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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Simple Diesel Train Fuel Consumption Model for Real-Time Train Applications

This paper introduces a simple diesel train energy consumption model that calculates the instantaneous energy consumption using vehicle operational input variables, including the instantaneous speed, acceleration, and roadway grade, which can be easily obtained from global positioning system (GPS) loggers. The model was tested against real-world data and produced an error of -1.33% for all data and errors ranging from -12.4% to +8.0% for energy consumption of four train datasets amounting to a total of 5854 km trips. The study also validated the proposed model with separate data that were collected between Valencia and Cuenca, Spain, which had a total length of 198 km and found that the model was accurate, yielding a relative error of -1.55% for the total energy consumption. These results show that the proposed model can be used by train operators, transportation planners, policy makers, and environmental engineers to evaluate the energy consumption effects of train operational projects and train simulation within intermodal transportation planning tools.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Density Matrix Implementation of the Fermi–Löwdin Orbital Self-Interaction Correction Method

The Fermi–Löwdin orbital self-interaction correction (FLOSIC) method effectively provides a transformation from canonical orbitals to localized Fermi–Löwdin orbitals which are used to remove the self-interaction error in the Perdew–Zunger (PZ) framework. This transformation is solely determined by a set of points in space, called Fermi–Löwdin descriptors (FODs), and the occupied canonical orbitals or the density matrix. In this work, we provide a detailed workflow for the implementation of the FLOSIC method for removal of self-interaction error in DFT calculations in an orbital-by-orbital basis that takes advantage of the unitary invariant nature of the FLOSIC method. In this way, it is possible to cast the self-consistent energy minimization at fixed FODs in the same manner than standard Kohn–Sham with one additional term in the Kohn–Sham Hamiltonian that introduces the PZ self-interaction correction. Each energy minimization iteration is divided in two substeps, one for the density matrix and one for the FODs. Expressions for the effective Kohn–Sham matrix and FOD gradients are provided such that its implementation is suitable for most electronic structure codes. Here, we analyze the convergence characteristics of the algorithm and present applications for the evaluation of NMR shielding constants and real-time time-dependent DFT simulations based on the Liouville–von Neumann equation to calculate excitation energies.

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

Preliminary Sensitivity Analysis for Sensors Impacts on Building Control Performance

This report describes the preliminary sensitivity analysis for sensor impacts on building control performance through the US Department of Energy’s Oak Ridge National Laboratory’s Flexible Research Platform (FRP-2) building. The rooftop unit system provides cooling and heating to the building. The main heating coil is a gas heating coil. Each zone is served by a variable air volume box with an electricity reheat coil. The rooftop unit and variable air volume box controls adopted the practical control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. For sensors, the incipient (time-changing) sensor errors, including bias sensor error and precision sensor error, are the inputs of interest. The outputs are energy consumption and thermal comfort (e.g., the predicted percentage of dissatisfied occupants). The large-scale simulation (3,600 cases) was conducted on a cloud platform by integrating sensor errors and ASHRAE Guideline 36 control sequences into an emulator based on the EnergyPlus simulation program with Python energy management system feature. The surrogate models were developed based on cloud simulation results. The uncertainty analysis showed that the sensor errors substantially affect building energy consumption and thermal comfort. The sensitivity analysis shows a ranking of sensor error impacts for each interested output item (e.g., cooling energy, reheat coil heating energy, predicted percentage of dissatisfied occupants). In FY 2022, sensor locations, types, and costs will be evaluated. The field test in Oak Ridge National Laboratory’s Flexible Research Platform building regarding sensor impacts will also be performed. Finally, a comparative analysis will be conducted based on the field test results and emulator results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1978

Evaluation of radiation interference in the Voyager Sun Sensor's cadmium sulfide detector

The simulation of radiation interference effects and the results of a radiation interference test on two Voyager Sun Sensor prototype detector assemblies are reported. The derivation of test levels and requirements are discussed and show that cobalt 60 gamma radiation is an effective and practical simulator of the ionization dose rate effects induced by high-energy electron flux incident on the spacecraft at a rate of 3.7 x 10 to the 8th e/sq cm-sec (10 rad(Si)/s) during closest approach to Jupiter. The test results provide information that is used to confirm an analytic correlation, and to predict satisfactory performance of a spacecraft sun sensing device having stringent angular resolution requirements. The measured detector response shows that at dose rates incident on the detector elements of 2 rad(Si)/sec, which is four times that expected during Jupiter encounter, the radiation-induced angle error is almost an order of magnitude less than that allowed by the acceptance criteria.

