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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

WE-B-304-03: Biological Treatment Planning

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning by the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys.

Rao, Ashwin P. (ORCID:0000000319312568)↗
DOE OSTI2017

Pressure Induced Phase Transformations in Ceramics

The study of materials with unusual properties offers new insight into structure-property relations as well as promise for the design of novel composites. In this spirit, the PIs seek to (1) understand fundamental mechanical phenomena in ceramics that exhibit pressure-induced phase transitions, negative coefficient of thermal expansion (CTE), and negative compressibility, and (2) explore the effect of these phenomena on the mechanical behavior of composites designed with such ceramics. The broad and long-term goal is to learn how to utilize these unusual behaviors to obtain desired mechanical responses. While the results are expected to be widely applicable to many ceramics, most of the present focus is on silicates, as they exhibit remarkable diversity in structure and properties. Eucryptite, a lithium aluminum silicate (LiAlSiO 4 ), is specifically targeted because it exhibits a pressure-induced phase transition at a sufficiently low pressure to be accessible during conventional materials processing. Thus, composites with eucryptite may be designed to exhibit a novel type of transformation toughening. The PIs have performed a combination of activities that encompass synthesis and processing to control structures, atomistic modeling to predict and understand structures, and characterization to study mechanical behavior. Several materials behavior discoveries were made. It was discovered that small amounts of Zn (as small as 0.1 percent by mol) reverse the sign of the coefficient of thermal expansion of beta-eucryptite from negative to slightly positive. The presence of Zn also significantly mitigates microcracking that occurs during thermal cycling of eucryptite. It is hypothesized that Zn disrupts the Li ordering in beta-eucryptite, thereby altering the thermal expansion behavior. A nanoindentation technique developed to characterize incipient plasticity was applied to examine the initial stages of the pressure induced phase transformation from beta to epsilon-eucryptite and show that the transformation nucleation is related to the motion of the tetrahedral units making up the structure. It was revealed that the conduction of Li ions through the structure is also dictated by the tetrahedral unit arrangement and how their positions change with temperature. The critical pressure to obtain the high pressure phase of eucryptite was shown to depend on the grain size. The structure of the high pressure phase was determined with a combination of atomistic modeling and in situ x-ray diffraction experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Aircraft measurements of BrO, IO, glyoxal, NO 2 , H 2 O, O 2 –O 2 and aerosol extinction profiles in the tropics: comparison with aircraft-/ship-based in situ and lidar measurements

