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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Genetic Algorithm for Optimization of Neural Networks for Bayesian Inference of Model Uncertainty

The objective of this work was to develop a genetic optimization algorithm that can design a neural network capable of producing uncertainty estimates along with predictions. This algorithm is necessary because the inclusion of uncertainty modeling in a neural network greatly complicates the network’s design space, making the development of a converging model extremely difficult and time consuming. The genetic algorithm presented in this work uses a number of value ranges for various configurable neural network parameters to create a randomly generated population of network architectures. The initially generated population is then evolved over the course of several generations, with the best performing models breeding to produce novel network configurations. Mutations are randomly applied to the network designs to facilitate the development of adaptations beneficial to the task being performed. An experiment was conducted to validate the proposed algorithm, in which the genetic optimizer was tasked with producing a neural network capable of predicting the sound pressure level (SPL) resulting from jet-surface interaction (JSI) noise. The data used for this task was generated at the NASA Glenn Research Center in the Aero-Acoustic Propulsion Laboratory. Starting with an initial population size of 35 randomly generated networks, and evolved over the course of 10 generations, the genetic algorithm produced a design able to predict SPL as a result of JSI noise within 0.272 dB, on average.

Genetic algorithm↗
DOE OSTI2017

Optimal current profile control for enhanced repeatability of L-mode and H-mode discharges in DIII-D

In this work, model-based control techniques are used to obtain target current profiles in low-confinement-mode (L-mode) as well as high-confinement-mode (H-mode) DIII-D discharges. The control problem is formulated as a trajectory optimization problem to search for a feasible path from the expected initial condition to the desired target. The result comprises a sequence of feedforward (open-loop) control requests and a corresponding state evolution from the initial condition to the desired target. On top of this optimal feedforward control sequence a feedback (closed-loop) controller based on a linearized model and optimal control design techniques is added to track the desired state evolution. Furthermore, the effectiveness of the control approach is demonstrated with experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Optimizing Discharge Capacity of Li-O 2 Batteries by Design of Air-Electrode Porous Structure: Multifidelity Modeling and Optimization

We develop a new mathematical framework to study the optimal design of air electrode microstructures for lithium-oxygen (Li-O2) batteries. It can eectively reduce the number of expensive experiments for testing dierent air-electrodes, thereby minimizing the cost in the design of Li-O2 batteries. The design parameters to characterize an air-electrode microstructure include the porosity, surface-to-volume ratio, and parameters associated with the pore-size distribution. A surrogate model (also known as response surface) for discharge capacity is rst constructed as a function of these design parameters. The surrogate model is accurate and easy to evaluate such that an optimization can be performed based on it. In particular, a Gaussian process regression method, co-kriging, is employed due to its accuracy and eciency in predicting high-dimensional responses from a combination of multidelity data. Specically, a small amount of data from high-delity simulations are combined with a large number of data obtained from computationally ecient low-delity simulations. The high-delity simulation is based on a multiscale modeling approach that couples the microscale (pore-scale) and macroscale (device-scale) models. Whereas, the low-delity simulation is based on an empirical macroscale model. The constructed response surface provides quantitative understanding and prediction about how air electrode microstructures aect the discharge performance of Li-O2 batteries. The succeeding sensitivity analysis via Sobol indices and optimization via genetic algorithm ultimately oer a reliable guidance on the optimal design of air electrode microstructures. The proposed mathematical framework can be generalized to investigate other new energy storage techniques and materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Comparing Designed Training Sets to Optimize Multivariate Regression Models for Pr, Nd, and Nitric Acid Using Spectrophotometry

Chemometric regression models were developed for the quantification of praseodymium (Pr, 0–1000 µg/mL), neodymium (Nd, 0–1000 µg/mL), and nitric acid (HNO 3 , 0.1–5 M) using spectrophotometry. Designed calibration sets were composed of 20 samples each: 10 model points and 10 lack-of-fit (LOF) points. The D-optimal designs effectively minimized the number of samples required to build models, and each design resulted in similar prediction performance, suggesting that statistical design of experiments can provide a reliable framework for selecting training set samples in three-variable systems. Partial least squares regression (PLSR) models were validated against a one-factor-at-a-time validation set composed of 125 samples (three variables, five levels). The top PLS-1 models resulted in average percent root mean square error of prediction error values of 3.5%, 1.7%, and 1.2% for Pr(III), Nd(III), and HNO 3 , respectively. Power set augmentations of the model and LOF samples were investigated to optimize the number of training set samples. PLSR models built using just required model points (10) had similar predictive capabilities as models including the LOF points (20) but with fewer samples. The number of validation samples was also varied systematically to learn how many samples are needed to validate regression models. This work addresses long-standing questions in the field of chemometrics to help make this approach amenable to the near-real-time quantification of hazardous species in remote settings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Developing an Ultra-Compact, Topology-Optimized Heat Exchanger using Additive Manufacturing

