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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Predictive Control to Further Reduce DC-Link Capacitor Current Stress for Segmented Inverter

Two three-phase interleaved inverters have been used in traction drive applications to reduce the current stress in a DC link capacitor bank. In such applications, either carrier-based or space vector modulation is used to select the optimum switching sequences, and the results show 50% less capacitor current than that of a single three-phase inverter. The switching state selection process for this inverter is tedious, and there has been no research to find the optimal switching state. To overcome this challenge, this research employed a simple finite set model predictive control to select the optimum switching sequence for a dual three-phase interleaved topology, called a segmented inverter. The results show that the predictive control algorithm can provide a simple solution and can reduce the current stress by 27% compared with traditional modulation techniques for the segmented inverters.

Ribeiro, Pedro↗
DOE OSTI2022

Medium-Scale Methanol Pool Fire Model Validation

In this work, medium scale (30 cm diameter) methanol pool fires were simulated using the latest fire modeling suite implemented in Sierra/Fuego, a low Mach number multiphysics reacting flow code. The sensitivity of model outputs to various model parameters was studied with the objective of providing model validation. This work also assesses model performance relative to other recently published large eddy simulations (LES) of the same validation case. Two pool surface boundary conditions were simulated. The first was a prescribed fuel mass flux and the second used an algorithm to predict mass flux based on a mass and energy balance at the fuel surface. Gray gas radiation model parameters (absorption coefficients and gas radiation sources) were varied to assess radiant heat losses to the surroundings and pool surface. The radiation model was calibrated by comparing the simulated radiant fraction of the plume to experimental data. The effects of mesh resolution were also quantified starting with a grid resolution representative of engineering type fire calculations and then uniformly refining that mesh in the plume region. Simulation data were compared to experimental data collected at the University of Waterloo and the National Institute of Standards and Technology (NIST). Validation data included plume temperature, radial and axial velocities, velocity temperature turbulent correlations, velocity velocity turbulent correlations, radiant and convective heat fluxes to the pool surface, and plume radiant fraction. Additional analyses were performed in the pool boundary layer to assess simulated flame anchoring and the effect on convective heat fluxes. This work assesses the capability of the latest Fuego physics and chemistry model suite and provides additional insight into pool fire modeling for nonluminous, non-sooting flames.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Risk-informed Predictive Analytics To Achieve Cost-effective Condition-based Monitoring And Maintenance Strategy

The research involves developing risk-informed predictive analytic capabilities to achieve condition-based monitoring and maintenance strategies to reduce overall maintenance costs. The research utilizes data (real-time data, periodic data, and institutional knowledge) related to a particular plant asset from a specific nuclear plant site to develop risk-informed predictive analytic algorithms. The developed algorithms and codes are used to optimize the maintenance strategy and estimate/forecast generation costs based on the state of health of the plant asset. Developed codes specifically include 1. Parameter estimation code based on Bayesian inference 2. Statistical data analysis code 3. Feature engineering code 4. Health classifier code 5. Diagnosis code 6. Prognosis code 7. Hazard code 8. Generation risk code 9. Economic code

Agarwal, Vivek↗
DOE OSTI2022

Scalable Predictive And Risk Technologies

The research involves developing scalable technologies for risk-informed predictive analytics to achieve condition-based monitoring and maintenance strategies to reduce overall maintenance costs. The research utilizes data (real-time data, periodic data, and institutional knowledge) related to a particular plant asset from a specific nuclear plant site to develop technologies to scale risk-informed predictive analytic algorithms across different plant assets at the plant site and across the nuclear fleet. The developed algorithms and codes are used to optimize the maintenance strategy and estimate/forecast generation costs based on the state of health of the plant asset. Developed codes specifically include 1. Parameter estimation using plant operation data 2. Federated and Transfer learning model 3. Feature group based Multi-kernel SVM 4. Three state markov model

Manjunatha, KoushikAraseethota↗
DOE OSTI2021

CERES: CRISPR Engineering for the Rapid Enhancement of Strains

Previous strain development efforts for cyanobacteria have failed to achieve the necessary productivities needed to support economic biofuel production. We proposed to develop CRISPR Engineering for Rapid Enhancement of Strains (CERES). We developed genetic and computational tools to enable future high-throughput screening of CRISPR interference (CRISPRi) libraries in the cyanobacterium Synechococcus sp. PCC 7002, including: (1) Operon- SEQer: an ensemble of algorithms for predicting operon pairs using RNA-seq data, (2) experimental characterization and machine learning prediction of gRNA design rules for CRISPRi, and (3) a shuttle vector for gene expression. These tools lay the foundation for CRISPR library screening to develop cyanobacterial strains that are optimized for growth or metabolite production under a wide range of environmental conditions. The optimization of cyanobacterial strains will directly advance U.S. energy and climate security by enabling domestic biofuel production while simultaneously mitigating atmospheric greenhouse gases through photoautotrophic fixation of carbon dioxide.