Here, tropospheric chemistry of halogens and organic carbon over tropical oceans modifies ozone and atmospheric aerosols, yet atmospheric models remain largely untested for lack of vertically resolved measurements of bromine monoxide (BrO), iodine monoxide (IO) and small oxygenated hydrocarbons like glyoxal (CHOCHO) in the tropical troposphere. BrO, IO, glyoxal, nitrogen dioxide (NO 2 ), water vapor (H 2 O) and O 2 –O 2 collision complexes (O 4 ) were measured by the University of Colorado Airborne Multi-AXis Differential Optical Absorption Spectroscopy (CU AMAX-DOAS) instrument, aerosol extinction by high spectral resolution lidar (HSRL), in situ aerosol size distributions by an ultra high sensitivity aerosol spectrometer (UHSAS) and in situ H 2 O by vertical-cavity surface-emitting laser (VCSEL) hygrometer. Data are presented from two research flights (RF12, RF17) aboard the National Science Foundation/National Center for Atmospheric Research Gulfstream V aircraft over the tropical Eastern Pacific Ocean (tEPO) as part of the "Tropical Ocean tRoposphere Exchange of Reactive halogens and Oxygenated hydrocarbons" (TORERO) project (January/February 2012). We assess the accuracy of O 4 slant column density (SCD) measurements in the presence and absence of aerosols. Our O 4 -inferred aerosol extinction profiles at 477 nm agree within 6% with HSRL in the boundary layer and closely resemble the renormalized profile shape of Mie calculations constrained by UHSAS at low (sub-Rayleigh) aerosol extinction in the free troposphere. CU AMAX-DOAS provides a flexible choice of geometry, which we exploit to minimize the SCD in the reference spectrum (SCD REF , maximize signal-to-noise ratio) and to test the robustness of BrO, IO and glyoxal differential SCDs. The RF12 case study was conducted in pristine marine and free tropospheric air. The RF17 case study was conducted above the NOAA RV Ka'imimoana (TORERO cruise, KA-12-01) and provides independent validation data from ship-based in situ cavity-enhanced DOAS and MAX-DOAS. Inside the marine boundary layer (MBL) no BrO was detected (smaller than 0.5 pptv), and 0.2–0.55 pptv IO and 32–36 pptv glyoxal were observed. The near-surface concentrations agree within 30% (IO) and 10% (glyoxal) between ship and aircraft. The BrO concentration strongly increased with altitude to 3.0 pptv at 14.5 km (RF12, 9.1 to 8.6° N; 101.2 to 97.4° W). At 14.5 km, 5–10 pptv NO 2 agree with model predictions and demonstrate good control over separating tropospheric from stratospheric absorbers (NO 2 and BrO). Our profile retrievals have 12–20 degrees of freedom (DoF) and up to 500 m vertical resolution. The tropospheric BrO vertical column density (VCD) was 1.5 × 10 13 molec cm –2 (RF12) and at least 0.5 × 10 13 molec cm –2 (RF17, 0–10 km, lower limit). Tropospheric IO VCDs correspond to 2.1 × 10 12 molec cm –2 (RF12) and 2.5 × 10 12 molec cm –2 (RF17) and glyoxal VCDs of 2.6 × 10 14 molec cm –2 (RF12) and 2.7 × 10 14 molec cm –2 (RF17). Surprisingly, essentially all BrO as well as the dominant IO and glyoxal VCD fraction was located above 2 km (IO: 58 ± 5%, 0.1–0.2 pptv; glyoxal: 52 ± 5%, 3–20 pptv). To our knowledge there are no previous vertically resolved measurements of BrO and glyoxal from aircraft in the tropical free troposphere. The atmospheric implications are briefly discussed. Future studies are necessary to better understand the sources and impacts of free tropospheric halogens and oxygenated hydrocarbons on tropospheric ozone, aerosols, mercury oxidation and the oxidation capacity of the atmosphere.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1989

Evaluation of a technique for predicting longitudinal pilot-induced-oscillations

A technique for predicting the susceptibility of an aircraft to longitudinal pilot-induced-oscillations (PIO's) is evaluated using 62 configurations from a pair of flight tests involving the NT-33 variable stability aircraft. The technique is based upon the characteristics of the open-loop pilot/vehicle system for attitude control as predicted by the Optimal Control Model (OCM) of the human pilot. The OCM is simplified so that only the index of performance weighting coefficients need to be considered as problem variables and a simple technique for generating these coefficients is reviewed.

Hess, R. A.↗
NASA NTRS1996

Rewetting of Monogroove Heat Pipe in Space Station Radiators

Experimental investigation of the rewetting characteristics of a uniformly heated grooved surface was performed, the results of which are presented in this work. It was found that, for a rewetting fluid of 2-propanol, the rewetting temperature was approx. 93-96 C for the upward-facing case and about 2 C lower for the downwardfacing case. When the initial plate temperature was higher than the rewetting temperature, the rewetting speed decreased with the initial plate temperature. The rewetting speed is also faster in the upward-facing case than in the downward-facing case for the same initial plate temperatures, which indicates a gravitational effect on rewetting. This trend is found to be consistent with the previously investigated end heating condition. The rewetting distance that is predicted by the conduction controlled model is found to be in fair agreement with the experimental data. Also, an apparatus that enables experiments to be performed in a reduced gravitational environment has been built and experiments are currently being performed. The design of this apparatus is presented along with preliminary data.

