solubility and high atomic misfit to aid in solid solution strengthening. Cracking of the mWAAM specimens was an early issue, and the Easton solidification cracking model (Easton et al., 2014a) was selected for addition to the MOBO function. High performing alloys fabricated by mWAAM included Ni 28 Cr 25 Co 26 Fe 15 V 8 and Ni 62 Cr 18 Co 1 Fe 3 W 15 . It was observed that even after adapting the mWAAM process for W, the W did not fully dissolve. To fully evaluate the Ni 62 Cr 18 Co 1 Fe 3 W 15 composition, a cored wire (80-20 NiCr sheath/powder core) was manufactured and printed via WAAM, and HIP’ing was utilized to homogenize and densify the printed alloy. The V and W alloys produced met metrics 1 (solid solution), 4 (solidification cracking), and 5 (cost). However, an unmodeled mechanism of thermal stress cracking was identified in the WAAM produced materials, perhaps exacerbated by the lack of grain boundary strengthening elements (B, C). Conclusions & Recommendations: A high-throughput (rapid) alloy design technique was applied to designing and manufacturing new high entropy alloys (HEAs) for extreme environments utilizing MOBO and mWAAM. The developed process was rapid and effective in addressing the mechanisms included in the model. The lack of grain boundary strengthening element additions (e.g., B, C) was a simplification that likely produced thermal stress cracking that turned into a large part of the investigation. Additions on the order of 0.005 wt% B and 0.05 wt% C likely would have minimized thermal stress grain boundary cracking. Overall, the high throughput design strategy is promising for rapid design of metrics-driven alloys for advanced ultra supercritical (A-USC) steam cycles for power generation. The MOBO and mWAAM process could be commercialized to accelerate metrics-driven alloy design. In addition, the cored-wire process utilized for scale-up is a promising high-volume process for WAAM alloy development and scale-up.

36 MATERIALS SCIENCE↗
NASA NTRS2017

Reconfigurable Model Execution in the OpenMDAO Framework

NASA's OpenMDAO framework facilitates constructing complex models and computing their derivatives for multidisciplinary design optimization. Decomposing a model into components that follow a prescribed interface enables OpenMDAO to assemble multidisciplinary derivatives from the component derivatives using what amounts to the adjoint method, direct method, chain rule, global sensitivity equations, or any combination thereof, using the MAUD architecture. OpenMDAO also handles the distribution of processors among the disciplines by hierarchically grouping the components, and it automates the data transfer between components that are on different processors. These features have made OpenMDAO useful for applications in aircraft design, satellite design, wind turbine design, and aircraft engine design, among others. This paper presents new algorithms for OpenMDAO that enable reconfigurable model execution. This concept refers to dynamically changing, during execution, one or more of: the variable sizes, solution algorithm, parallel load balancing, or set of variables-i.e., adding and removing components, perhaps to switch to a higher-fidelity sub-model. Any component can reconfigure at any point, even when running in parallel with other components, and the reconfiguration algorithm presented here performs the synchronized updates to all other components that are affected. A reconfigurable software framework for multidisciplinary design optimization enables new adaptive solvers, adaptive parallelization, and new applications such as gradient-based optimization with overset flow solvers and adaptive mesh refinement. Benchmarking results demonstrate the time savings for reconfiguration compared to setting up the model again from scratch, which can be significant in large-scale problems. Additionally, the new reconfigurability feature is applied to a mission profile optimization problem for commercial aircraft where both the parametrization of the mission profile and the time discretization are adaptively refined, resulting in computational savings of roughly 10% and the elimination of oscillations in the optimized altitude profile.

Hwang, John T.↗
DOE OSTI2017

Understanding Film Formation Morphology and Orientation in High Member 2D Ruddlesden-Popper Perovskites for High-Efficiency Solar Cells

We report that 2D Ruddlesden–Popper (RP) perovskites have recently emerged as promising candidates for hybrid perovskite photovoltaic cells, realizing power-conversion efficiencies (PCEs) of over 10% with technologically relevant stability. To achieve solar cell performance comparable to the state-of-the-art 3D perovskite cells, it is highly desirable to increase the conductivity and lower the optical bandgap for enhanced near-IR region absorption by increasing the perovskite slab thickness. Here, the use of the 2D higher member (n = 5) RP perovskite (n-butyl-NH 3 ) 2 (MeNH 3 ) 4 Pb 5 I 16 in depositing highly oriented thin films from dimethylformamide/dimethylsulfoxide mixtures using the hot-casting method is reported. In addition, they exhibit superior environmental stability over thin films of their 3D counterpart. These films are assembled into high-efficiency solar cells with an open-circuit voltage of ≈1 V and PCE of up to 10%. This is achieved by fine-tuning the solvent ratio, crystal growth orientation, and grain size in the thin films. Finally, the enhanced performance of the optimized devices is ascribed to the growth of micrometer-sized grains as opposed to more typically obtained nanometer grain size and highly crystalline, densely packed microstructures with the majority of the inorganic slabs preferentially aligned out of plane to the substrate, as confirmed by X-ray diffraction and grazing-incidence wide-angle X-ray scattering mapping.

