Results for “Lead optimization” — Page 86

DOE OSTI2022

Plasma Facing Components with Capillary Porous System and Liquid Metal Coolant Flow

Liquid metal can create a renewable protective surface on plasma facing components (PFC), with an additional advantage of deuterium pumping and the prospect of tritium extraction if liquid lithium (LL) is used and maintained below 450 C, the temperature above which LL vapor pressure begins to contaminate the plasma. LM can also be utilized as an efficient coolant, driven by the Lorentz force created with the help of the magnetic field in fusion devices. Capillary porous systems can serve as a conduit of LM and simultaneously provide stabilization of the LM flow, protecting against spills into the plasma. Recently a combination of a fast-flowing LM cooling system with a porous plasma facing wall (CPSF) was investigated [Khodak and Maingi (2021)]. The system takes an advantage of a magnetohydrodynamics velocity profile, as well as attractive LM properties to promote efficient heat transfer from the plasma to the LL at low pumping energy cost, relative to the incident heat flux on the PFC. In case of a disruption leading to excessive heat flux from the plasma to the LM PFCs, LL evaporation can stabilize the PFC surface temperature, due to high evaporation heat and apparent vapor shielding. The proposed CPSF was optimized analytically for the conditions of a Fusion Nuclear Science Facility [Kessel et al. (2019)]: 10T toroidal field and 10 MW/m2 peak incident heat flux. Computational fluid dynamics analysis confirmed that a CPSF system with 2.5 mm square channels can pump enough LL so that no additional coolant is needed.

High Heat Flux↗

DOE OSTI2024

A Preliminary Feasibility Study on Multi-Cavity Cryomodule Integration for the Electron Ion Collider Energy Recover Linac Cooler

The Electron-Ion Collider (EIC) is a state-of-the-art accelerator designed for collisions between highly polarized electrons and ions. To achieve optimal luminosity, the ion beam is cooled using an electron beam sourced from an energy recovery linac (ERL). In the current ERL baseline design, one BNL type RF cavity is used per cryomodule, leading to spatial and cost challenges. This study examines the feasibility of using more compact PERLE (Powerful Energy Recovery Linac for Experiments) type cavities, which can house multiple cavities within a single cryomodule, by evaluating their Beam Breakup (BBU) instability performance. Higher Order Modes (HOM) parameters were obtained through frequency scaling, assuming constant quality factor Q_L, R/Q, and linearly scaling HOM frequencies. To predict the BBU threshold current and ensure accuracy, two different BBU tracking simulations are used for cross-verification. Although the reduced footprint of PERLE-type cavities is advantageous, maintaining sufficient damping of HOMs remains crucial. Finally, we compare the HOM damping efficiency of both cavity types and suggest a pathway forward.

Deitrick, K.↗

DOE OSTI2022

Advancement of Certification Methods and Applications for Industrial Deployments of Components Derived from Advanced Manufacturing Technologies

One goal of the Advanced Materials and Manufacturing Technologies subprogram, within the Department of Energy’s (DOE) crosscutting technology development program, is to advance the qualification and certification of parts and components created via additive manufacturing (AM). AM offers a new paradigm for the design and optimization of new nuclear components, and for observing and tracking manufacturing performance and potential defects in a way that is not possible with traditional approaches. Tracking and evaluating these in situ data can lead to the estimation of local material properties and, with the use of engineering analysis tools, the estimation of component performance during operation. To achieve these objectives, a digital platform certification approach was developed and is summarized in this report.

36 MATERIALS SCIENCE↗

NASA NTRS1972

Bounded state variables and the calculus of variations

An optimal control problem with bounded state variables is transformed into a Lagrange problem by means of differentiable mappings which take some Euclidean space onto the control and state regions. Whereas all such mappings lead to a Lagrange problem, it is shown that only those which are defined as acceptable pairs of transformations are suitable in the sense that solutions to the transformed Lagrange problem will lead to solutions to the original bounded state problem and vice versa. In particular, an acceptable pair of transformations is exhibited for the case when the control and state regions are right parallelepipeds. Finally, a description of the necessary conditions for the bounded state problem which were obtained by this method is given.

