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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.

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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

The experience factory: Can it make you a 5? or what is its relationship to other quality and improvement concepts?

The concepts of quality improvements have permeated many businesses. It is clear that the nineties will be the quality era for software and there is a growing need to develop or adapt quality improvement approaches to the software business. Thus we must understand software as an artifact and software as a business. Since the business we are dealing with is software, we must understand the nature of software and software development. The software discipline is evolutionary and experimental; it is a laboratory science. Software is development not production. The technologies of the discipline are human based. There is a lack of models that allow us to reason about the process and the product. All software is not the same; process is a variable, goals are variable, etc. Packaged, reusable, experiences require additional resources in the form of organization, processes, people, etc. There have been a variety of organizational frameworks proposed to improve quality for various businesses. The ones discussed in this presentation include: Plan-Do-Check-Act, a quality improvement process based upon a feedback cycle for optimizing a single process model/production line; the Experience Factory/Quality Improvement Paradigm, continuous improvements through the experimentation, packaging, and reuse of experiences based upon a business's needs; Total Quality Management, a management approach to long term success through customer satisfaction based on the participation of all members of an organization; the SEI capability maturity model, a staged process improvement based upon assessment with regard to a set of key process areas until you reach a level 5 which represents a continuous process improvement; and Lean (software) Development, a principle supporting the concentration of the production on 'value added' activities and the elimination of reduction of 'not value added' activities.

Basili, Victor R.↗
DOE OSTI2026

Efficient Optimization of Plasma Radiation Detector Configurations using Imperfect Inference Models

The configurations of instruments fielded on an experiment affect the amount of information captured and the quality of subsequent inference. Here, we investigate the problem of optimizing plasma x-ray radiation detectors in a magneto-inertial fusion experiment at Sandia National Laboratories. It is impossible to directly measure properties such as the temperature of the thermonuclear fusion plasma produced in these experiments because of the extreme environment and destructive nature of the experiment. Among other diagnostics, several detectors are placed with significant standoff from the fusion target to capture the x-rays emitted by the fusion plasma, which can be used to infer some of its properties. To optimize the configuration of these detectors, a high-fidelity model (HFM) is used for simulating outputs and a low-fidelity model (LFM) is used for inference. We develop methods based on A- and L-optimality criteria that are efficient to compute while explicitly accounting for the discrepancy between the HFM and the LFM. The method allows us to find detector configurations that perform similarly to or better than the configuration obtained using an existing sampling-based optimization method while decreasing computational time by a factor of 50. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Bayesian optimization↗
NASA NTRS2016

Conflict Resolution for Wind-Optimal Aircraft Trajectories in North Atlantic Oceanic Airspace with Wind Uncertainties

Air traffic in the North Atlantic oceanic airspace (NAT) experiences very strong winds caused by jet streams. Flying wind-optimal trajectories increases individual flight efficiency, which is advantageous when operating in the NAT. However, as the NAT is highly congested during peak hours, a large number of potential conflicts between flights are detected for the sets of wind-optimal trajectories. Conflict resolution performed at the strategic level of flight planning can significantly reduce the airspace congestion. However, being completed far in advance, strategic planning can only use predicted environmental conditions that may significantly differ from the real conditions experienced further by aircraft. The forecast uncertainties result in uncertainties in conflict prediction, and thus, conflict resolution becomes less efficient. This work considers wind uncertainties in order to improve the robustness of conflict resolution in the NAT. First, the influence of wind uncertainties on conflict prediction is investigated. Then, conflict resolution methods accounting for wind uncertainties are proposed.

wind uncertainties↗
DOE OSTI2023

Yet another parameter-free shape optimization method

The use of node coordinates as design variables in shape optimization offers a larger design space than computer-aided design (CAD)-based shape parameterizations. It also allows for the optimization of legacy designs, i.e., a finite element mesh from an existing design can be readily optimized to meet new performance requirements without involving a CAD model. However, it is well known that the node coordinate parameterization method is fraught with numerical difficulties, which makes it impractical to use. This has led to several of “parameter-free” shape optimization methods that seek the advantages and avoid the pitfalls of the naïve node coordinate parameterization method. These methods come in two main varieties: sensitivity filtering (or gradient smoothing) and consistent filtering. The latter is analogous to the density filter method used in topology optimization (TO). In this work, we use the PDE filter from TO and energy-based filters to implement consistent shape optimization filtering schemes easily and efficiently. Numerical experiments demonstrate that consistent methods are more robust than sensitivity filtering methods.