14 SOLAR ENERGY↗
DOE OSTI2024

Creating Accurate Methane Emission Inventories through Data-Driven Airborne Survey Strategies: Methods and Results from the Haynesville, Anadarko, and Permian Basins

Significantly reducing methane emissions from the oil and gas sector can decrease the rate of climate change over the next two decades, buying critical time for a global energy transition. However, emissions inventories that can be used by oil and gas operators and environmental regulators to identify optimal methane emission mitigation strategies are either based on conservative emission factor methods, or are inconsistent between studies due to differences in sampling strategies or survey technologies. We developed a new approach for methane emissions survey design that yields representative basinwide methane emissions inventories by surveying a subset of total assets in a given oil and gas basin. We identify several sampling and analysis principles, including large sample sizes, balanced sampling across oil and gas production, careful survey area definition, and a unified protocol for analysis, to be vital to producing an unbiased estimate of basin-scale emissions that can be reconciled with future studies. We further present results from deploying this strategy in two oil and gas producing regions in the United States: the Haynesville Basin in Texas and Louisiana, and the Woodford Shale in the Anadarko Basin in Oklahoma. Aerial surveys were performed in 2023 using the Insight M LeakSurveyor™ technology. Preliminary results from methane emissions detected by Insight M indicate that aerially detected emissions above roughly 30 kg(CH4)/hr by themselves contribute a fractional loss rate of 1.13% of gross gas production across oil and gas operations in the Haynesville Basin, with aerially detected emissions equivalent to 2.67% of gross gas production in the Woodford Shale. We supplement these aerial estimates with modeled emissions that are below the LeakSurveyor’s survey sensitivity using a recently published inventory-based model of methane emissions, which we update for our survey areas. We then combine our aerial detections with modeled emissions to yield methane emission distributions and inventories that incorporate the full range of potential methane emissions from the smallest to the largest. These results can be used to identify the most effective methane mitigation strategies for our study areas, and can be reconciled with future methane emissions surveys that use different technologies.

Sherwin, Evan (ORCID:0000000321804297)↗
DOE OSTI2020

Computational Optimization of A 3D Printed Collimator

This contribution describes the computational methodology behind an optimization procedure for a scattered beam collimator. The workflow includes producing a file that can be manufactured via additive methods. A conical collimator, optimized for neutron diffraction experiments in a high pressure clamp cell, is presented as an example. In such a case the scattering from the sample is much smaller than that of the pressure cell. Monte Carlo Ray tracing in MCViNE was used to model scattering from a Si powder sample and the cell. A collimator was inserted into the simulation and the number and size of channels were optimized to maximize the rejection of the parasitic signal coming from the complex sample environment. Constraints, provided by the additive manufacturing process as well as a specific neutron diffractometer, were also included in the optimization. The source code and the tutorials are available in c3dp (Islam (2019)).

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Microstructural and Hall–Petch Analysis of Additively Manufactured Ferritic Alloy Using 2507 Duplex Stainless Steel Powder

The powder bed fusion–laser beam (PBF-LB) process, a method of additive manufacturing (AM), was used to print duplex stainless steel (DSS) using commercial-grade 2507 powders. While conventionally processed DSS has a two-phase microstructure consisting of 50% austenite and 50% ferrite, the PBF-LB-printed 2507 alloy was nearly 100% ferrite. Optimal processing conditions that minimized porosity were determined to be 290 W laser power and 1000 mm/s scan speed, and grain size, texture, and phases were characterized as a function of laser power and scan speed. Grain size increased with increasing laser power but decreased with increasing scan speed. A <100> texture diminished with increasing scan speed from 1000 mm/s to 1400 mm/s. No austenite phase was detected. Transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) characterization revealed nanoscale chromium nitride precipitates in the ferritic matrix (incoherent hexagonal close-packed (HCP) precipitates at grain boundaries and coherent body-centered cubic (BCC) precipitates within the grains) and a high density of tangled dislocations. Tensile tests of as-printed alloys showed a yield strength of 570 MPa, an ultimate tensile strength of 756 MPa, and an elongation to failure of 10%. The tensile properties were analyzed based on the observed microstructure considering grain size, nanoscale precipitates, and the high density of dislocations.

Crystallography↗
DOE OSTI2023

A General Method for Estimating Zonal Transmission Interface Limits from Nodal Network Data: Preprint

Capacity expansion models for the electric power system often employ zonal (rather than nodal) resolution, necessitating estimates of aggregate power transfer limits across the interfaces between model zones. Interface limits between planning areas are sometimes published, but they are not generalizable to arbitrary zone shapes. There is thus a need for a reproducible method for estimating interface transfer limits (ITLs) between user-defined zones directly from nodal transmission system data. Here, we present a simple method for estimating ITLs using a DC power flow approximation via the power transfer distribution factor (PTDF) matrix. Linear optimization is performed to identify the distribution of power flows that maximizes the total flow on interface-crossing lines, subject to individual line ratings, limits on bus injection/withdrawal, and the relationships among flows, injections, and withdrawals imposed by the PTDF matrix. We demonstrate the application of the method on a 134-zone ~65000-bus system, and we explore the influence of flow direction, contingency level, and zone size on the estimated ITLs. There is significant heterogeneity in the ratio of the ITL to the sum of interface-crossing line ratings, which highlights the importance of accounting for the physical constraints on power flows imposed by Kirchhoff's laws when estimating zonal ITLs.

