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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 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Dynamic Evolution of Cement Composition and Transport Properties under Conditions Relevant to Geological Carbon Sequestration

Assessing the possibility of CO{sub 2} leakage is one of the major challenges for geological carbon sequestration. Injected CO{sub 2} can react with wellbore cement, which can potentially change cement composition and transport properties. In this work, we develop a reactive transport model based on experimental observations to understand and predict the property evolution of cement in direct contact with CO{sub 2}-saturated brine under diffusion-controlled conditions. The model reproduced the observed zones of portlandite depletion and calcite formation. Cement alteration is initially fast and slows down at later times. This work also quantified the role of initial cement properties, in particular the ratio of the initial portlandite content to porosity (defined here as φ), in determining the evolution of cement properties. Portlandite-rich cement with large φ values results in a localized “sharp” reactive diffusive front characterized by calcite precipitation, leading to significant porosity reduction, which eventually clogs the pore space and prevents further acid penetration. Severe degradation occurs at the cement–brine interface with large φ values. This alteration increases effective permeability by orders of magnitude for fluids that preferentially flow through the degraded zone. The significant porosity decrease in the calcite zone also leads to orders of magnitude decrease in effective permeability, where fluids flow through the low-permeability calcite zone. The developed reactive transport model provides a valuable tool to link cement–CO{sub 2} reactions with the evolution of porosity and permeability. It can be used to quantify and predict long-term wellbore cement behavior and can facilitate the risk assessment associated with geological CO{sub 2} sequestration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Perspectives on Machine Learning-assisted Plasma Medicine: Towards Automated Plasma Treatment

Cold atmospheric plasmas (CAPs) have shown great promise for medical applications through their synergistic chemical, electrical, and thermal effects, which can induce therapeutic outcomes. However, safe and reproducible plasma treatment of complex biological surfaces poses a major hurdle to the widespread adoption of CAPs for medical applications. Predictive modeling of the mutual interactions between the plasma and biological surfaces and, thus, systematic approaches to quantify and predict plasma treatment outcomes remain largely elusive due to the lack of mechanistic understanding of plasma-surface interactions that can span across vastly different length-and time-scales. In addition, real-time sensing capabilities in biomedical CAP devices are often limited, which can be detrimental to plasma treatment due to the intrinsic plasma and surface variability during the treatment, as well as sensitivity to external perturbations. All of these challenges can make reproducible and effective plasma treatment of biological surfaces difficult to realize, which is further compounded by errors due to human operation of hand-held CAP devices. Machine learning and data-driven approaches can be particularly useful in addressing these challenges in three major ways: (i) data-driven modeling of hard-to-model plasma-surface interactions and plasma treatment outcomes; (ii) learning data analytics for plasma and surface diagnostics in real-time; and (iii) developing predictive controllers that enable reliable and effective CAP treatments. Furthermore, this paper discusses the promise of machine learning to accelerate plasma medicine research in these areas, toward machine learning-assisted and automated CAP treatment of complex biological surfaces.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

RF pulse shape control in the compact linear collider test facility

The Compact Linear Collider (CLIC) is a study for an electron–positron machine aiming at accelerating and colliding particles at the next energy frontier. The CLIC concept is based on the novel two-beam acceleration scheme, where a high-current low-energy drive beam generates RF in series of power extraction and transfer structures accelerating the low-current main beam. To compensate for the transient beam-loading and meet the energy spread specification requirements for the main linac, the RF pulse shape must be carefully optimized. This was recently modelled by varying the drive beam phase switch times in the sub-harmonic buncher so that, when combined, the drive beam modulation translates into the required voltage modulation of the accelerating pulse. In this paper, the control over the RF pulse shape with the phase switches, that is crucial for the success of the developed compensation model, is studied. The results on the experimental verification of this control method are presented and a good agreement with the numerical predictions is demonstrated. Implications for the CLIC beam-loading compensation model are also discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

MMMnet: A Neural Network Surrogate for Real-Time Transport Prediction Based on the Updated Multi-Mode Model

The Multi-Mode Model (MMM) is a physics-based anomalous transport model integrated into TRANSP for predicting electron and ion thermal transport, electron and impurity particle transport, and toroidal and poloidal momentum transport. While MMM provides valuable predictive capabilities, its computational cost, although manageable for standard simulations, is too high for real-time control applications. MMMnet, a neural network-based surrogate model, is developed to address this challenge by significantly reducing computation time while maintaining high accuracy. Trained on TRANSP simulations of DIII-D discharges, MMMnet incorporates an updated version of MMM (9.0.10) with enhanced physics, including isotopic effects, plasma shaping via effective magnetic shear, unified correlation lengths for ion-scale modes, and a new physics-based model for the electromagnetic electron temperature gradient mode. A key advancement is MMMnet’s ability to predict all six transport coefficients, providing a comprehensive representation of plasma transport dynamics. MMMnet achieves a two-order-of-magnitude speed improvement while maintaining strong correlation with MMM diffusivities, making it well-suited for real-time tokamak control and scenario optimization.