42 ENGINEERING↗
DOE OSTI2024

A Gaussian process based surrogate approach for the optimization of cylindrical targets

Simulating direct-drive inertial confinement experiments presents significant computational challenges, both due to the complexity of the codes required for such simulations and the substantial computational expense associated with target design studies. Machine learning models, and in particular, surrogate models, offer a solution by replacing simulation results with a simplified approximation. In this study, we apply surrogate modeling and optimization techniques that are well established in the existing literature to one-dimensional simulation data of a new cylindrical target design containing deuterium–tritium fuel. These models predict yields without the need for expensive simulations. We find that Bayesian optimization with Gaussian process surrogates enhances sampling efficiency in low-dimensional design spaces but becomes less efficient as dimensionality increases. Nonetheless, optimization routines within two-dimensional and five-dimensional design spaces can identify designs that maximize yield, while also aligning with established physical intuition. Optimization routines, which ignore constraints on hydrodynamic instability growth, are shown to lead to unstable designs in 2D, resulting in yield loss. However, routines that utilize 1D simulations and impose constraints on the in-flight aspect ratio converge on novel cylindrical target designs that are stable against hydrodynamic instability growth in 2D and achieve high yield.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Chelation-Driven Chemistry Controls Dissolution Pathways for Facile Critical Mineral Recovery from Ultramafic Resources

The efficient recovery of critical minerals, such as nickel (Ni) and manganese (Mn) from the subsurface is of vital importance, given their role in modern technologies ranging from batteries to advanced alloys. This study explores an approach that employs chelation to enhance critical mineral recovery from ultramafic rocks using chelating ligands under ambient pressures and systematically evaluates the extraction process for the first time. Experimental results demonstrate the superior performance of EDTA and PrDTA in achieving high extraction efficiencies for both Ni and Mn. To further highlight the ability for optimization, the fluid exchange and variation of fluid-to-rock ratio experiments were conducted, revealing tunable controls for engineering recovery. We show that under optimized conditions, a Ni extraction efficiency of ?82% and a Mn extraction efficiency of ~60% is achieved at ambient pressures, which is equivalent to over 23 times the current global Ni production, and 2 million metric tonnes (MMT) greater than the current global Mn production. From both in-situ and ex-situ mining perspectives, the results highlight the potential of chelation to advance sustainable mineral processing. Overall, this work highlights the environmental benefits of innovative ligand-assisted recovery methods, addressing the critical need for the efficient utilization of olivine resources.

Krishnan, Keerthana↗
DOE OSTI2024

Towards Autonomous Experiments by Connecting High Performance Microscopy with High Performance Computing

The digitization of controls, data, and analysis in microscopy is bringing the idea of autonomous microscopes closer to reality than ever before. Automated transmission electron microscopy (TEM) is already fairly routine for some experiments the only require simple repetitive tasks such as imaging biological macromolecules for single particle cryoEM [1], tilt series for electron tomography [2], and movies for crystallography [3]. The vast majority of TEM experiments are conducted completely by human operators who choose the regions of interest, optimize experimental parameters, and make decisions about data quality visually during an experiment. The field is still a long way from having completely autonomous TEMs that can adapt to sample difficulties and tune experimental parameters based on data quality and desired experimental outcomes. Part of the issue is the lack of capability for feeding information learned from on-line, live data analysis back into the on-going experiment [4]. Furthermore, this presentation will discuss current capabilities for large scale data reduction and analysis using high performance computing (i.e. supercomputing) and progress towards developing a true feed-back loop that places data analysis and theory in the experimental loop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The FRIB Decay Station: New Horizons with Rare Isotopes