17 WIND ENERGY↗
NASA NTRS1982

Conceptual design of the 6 MW Mod-5A wind turbine generator

The General Electric Company, Advanced Energy Programs Department, is designing under DOE/NASA sponsorship the MOD-5A wind turbine system which must generate electricity for 3.75 cent/KWH (1980) or less. During the Conceptual Design Phase, completed in March, 1981, the MOD-5A WTG system size and features were established as a result of tradeoff and optimization studies driven by minimizing the system cost of energy (COE). This led to a 400' rotor diameter size. The MOD-5A system which resulted is defined in this paper along with the operational and environmental factors that drive various portions of the design. Development of weight and cost estimating relationships (WCER's) and their use in optimizing the MOD-5A are discussed. The results of major tradeoff studies are also presented. Subsystem COE contributions for the 100th unit are shown along with the method of computation. Detailed descriptions of the major subsystems are given, in order that the results of the various trade and optimization studies can be more readily visualized.

Barton, R. S.↗
NASA NTRS1984

Optimization of Antenna Structure Design

Optimality criteria design is applied for large antenna structures with multiple constraints on microwave performance. The constraints are on accuracy of the structure: restrictions on the microwave pathlength error, and the antenna pointing error. The examples given show convergence to low-weight feasible designs that satisfy the constraints. Truss-member sizes are automatically selected from tables of commercially available structural shapes, and approximations results from the method of selection are found moderate. The multiple constraint design is shown to be more effective in meeting constraints than the old envelop method. For practical structures, the new design method can be performed within reasonable core size and computation time.

Levy, R.↗
NASA NTRS1989

Advances in ambient temperature secondary lithium cells

The Jet Propulsion Laboratory is involved in a Research and Development program sponsored by NASA/OAST on the development of ambient temperature secondary lithium cells for future space applications. Some of the projected applications are planetary spacecraft, planetary rovers, and astronaut equipment. The main objective is to develop secondary lithium cells with greater than 100 Wh/kg specific energy while delivering 1000 cycles at 50 percent Depth of Discharge (DOD). To realize these ambitious goals, the work was initially focused on several important basic issues related to the cell chemistry, selection of cathode materials and electrolytes, and component development. The performance potential of Li-TiS2, Li-MoS3, Li-V6O13 and Li-NbSe3 electrochemical systems was examined. Among these four, the Li-TiS2 system was found to be the most promising system in terms of realizable specific energy and cycle life. Some of the major advancements made so far in the development of Li-TiS2 cells are in the areas of cathode processing technology, mixed solvent electrolytes, and cell assembly. Methods were developed for the fabrication of large size high performance TiS2 cathodes. Among the various electrolytes examined, 1.5M LiAsF6/EC + 2-MeTHF mixed solvent electrolyte was found to be more stable towards lithium. Experimental cells activated with this electrolyte exhibited more than 300 cycles at 100 percent Depth of Discharge. Work is in progress in other areas such as selection of lithium alloys as candidate anode materials, optimization of cell design, and development of 5 Ah cells. The advances made at the Jet Propulsion Laboratory on the development of secondary lithium cells are summarized.

Subbarao, S.↗
NASA NTRSDate not supplied

Modeling of Multicomponent Trace Contaminant Adsorption on Activated Carbon

Modeling of multicomponent trace contaminant adsorption on activated carbon for spacecraft life support applications presents challenges due to the very low contaminant concentrations in cabin air coupled with very strong adsorption of heavier components such as cyclic polydimethylsiloxanes. Both gas-phase and solid-phase micropore mass transfer resistances can be important, and competitive interactions between adsorbed components can result in behavior such as roll-up where lighter components displaced by heavier components have a higher concentration at the outlet than at the inlet. This paper describes the further development of a dynamic trace contaminant adsorption model based on Ideal Adsorbed Solution Theory (IAST). Alternative 1-dimensional and 2-dimensional versions of the model are described with different mass transfer resistance and driving force assumptions. Optimization of solution methods for speed and stability is also described. Mass balance equations are scaled to provide sensitivity over the wide range of component concentrations in cabin air. Model parameters are estimated from isotherm and breakthrough test data on single adsorbate and binary adsorbate systems. Multicomponent results are compared against predictions of a heritage model used extensively by NASA for trace contaminant control system (TCCS) carbon bed sizing. Finally, efforts to implement the model in a second software platform for wider access are described.

Kevin E Lange↗