09 BIOMASS FUELS↗
DOE OSTI2021

Verification and validation of developed short-term forecasting models

Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provide an opportunity for leveraging data-driven algorithms to predict future nuclear power plant (NPP) operating conditions by using recorded plant process data. Successfully implementing these models can lead to cost-reducing, conditioned-based predictive maintenance through optimized maintenance schedules and a reduction of unnecessary maintenance activities. This report discusses the verification and validation of short-term forecasting processes (i.e., data cleaning, feature selection, model optimization, and forecasting) developed in previous reports. The verification and validation (V&V) process demonstrates the expected precision and accuracy when the ML model encounters new datasets from different systems. Shapley additive explanations were used as the primary means of feature selection across these different data set. Individual models were trained for each data set, then validated through a cross-validation procedure. In this report, two different ML models were tasked to predict variables from three different plant process data sets with varying prediction horizons. The results indicate that support vector regression (SVR) outperformed long short-term memory (LSTM) neural networks in regard to each data set and each prediction horizon in this study, but further tuning and optimization could improve long short-term memory results. However, each forecasting model showed reduced performance as the prediction horizon was extended from 1 hour to 1 day ahead. Research is ongoing to evaluate the optimal input variable space, which is based on a given set of process parameters, to further improve forecasting accuracy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation

Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R 2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m 3 /m 3 ). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict

The reconstruction of complete microbial metabolic pathways using ‘omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1975

Duct liner optimization for turbomachinery noise sources

An acoustical field theory for axisymmetric, multisectioned lined ducts with uniform flow profiles was combined with a numerical minimization algorithm to predict optimal liner configurations having one, two, and three sections. Source models studied include a point source located on the axis of the duct and rotor/outlet-stator viscous wake interaction effects for a typical research compressor operating at an axial flow Mach number of about 0.4. For this latter source, optimal liners for equipartition-of energy, zero-phase, and least-attenuated-mode source variations were also calculated and compared with exact results. It is found that the potential benefits of liner segmentation for the attenuation of turbomachinery noise is greater than would be predicted from point source results. Furthermore, effective liner design requires precise knowledge of the circumferential and radial modal distributions.

Lester, H. C.↗
NASA NTRS1980

Structural assembly in space

A cost algorithm for predicting assembly costs for large space structures is given. Assembly scenarios are summarized which describe the erection, deployment, and fabrication tasks for five large space structures. The major activities that impact total costs for structure assembly from launch through deployment and assembly to scientific instrument installation and checkout are described. Individual cost elements such as assembly fixtures, handrails, or remote minipulators are also presented.

Stokes, J. W.↗
NASA NTRS1984

An attitude reorientation algorithm for the Galileo spacecraft

This paper presents an onboard closed loop attitude reorientation algorithm for a dual-spin spacecraft, Galileo. The basic concept for the design uses the despun stator as a reference and fires a sequence of fixed duration thruster pulses to precess the angular momentum vector to its commanded orientation. The error signals used to despin the stator and to perform the turn are derived from the stator error quaternion which is computed using gyro outputs. Constraints considered in the design include gyro rate saturation, plume impingement effects, system parameter variations, as well as software reliability and flexibility. The performance of the algorithm was predicted by analysis and confirmed by computer simulation results. It was concluded that a reorientation accuracy of better than 1 milliradian with a nutation angle of less than 1 degree during the turn can be achieved.

Lin, H.-S.↗
NASA NTRS1986

Direct solar-pumped iodine laser amplifier

In order to evaluate the feasibility of the solar pumped dye laser, the parametric study of a dye laser amplifier pumped by a solar simulator and flashlamp was carried out, and the amplifier gains were measured at various pump beam irradiances on the dye cell. Rhodamine 6G was considered as a candidate for the solar pumped laser because of its good utilization of the solar spectrum and high quantum efficiency. The measurement shows that a solar concentration of 20,000 is required to reach the threshold of the dye. The work to construct a kinetic model algorithm which predicts the output parameter of laser was progressed. The kinetic model was improved such that there is good agreement between the theoretical model and experimental data for the systems defined previously as flashlamp pumped laser oscillator, and the long path length solar pumped laser.

Han, Kwang S.↗
NASA NTRS1988

Analysis and improvement of the vector quantization in SELP (Stochastically Excited Linear Prediction)

The Stochastically Excited Linear Prediction (SELP) algorithm is described as a speech coding method employing a two-stage vector quantization. The first stage uses an adaptive codebook which efficiently encodes the periodicity of voiced speech, and the second stage uses a stochastic codebook to encode the remainder of the excitation signal. The adaptive codebook performs well when the pitch period of the speech signal is larger than the frame size. An extension is introduced, which increases its performance for the case that the frame size is longer than the pitch period. The performance of the stochastic stage, which improves with frame length, is shown to be best in those sections of the speech signal where a high level of short-term correlations is present. It can be concluded that the SELP algorithm performs best during voiced speech where the pitch period is longer than the frame length.

Kleijn, W. B.↗
NASA NTRS1989

A new method for analysis of limit cycle behavior of the NASA/JPL 70-meter antenna axis servos

A piecewise linear method of analyzing the effects of discontinuous nonlinearities on control system performance is described. The limit cycle oscillatory behavior of the system resulting from the nonlinearities is described in terms of a sequence of linear system transient responses. The equations are derived which relate the initial and the terminal conditions of successive transients and the boundary conditions imposed by the non-linearities. The method leads to a convenient computation algorithm for prediction of limit cycle characteristics resulting from discontinuous nonlinearities such as friction, deadzones, and hysteresis.

Hill, R. E.↗