DIII-D↗
NASA NTRS2015

Thermal Analysis of Brazing Seal and Sterilizing Technique to Break Contamination Chain for Mars Sample Return

The potential to return Martian samples to Earth for extensive analysis is in great interest of the planetary science community. It is important to make sure the mission would securely contain any microbes that may possibly exist on Mars so that they would not be able to cause any adverse effects on Earth's environment. A brazing sealing and sterilizing technique has been proposed to break the Mars-to-Earth contamination chain. Thermal analysis of the brazing process was conducted for several conceptual designs that apply the technique. Control of the increase of the temperature of the Martian samples is a challenge. The temperature profiles of the Martian samples being sealed in the container were predicted by finite element thermal models. The results show that the sealing and sterilization process can be controlled such that the samples' temperature is maintained below the potentially required level, and that the brazing technique is a feasible approach to break the contamination chain.

Bao, Xiaoqi↗
DOE OSTI2019

Understanding Transient Combustion Phenomena in Low-NO x Gas Turbines

The goal of this work was to understand, quantify, and eventually predict combustion instability during operational transients in low-NO x gas turbine combustors. Transient operation is defined as a mode where one or more key operating parameters, including equivalence ratio, fuel composition, or fuel splitting between nozzles in a combustor, are varying in time. The timescale over which this variation occurs will be referred to as the transient timescale, the magnitude of the change in the transient parameter will be referred to as the transient amplitude, and the direction of the variation will be referred to as the transient direction. Transients occur regularly in power generation gas turbine operation. For example, load-following gas turbines vary load to meet power demands while accounting for variation in unsteady grid contributors like wind and solar power. Operators achieve this by varying equivalence ratio and fuel splitting inside the combustors to produce more or less power. Another example of transient operation is during fuel-switching operations, a particular issue for turbines operating with syngas fuels in a co-generation facility. In this case, the composition of the fuel stream may vary in time either as a result of fluctuations in the pipeline, or in the case of a co-generation plant, variations in feedstock or gasification processes. This fluctuation in fuel composition can have significant implications for combustion processes, particularly with high-hydrogen fuels. A small number of studies have investigated transient behavior in gas turbine combustors, particularly in the context of flame blow-off; here, investigators have typically focused on issues of transient amplitude, though without the depth and physics-based experimental design that is proposed in this program. To our knowledge, there have been no studies that have quantified the impact of the transient timescale on combustion. The work helped us understand the impact of several types of transients on combustion stability. Both static and dynamic combustion stability will be considered. The transients will be quantified using their amplitude, direction, and timescale, and we will consider transients in equivalence ratio, fuel composition, and fuel splitting. There were two main objectives of this work: 1. Provide a fundamental understanding of combustion stability during transient operation using data obtained from a number of high-speed diagnostic techniques on experimental configurations that capture important gas turbine combustor features and operating conditions. This goal was met in conjunction with our industrial partner, GE Global Research, with whom we worked closely throughout the program. 2. Develop a stability prediction framework that will allow for the prediction, and possible control, of instabilities as a function of the transient amplitude, direction, and timescale. This input/output model should be physics-based and allow for screening of operating conditions and prediction of unstable behavior at the end of a transient by monitoring combustor health and precursor signals during operation.

03 NATURAL GAS↗
NASA NTRS1947

Comparison of Wind-Tunnel Predictions with Flight Measurements of the Longitudinal-Stability and -Control Characteristics of a Douglas BTD-1 Airplane

Low Mach number longitudinal-stability and control characteristics as predicted by use of wind tunnel data from a powered 3/16-scale model are compared with flight test measurements of a Navy BTD-1 airplane. The accuracy of the wind tunnel data and the discrepancies involved in attempting to correlate with flight data are discussed and analyzed. The comparison showed that wind tunnel predictions were, in general, in good agreement with flight test data. The predicted values were for the most part sufficiently accurate to show the satisfactory and unsatisfactory characteristics in the preliminary design stage and to indicate possible methods of improvement. The discrepancies which did occur were attributed principally to physical dissimilarities between model and airplane and the instability to determine accurately the flight power conditions. The effect of Mach number was considered negligible since the maximum flight test value was about 0.5. In order to simulate more closely the flight conditions and hence obtain more accurate data for predictions, it appears desirable to perform large-scale tests of unorthodox control surfaces such as the sealed vaned elevators with which the airplane was equipped.

