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NASA NTRSDate not supplied

Demonstration of How Manufacturing Innovations Challenge Conventional Structural Design

For almost 100 years, commercial aircraft have been fabricated using riveted aluminum alloy structures. Aside from refining aircraft designs for better aerodynamic efficiency, improving alloy compositions, and automating assembly steps, fuselage construction remains largely unchanged today. The intent of the lightweight metallic fuselage prototype undertaken in NASA’s Advanced Air Transport Technology project was to demonstrate innovative forming and joining processes that advance the design paradigm, i.e. achieve high-rate manufacturing and reduce assembly time/costs. Over the past decade, researchers at NASA Langley Research Center have evaluated the potential to modify the flow forming process to produce near-net shaped cylinders, integrally stiffened along the cylinder axis, for space launch vehicles. The production of integral stiffeners in a formed aluminum cylinder, the size of the Space Shuttle external tank, replaced machined thick plate and welding steps to eliminate > 500,000 pounds of machining chips and ~ 0.5 miles of welds. The flow forming method, called the Integrally Stiffened Cylinder (ISC) process, was successfully demonstrated up to the 10-foot diameter scale, which laid the groundwork for the current investigation of metallic fuselage structures. Using the ISC process as the basis for the re-design of a metallic fuselage eliminates hundreds of thousands of holes and rivets, while significantly reducing assembly time and crack initiation sites in the integral structure. However, the ISC process is not currently configured to fabricate circumferential ring frames for carrying fuselage internal pressure loads. Consequently, a trade study was performed to assess existing and advanced manufacturing processes, including additive manufacturing, forming, and welding. The approach with the lowest barriers to success, while simultaneously improving manufacturing time and cost, was to use formed ring frame segments attached to the ISC via Refill Friction Stir Spot Welding (RFSSW). The RFSSW process is five times faster than drilling, reaming, and riveting and provides similar mechanical performance. Finishing the fuselage structure with conventional windows, floor beams, and floor panels can then be accomplished using incumbent assembly methods, thereby maximizing reuse of existing infrastructure for aircraft construction. Structural analyses were performed to assess and optimize the geometric variables of integrated skin and stiffener configurations. A cost benefit and manufacturing rate analysis was also performed to compare against the current state-of-the-art for single aisle transport class aircraft. The resulting structure offers a weight reduction that rivals current graphite-epoxy composite fuselage structures. The projected manufacturing rate is close to double current metallic fuselages and six times faster than current composite manufacturing practices. The damage tolerance properties of a monocoque fuselage structure have yet to be assessed, but past integral airframe structural work has exploited geometric features to blunt or turn cracks. This concept offers promise that integrated structures can meet stringent aircraft durability specifications. An important benefit is that such aluminum fuselage structures may be inspected and repaired using established practices and existing expertise. Finally, pursuit of advanced manufacturing processes for future aluminum fuselages minimizes waste and the structure is 100% recyclable at the end-of-life for maximum sustainability.

aluminum↗
DOE OSTI2022

Natural orbitals for the ab initio no-core configuration interaction approach

Ab initio no-core configuration interaction (NCCI) calculations for the nuclear many-body problem have traditionally relied upon an antisymmetrized product (Slater determinant) basis built from harmonic oscillator orbitals. The accuracy of such calculations is limited by the finite dimensions which are computationally feasible for the truncated many-body space. We therefore seek to improve the accuracy obtained for a given basis size by optimizing the choice of single-particle orbitals. Natural orbitals, which diagonalize the one-body density matrix, provide a basis which maximizes the occupation of low-lying orbitals, thus accelerating convergence in a configuration-interaction basis, while also possibly providing physical insight into the single-particle structure of the many-body wave function. We describe the implementation of natural orbitals in the NCCI framework and examine the nature of the natural orbitals thus obtained, the properties of the resulting many-body wave functions, and the convergence of observables. After taking 3 He as an illustrative testbed, we explore aspects of NCCI calculations with natural orbitals for the ground state of the p-shell neutron halo nucleus 6 He .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2005

Buckling of Carbon Nanotube-Reinforced Polymer Laminated Composite Materials Subjected to Axial Compression and Shear Loadings