Traditional heat exchanger designs have relied on a plate-fin architecture, assembled in a crossflow configuration. These HX designs are limited in their maximum allowable temperature and, therefore, efficiency. More advanced designs, such as those containing counter-flow, microtube cores, can yield higher efficiencies, but generally require complex headers. The design of such headers is usually determined based on user experience rather than on rigorous design tools, which can lead to lossy architectures; conventional headers can generate up to 50% of the total pressure loss through the HX. Furthermore, the consequences of manufacturing techniques are rarely considered during the design phase. Our team has developed an ultra-compact, topology-optimized heat exchanger capable of operating in environments with temperatures and pressures up to 800°C (1472°F) and 250 bar (3626 psi), respectively. Throughout the program, the team leveraged extensive additive manufacturing research and experience in supercritical carbon dioxide (sCO 2 ) power generation experience to develop and commercialize the technology. The final power density numbers for the subscale and full-scale heat exchangers are >15 kW/kg and >50 kW/L, which includes the core and the headers. The team has involved the RTX business units, notably Collins Aerospace, in discussions for commercializing the technology.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Control of low-mode drive asymmetry in an efficient long-pulse low gas-fill density Hohlraum

Laser-driven Hohlraums filled with gas at lower densities (<0.6 mg/cc) have higher efficiency compared to original ≥ 0.96 mg/cc fill because of reduced backscatter losses [Hall et al., Phys. Plasmas 24, 052706 (2017)]. However, using low-density filled Hohlraums with longer drive required for lower adiabat implosions, and hence potentially higher inertial confinement fusion gain designs, has been challenging since the Hohlraum wall blow-off is less tamped, thus altering the laser beam absorption regions and drive symmetry. A series of NIF experiments using optimized pulse shaping, beam pointing, and temporal phasing have demonstrated, through imaging of the Hohlraum and capsule dynamics, that a symmetric implosion using a 14-ns low-adiabat drive pulse {2× longer than high-density-carbon ablator designs using low gas-fill density Hohlraums [Divol et al., Phys. Plasmas 24, 056309 (2017)]} is possible in a low backscatter loss 0.45 mg/cc He-filled Hohlraum. The ingress of the Hohlraum walls was mitigated by revisiting the adiabat-shaped design [Clark et al., Phys. Plasmas 21, 112705 (2014)] that uses a low-power (1 TW) trough that delays the wall expansion. Low-mode P 2 and P 4 drive asymmetry swings caused by the drift of the laser spots were essentially zeroed out by employing temporal beam phasing between cones of beams [Turner et al., Phys. Plasmas 7, 333 (2000)]. So the results also indicate an improved coupling efficiency of ~30% compared to an earlier design using higher density filled Hohlraums and pave the way for revisiting low-adiabat, high convergence drives using CH ablators.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Experiments to Measure the Effect of Tantalum on Critical Systems

Sandia National Laboratories (SNL) and Oak Ridge National Laboratory (ORNL) collaborated to develop a capability to test the epithermal/intermediate cross sections of materials at the SNL critical experiment facility using the Seven Percent Critical Experiment (7uPCX) fuel. As a result, a new set of critical experiments has been designed to target the epithermal cross sections of tantalum (Ta) and is scheduled to be performed at SNL in 2023. These critical experiments will be evaluated for inclusion in the 2024 edition of the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook. The focus of these critical experiments is to measure the effects of Ta on the critical array size. The critical array size will be determined by an approach-to-critical experiment with the number of fuel rods in the array as a free parameter. The core configurations are designed to optimize the reactivity worth of Ta and the overall percentage of Ta absorption rates in the epithermal/intermediate energy range (0.625 eV – 100 keV). The baseline core configuration includes 7uPCX fuel rods set at a triangular pitch of 1.016 cm and a central dry test region that utilizes a cadmium liner for filtering out thermal neutrons. The central test region has locations for 85 Ta rods set at a triangular pitch of 0.813 cm. The Ta reactivity worth for the case with 85 Ta rods is approximately 2.55%, with the percentage of Ta absorption rates within the intermediate energy range at nearly 90%.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

RxnRover/amlro

AMLRO (Active Machine Learning Reaction Optimizer) is an open-source framework designed to accelerate chemical reaction optimization using active learning with classical machine learning regression models. AMLRO integrates space-filling sampling strategies (e.g., Sobol and Latin Hypercube sampling) with iterative model training, prediction, and experiment selection to efficiently navigate complex reaction spaces. The platform supports multiple regression models, flexible multi-objective definitions, and user-defined parameter bounds, enabling data-efficient optimization from small initial datasets. AMLRO is designed for ease of use by experimentalists and can operate as a standalone decision-support tool or be integrated into closed-loop automated experimentation workflows.