Clarke, T. C.↗
NASA NTRS1994

An assessment of satellite temperature distributions used to derive the net diabatic transport for zonally averaged models of the middle atmosphere

Determinations of the zonally averaged and diabatically derived residual mean circulation (RMC) are particularly sensitive to the assumed zonal mean temperature distribution used as input. Several different middle atmosphere satellite temperature distributions have been employed in models and are compared here: a 4-year (late 1978 to early 1982) National Meteorological Center (NMC) climatology, the Barnett and Corney (or BC) climatology, and the 7 months of Nimbus 7 limb infrared monitor of the stratosphere (LIMS) temperatures. All three climatologies are generally accurate below the 10 hPa level, but there are systematic differences between them of up to +/-5 K in the upper stratosphere and lower mesosphere. The NMC/LIMS differences are evaluated using time series of rocketsonde and reconstructed satellite temperatures at station locations. Much of those biases can be explained by the differing vertical resolutions for the satellite-derived temperatures; the time series of reconstructed LIMS profiles have higher resolution and are more accurate. Because the LIMS temperatures are limited to just two full seasons, one cannot obtain monthly RMCs from them for an annual model calculation. Two alternate monthly climatologies are examined briefly: the 4-year Nimbus 7 stratospheric and mesospheric sounder (SAMS) temperatures and for the mesosphere the distribution from the Solar Mesosphere Explorer (SME), both of which are limb viewers of medium vertical resolution. There are also differences of the order of +/-5 K for those data sets. It is concluded that a major source of error in the determination of diabatic RMCs is a persistent pattern of temperature bias whose characteristics vary according to the vertical resolution of each individual climatology.

Remsberg, Ellis E.↗
NASA NTRS2008

Instrumentation System Diagnoses a Thermocouple

An improved self-validating thermocouple (SVT) instrumentation system not only acquires readings from a thermocouple but is also capable of detecting deterioration and a variety of discrete faults in the thermocouple and its lead wires. Prime examples of detectable discrete faults and deterioration include open- and short-circuit conditions and debonding of the thermocouple junction from the object, the temperature of which one seeks to measure. Debonding is the most common cause of errors in thermocouple measurements, but most prior SVT instrumentation systems have not been capable of detecting debonding. The improved SVT instrumentation system includes power circuitry, a cold-junction compensator, signal-conditioning circuitry, pulse-width-modulation (PWM) thermocouple-excitation circuitry, an analog-to-digital converter (ADC), a digital data processor, and a universal serial bus (USB) interface. The system can operate in any of the following three modes: temperature measurement, thermocouple validation, and bonding/debonding detection. The software running in the processor includes components that implement statistical algorithms to evaluate the state of the thermocouple and the instrumentation system. When the power is first turned on, the user can elect to start a diagnosis/ monitoring sequence, in which the PWM is used to estimate the characteristic times corresponding to the correct configuration. The user also has the option of using previous diagnostic values, which are stored in an electrically erasable, programmable read-only memory so that they are available every time the power is turned on.

Perotti, Jose↗
NASA NTRSDate not supplied

Validation of Self-Scheduling Countermeasures in NASA's HERA Campaign 6

Enhancing crew capabilities for planning and scheduling activities is critical for periods of increased crew autonomy in future long-duration missions where communication delays preclude real-time ground support from Earth. Our study focuses on how to empower astronauts to manage their timelines independently from the experts in the mission control center (MCC). Our objective was to evaluate the impact of scheduling countermeasures on crew scheduling performance, workload, and usability in an analog mission environment. The study involved 16 crew members across four missions in the Limited Autonomy phase of Human Exploration Research Analog (HERA) Campaign 6. Crew members used Playbook to schedule one operational day for the entire crew. Half the participants accessed scheduling aids, and we compared their performance to a control group with no aids. Performance, workload, and usability were assessed using time on task, violation counts, NASA Task Load Index (NASA-TLX), and System Usability Scale (SUS). Participants using scheduling aids completed sessions 20% faster and committed 33% fewer violations. While these differences were not statistically significant due to the study’s operational limitations, trends indicate that scheduling aids may reduce errors and improve efficiency. These results can inform the design of scheduling tools to enhance astronauts’ autonomy in long-duration space missions, contributing to improved crew performance and reduced reliance on ground support.