In May 2022, the Facility for Rare Isotope Beams (FRIB), located on the campus of Michigan State University (MSU), began delivering exotic isotopes to an international community of scientists. New discoveries are now being reported from radioactive decay of neutron-rich nuclei near N = 20 and N = 28.FRIB is expected to produce roughly 80% of the unstable or radioactive isotopes predicted to exist up to uranium (Z = 92). The new user facility is supported by the U.S. Department of Energy, and it is operated by MSU. A high-power superconducting linear accelerator, shaped like a paper-clip, drives the production of these rare isotopes by colliding stable nuclei moving at half the speed of light with a rotating, water-cooled graphite tar-get. These collisions cause the primary stable beam to fragment into a wide variety of unstable nuclei, which can be subsequently filtered through a multistage magnetic separator, the Advanced Rare Isotope Separator, and transported to one of several experimental stations. The FRIB Decay Station initiator (FDSi) (see Figure 1) was developed to enable comprehensive radio-active decay studies of the exotic nuclei produced by FRIB and it was used in the first two experiments in 2022. Further, the FDSi is a highly reconfigurable multidetector system with two focal planes (FP1 for discrete spectroscopy and FP2 for total absorption spectroscopy) that can be optimized for the specific science goals of each experiment. It is designed, built, and operated by a community of users with the sup-port of U.S. funding agencies, including the Department of Energy and National Science Foundation.

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRS1979

Acoustic duct liner optimization using finite elements

Determination of a mathematical model which may be used for obtaining optimum acoustic liners in aeroengine ducts was the goal of this study. Starting with a baseline finite element model which consumed too much computer time to permit optimum liner evaluation for realistic ducts and source frequencies, a survey was conducted of candidate methods for reducing computational effort. A highly effective boundary exclusive decomposition method was developed which enables L-U decomposition of a major partition of the global matrix to be performed once only. Timing experiments using the technique in duct liner optimization show a reduction of between one and two orders of magnitude in the central processor unit (CPU) time of the baseline model.

Abrahamson, A. L.↗
NASA NTRS1975

A parallel Jacobson-Oksman optimization algorithm

A gradient-dependent optimization technique which exploits the vector-streaming or parallel-computing capabilities of some modern computers is presented. The algorithm, derived by assuming that the function to be minimized is homogeneous, is a modification of the Jacobson-Oksman serial minimization method. In addition to describing the algorithm, conditions insuring the convergence of the iterates of the algorithm and the results of numerical experiments on a group of sample test functions are presented. The results of these experiments indicate that this algorithm will solve optimization problems in less computing time than conventional serial methods on machines having vector-streaming or parallel-computing capabilities.

Straeter, T. A.↗
DOE OSTI2023

A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)

Tropical forest dynamics play a crucial role in the global carbon, water, and energy cycles. However, realistically simulating the dynamics of competition and coexistence between different plant functional types (PFTs) in tropical forests remains a significant challenge. This study aims to improve the modeling of PFT coexistence in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a vegetation demography model implemented in the Energy Exascale Earth System Model (E3SM) land model (ELM), ELM-FATES. Specifically, we explore (1) whether plant trait relationships established from field measurements can constrain ELM-FATES simulations and (2) whether machine learning (ML)-based surrogate models can emulate the complex ELM-FATES model and optimize parameter selections to improve PFT coexistence modeling. We conducted three ensembles of ELM-FATES experiments at a tropical forest site near Manaus, Brazil. By comparing the ensemble experiments without (Exp-CTR) and with (Exp-OBS) consideration of observed trait relationships, we found that accounting for these relationships slightly improves the simulations of water, energy, and carbon variables when compared to observations but degrades the simulation of PFT coexistence. Using ML-based surrogate models trained on Exp-CTR, we optimized the trait parameters in ELM-FATES and conducted another ensemble of experiments (Exp-ML) with these optimized parameters. The proportion of PFT coexistence experiments significantly increased from 21 % in Exp-CTR to 73 % in Exp-ML. After filtering the experiments that allow for PFT coexistence to agree with observations (within 15 % tolerance), 33 % of the Exp-ML experiments were retained, which is a significant improvement compared to the 1.4 % in Exp-CTR. Exp-ML also accurately reproduces the annual means and seasonal variations in water, energy, and carbon fluxes and the field inventory of aboveground biomass. This study represents a reproducible method that utilizes machine learning to identify parameter values that improve model fidelity against observations and PFT coexistence in vegetation demography models for diverse ecosystems. Our study also suggests the need for new mechanisms to enhance the robust simulation of coexisting plants in ELM-FATES and has significant implications for modeling the response and feedbacks of ecosystem dynamics to climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1987

Numerical optimization of conical flow waveriders including detailed viscous effects