Bunnell, Mort V.↗
DOE OSTI2025

Hydraulic constraints to stomatal conductance in flooded trees

Stomatal closure is a pervasive response among trees exposed to flooded soil. We tested whether this response is caused by reduced hydraulic conductance in the soil-to-leaf hydraulic continuum (k total ), and particularly by reduced root hydraulic conductance (k root ), which has been widely hypothesized. We tracked stomatal conductance at the leaf level (g s ) and canopy scale (G s ) along with physiological conditions in two temperate tree species, Magnolia grandiflora and Quercus virginiana, that were subjected to flood and control conditions in a greenhouse experiment. Flooding reduced g s , G s , k root and k total . Path analysis showed strong support for direct effects of k total on g s and for flood duration on k total , but not k root on k total . A process-based model that accounted for the k total reduction predicted the timeseries of G s in flood and control treatment trees reasonably well (predicted versus observed G s R 2 = 0.80 and 0.51 for M. grandiflora and Q. virginiana, respectively). However, accounting only for k root reduction in flooded trees was insufficient for predicting observed G s reduction. Together, these results suggest that hydraulic constraints were not limited to roots and highlight the need to account for flooding effects on k total when projecting forest ecosystem function using process-based models.

Plant stress↗
DOE OSTI2021

Reservoir Computing as a Tool for Climate Predictability Studies

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate models. In this context, the linear inverse modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that reservoir computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea surface temperature in the North Atlantic in the preindustrial control simulation of a popular earth system model, the Community Earth System Model so that we can compare the performance of the new RC-based approach with the traditional LIM approach both when learning data are plentiful and when such data are more limited. The improved predictive skill of the RC approach over a wide range of conditions—larger number of retained EOF coefficients, extending well into the limited data regime, etc.—suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator—the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory—is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

Success Stories in Control: Nonlinear Dynamic Inversion Control

NASA plays an important role in advancing the state of the art in flight control systems. In the case of Nonlinear Dynamic Inversion (NDI) NASA supported initial implementation of the theory in an aircraft and demonstration in a space vehicle. Dr. Dale Enns of Honeywell Aerospace Advanced Technology performed this work in cooperation with NASA and under NASA contract. Honeywell and Lockheed Martin were subsequently contracted by AFRL to create "Design Guidelines for Multivariable Control Theory". This foundational work directly contributed to the advancement of the technology and the credibility of the control law as a design option. As a result Honeywell collaborated with Lockheed Martin to produce a Nonlinear Dynamic Inversion controller for the X-35 and subsequently Lockheed Martin did the same for the production Lockheed Martin F-35 vehicle. The theory behind NDI is to use a systematic generalized approach to controlling a vehicle. Using general aircraft nonlinear equations of motion and onboard aerodynamic, mass properties, and engine models specific to the vehicle, a relationship between control effectors and desired aircraft motion can be formulated. Using this formulation a control combination is used that provides a predictable response to commanded motion. Control loops around this formulation shape the response as desired and provide robustness to modeling errors. Once the control law is designed it can be used on a similar class of vehicle with only an update to the vehicle specific onboard models.

Bosworth, John T.↗
NASA NTRS2021

Designing a Decontamination Solution for the Low-Earth-Orbit, Cryogenic SPHEREx Mission

To address the scientific goals of NASA’s astrophysics division, the JPL and Caltech’s SPHEREx mission conducts the first near-infrared all-sky spectral survey in low-earth-orbit using a passively-cooled, cryogenic telescope. Several unique water contamination issues arise due to the combination of cryogenic operation temperatures and the limited temperature control authority of passive cooling. Because water molecules have adsorption bands within the wavelengths of interest for SPHEREx’s survey, it is imperative that water contamination be controlled and minimized. In this work, a model is developed for the SPHEREx mission to predict the transport and accumulation of outgassed water onto sensitive payload components. The model utilizes the time dependent thermal profiles of the individual payload components, initial water content, and spacecraft geometry to calculate the time dependent water diffusion, transport, and adsorption. This model is subsequently used to predict water contamination risks and design decontamination solutions for each risk. Two major water contamination risks were predicted, water accumulation during cooldown and on cryogenic surfaces during the mission. The first risk occurs during the cooldown of the payload to cryogenic temperatures, predicting water accumulation on optical surfaces in excess of the allowable levels. To mitigate this accumulation, the temperature of the optical surfaces is controlled during cooldown through a combination of heaters and spacecraft pointing. The second risk is that over the course of the mission, is unavoidable water accumulation onto the payload thermal system’s cryogenic surfaces, possibly jeopardizing thermal performance and temperature stability, both of which are required for science success. To decontaminate any water that had collected onto cryogenic thermal system surfaces, a decontamination maneuver was designed. In this decontamination maneuver, cryogenic surfaces of the thermal system are warmed to a temperature where water will desorb by spacecraft pointing while still meeting all avoidance constraints, and thereby avoiding undue risk to the observatory hardware. Through application of the developed analytical model and inclusion of the decontamination maneuver in the mission design, SPHEREx can confidently demonstrate that it is able to decontaminate, at the start of and during the mission as needed to meet its end of life science performance requirements.