space robotics↗
NASA NTRSDate not supplied

Validation of Self-Scheduling Countermeasures in NASA's HERA Campaign 6

Enhancing crew capabilities for planning and scheduling activities is critical for periods of increased crew autonomy in future long-duration missions where communication delays preclude real-time ground support from Earth. Our study focuses on how to empower astronauts to manage their timelines independently from the experts in the mission control center (MCC). Our objective was to evaluate the impact of scheduling countermeasures on crew scheduling performance, workload, and usability in an analog mission environment. The study involved 16 crew members across four missions in the Limited Autonomy phase of Human Exploration Research Analog (HERA) Campaign 6. Crew members used Playbook to schedule one operational day for the entire crew. Half the participants accessed scheduling aids, and we compared their performance to a control group with no aids. Performance, workload, and usability were assessed using time on task, violation counts, NASA Task Load Index (NASA-TLX), and System Usability Scale (SUS). Participants using scheduling aids completed sessions 20% faster and committed 33% fewer violations. While these differences were not statistically significant due to the study’s operational limitations, trends indicate that scheduling aids may reduce errors and improve efficiency. These results can inform the design of scheduling tools to enhance astronauts’ autonomy in long-duration space missions, contributing to improved crew performance and reduced reliance on ground support.

space robotics↗
DOE OSTI2024

Risk-Aware Framework Development for Disruption Prediction: Alcator C-Mod and DIII-D Survival Analysis

Abstract Survival regression models can achieve longer warning times at similar receiver operating characteristic performance than previously investigated models. Survival regression models are also shown to predict the time until a disruption will occur with lower error than other predictors. Time-to-event predictions from time-series data can be obtained with a survival analysis statistical framework, and there have been many tools developed for this task which we aim to apply to disruption prediction. Using the open-source Auton-Survival package we have implemented disruption predictors with the survival regression models Cox Proportional Hazards, Deep Cox Proportional Hazards, and Deep Survival Machines. To compare with previous work, we also include predictors using a Random Forest binary classifier, and a conditional Kaplan-Meier formalism. We benchmarked the performance of these five predictors using experimental data from the Alcator C-Mod and DIII-D tokamaks by simulating alarms on each individual shot. We find that developing machine-relevant metrics to evaluate models is an important area for future work. While this study finds cases where disruptive conditions are not predicted, there are instances where the desired outcome is produced. Giving the plasma control system the expected time-to-disruption will allow it to determine the optimal actuator response in real time to minimize risk of damage to the device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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↗
NASA NTRS1986

Adaptive filtering of biodynamic stick feedthrough in manipulation tasks on board moving platforms

A novel approach to suppress the effects of biodynamic interference is presented. An adaptive noise canceling technique is employed for substracting the platform motion correlated components from the control stick output. The effects of biodynamic interference and its suppression by adaptive noise cancellation has been evaluated in a series of tracking tasks performed in a moving base simulator. Simulator motions were in pitch, roll and combined pitch and roll. Human operator performance was assessed from the mean square values of the tracking error and the control activity. The tracking error and the total stick output signal were found to increase significantly with motion and to diminish substantially with adaptive noise cancellation, thus providing a considerable improvement in tracking performance under conditions in which platform motion were present. The adaptive filter was found to cause a significant increase in the cross-over frequency and decrease in the phase margin. Moreover, the adaptive filter was found to significantly improve the human operator visual motor response. This improvement is manifested as an increased human operator gain, a smaller time delay and lower pilot workload.

Velger, M.↗
NASA NTRS2002

Comparison of Two Methods for Estimating the Sampling-Related Uncertainty of Satellite Rainfall Averages Based on a Large Radar Data Set

The uncertainty of rainfall estimated from averages of discrete samples collected by a satellite is assessed using a multi-year radar data set covering a large portion of the United States. The sampling-related uncertainty of rainfall estimates is evaluated for all combinations of 100 km, 200 km, and 500 km space domains, 1 day, 5 day, and 30 day rainfall accumulations, and regular sampling time intervals of 1 h, 3 h, 6 h, 8 h, and 12 h. These extensive analyses are combined to characterize the sampling uncertainty as a function of space and time domain, sampling frequency, and rainfall characteristics by means of a simple scaling law. Moreover, it is shown that both parametric and non-parametric statistical techniques of estimating the sampling uncertainty produce comparable results. Sampling uncertainty estimates, however, do depend on the choice of technique for obtaining them. They can also vary considerably from case to case, reflecting the great variability of natural rainfall, and should therefore be expressed in probabilistic terms. Rainfall calibration errors are shown to affect comparison of results obtained by studies based on data from different climate regions and/or observation platforms.

Lau, William K. M.↗
NASA NTRS2021

Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a

Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, ChlBS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.

MERIS timeseries↗
NASA NTRS1981

An evaluation of ISOCLS and CLASSY clustering algorithms for forest classification in northern Idaho

Both the iterative self-organizing clustering system (ISOCLS) and the CLASSY algorithms were applied to forest and nonforest classes for one 1:24,000 quadrangle map of northern Idaho and the classification and mapping accuracies were evaluated with 1:30,000 color infrared aerial photography. Confusion matrices for the two clustering algorithms were generated and studied to determine which is most applicable to forest and rangeland inventories in future projects. In an unsupervised mode, ISOCLS requires many trial-and-error runs to find the proper parameters to separate desired information classes. CLASSY tells more in a single run concerning the classes that can be separated, shows more promise for forest stratification than ISOCLS, and shows more promise for consistency. One major drawback to CLASSY is that important forest and range classes that are smaller than a minimum cluster size will be combined with other classes. The algorithm requires so much computer storage that only data sets as small as a quadrangle can be used at one time.