Chan, S. H.↗
NASA NTRS2001

A Roadmap for Aircraft Engine Life Extending Control

The concept of Aircraft Engine Life Extending Control is introduced. A brief description of the tradeoffs between performance and engine life are first explained. The overall goal of the life extending controller is to reduce the engine operating cost by extending the on-wing engine life while improving operational safety. The research results for NASA's Rocket Engine life extending control program are also briefly described. Major building blocks of the Engine Life Extending Control architecture are examined. These blocks include: life prediction models, engine operation models, stress and thermal analysis tools, control schemes, and intelligent control systems. The technology areas that would likely impact the successful implementation of an aircraft engine life extending control are also briefly described. Near, intermediate, and long term goals of NASA's activities are also presented.

Guo, Ten-Huei↗
NASA NTRS2015

Aircraft Measurements of BrO, IO, Glyoxal, NO2, H2O, O2-O2 and Aerosol Extinction Profiles in the Tropics: Comparison with Aircraft-/Ship-Based in Situ and Lidar Measurements

Tropospheric chemistry of halogens and organic carbon over tropical oceans modifies ozone and atmospheric aerosols, yet atmospheric models remain largely untested for lack of vertically resolved measurements of bromine monoxide (BrO), iodine monoxide (IO) and small oxygenated hydrocarbons like glyoxal (CHOCHO) in the tropical troposphere. BrO, IO, glyoxal, nitrogen dioxide (NO2), water vapor (H2O) and O2-O2 collision complexes (O4/ were measured by the University of Colorado Airborne Multi-AXis Differential Optical Absorption Spectroscopy (CU AMAXDOAS) instrument, aerosol extinction by high spectral resolution lidar (HSRL), in situ aerosol size distributions by an ultra high sensitivity aerosol spectrometer (UHSAS) and in situ H2O by vertical-cavity surface-emitting laser (VCSEL) hygrometer. Data are presented from two research flights (RF12, RF17) aboard the National Science Foundation/ National Center for Atmospheric Research Gulfstream V aircraft over the tropical Eastern Pacific Ocean (tEPO) as part of the "Tropical Ocean tRoposphere Exchange of Reactive halogens and Oxygenated hydrocarbons" (TORERO) project (January/February 2012). We assess the accuracy of O4 slant column density (SCD) measurements in the presence and absence of aerosols. Our O4-inferred aerosol extinction profiles at 477 nm agree within 6% with HSRL in the boundary layer and closely resemble the renormalized profile shape of Mie calculations constrained by UHSAS at low (sub-Rayleigh) aerosol extinction in the free troposphere. CU AMAX-DOAS provides a flexible choice of geometry, which we exploit to minimize the SCD in the reference spectrum (SCDREF, maximize signal-to-noise ratio) and to test the robustness of BrO, IO and glyoxal differential SCDs. The RF12 case study was conducted in pristine marine and free tropospheric air. The RF17 case study was conducted above the NOAA RV Ka'imimoana (TORERO cruise, KA-12-01) and provides independent validation data from ship-based in situ cavity-enhanced DOAS and MAX-DOAS. Inside the marine boundary layer (MBL) no BrO was detected (smaller than 0.5 pptv), and 0.2-0.55 pptv IO and 32-36 pptv glyoxal were observed. The near-surface concentrations agree within 30% (IO) and 10% (glyoxal) between ship and air-craft. The BrO concentration strongly increased with altitude to 3.0 pptv at 14.5 km (RF12, 9.1 to 8.6 deg N; 101.2 to 97.4 deg W). At 14.5 km, 5-10 pptv NO2 agree with model predictions and demonstrate good control over separating tropospheric from stratospheric absorbers (NO2 and BrO). Our profile retrievals have 12-20 degrees of freedom (DoF) and up to 500m vertical resolution. The tropospheric BrO vertical column density (VCD) was 1.5 x 10(exp 13) molec cm(exp -2) (RF12) and at least 0.5 x 10(exp 13) molec cm(exp -2) (RF17, 0- 10 km, lower limit). Tropospheric IO VCDs correspond to 2.1 x 10(exp 12) molec cm(exp -2) (RF12) and 2.5 x 10(exp 12) molec cm(exp -2) (RF17) and glyoxal VCDs of 2.6 x 10(exp 14) molec cm(exp -2) (RF12) and 2.7 x 10(exp 14) molec cm(exp -2) (RF17). Surprisingly, essentially all BrO as well as the dominant IO and glyoxal VCD fraction was located above 2 km (IO: 58 plus or minus 5 %, 0.1-0.2 pptv; glyoxal: 52 plus or minus 5 %, 3-20 pptv). To our knowledge there are no previous vertically resolved measurements of BrO and glyoxal from aircraft in the tropical free troposphere. The atmospheric implications are briefly discussed. Future studies are necessary to better understand the sources and impacts of free tropospheric halogens and oxygenated hydrocarbons on tropospheric ozone, aerosols, mercury oxidation and the oxidation capacity of the atmosphere.