A multi-scale method to predict the stiffness and stability properties of carbon nanotube-reinforced laminates has been developed. This method is used in the prediction of the buckling behavior of laminated carbon nanotube-polyethylene composites formed by stacking layers of carbon nanotube-reinforced polymer with the nanotube alignment axes of each layer oriented in different directions. Linking of intrinsic, nanoscale-material definitions to finite scale-structural properties is achieved via a hierarchical approach in which the elastic properties of the reinforced layers are predicted by an equivalent continuum modeling technique. Solutions for infinitely long symmetrically laminated nanotube-reinforced laminates with simply-supported or clamped edges subjected to axial compression and shear loadings are presented. The study focuses on the influence of nanotube volume fraction, length, orientation, and functionalization on finite-scale laminate response. Results indicate that for the selected laminate configurations considered in this study, angle-ply laminates composed of aligned, non-functionalized carbon nanotube-reinforced lamina exhibit the greatest buckling resistance with 1% nanotube volume fraction of 450 nm uniformly-distributed carbon nanotubes. In addition, hybrid laminates were considered by varying either the volume fraction or nanotube length through-the-thickness of a quasi-isotropic laminate. The ratio of buckling load-to-nanotube weight percent for the hybrid laminates considered indicate the potential for increasing the buckling efficiency of nanotube-reinforced laminates by optimizing nanotube size and proportion with respect to laminate configuration.

Riddick, J. C.↗
DOE OSTI2023

A Fast and Scalable Genetic Algorithm-Based Approach for Planning of Microgrids in Distribution Networks

As a result of climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution utilities and system operators to ensure that there is uninterrupted power supply to critical loads in their networks; thus, the level of proactive preparation of the distribution system to be able to handle severe impacts of extreme weather events represents the system's resilience. One method that distribution system planners can use to prepare for future extreme events is to plan multiple microgrids which can use local generation as much as possible to supply critical loads. But partitioning an existing distribution system such that multiple feasible islands are planned and which are capable of supporting critical loads is still challenging for distribution systems - first, because of the size of the network graph partitioning problem and, second, because of the difficulty in properly formulating the desired attributes of such islands or microgrids. Therefore, this paper presents a genetic algorithm based approach that facilitates incorporating multiple objectives for grid partitioning by formulating two types of problems - node allocation and edge elimination - and it considers multiple topological and resilience-enhancing objectives. The performance of the proposed genetic algorithm-based approach is numerically evaluated on multiple test systems as well as on a real distribution feeder in Colorado, United States.

genetic algorithm↗
NASA NTRS2015

Open-Source Conceptual Sizing Models for the Hyperloop Passenger Pod

Hyperloop is a new mode of transportation proposed as an alternative to California's high speed rail project, with the intended benefits of higher performance at lower overall costs. It consists of a passenger pod traveling through a tube under a light vacuum and suspended on air bearings. The pod travels up to transonic speeds resulting in a 35 minute travel time between the intended route from Los Angeles and San Francisco. Of the two variants outlined, the smaller system includes a 1.1 meter tall passenger capsule traveling through a 2.2 meter tube at 700 miles per hour. The passenger pod features water-based heat exchangers as well as an on-board compression system that reduces the aerodynamic drag as it moves through the tube. Although the original proposal looks very promising, it assumes that tube and pod dimensions are independently sizable without fully acknowledging the constraints of the compressor system on the pod geometry. This work focuses on the aerodynamic and thermodynamic interactions between the two largest systems; the tube and the pod. Using open-source toolsets, a new sizing method is developed based on one-dimensional thermodynamic relationships that accounts for the strong interactions between these sub-systems. These additional considerations require a tube nearly twice the size originally considered and limit the maximum pod travel speed to about 620 miles per hour. Although the results indicate that Hyperloop will need to be larger and slightly slower than originally intended, the estimated travel time only increases by approximately five minutes, so the overall performance is not dramatically affected. In addition, the proposed on-board heat exchanger is not an ideal solution to achieve reasonable equilibrium air temperatures within the tube. Removal of this subsystem represents a potential reduction in weight, energy requirements and complexity of the pod. In light of these finding, the core concept still remains a compelling possibility, although additional engineering and economic analyses are markedly necessary before a more complete design can be developed.