Soares, Carolos E.↗
DOE OSTI2017

Reassessing the roles of PIN proteins and anticlinal microtubules during pavement cell morphogenesis

The leaf epidermis is a biomechanical shell that influences the size and shape of the organ. Its morphogenesis is a multiscale process in which nanometer-scale cytoskeletal protein complexes, individual cells, and groups of cells pattern growth and define macroscopic leaf traits. Interdigitated growth of neighboring cells is an evolutionarily conserved developmental strategy. Understanding how signaling pathways and cytoskeletal proteins pattern cell walls during this form of tissue morphogenesis is an important research challenge. The cellular and molecular control of a lobed cell morphology is currently thought to involve PIN-FORMED (PIN)-type plasma membrane efflux carriers that generate subcellular auxin gradients. Auxin gradients were proposed to function across cell boundaries to encode stable offset patterns of cortical microtubules and actin filaments between adjacent cells. Many models suggest that long-lived microtubules along the anticlinal cell wall generate local cell wall heterogeneities that restrict local growth and specify the timing and location of lobe formation. Here we used Arabidopsis reverse genetics and multivariate long-term time-lapse imaging to test current cell shape control models. We found that neither PIN proteins nor microtubules along the anticlinal wall predict the patterns of lobe formation. In fields of lobing cells, anticlinal microtubules are not correlated with cell shape and are unstable at the time scales of cell expansion. Our analyses indicate that anticlinal microtubules have multiple functions in pavement cells, and that lobe initiation is likely controlled by complex interactions among cell geometry, cell wall stress patterns, and transient microtubule networks that span the anticlinal and periclinal walls.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2019

Input Constrained M-MRAC for Multirotors Operating in an Urban Environment

The paper presents a modified model reference adaptive control (M-MRAC) for multi-input multi-output nonlinear dynamical systems with time varying parametric uncertainties and bounded external disturbances. It uses a prediction model to rapidly generate adaptive estimates of the system's uncertainties with adjustable errors that converge to a small neighborhood of the origin. A sufficient condition is derived to specify the region of attraction in the space of initialization errors, design parameters and external commends. The approach is applied to thrust controlled multi-rotor air vehicles operating in an urban environment. It is shown that the designed controller can provide a good tracking of a given trajectory in the unknown urban wind field, assuming that the maximum thrust generated by the rotors is known. The performance of the algorithms are demonstrated in simulations.

Stepanyan, Vahram↗
DOE OSTI2015

Evaluation and comparison of benchmark QSAR models to predict a relevant REACH endpoint: The bioconcentration factor (BCF)