Volkamer, R.↗
NASA NTRSDate not supplied

Investigating Solar Radiation Pressure Modeling for Operations in Near Rectilinear Halo Orbit

NASA’s Gateway program will build a crew-tended station in an Earth-Moon Near Rectilinear Halo Orbit (NRHO). Deep space operations differ considerably from Low Earth Orbit (LEO) operations in the environmental modeling, orbit geometry, and propagation timespans in operations. The cislunar environment, as opposed to the LEO environment, lacks atmospheric drag and is simultaneously affected by the gravity of both the Earth and the Moon, and solar radiation pressure (SRP) has a significant effect. This paper investigates the impacts of various SRP models on prediction accuracy, attitude control accuracy, orbit determination performance, and computational burden.

Guidance↗
DOE OSTI2012

Sootblowing optimization for improved boiler performance

A sootblowing control system that uses predictive models to bridge the gap between sootblower operation and boiler performance goals. The system uses predictive modeling and heuristics (rules) associated with different zones in a boiler to determine an optimal sequence of sootblower operations and achieve boiler performance targets. The system performs the sootblower optimization while observing any operational constraints placed on the sootblowers.

42 ENGINEERING↗
DOE OSTI2013

Sootblowing optimization for improved boiler performance

A sootblowing control system that uses predictive models to bridge the gap between sootblower operation and boiler performance goals. The system uses predictive modeling and heuristics (rules) associated with different zones in a boiler to determine an optimal sequence of sootblower operations and achieve boiler performance targets. The system performs the sootblower optimization while observing any operational constraints placed on the sootblowers.

42 ENGINEERING↗
DOE OSTI2025

Neural Network Analysis of Nuclear Magnetic Resonance and Infrared Spectra

Nuclear magnetic resonance (NMR) spectroscopy and infrared (IR) spectroscopy are powerful chemical characterization techniques with broad general usage. However, the manual evaluation of the resulting spectra is time-consuming and requires significant expertise, preventing insights from being used in real-time applications. With recent advances in computation and artificial intelligence (AI), new tools are available for automating spectral interpretation. In this work, machine learning (ML) algorithms using 1-dimensional convolutional neural networks (CNNs) were applied to identify common functional groups from spectral information. Raw spectra were collected virtually from the Human Metabolome Database (HMDB) and National Institute of Standards and Technology (NIST) Chemistry WebBook and processed into a suitable standard. Algorithm design was tailored to best fit the nature of the problem, with built-in flexibility to accommodate relevant parameters beyond the raw spectral input, specifically solvent identity and magnetic frequency for NMR. The predictive capability of the algorithm in identifying functional groups is displayed in several examples. This methodology has been compiled into a code repository and could easily be modified to adapt alternative data sources, including other spectrum types. To mitigate overfitting, a common problem in mathematical modeling where overfamiliarity with training data produces trends that are not representative of the general data, a novel metric was developed, referred to as Accufit. Accufit includes a parameter that penalizes substantial differences in the training accuracy and the accuracy of an independent validation set. Examples are presented showing the effectiveness of Accufit in maintaining the model’s predictive capability while controlling the overfitting when used as a custom metric for hyperparameter tuning.