design analysis↗
DOE OSTI2021

Accelerate Nuclear Research and Development by Reducing Time and Cost Spend in the Pre-conceptual Design Phase of Advanced Reactor Experiments

The design process of every new concept, such as advanced nuclear reactors or associated experiments, starts with the pre-conceptual design phase. In this phase, the viability of a wide range of design options needs to be assessed quickly, to understand the operating envelope and its feasibility. A variety of physics models (thermal-hydraulics, neutronics, mechanical design, etc.) has to be considered at this very first design stage and optimum component sizes and materials (e.g. heat exchangers, piping, turbomachinery, coolant type, etc.) have to be chosen for a given set of boundary conditions (e.g. heat source, heat sink, flow rate, etc.). Detailed solutions such as provided by high fidelity methods like computational fluid dynamics (CFD), Monte Carlo methods, etc. and even lower fidelity tools such as system or subchannel codes, etc. are usually not used during the pre-conceptual design due to the relatively long time needed to create input models, the computational time to obtain a solution and the lack of flexibility to quickly investigate different combinations of components, individual component sizes and material properties. High fidelity tools are usually only employed in the conceptual design and later phases once a base concept has been identified during the pre-conceptual design stage. The current practice during the pre-conceptual design stage is that analysts collect the needed equations, material properties, closure laws, etc. and create ad-hoc solutions form scratch for every new problem. There clearly is a lack of a flexible scoping tool that can be used during pre-conceptional design before higher fidelity tools (as described above) come into play. To reduce user errors in ad-hoc solutions and increase fidelity and efficiency, this project aims to investigate and develop a user-friendly scoping tool to address the thermal-hydraulic designing needs during preconceptual experiment design, i.e. Thermal-hydraulic Research Universal Scoping Tool (TRUST). The success of TRUST will provide the nuclear engineers with an easy-to-use and affordable calculator for early reactor system design and optimization.

42 ENGINEERING↗
DOE OSTI2025

Incident beam optics optimization for the single crystal neutron diffractometer Pioneer with a polarized beam option

Pioneer, a next-generation single-crystal neutron diffractometer, is under development for Oak Ridge National Laboratory’s Second Target Station. Designed to address a wide range of scientific questions, Pioneer will deliver homogeneous neutron beams with customizable size and divergence and provide a polarized beam option. This article introduces its incident beam optics, highlighting the optimization methodology and the simulated performance. Pioneer will utilize a modified elliptical-straight guide for neutron transport and deploy slit packages and insertable apertures to control beam size and divergence. The optimized guide geometry matches the optimal-and-full-sample-illumination condition, and the beam control system effectively filters out unwanted neutrons while preserving the desired ones. In addition, we have found that polygon-approximated guides provide satisfactory transport efficiency and beam homogeneity, eliminating the need for truly curved guides. Further, to enhance neutronics performance and reduce cost, the coatings of supermirror elements are individually optimized to the lowest half-integer m-values that are sufficient to deliver the desired neutrons. After evaluating polarizing V-cavities and 3 He spin filters over the default polarized wavelength band of 1.2–5.5 Å, we selected a translatable multichannel polarizing V-cavity as the incident beam polarizer. Strategically placed at a location where the beam divergence is low and a large in-guide gap has negligible impact on transport efficiency, the optimized V-cavity achieves an average P 2 T of ~35%.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Dilute Combustion Control Using Spiking Neural Networks