Werth, L. F.↗
NASA NTRS2002

Modeling and control for closed environment plant production systems

A computer program was developed to study multiple crop production and control in controlled environment plant production systems. The program simulates crop growth and development under nominal and off-nominal environments. Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller. The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling. The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting. The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations. Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.

NASA Discipline Life Support Systems↗
NASA NTRS2006

Estimation of Stability and Control Derivatives of an F-15

A technique for real-time estimation of stability and control derivatives (derivatives of moment coefficients with respect to control-surface deflection angles) was used to support a flight demonstration of a concept of an indirect-adaptive intelligent flight control system (IFCS). Traditionally, parameter identification, including estimation of stability and control derivatives, is done post-flight. However, for the indirect-adaptive IFCS concept, parameter identification is required during flight so that the system can modify control laws for a damaged aircraft. The flight demonstration was carried out on a highly modified F-15 airplane (see Figure 1). The main objective was to estimate the stability and control derivatives of the airplane in nearly real time. A secondary goal was to develop a system to automatically assess the quality of the results, so as to be able to tell a learning neural network which data to use. Parameter estimation was performed by use of Fourier-transform regression (FTR) a technique developed at NASA Langley Research Center. FTR is an equation- error technique that operates in the frequency domain. Data are put into the frequency domain by use of a recursive Fourier transform for a discrete frequency set. This calculation simplifies many subsequent calculations, removes biases, and automatically filters out data beyond the chosen frequency range. FTR as applied here was tailored to work with pilot inputs, which produce correlated surface positions that prevent accurate parameter estimates, by replacing half the derivatives with predicted values. FTR was also set up to work only on a recent window of data, to accommodate changes in flight condition. A system of confidence measures was developed to identify quality-parameter estimates that a learning neural network could use. This system judged the estimates primarily on the basis of their estimated variances and of the level of aircraft response. The resulting FTR system was implemented in the Simulink software system and auto-coded in the C programming language for use on the Airborne Research Test System (ARTS II) computer installed in the F-15 airplane. The Simulink model was also used in a control room that utilizes the Ring Buffered Network Bus hardware and software, making it possible to evaluate test points during flights. In-flight parameter estimation was done for piloted and automated maneuvers, primarily at three test conditions. Figure 2 shows results for pitching moment due to symmetric stabilator actuations for a series of three pitch doublet maneuvers (in a doublet maneuver, a command to change attitude in a given direction by a given amount is followed immediately by a command to change attitude in the opposite direction by the same amount). A time window of 5 seconds was used. The portions of the curves shown in red are those that passed the confidence tests. The technique showed good convergence for most derivatives for both kinds of maneuvers - typically within a few seconds. The confidence tests were marginally successful, and it would be necessary to refine them for use in an IFCS.

Smith, Mark↗
DOE OSTI2024

A Comprehensive Analysis of Uncertainties in Warm-Rain Parameterizations in Climate Models Based on In Situ Measurements

Abstract Because of the coarse grid size of Earth system models (ESMs), representing warm-rain processes in ESMs is a challenging task involving multiple sources of uncertainty. Previous studies evaluated warm-rain parameterizations mainly according to their performance in emulating collision–coalescence rates for local droplet populations over a short period of a few seconds. The representativeness of these local process rates comes into question when applied in ESMs for grid sizes on the order of 100 km and time steps on the order of 20–30 min. We evaluate several widely used warm-rain parameterizations in ESM application scenarios. In the comparison of local and instantaneous autoconversion rates, the two parameterization schemes based on numerical fitting to stochastic collection equation (SCE) results perform best. However, because of Jessen’s inequality, their performance deteriorates when grid-mean, instead of locally resolved, cloud properties are used in their simulations. In contrast, the effect of Jessen’s inequality partly cancels the overestimation problem of two semianalytical schemes, leading to an improvement in the ESM-like comparison. In the assessment of uncertainty due to the large time step of ESMs, it is found that the rainwater tendency simulated by the SCE is roughly linear for time steps smaller than 10 min, but the nonlinearity effect becomes significant for larger time steps, leading to errors up to a factor of 4 for a time step of 20 min. After considering all uncertainties, the grid-mean and time-averaged rainwater tendency based on the parameterization schemes is mostly within a factor of 4 of the local benchmark results simulated by SCE.

Meteorology & Atmospheric Sciences↗