Sturgill, James↗
DOE OSTI2024

Machine learning-enhanced model-based scenario optimization for DIII-D

Abstract Scenario development in tokamaks is an open area of investigation that can be approached in a variety of different ways. Experimental trial and error has been the traditional method, but this required a massive amount of experimental time and resources. As high fidelity predictive models have become available, offline development and testing of proposed scenarios has become an option to reduce the required experimental resources. The use of predictive models also offers the possibility of using a numerical optimization process to find the controllable inputs that most closely achieve the desired plasma state. However, this type of optimization can require as many as hundreds or thousands of predictive simulation cases to converge to a solution; many of the commonly used high fidelity models have high computational burdens, so it is only reasonable to run a handful of predictive simulations. In order to make use of numerical optimization approaches, a compromise needs to be found between model fidelity and computational burden. This compromise can be achieved using neural networks surrogates of high fidelity models that retain nearly the same level of accuracy as the models they are trained to replicate while reducing the computation time by orders of magnitude. In this work, a model-based numerical optimization tool for scenario development is described. The predictive model used by the optimizer includes neural network surrogate models integrated into the fast Control-Oriented Transport simulation framework. This optimization scheme is able to converge to the optimal values of the controllable inputs that produce the target plasma scenario by running thousands of predictive simulations in under an hour without sacrificing too much prediction accuracy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2000

Controlling Microbial Byproducts using Model-Based Substrate Monitoring and Control Strategies

We have developed a computer-controlled bioreactor system to study various aspects of microbially-mediated nitrogen cycling. The system has been used to investigate methods for controlling microbial denitrification (the dissimilatory reduction of nitrate to N2O and N2) in hydroponic plant growth chambers. Such chambers are key elements of advanced life support systems being designed for use on long duration space missions, but nitrogen use efficiency in them is reduced by denitrification. Control software architecture was designed which permits the heterogeneous control of system hardware using traditional feedback control, and quantitative and qualitative models of various system features. Model-based feed forward control entails prediction of future systems in states and automated regulation of system parameters to achieve desired and avoid undesirable system states. A bacterial growth rate model based on the classic Monod model of saturation kinetics was used to evaluate the response of several individual denitrifying species to varying environmental conditions. The system and models are now being applied to mixed microbial communities harvested from the root zone of a hydroponic growth chamber. The use of a modified Monod organism interaction model was evaluated as a means of achieving more accurate description of the dynamic behavior of the communities. A minimum variance parameter estimation routine was also' used to calibrate the constant parameters in the model by iterative evaluation of substrate (nitrate) uptake and growth kinetics. This representation of processes and interactions aids in the formulation of control laws. The feed forward control strategy being developed will increase system autonomy, reduce crew intervention and limit the accumulation of undesirable waste products (NOx).

Smernoff, David T.↗
DOE OSTI2015

Controlling bimetallic nanostructures by the microemulsion method with subnanometer resolution using a prediction model

In this study, we present a theoretical model to predict the atomic structure of Au/Pt nanoparticles synthesized in microemulsions. Excellent concordance with the experimental results shows that the structure of the nanoparticles can be controlled at sub-nanometer resolution simply by changing the reactants concentration. The results of this study not only offer a better understanding of the complex mechanisms governing reactions in microemulsions, but open up a simple new way to synthesize bimetallic nanoparticles with ad-hoc controlled nanostructures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1971

A control-theory model for human decision-making

A model for human decision making is an adaptation of an optimal control model for pilot/vehicle systems. The models for decision and control both contain concepts of time delay, observation noise, optimal prediction, and optimal estimation. The decision making model was intended for situations in which the human bases his decision on his estimate of the state of a linear plant. Experiments are described for the following task situations: (a) single decision tasks, (b) two-decision tasks, and (c) simultaneous manual control and decision making. Using fixed values for model parameters, single-task and two-task decision performance can be predicted to within an accuracy of 10 percent. Agreement is less good for the simultaneous decision and control situation.

Levison, W. H.↗
DOE OSTI2016

Predictive modeling and reducing cyclic variability in autoignition engines

Methods and systems are provided for controlling a vehicle engine to reduce cycle-to-cycle combustion variation. A predictive model is applied to predict cycle-to-cycle combustion behavior of an engine based on observed engine performance variables. Conditions are identified, based on the predicted cycle-to-cycle combustion behavior, that indicate high cycle-to-cycle combustion variation. Corrective measures are then applied to prevent the predicted high cycle-to-cycle combustion variation.

33 ADVANCED PROPULSION SYSTEMS↗