A family of optimized hypersonic waveriders is generated and studied wherein detailed viscous effects are included within the optimization process itself. This is in contrast to previous optimized waverider work, wherein purely inviscid flow is used to obtain the waverider shapes. For the present waveriders, the undersurface is a streamsurface of an inviscid conical flowfield, the upper surface is a streamsurface of the inviscid flow over a tapered cylinder (calculated by the axisymmetric method of characteristics), and the viscous effects are treated by integral solutions of the boundary layer equations. Transition from laminar to turbulent flow is included within the viscous calculations. The optimization is carried out using a nonlinear simplex method. The resulting family of viscous hypersonic waveriders yields predicted high values of lift/drag, high enough to break the L/D barrier based on experience with other hypersonic configurations. Moreover, the numerical optimization process for the viscous waveriders results in distinctly different shapes compared to previous work with inviscid-designed waveriders. Also, the fine details of the viscous solution, such as how the shear stress is distributed over the surface, and the location of transition, are crucial to the details of the resulting waverider geometry. Finally, the moment coefficient variations and heat transfer distributions associated with the viscous optimized waveriders are studied.

Bowcutt, Kevin G.↗
DOE OSTI2021

Optimization of Magnesium‐Doped Lithium Metal Anode for High Performance Lithium Metal Batteries through Modeling and Experiment

Abstract Lithium (Li)‐magnesium (Mg) alloy with limited Mg amount, which can also be called Mg‐doped Li (Li‐Mg), has been considered as a potential alternative anode for high energy density rechargeable Li metal batteries. However, the optimum doping‐content of Mg in Li‐Mg anode and the mechanism of the improved performance are not well understood. Herein, density functional theory (DFT) calculations are used to investigate the effect of Mg amount in Li‐Mg anode. The Li‐Mg with about 5 wt. % Mg (abbreviated as Li‐Mg5) has the lowest absorption energy of Li, thus all the surface area can be “controlled” by Mg atoms, leading to the smooth and continuous deposition of Li on the surface around the Mg center. A localized high concentration electrolyte enables Li‐Mg5 to exhibit the best cycling stability in Li metal batteries with high‐loading cathode and lean electrolyte under 4.4 V high‐voltage, which is approaching the demand of practical application. This electrolyte also helps generate an inorganic‐rich solid electrolyte interphase, which leads to smooth, compact and less corrosion layer on the Li‐Mg5 surface. Both theoretical simulations and experimental results prove that Li‐Mg5 has optimum Mg content and gives best battery cycling performance.

Gao, Peiyuan↗
DOE OSTI2024

Oak Ridge National Laboratory Neutron Spin Echo Beamline on NB-2

Neutron Spin Echo (NSE) spectroscopy uniquely measures the Q-dependence of slow relaxation dynamics, and having such a machine at HFIR will advance polarized neutron spectroscopy and promote the study of biophysical and energy materials using neutrons. The 2018 Instrument Advisory Board (IAB) advises Neutron Sciences Directorate, ORNL to upgrade the cold neutron delivery system at HFIR, addressing geometrical challenges in neutron transfer from the bright cold source. This is being optimized now: the entrance to the guide system is moving closer to the source, and more guides are being added. However, there are still significant challenges to making the cold flux at the sample world-class. With the proposed guide system, NB-2 can deliver more than 10 6 polarized neutrons per cm 2 /sec below 8Å, and probably 10Å with further optimization. With no potential for running user experiments with neutrons longer than 15Å, the utility of such a machine for biological studies is limited. However, there is a large demand for this class of spectrometer in materials science, chemical engineering, and nanotechnology. Workshops held over the past decade and the three-source vision have highlighted the community's need for a low-angle, high-energy resolution spectrometer at HFIR. A detailed design study of this spectrometer is needed, focusing on optimizing the neutron delivery system for SANS-type studies. This neutron spectrometer would complement studies already performed on both SNSNSE and BASIS by overlapping and extend the dynamic range accessible, while acknowledging the proposed machine, EXPANSE, at the Second Target Station. Two technologies currently exist for such a spectrometer: traditional DC solenoids like those on BL-15 at SNS and IN15 at the ILL, and RF-flipper-based machines similar to RESEDA at FRMII. Either instrument would satisfy the scientific justification. However, there are strong business and scientific cases to utilize the thermal flux, the resonant expertise developed at ORNL, and the potential to utilize an entangled beam of neutrons to probe quantum matter by adopting the neutron resonant spin-echo configuration.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2014