The bioconcentration factor (BCF) is an important bioaccumulation hazard assessment metric in many regulatory contexts. Its assessment is required by the REACH regulation (Registration, Evaluation, Authorization and Restriction of Chemicals) and by CLP (Classification, Labeling and Packaging). We challenged nine well-known and widely used BCF QSAR models against 851 compounds stored in an ad-hoc created database. The goodness of the regression analysis was assessed by considering the determination coefficient (R{sup 2}) and the Root Mean Square Error (RMSE); Cooper's statistics and Matthew's Correlation Coefficient (MCC) were calculated for all the thresholds relevant for regulatory purposes (i.e. 100 L/kg for Chemical Safety Assessment; 500 L/kg for Classification and Labeling; 2000 and 5000 L/kg for Persistent, Bioaccumulative and Toxic (PBT) and very Persistent, very Bioaccumulative (vPvB) assessment) to assess the classification, with particular attention to the models' ability to control the occurrence of false negatives. As a first step, statistical analysis was performed for the predictions of the entire dataset; R{sup 2}>0.70 was obtained using CORAL, T.E.S.T. and EPISuite Arnot–Gobas models. As classifiers, ACD and log P-based equations were the best in terms of sensitivity, ranging from 0.75 to 0.94. External compound predictions were carried out for the models that had their own training sets. CORAL model returned the best performance (R{sup 2}{sub ext}=0.59), followed by the EPISuite Meylan model (R{sup 2}{sub ext}=0.58). The latter gave also the highest sensitivity on external compounds with values from 0.55 to 0.85, depending on the thresholds. Statistics were also compiled for compounds falling into the models Applicability Domain (AD), giving better performances. In this respect, VEGA CAESAR was the best model in terms of regression (R{sup 2}=0.94) and classification (average sensitivity>0.80). This model also showed the best regression (R{sup 2}=0.85) and sensitivity (average>0.70) for new compounds in the AD but not present in the training set. However, no single optimal model exists and, thus, it would be wise a case-by-case assessment. Yet, integrating the wealth of information from multiple models remains the winner approach. - Highlights: • REACH encourages the use of in silico methods in the assessment of chemicals safety. • The performances of nine BCF models were evaluated on a benchmark database of 851 chemicals. • We compared the models on the basis of both regression and classification performance. • Statistics on chemicals out of the training set and/or within the applicability domain were compiled. • The results show that QSAR models are useful as weight-of-evidence in support to other methods.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Analysis of a VTOL hover task with predictor displays using an optimal control model of the human operator

The influence of different types of predictor displays in a longitudinal VTOL hover task is analyzed in a theoretical study. It was assumed that pitch angle and position are presented to the pilot in separate displays namely the artificial horizon and position display. The predictive information is calculated by means of a Taylor series. From earlier experimental studies it is well known that predictor displays improve human and system performance and result in reducing human workload. In this study, an optimal control model is used to prove this effect theoretically. Several cases with differing amounts of predictive and rate information are compared.

Johannsen, G.↗
NASA NTRS1988

Space communication link propagation data for selected cities within the multiple beam and steerable antenna coverage areas of the advanced communications technology satellite

Rain attenuation propagation data for 68 cities within the coverage area of the multiple beam and steerable antennas of the Advanced Communications Technology Satellite (ACTS) are presented. These data provide the necessary data base for purposes of communication link power budgeting and rain attenuation mitigation controller design. These propagation parameters are derived by applying the ACTS Rain Attenuation Prediction Model to these 68 locations. The propagation parameters enumerated in tabular form for each location are as follows: (1) physical description of the link and location (e.g., latitude, longitude, antenna elevation angle, etc.), link availability versus attenuation margin (also in graphical form), fading time across fade depths of 3, 5, 8, and 15 dB versus fade duration, and required fade control response time for controller availabilities of 99.999, 99.99, 99.9, and 99 percent versus sub-threshold attenuation levels. The data for these specific locations can be taken to be representative of regions near these locations.

Manning, Robert M.↗
NASA NTRS2014

Development and Implementation of a Hardware In-the-Loop Test Bed for Unmanned Aerial Vehicle Control Algorithms

Successful prediction and management of battery life using prognostic algorithms through ground and flight tests is important for performance evaluation of electrical systems. This paper details the design of test beds suitable for replicating loading profiles that would be encountered in deployed electrical systems. The test bed data will be used to develop and validate prognostic algorithms for predicting battery discharge time and battery failure time. Online battery prognostic algorithms will enable health management strategies. The platform used for algorithm demonstration is the EDGE 540T electric unmanned aerial vehicle (UAV). The fully designed test beds developed and detailed in this paper can be used to conduct battery life tests by controlling current and recording voltage and temperature to develop a model that makes a prediction of end-of-charge and end-of-life of the system based on rapid state of health (SOH) assessment.

Battery Testbed↗
DOE OSTI2015

Carbon nanotube biconvex microcavities

Developing highly efficient microcavities with predictive narrow-band resonance frequencies using the least amount of material will allow the applications in nonlinear photonic devices. We have developed a microcavity array that comprised multi-walled carbon nanotubes (MWCNT) organized in a biconvex pattern. The finite element model allowed designing microcavity arrays with predictive transmission properties and assessing the effects of the microarray geometry. The microcavity array demonstrated negative index and produced high Q factors. 2–3 μm tall MWCNTs were patterned as biconvex microcavities, which were separated by 10 μm in an array. The microcavity was iridescent and had optical control over the diffracted elliptical patterns with a far-field pattern, whose properties were predicted by the model. It is anticipated that the MWCNT biconvex microcavities will have implications for the development of highly efficient lenses, metamaterial antennas, and photonic circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