Kulathunga, Dulitha Prasanna [Iowa State Universit↗
DOE OSTI2024

Systematic engineering for production of anti-aging sunscreen compound in Pseudomonas putida

Sunscreen has been used for thousands of years to protect skin from ultraviolet radiation. However, the use of modern commercial sunscreen containing oxybenzone, ZnO, and TiO 2 has raised concerns due to their negative effects on human health and the environment. In this study, we aim to establish an efficient microbial platform for production of shinorine, a UV light absorbing compound with anti-aging properties. First, we methodically selected an appropriate host for shinorine production by analyzing central carbon flux distribution data from prior studies alongside predictions from genome-scale metabolic models (GEMs). We enhanced shinorine productivity through CRISPRi-mediated downregulation and utilized shotgun proteomics to pinpoint potential competing pathways. Simultaneously, we improved the shinorine biosynthetic pathway by refining its design, optimizing promoter usage, and altering the strength of ribosome binding sites. Finally, we conducted amino acid feeding experiments under various conditions to identify the key limiting factors in shinorine production. The study combines meta-analysis of 13 C-metabolic flux analysis, GEMs, synthetic biology, CRISPRi-mediated gene downregulation, and omics analysis to improve shinorine production, demonstrating the potential of Pseudomonas putida KT2440 as platform for shinorine production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Cross-geography scientific data transferring trends and behavior

Wide area data transfers play an important role in many science applications but rely on expensive infrastructure that often delivers disappointing performance in practice. In response, we present a systematic examination of a large set of data transfer log data to characterize transfer characteristics, including the nature of the datasets transferred, achieved throughput, user behavior, and resource usage. This analysis yields new insights that can help design better data transfer tools, optimize networking and edge resources used for transfers, and improve the performance and experience for end users. Our analysis shows that (i) most of the datasets as well as individual files transferred are very small; (ii) data corruption is not negligible for large data transfers; and (iii) the data transfer nodes utilization is low. Insights gained from our analysis suggest directions for further analysis.

Liu, Zhengchun↗
DOE OSTI2019

Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization

© 2019 Association for Computing Machinery. The goal of this work is to investigate the impact of missing values in clustering joint categorical social sequences. Identifying patterns in sociodemographic longitudinal data is important in a number of social science settings. However, performing analytical operations, such as clustering on life course trajectories, is challenging due to the categorical and multidimensional nature of the data, their mixed data types, and corruption by missing and inconsistent values. Data quality issues were investigated previously on single variable sequences. To understand their effects on multivariate sequence analysis, we employ a dataset of mixed data types and missing values, a dissimilarity measure designed for joint categorical sequence data, together with dimensionality reduction methodologies in a systematic design of sequence clustering experiments. Given the categorical nature of our data, we employ an “edit” distance using optimal matching. Because each data record has multiple variables of different types, we investigate the impact of mixing these variables in a single dissimilarity measure. Between variables with binary values and those with multiple nominal values, we find that the ability to overcome missing data problems is more difficult in the nominal domain than in the binary domain. Additionally, alignment of leading missing values can result in systematic biases in dissimilarity matrices and subsequently introduce both artificial clusters and unrealistic interpretations of associated data domains. We demonstrate the usage of t-distributed stochastic neighborhood embedding to visually guide mitigation of such biases by tuning the missing value substitution cost parameter or determining an optimal sequence span.

Lazar, Alina↗
DOE OSTI2022

Atomically Precise Manufacturing for 2D-Designed Materials

The primary purpose of this program was to develop atomically precise fabrication techniques for semiconductor systems that places dopant atoms in a single buried (100) atomic plane in silicon with near atomic precision to create unprecedented structures that can be used for a wide variety of quantum experiments, devices, and potentially designer quantum materials. The potential impacts on reducing industrial energy use are many: 1) optimized and more energy efficient industrial processes via more efficient computational approaches, 2) dramatically improved materials for industrial use including higher critical-temperature superconductors eliminating losses in electrical transmission, 3) the better understanding of quantum chemistry via Analog Quantum Simulation(AQS) in the near term and universal quantum computing in the longer term that will lead to new industrial processes with smaller or zero production of greenhouse gases.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Design and aerodynamic performance evaluation of a high-work mixed flow turbine stage