Hanafy, L. M.↗

NASA NTRS1980

Damping in tapered annular seals for an incompressible fluid

Damping in annular seals is calculated for an incompressible fluid. Results show that damping in tapered seals optimized for stiffness is considerably less than that in straight seals for the same minimum clearance. Damping in rotating seals can promote fractional frequency whirl. Neglecting fluid acceleration makes solution much easier, but leads to errors in calculated damping of up to 16 percent.

Fleming, D. P.↗

NASA NTRS1985

Crystallization, Optical and Chemical Properties of Fluoride Glasses

Fluoride glasses have great promise as infrared optical components, especially fibers, because they are transparent to 8 micrometers and higher. In order to optimize properties, different glass compositions are needed. Some are hard to form in a container, and may possibly be formable in a containerless furnace. Understanding of crystallization with and without a container could lead to glasses with optimum properties. Chemical durability (attack by water) can limit or extend the applicability of fluoride glasses. Progress to date is given.

Doremus, R. H.↗

NASA NTRS1991

An aeroelastic analysis with a generalized dynamic wake

An aeroelastic model with generalized dynamic wake is developed for application in the integration of aerodynamic, dynamic, and structural optimization of a rotor blade. The investigation is carried out with special attention to efficiency and accuracy of aeroelastic modeling. Each blade is assumed to be an elastic beam undergoing flap bending, lead-lag bending, elastic twist and axial deflections. The nonuniform blade is discretized into finite beam elements, each of which consists of twelve degrees of freedom. Such important blade design variables as pretwist, and chordwise offsets of the blade center of gravity and of the aerodynamic center from the elastic axis have been included in the analysis. Aerodynamic loads are computed from unsteady blade element theory where the rotor three-dimensional unsteady wake is modeled using a generalized dynamic wake theory. The noncirculatory loads based on unsteady thin airfoil theory are also included.

He, Cheng J.↗

NASA NTRS2005

Acoustic Emission Weld Monitoring in the 2195 Aluminum-Lithium Alloy

Due to its low density, the 2195 aluminum-lithium alloy was developed as a replacement for alloy 2219 in the Space Shuttle External Tank (ET). The external tank is the single largest component of the space shuttle system. It is 154 feet long and 27.6 feet in diameter, and serves as the structural backbone for the shuttle during launch, absorbing most of the 7 million plus pounds of thrust produced. The almost 4% decrease in density between the two materials provides an extra 7500 pounds of payload capacity necessary to put the International Space Station components into orbit. The ET is an all-welded structure; hence, the requirement is for up to five rewelds without hot cracking. Unfortunately, hot cracking during re-welding or repair operations was occurring and had to be dealt with before the new super lightweight tank could be used. Weld metal porosity formation was also of concern because it leads to hot cracking during weld repairs. Accordingly, acoustic emission (AE) nondestructive testing was employed to monitor the formation of porosity and hot cracks in order to select the best filler metal and optimize the weld schedule. The purpose of this work is to determine the feasibility of detecting hot cracking in welded aluminum-lithium (Al-Li) structures through the analysis of acoustic emission data. By acoustically characterizing the effects of reheating during a repair operation, the potential for hidden flaws coalescing and becoming "unstable" as the panel is repaired could be reduced. Identification of regions where microcrack growth is likely to occur and the location of active flaw growth in the repair weld will provide the welder with direct feedback as to the current weld quality enabling adjustments to the repair process be made in the field. An acoustic emission analysis of the source mechanisms present during welding has been conducted with the goals of locating regions in the weld line that are susceptible to damage from a repair operation, identifying the formation of critically sized flaws and providing accept/reject criteria for the quality of a weld as it is performed.