Optimal Stimulus Amplitude for Vestibular Stochastic Stimulation to Improve Sensorimotor Function

Sensorimotor changes such as postural and gait instabilities can affect the functional performance of astronauts when they transition across different gravity environments. We are developing a method, based on stochastic resonance (SR), to enhance information transfer by applying non-zero levels of external noise on the vestibular system (vestibular stochastic resonance, VSR). Our previous work has shown the advantageous effects of VSR in a balance task of standing on an unstable surface. This technique to improve detection of vestibular signals uses a stimulus delivery system that is wearable or portable and provides imperceptibly low levels of white noise-based binaural bipolar electrical stimulation of the vestibular system. The goal of this project is to determine optimal levels of stimulation for SR applications by using a defined vestibular threshold of motion detection. A series of experiments were carried out to determine a robust paradigm to identify a vestibular threshold that can then be used to recommend optimal stimulation levels for SR training applications customized to each crewmember. Customizing stimulus intensity can maximize treatment effects. The amplitude of stimulation to be used in the VSR application has varied across studies in the literature such as 60% of nociceptive stimulus thresholds. We compared subjects' perceptual threshold with that obtained from two measures of body sway. Each test session was 463s long and consisted of several 15s sinusoidal stimuli, at different current amplitudes (0-2 mA), interspersed with 20-20.5s periods of no stimulation. Subjects sat on a chair with their eyes closed and had to report their perception of motion through a joystick. A force plate underneath the chair recorded medio-lateral shear forces and roll moments. First we determined the percent time during stimulation periods for which perception of motion (activity above a pre-defined threshold) was reported using the joystick, and body sway (two standard deviation of the noise level in the baseline measurement) was detected by the sensors. The percentage time at each stimulation level for motion detection was normalized with respect to the largest value and a logistic regression curve fit was applied to these data. The threshold was defined at the 50% probability of motion detection. Comparison of threshold of motion detection obtained from joystick data versus body sway suggests that perceptual thresholds were significantly lower, and were not impacted by system noise. Further, in order to determine optimal stimulation amplitude to improve balance, two sets of experiments were carried out. In the first set of experiments, all subjects received the same level of stimuli and the intensity of optimal performance was projected back on subjects' vestibular threshold curve. In the second set of experiments, on different subjects, stimulation was administered from 20-400% of subjects' vestibular threshold obtained from joystick data. Preliminary results of our study show that, in general, using stimulation amplitudes at 40-60% of perceptual motion threshold improved balance performance significantly compared to control (no stimulation). The amplitude of vestibular stimulation that improved balance function was predominantly in the range of +/- 100 to +/- 400 micro A. We hypothesize that VSR stimulation will act synergistically with sensorimotor adaptability (SA) training to improve adaptability by increasing utilization of vestibular information and therefore will help us to optimize and personalize a SA countermeasure prescription. This combination will help to significantly reduce the number of days required to recover functional performance to preflight levels after long-duration spaceflight.

Goel, R.↗
DOE OSTI2023

Trigger Detection for the sPHENIX Experiment via Bipartite Graph Networks with Set Transformer

Trigger (interesting events) detection is crucial to high-energy and nuclear physics experiments because it improves data acquisition efficiency. It also plays a vital role in facilitating the downstream offline data analysis process. The sPHENIX detector, located at the Relativistic Heavy Ion Collider in Brookhaven National Laboratory, is one of the largest nuclear physics experiments on a world scale and is optimized to detect physics processes involving charm and beauty quarks. Furthermore, these particles are produced in collisions involving two proton beams, two gold nuclei beams, or a combination of the two and give critical insights into the formation of the early universe. This paper presents a model architecture for trigger detection with geometric information from two fast silicon detectors. Transverse momentum is introduced as an intermediate feature from physics heuristics. We also prove its importance through our training experiments. Each event consists of tracks and can be viewed as a graph. A bipartite graph neural network is integrated with the attention mechanism to design a binary classification model. Compared with the state-of-the-art algorithm for trigger detection, our model is parsimonious and increases the accuracy and the AUC score by more than 15%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Optimization of key energy and performance metrics for drug product manufacturing

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors, this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. Here, the results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

59 BASIC BIOLOGICAL SCIENCES↗