Dilute combustion with exhaust gas recirculation (EGR) in spark-ignition engines presents a cost-effective method for achieving higher levels of engine efficiency. At high levels of EGR, however, cycle-to-cycle variability (CCV) of the combustion process is exacerbated by sporadic occurrences of misfires and partial burns. Previous studies have shown that temporal deterministic patterns emerge at such conditions and certain combustion cycles have a significant influence over future events. Due to the complexity of the combustion process and the nature of CCV, harnessing all the deterministic information for control purposes has remained challenging even with physics based 0-D, 1-D, and high-fidelity computational fluid dynamics (CFD) models. In this study, we present a data-driven approach to optimize the combustion process by controlling CCV adjusting the cycle-to-cycle fuel injection quantity. Readily available data from in-cylinder pressure was used to train a spiking neural network (SNN) which learns the optimal way to manage fuel injection in order to reduce CCV while maintaining acceptable levels of fuel consumption. SNNs are particularly well suited for powertrain control applications due to their ability to be deployed on FPGA-based neuromorphic hardware which are small, inexpensive, and have a low power demand. The high-performance computing (HPC) resources of Oak Ridge National Laboratory were used to run an evolutionary-based training approach for choosing the best SNN configuration that minimizes the size of the network while achieving the desired goal. The neuromorphic hardware with the optimized SNN deployed was connected to the rapid prototyping engine control system for real-time control implementation and tested on a single cylinder version of a GM LNF 4-cylinder engine. The results show a significant reduction of CCV with a small percentage of additional fuel used to stabilize the charge.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Processing of Ice Cloud In Situ Data Collected by Bulk Water, Scattering, and Imaging Probes: Fundamentals, Uncertainties, and Efforts toward Consistency

Abstract In situ observations of cloud properties made by airborne probes play a critical role in ice cloud research through their role in process studies, parameterization development, and evaluation of simulations and remote sensing retrievals. To determine how cloud properties vary with environmental conditions, in situ data collected during different field projects processed by different groups must be used. However, because of the diverse algorithms and codes that are used to process measurements, it can be challenging to compare the results. Therefore it is vital to understand both the limitations of specific probes and uncertainties introduced by processing algorithms. Since there is currently no universally accepted framework regarding how in situ measurements should be processed, there is a need for a general reference that describes the most commonly applied algorithms along with their strengths and weaknesses. Methods used to process data from bulk water probes, single-particle light-scattering spectrometers and cloud-imaging probes are reviewed herein, with emphasis on measurements of the ice phase. Particular attention is paid to how uncertainties, caveats, and assumptions in processing algorithms affect derived products since there is currently no consensus on the optimal way of analyzing data. Recommendations for improving the analysis and interpretation of in situ data include the following: establishment of a common reference library of individual processing algorithms, better documentation of assumptions used in these algorithms, development and maintenance of sustainable community software for processing in situ observations, and more studies that compare different algorithms with the same benchmark datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Super Resolving Unrolled Neural Networks for Remote Sensing

In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Mass, momentum, and energy transfer in supersonic aerosol deposition processes

Aerosol deposition (AD) can lead to the formation of dense coatings via deposition of particles from a gas flow; in AD the aerosol is passed through a converging-diverging nozzle, facilitating inertial particle impaction on a desired substrate at supersonic particle velocities. Unique from thermal spray methods, AD can be applied near room temperature and unique from cold spray, in AD the aerosol is typically at atmospheric pressure upstream of the nozzle. Though AD has been successfully demonstrated previously, a number of aspects related to particle motion in AD systems remain poorly understood. In this work, we simulated compressible flow field profiles and particle trajectories for typical AD working conditions for a slit type converging-diverging nozzle with a planar substrate. In examining the fluid flow profile, we show that the velocity and pressure profiles, as well as the shock structure are sensitive to the upstream and downstream operating pressures of the nozzle. These ultimately affect particle impaction speed. Importantly, in AD, the particle drag regime is dynamic; both particle Knudsen numbers and Mach numbers can vary by orders of magnitude. To aid particle trajectory simulations, we trained a neural network to predict the drag force on the particles based on existing experimental data, theoretical limits, and new direct simulation Monte Carlo (DMSC) results. The neural network based drag law, which depends upon both Mach and Knudsen numbers, shows better agreement with the DSMC simulation data than pre-existing correlations. With it, particle trajectory simulation results reveal that for a given particle density, there exists an optimal particle diameter to maximize particle impaction speed. We also find that in AD particles undergo size dependent inertial focusing, i.e. there is a particular particle diameter where the particle deposition linewidth is minimized. Particles smaller than this diameter are underfocused, and particles larger than this are overfocused, and hence have larger deposition linewidths in both cases. Using trajectory simulations, we additionally develop a framework that can be used to evaluate the position-dependent mass, momentum and kinetic energy fluxes to the deposition substrate for any aerosol size distribution function upstream the nozzle. It is shown for typical aerosol concentrations achievable in the laboratory, the kinetic energy flux can approach a magnitude normally observed in convective heat transfer with phase change, hence translational kinetic energy to thermal energy transfer in AD is likely the key contributor to the formation of dense coatings.