Walker, James L.↗

NASA NTRS2005

Managing Space Radiation Risks On Lunar and Mars Missions: Risk Assessment and Mitigation

Radiation-induced health risks are a primary concern for human exploration outside the Earth's magnetosphere, and require improved approaches to risk estimation and tools for mitigation including shielding and biological countermeasures. Solar proton events are the major concern for short-term lunar missions (<60 d), and for long-term missions (>60 d) such as Mars exploration, the exposures to the high energy and charge (HZE) ions that make-up the galactic cosmic rays are the major concern. Health risks from radiation exposure are chronic risks including carcinogenesis and degenerative tissue risks, central nervous system effects, and acute risk such as radiation sickness or early lethality. The current estimate is that a more than four-fold uncertainty exists in the projection of lifetime mortality risk from cosmic rays, which severely limits analysis of possible benefits of shielding or biological countermeasure designs. Uncertainties in risk projections are largely due to insufficient knowledge of HZE ion radiobiology, which has led NASA to develop a unique probabilistic approach to radiation protection. We review NASA's approach to radiation risk assessment including its impact on astronaut dose limits and application of the ALARA (As Low as Reasonably Achievable) principle. The recently opened NASA Space Radiation Laboratory (NSRL) provides the capability to simulate the cosmic rays in controlled ground-based experiments with biological and shielding models. We discuss how research at NSRL will lead to reductions in the uncertainties in risk projection models. In developing mission designs, the reduction of health risks and mission constraints including costs are competing concerns that need to be addressed through optimization procedures. Mitigating the risks from space radiation is a multi-factorial problem involving individual factors (age, gender, genetic makeup, and exposure history), operational factors (planetary destination, mission length, and period in the solar cycle), and shielding characteristics (materials, mass, and topology). We review optimization metrics for radiation protection including scenarios that integrate biophysics models of radiation risks, operational variables, and shielding design tools needed to assess exploration mission designs. We discuss the application of a crosscutting metric, based on probabilistic risk assessment, to lunar and Mars mission trade studies including the assessment of multi-factorial problems and the potential benefits of new radiation health research strategies or mitigation technologies.

Cucinotta, F. A.↗

NASA NTRS2005

Managing Space Radiation Risks on Lunar and Mars Missions: Risk Assessment and Mitigation

Radiation-induced health risks are a primary concern for human exploration outside the Earth's magnetosphere, and require improved approaches to risk estimation and tools for mitigation including shielding and biological countermeasures. Solar proton events are the major concern for short-term lunar missions (<60 d), and for long-term missions (>60 d) such as Mars exploration, the exposures to the high energy and charge (HZE) ions that make-up the galactic cosmic rays are the major concern. Health risks from radiation exposure are chronic risks including carcinogenesis and degenerative tissue risks, central nervous system effects, and acute risk such as radiation sickness or early lethality. The current estimate is that a more than four-fold uncertainty exists in the projection of lifetime mortality risk from cosmic rays, which severely limits analysis of possible benefits of shielding or biological countermeasure designs. Uncertainties in risk projections are largely due to insufficient knowledge of HZE ion radiobiology, which has led NASA to develop a unique probabilistic approach to radiation protection. We review NASA's approach to radiation risk assessment including its impact on astronaut dose limits and application of the ALARA (As Low as Reasonably Achievable) principle. The recently opened NASA Space Radiation Laboratory (NSRL) provides the capability to simulate the cosmic rays in controlled ground-based experiments with biological and shielding models. We discuss how research at NSRL will lead to reductions in the uncertainties in risk projection models. In developing mission designs, the reduction of health risks and mission constraints including costs are competing concerns that need to be addressed through optimization procedures. Mitigating the risks from space radiation is a multi-factorial problem involving individual factors (age, gender, genetic makeup, and exposure history), operational factors (planetary destination, mission length, and period in the solar cycle), and shielding characteristics (materials, mass, and topology). We review optimization metrics for radiation protection including scenarios that integrate biophysics models of radiation risks, operational variables, and shielding design tools needed to assess exploration mission designs. We discuss the application of a crosscutting metric, based on probabilistic risk assessment, to lunar and Mars mission trade studies including the assessment of multi-factorial problems and the potential benefits of new radiation health research strategies or mitigation technologies.