As axial and radial turbine designs have been pushed to their aerothermodynamic and mechanical limits, the mixed-flow turbine (MFT) concept has been projected to offer performance and durability improvements, especially when ceramic materials are considered. The objective of this NASA/U.S. Army sponsored mixed-flow turbine (AMFT) program was to determine the level of performance attainable with MFT technology within the mechanical constraints of 1997 projected ceramic material properties. The MFT geometry is similar to a radial turbine, exhibiting a large radius change from inlet to exit, but differing in that the inlet flowpath is not purely radial, nor axial, but mixed; it is the inlet geometry that gives rise to the name 'mixed-flow'. The 'mixed' orientation of the turbine inlet offers several advantages over radial designs by allowing a nonzero inlet blade angle yet maintaining radial-element blades. The oblique inlet not only improves the particle-impact survivability of the design, but improves the aerodynamic performance by reducing the incidence at the blade inlet. The difficulty, however, of using mixed-flow geometry lies in the scarcity of detailed data and documented design experience. This paper reports the design of a MFT stage designed with the intent to maximize aerodynamic performance by optimizing design parameters such as stage reaction, rotor incidence, flowpath shape, blade shape, vane geometry, and airfoil counts using 2-D, 3-D inviscid, and 3-D viscous computational fluid dynamics code. The aerodynamic optimization was accomplished while maintaining mechanical integrity with respect to vibration and stress levels in the rotor. A full-scale cold-flow rig test was performed with metallic hardware fabricated to the specifications of the hot ceramic geometry to evaluate the stage performance.

Neri, Remo N.↗
NASA NTRS2004

Designing Domain-Specific HUMS Architectures: An Automated Approach

The HUMS automation system automates the design of HUMS architectures. The automated design process involves selection of solutions from a large space of designs as well as pure synthesis of designs. Hence the whole objective is to efficiently search for or synthesize designs or parts of designs in the database and to integrate them to form the entire system design. The automation system adopts two approaches in order to produce the designs: (a) Bottom-up approach and (b) Top down approach. Both the approaches are endowed with a Suite of quantitative and quantitative techniques that enable a) the selection of matching component instances, b) the determination of design parameters, c) the evaluation of candidate designs at component-level and at system-level, d) the performance of cost-benefit analyses, e) the performance of trade-off analyses, etc. In short, the automation system attempts to capitalize on the knowledge developed from years of experience in engineering, system design and operation of the HUMS systems in order to economically produce the most optimal and domain-specific designs.

Mukkamala, Ravi↗
DOE OSTI2016

Liquid VI Detector Time Response

Presentation describes research into use of LightTools, an illumination design program, to optimize time and sensitivity performance in Liquid-VI gamma detectors for neutron-diagnosed experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Implementation of a new gamma spectrometer on the MERARG loop: Application to the volatile fission products release measurement

The MERARG facility initially aims at the annealing of irradiated fuel samples to study the gaseous fission products release kinetics. In order to complete the evaluation of the source term potentially released during accidental situation, the MERARG experimental circuit has been enhanced with a new gamma spectrometer. This one is directly sighting the fuel and is devoted to the fission products release kinetics. Because of the specificities of the fuel measurements, it has been dimensioned and designed to match the specific requirements. The acquisition chain and the collimation system have been optimized for this purpose and a first set of two experiments have shown the good functioning of this new spectrometry facility. (authors)

42 ENGINEERING↗
NASA NTRS1996

Parameter Design and Optimal Control of an Open Core Flywheel Energy Storage System

In low earth orbit (LEO) satellite applications spacecraft power is provided by photovoltaic cells and batteries. To overcome battery shortcomings the University of Maryland, working in cooperation with NASA/GSFC and NASA/LeRC, has developed a magnetically suspended flywheel for energy storage applications. The system is referred to as an Open Core Composite Flywheel (OCCF) energy storage system. Successful application of flywheel energy storage requires integration of several technologies, viz. bearings, rotor design, motor/generator, power conditioning, and system control. In this paper we present a parameter design method which has been developed for analyzing the linear SISO model of the magnetic bearing controller for the OCCF. The objective of this continued research is to principally analyze the magnetic bearing system for nonlinear effects in order to increase the region of stability, as determined by high speed and large air gap control. This is achieved by four tasks: (1) physical modeling, design, prototyping, and testing of an improved magnetically suspended flywheel energy storage system, (2) identification of problems that limit performance and their corresponding solutions, (3) development of a design methodology for magnetic bearings, and (4) design of an optimal controller for future high speed applications. Both nonlinear SISO and MIMO models of the magnetic system were built to study limit cycle oscillations and power amplifier saturation phenomenon observed in experiments. The nonlinear models include the inductance of EM coils, the power amplifier saturation, and the physical limitation of the flywheel movement as discussed earlier. The control program EASY5 is used to study the nonlinear SISO and MIMO models. Our results have shown that the characteristics and frequency responses of the magnetic bearing system obtained from modeling are comparable to those obtained experimentally. Although magnetic saturation is shown in the bearings, there are good correlations between the theoretical model and experimental data. Both simulation and experiment confirm large variations of the magnetic bearing characteristics due to air gap growth. Therefore, the gap growth effect should be considered in the magnetic bearing system design. Additionally, the magnetic bearing control system will be compared to other design methods using not only parameter design but H-infinity optimal control and mu synthesis.

Pang, D.↗