42 ENGINEERING↗
DOE OSTI2024

Feasibility of using nuclear microreactor process heat for bioconversion and agricultural processes

Introduction There is a global goal to reduce greenhouse gas emissions by 43% by 2023. Nuclear microreactors, a subset of small modular reactors, offer a potential solution due to their compact size, transportability, and carbon-neutral power generation capabilities. Methods This study explores the feasibility of using heat from nuclear microreactors for bioconversion and agricultural processes, including transforming biomass into energy carriers and products such as syngas, bio-oil, and pasteurized milk. Operating requirements for gasification, pyrolysis, hydrothermal carbonization, hydrothermal liquefaction, hydrothermal gasification, ethanol production, anaerobic digestion, and pasteurization were obtained through a literature review. A Brayton cycle model based on the eVinci TM microreactor was developed to assess the feasibility of powering these processes using nuclear microreactor heat. Results and Discussion Exergetic efficiency values for high-temperature processes ranged from 72% to 100%, whereas lower-temperature processes ranged from 2% to 53%. These efficiencies depend on the available source temperature for each microreactor design. There were trade-offs between producing net power and using process heat, particularly for high-temperature processes. Three heat exchanger locations were considered: before the turbine (600 ℃ ), between the turbine and regenerator (370 ℃ ), and after the regenerator (192 ℃ ). High-temperature processes like gasification require temperatures too high for feasibility. Middle temperature processes are better suited to a heat exchanger between the turbine and regenerator, while also operable before the turbine. Lower-temperature processes like pasteurization and anaerobic digestion can use waste heat after the regenerator and do not impact power production. These findings are valuable for optimizing nuclear microreactor heat use and aligning with global climate initiatives.

09 BIOMASS FUELS↗
NASA NTRS2010

From Shuttle Main Engine to the Human Heart: A Presentation to the Federal Lab Consortium for Technology Transfer

A NASA engineer received a heart transplant performed by Drs. DeBakey and Noon after suffering a serious heart attack. 6 months later that engineer returned to work at NASA determined to use space technology to help people with heart disease. A relationship between NASA and Drs. DeBakey and Noon was formed and the group worked to develop a low cost, low power implantable ventricular assist device (VAD). NASA patented the method to reduce pumping damage to red blood cells and the design of a continuous flow heart pump (#5,678,306 and #5,947,892). The technology and methodology were licensed exclusively to MicroMed Technology, Inc.. In late 1998 MicroMed received international quality and electronic certifications and began clinical trials in Europe. Ventricular assist devices were developed to bridge the gap between heart failure and transplant. Early devices were cumbersome, damaged red blood cells, and increased the risk of developing dangerous blood clots. Application emerged from NASA turbopump technology and computational fluid dynamics analysis capabilities. To develop the high performance required of the Space Shuttle main engines, NASA pushed the state of the art in the technology of turbopump design. NASA supercomputers and computational fluid dynamics software developed for use in the modeling analysis of fuel and oxidizer flow through rocket engines was used in the miniaturization and optimization of a very small heart pump. Approximately 5 million people worldwide suffer from chronic heart failure at a cost of 40 billion dollars In the US, more than 5000 people are on the transplant list and less than 3000 transplants are performed each year due to the lack of donors. The success of ventricular assist devices has led to an application as a therapeutic destination as well as a bridge to transplant. This success has been attributed to smaller size, improved efficiency, and reduced complications such as the formation of blood clots and infection.