Cucinotta, F. A.↗

NASA NTRS2006

Managing Space Radiation Risks on Lunar and Mars Missions: Risk Assessment and Mitigation

Radiation-induced health risks are a primary concern for human exploration outside the Earth's magnetosphere, and require improved approaches to risk estimation and tools for mitigation including shielding and biological countermeasures. Solar proton events are the major concern for short-term lunar missions (<60 d), and for long-term missions (>60 d) such as Mars exploration, the exposures to the high energy and charge (HZE) ions that make-up the galactic cosmic rays are the major concern. Health risks from radiation exposure are chronic risks including carcinogenesis and degenerative tissue risks, central nervous system effects, and acute risk such as radiation sickness or early lethality. The current estimate is that a more than four-fold uncertainty exists in the projection of lifetime mortality risk from cosmic rays, which severely limits analysis of possible benefits of shielding or biological countermeasure designs. Uncertainties in risk projections are largely due to insufficient knowledge of HZE ion radiobiology, which has led NASA to develop a unique probabilistic approach to radiation protection. We review NASA's approach to radiation risk assessment including its impact on astronaut dose limits and application of the ALARA (As Low as Reasonably Achievable) principle. The recently opened NASA Space Radiation Laboratory (NSRL) provides the capability to simulate the cosmic rays in controlled ground-based experiments with biological and shielding models. We discuss how research at NSRL will lead to reductions in the uncertainties in risk projection models. In developing mission designs, the reduction of health risks and mission constraints including costs are competing concerns that need to be addressed through optimization procedures. Mitigating the risks from space radiation is a multi-factorial problem involving individual factors (age, gender, genetic makeup, and exposure history), operational factors (planetary destination, mission length, and period in the solar cycle), and shielding characteristics (materials, mass, and topology). We review optimization metrics for radiation protection including scenarios that integrate biophysics models of radiation risks, operational variables, and shielding design tools needed to assess exploration mission designs. We discuss the application of a crosscutting metric, based on probabilistic risk assessment, to lunar and Mars mission trade studies including the assessment of multi-factorial problems and the potential benefits of new radiation health research strategies or mitigation technologies.

Cucinotta, F. A.↗

NASA NTRS2012

A Concept for Airborne Precision Spacing for Dependent Parallel Approaches

The Airborne Precision Spacing concept of operations has been previously developed to support the precise delivery of aircraft landing successively on the same runway. The high-precision and consistent delivery of inter-aircraft spacing allows for increased runway throughput and the use of energy-efficient arrivals routes such as Continuous Descent Arrivals and Optimized Profile Descents. This paper describes an extension to the Airborne Precision Spacing concept to enable dependent parallel approach operations where the spacing aircraft must manage their in-trail spacing from a leading aircraft on approach to the same runway and spacing from an aircraft on approach to a parallel runway. Functionality for supporting automation is discussed as well as procedures for pilots and controllers. An analysis is performed to identify the required information and a new ADS-B report is proposed to support these information needs. Finally, several scenarios are described in detail.

Barmore, Bryan E.↗

NASA NTRSDate not supplied

Creating Near Real-time Hourly VIIRS Radiance Mosaic over Polar Regions from SatCORPS Global Cloud Composite (GCC)

While polar orbiting satellites are essential in providing the needed observations over polar regions for data assimilation, weather diagnoses/forecasting, and flight guidance and situation awareness in airborne field campaigns, there is limited temporal coverage. Often, polar mosaics are created by overlaying multiple satellite imagers on top of each other which can lead to transition lines and discontinuities in the data products. To help ease some of these shortcomings, we utilize the NASA Langley Satellite ClOud and Radiation Property retrieval System (SatCORPS) Mosaic algorithm for optimally fusing and merging selected imager radiances from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the Suomi National polar-orbiting Partnership (SNPP), Joint Polar Satellite System (JPSS-1), and JPSS-2 satellites, and the MODerate resolution Imaging Spectroradiometer (MODIS) instruments onboard AQUA and TERRA satellites. Since multiple satellites overpasses and instruments are used in creating hourly 3-km composites over the polar regions, selection of the best satellite data for each 3-km composite pixel is based on aggregated ranking of satellite resolution, time, viewing zenith angle, and distance from terminator. This ranking system provides a smoother transition and improves data discontinuity in the merged output resulting in a more seamless polar region dataset. We will discuss the use of this near real-time dataset to support polar airborne field campaigns and SatCORPS satellite-derived Global Cloud Composite (GCC) generation.

Satellite composites↗

DOE OSTI2019

Stripping off the implementation complexity of physics-based model predictive control for buildings via deep learning

Over the past decade, model predictive control (MPC) has been considered as the most promising solution for intelligent building operation. Despite extensive effort, transfer of this technology into practice is hampered by the need to obtain an accurate controller model with minimum effort, the need of expert knowledge to set it up, and the need of increased computational power and dedicated software to run it. A promising direction that tackles the last two problems was proposed by approximate explicit MPC where the optimal control policies are learned from MPC data via a suitable function approximator, e.g., a deep learning (DL) model. The main advantage of the proposed approach stems from simple evaluation at execution time leading to low computational footprints and easy deployment on embedded HW platforms. We present the energy savings potential of physics-based (also called 'white-box') MPC applied to an office building in Belgium. Moreover, we demonstrate how deep learning approximators can be used to cut the implementation and maintenance costs of MPC deployment without compromising performance. We also critically assess the presented approach by pointing out the major challenges and remaining open-research questions.

Drgona, Jan↗

DOE OSTI2024

Performance Testing of an Integrated Magnetic Power Take-Off

A wave energy converter (WEC) and the power take-off (PTO) generator system can be represented by using a mass-spring-dampener model. By incorporating a negative stiffness spring within the PTO, the overall stiffness of the PTO can be lowered allowing the impedance of the PTO to be more closely matched with the WEC. By designing for impedance matching the WEC can greatly enhance its power generation capability. This project has involved the design, fabrication and testing of a new type of linear-stroke length and rotary stroke length adjustable negative stiffness magnetic springs for use within a wave energy converter (WEC). The magnetic springs were studied by using 3-D finite element analysis with the objective of creating high energy density and a long linear stroke length. After the construction and testing of both a proof-of-principle adjustable linear and rotary magnetic spring prototype the rotary (torsional) magnetic spring was selected for scaling up analysis. The selected scaled-up proof-of-principle magnetic spring had a peak torque of 850 Nm with a ±45 degree stroke length. By translating the inner rotor relative to the outer rotor, the stiffness could be adjusted to be either negative or positive stiffness. During this project a magnetic lead screw was also studied, and it was shown that by combining the magnetic lead screw with a linearly translating magnetic spring a very long rotary stroke length could be attained. However, as the magnetic lead screw increased complexity and reduced overall energy density relative to a rotary (torsional) spring, this design approach was not further pursed. Dynamometer testing of the variable stiffness magnetic springs was first completed by Portland State University and following this the magnetic spring performance was independently verified by Sandia National Laboratory (Sandia). A WEC analysis when using a variable stiffness magnetic spring was completed by using the WecOptTool. WecOptTool is an open-source WEC optimization software developed by Sandia that supports efficient power take-off (PTO) and control optimization. Wave condition data from the Oregon PacWave test-site was used in this study. The analysis showed that a WEC with a tunable stiffness value could consistently achieve about 80% of its maximum theoretical power production. The use of a tunable stiffness WEC, rather than zero-stiffness or a constant stiffness WEC was also shown to lead to a smaller maximum PTO force requirement. The variable stiffness magnetic spring was integrated into an experimental WEC developed by Sandia, called a Wave-Bot. Sandia successfully completed water-tank testing of the Wave-Bot at the Navy’s Carderock, Maryland, wave-basin test site. The wave-basin testing helped to experimentally demonstrate the operating capabilities and increased power generation capability of a WEC containing an integrated variable stiffness magnetic spring. A WEC capacity factor analysis was also completed. The capacity factor was defined as the ratio of annual average WEC generator power to the maximum (RMS) generated power. This capacity factor provided a means of identifying the ratio of potential revenue to cost. It was calculated that if a tunable variable stiffness magnetic spring has RMS constraints it could operate with a capacity factor above 30%. This is comparable to a wind turbine’s capacity factor.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

Optimal Control of Biomass Feedstock Processing System Under Uncertainty in Biomass Quality