Fogarty, Jennifer A.↗
NASA NTRS2016

Subscale Carbon-Carbon Nozzle Extension Development and Hot Fire Testing in Support of Upper Stage Liquid Rocket Engines

Upper stage and in-space liquid rocket engines are optimized for performance through the use of high area ratio nozzles to fully expand combustion gases to low exit pressures increasing exhaust velocities. Due to the large size of such nozzles and the related engine performance requirements, carbon-carbon (C/C) composite nozzle extensions are being considered for use in order to reduce weight impacts. NASA and industry partner Carbon-Carbon Advanced Technologies (C-CAT) are working towards advancing the technology readiness level of large-scale, domestically-fabricated, C/C nozzle extensions. These C/C extensions have the ability to reduce the overall costs of extensions relative to heritage metallic and composite extensions and to decrease weight by 50%. Material process and coating developments have advanced over the last several years, but hot fire testing to fully evaluate C/C nozzle extensions in relevant environments has been very limited. NASA and C-CAT have designed, fabricated and hot fire tested multiple subscale nozzle extension test articles of various C/C material systems, with the goal of assessing and advancing the manufacturability of these domestically producible materials as well as characterizing their performance when subjected to the typical environments found in a variety of liquid rocket and scramjet engines. Testing at the MSFC Test Stand 115 evaluated heritage and state-of-the-art C/C materials and coatings, demonstrating the capabilities of the high temperature materials and their fabrication methods. This paper discusses the design and fabrication of the 1.2k-lbf sized carbon-carbon nozzle extensions, provides an overview of the test campaign, presents results of the hot fire testing, and discusses potential follow-on development work.

Gradl, Paul↗
NASA NTRS2016

Successful Completion of the JWST OGSE2 Cryogenic Test at JSC Chamber-A While Managing Numerous Challenges

The James Webb Space Telescope (JWST) Optical Telescope Element (OTE) assembly is the largest optically stable infrared-optimized telescope currently being manufactured and assembled, and scheduled for launch in 2018. The JWST OTE, including the primary mirrors, secondary mirror, and the Aft Optics Subsystems (AOS) are designed to be passively cooled and operate at near 45 degrees Kelvin. Due to the size of its large sunshield in relation to existing test facilities, JWST cannot be optically or thermally tested as a complete observatory-level system at flight temperatures. As a result, the telescope portion along with its instrument complement will be tested as a single unit very late in the program, and on the program schedule critical path. To mitigate schedule risks, a set of 'pathfinder' cryogenic tests will be performed to reduce program risks by demonstrating the optical testing capabilities of the facility, characterizing telescope thermal performance, and allowing project personnel to learn valuable testing lessons off-line. This paper describes the 'pathfinder' cryogenic test program, focusing on the recently completed second test in the series called the Optical Ground Support Equipment 2 (OGSE2) test. The JWST OGSE2 was successfully completed within the allocated project schedule while faced with numerous conflicting thermal requirements during cool-down to the final cryogenic operational temperatures, and during warm-up after the cryo-stable optical tests. The challenges include developing a pre-test cool-down and warm-up profiles without a reliable method to predict the thermal behaviors in a rarified helium environment, and managing the test article hardware safety driven by the project Limits and Constraints (L&C's). Furthermore, OGSE2 test included the time critical Aft Optics Subsystem (AOS), a part of the flight Optical Telescope Element that would need to be placed back in the overall telescope assembly integrations. The OGSE2 test requirements included the strict adherence of the project contamination controls due to the presence of the contamination sensitive flight optical elements. The test operations required close coordination of numerous personnel while they being exposed and trained for the 'final' combined OTE and instrument cryo-test in 2017. This paper will also encompass the OGSE2 thermal data look-back review.

JWST OGSE2 Cryogenic Test↗
NASA NTRSDate not supplied

A Multiphysics Study to Improve Specific Energy of Primary Batteries for Low Temperature Operation for Deep Space Missions

Several lander missions on the outer planets such as Europa, Enceladus, and Titan require electrical power to operate scientific and communication equipment. The traditional power generation methods, such as a photovoltaic array, are not feasible as their efficiency drops significantly at these vast distances. The novel radioisotope power systems are not practical today based on current lander designs and the effectiveness of these systems. To perform in situ science on distant planets, a high specific energy battery (>700 Wh/kg) needs to operate for about 480 hours under cold temperatures (-40C or 0C) [1]. While a primary battery such as Li-CFx can provide high specific energy at room temperature, its specific capacity decreases significantly at low temperatures. One of the causes for this drop is low ion and electrical conductivity, and slower reaction kinetics. Slower transport and facile kinetics lead to an increase in the battery’s resistance and higher voltage drops during the cell operation, thus reducing specific capacity. Both the transport and kinetics show a strong dependence on temperature. Thus, a small temperature rise can lead to an increase in the reaction rate and ion conductivity; since the temperature, cell resistance, and specific capacity are interdependent. A conventional battery model accounts for ohmic, thermodynamic, and, electrochemical, and chemical decomposition heating. The ohmic heating can be controlled by designing a resistive microstructure and varying the ratios of the active materials [2]. The kinetics can be improved by increasing the surface area, reducing the particle size, or adding a catalyst. These parameters are often optimized to achieve high specific energy at room temperatures. A similar optimization study is not available at low temperatures and for a primary (high specific energy) battery. For this presentation, we will explore the effect of geometrical, microstructural, and material properties on optimal specific capacity at low temperatures through multiphysics simulations. The ion transport resistance depends on the porosity and the tortuosity of an electrode and the separator.

M. Mehta↗
DOE OSTI2022

Revenue prediction for integrated renewable energy and energy storage system using machine learning techniques

Revenue estimation for integrated renewable energy and energy storage systems is important to support plant owners or operators’ decisions in battery sizing selection that leads to maximized financial performances. A common approach to optimizing revenues of a hybrid hydro and energy storage system is using mixed-integer linear programming (MILP). Although MILP models can provide accurate production cost estimations, they are typically very computationally expensive. To provide a fast yet accurate first-step information to hydropower plant owners or operators who consider integrating energy storage systems, we propose an innovative approach to predicting optimal revenues of an integrated energy generation and storage system. In this study, we examined the performance of two prediction techniques: Generalized Additive Models (GAMs) and machine learning (ML) models developed based on artificial neural networks (ANN). Predictive equations and models are generated based on optimized solutions from a market participation optimization model, the Conventional Hydropower Energy and Environmental Resource System (CHEERS) model. The two predicting techniques reduce the computational time to evaluate annual revenue for one set of battery configurations from 3 h to 1 to 4 min per run while also being implementable with significantly less data. The model validation prediction errors of developed GAMs and ML models are generally below 5%; for model testing predictions, the ML models consistently outperform the regression equations in terms of root mean square errors. This new approach allows plant owners, operators, or potential investors to quickly access multiple battery configurations under different energy generation and market scenarios. This new revenue prediction method will therefore help reduce the barriers, and thereby promoting the deployment of battery hybridization with existing renewable energy sources.

13 HYDRO ENERGY↗
DOE OSTI2025

Control of core–shell nanoparticles properties through plasma synthesis: a computational study

The improved properties of core–shell nanoparticles (CSNPs) over homogeneous nanoparticles (NPs) have expanded and diversified the applications of these nanomaterials. However, controlling the properties of CSNPs can be a challenging task. Low temperature plasmas have proven to be an effective method of producing NPs with uniform size and morphology, and high yield. That said, NP transport and growth dynamics are sensitive to LTP properties. We report on a computational investigation of the evolution of Ge–Si CSNP properties as a function of operating conditions through the modeling of a flowing, two-zone inductively coupled plasma (ICP) reactor. Ar/GeH 4 and Ar/SiH 4 gas mixtures were supplied to separate plasma zones at a pressure of 1 Torr to promote growth of Ge cores and Si shells. The negatively charged CSNPs are trapped electrostatically in the vicinity of the antennas where the plasma is generated and where the majority of particle growth occurs. Particles that grow to a critical size are then de-trapped by fluid drag due to neutral gas flow. A two-dimensional hybrid plasma model coupled with a three-dimensional kinetic NP transport model were utilized to resolve plasma chemistry and NP growth processes that take place on distinct timescales. The trends in CSNP properties and trapping mechanisms associated with flow rate, applied ICP power and inlet precursor fraction are discussed. While the spatial distribution of plasma produced radical species can have significant impact on the NP growth process, the NP transport dynamics are what ultimately dictates the growth environment that is unique to each particle and so determines their final dimension and composition. The key to optimizing reactor conditions involves controlling the spatial density of growth species and plasma profile as a means to tailor particle trapping dynamics suitable to produce CSNPs for a specific application.

36 MATERIALS SCIENCE↗