Planning of biorefinery operations is complicated by the stochastic nature of physical and chemical characteristics of biomass feedstock, such as, moisture level and carbohydrate content. Biomass characteristics affect the performance of the equipment which feed the reactor and the efficiency of the conversion process in a biorefinery. We propose a stochastic optimization model to identify a blend of feedstocks, inventory levels, and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor while meeting the requirements of the biochemical conversion process. We propose a sample average approximation (SAA) of the model, and develop an efficient algorithm to solve the SAA model. A feedstock preprocessing process consists of two-stage grinding and pelleting is used to develop a case study. Extensive numerical analysis are conducted which lead to a number of observations. Our main observation is that sequencing bales based on moisture level and carbohydrate content leads to robust solutions that improve processing time and processing rate of the reactor. We provide a number of managerial insights that facilitate the implementation of the model proposed. Note to Practitioners—This paper is motivated by the challenges faced in the bioenergy industry. The focus of this paper is on plants which use the biochemical conversion process to generate liquid fuels. It has been observed that variations in biomass characteristics, such as moisture content, cause variations in feeding of the system which lead to under-utilization of equipment. A requirement of biochemical conversion process is to maintain the carbohydrate content of biomass processed by the reactor, larger than a threshold. We propose a model that identifies the inventory levels and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor. The goal is to improve equipment utilization while satisfying the requirements of the conversion process. The model is tested using real-life data. We found out that by sequencing bales based on moisture level and carbohydrate content, a plant can reduce variability in the system leading to improved system reliability, higher processing rates of the reactor, and higher throughput.

09 BIOMASS FUELS↗

DOE OSTI2026

Optimal Client Sampling in Federated Learning with Client-level Heterogeneous Differential Privacy

Federated Learning with client-level differential privacy (DP) provides a promising framework for collaboratively training models while rigorously protecting clients’ privacy. However, classic approaches like DP-FedAvg struggle when clients have heterogeneous privacy requirements, as they must uniformly enforce the strictest privacy level across all clients, leading to excessive DP noise and significant degradation in model utility. Existing methods to improve the model utility in such heterogeneous privacy settings often assume a trusted server and are largely heuristic, resulting in suboptimal performance and lacking strong theoretical foundations. Here, in this work, we address these challenges under a practical attack model where both clients and the server are honest-but-curious. We propose GDPFed, which partitions clients into groups based on their privacy budgets and achieves client-level DP within each group to reduce the privacy budget waste and hence improve the model utility. Based on the privacy and convergence analysis of GDPFed, we find that the magnitude of DP noise depends on both model dimensionality and the per-group client sampling ratios. To further improve the performance of GDPFed, we introduce GDPFed+, which integrates model sparsification to eliminate unnecessary noise and optimizes per-group client sampling ratios to minimize convergence error. Extensive empirical evaluations on multiple benchmark datasets demonstrate the effectiveness of GDPFed+, showing substantial performance gains compared with state-of-the-art methods.

Xu, Jiahao [Univ. of Nevada, Reno, NV (United Stat↗

DOE OSTI2022

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

The pressing need for novel materials that can serve rising demands in solar cell and optoelectronic technologies makes the nexus of halide perovskites, high-throughput computations, and machine learning, very promising. Ever increasing amounts of data on the structure, fundamental properties, and device performance of halide perovskites provide opportunities for learning chemical rules and design principles that make these materials attractive, and applying them across wide chemical spaces. In this work, we show that impurity properties of halide perovskites computed using density functional theory (DFT) can be combined with machine learning (ML) to deliver predictive models and quick identification of optoelectronically active impurity atoms. Our computation lead to the largest reported dataset of the formation energies and charge transition levels of Pb-site impurities in methylammonium lead halide (MAPbX 3 ) perovskites. Descriptors are defined to uniquely represent any impurity atom in any MAPbX 3 compound and mapped to the computed impurity properties using regression techniques such as Gaussian process regression, neural networks, and random forests. We use the best optimized predictive models to make predictions for hundreds of impurities across 9 MAPbX 3 compounds and create lists of dominating impurities, that is, impurities that can shift the equilibrium Fermi level in the perovskite as determined by native point defects. Finally, this accelerated screening powered by computations and machine learning can guide the identification of problematic impurities that may cause undesired recombination of charge carriers, as well as impurities that can be deliberately introduced to tune the perovskite conductivity and resulting photovoltaic absorption.

36 MATERIALS SCIENCE↗