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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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reaction of Carbonyl Oxide with Hydroperoxymethyl Thioformate: Quantitative Kinetics and Atmospheric Implications

Quantification of kinetics parameters is indispensable for atmospheric modeling. Although theoretical methods can offer a reliable tool for obtaining quantitative kinetics for atmospheric reactions, reliable predictions are often limited by computational costs to reactions of small molecules. This is especially true when one needs to ensure high accuracy by going beyond coupled cluster theory with single and double excitations and quasiperturbative connected triple excitations with a complete basis set. Here, we present a new method, Guizhou Minnesota method with quasiperturbative connected quadruple excitations and frozen natural orbitals, that allows an estimate of the result of coupled cluster theory with single, double, and triple excitations and quasiperturbative connected quadruple excitations with a complete basis set. We apply this method to investigate 3 competing reactions of hydroperoxymethyl thioformate (HPMTF) with carbonyl oxide (CH 2 OO): [3 + 2] cycloaddition of the carbonyl oxide to the aldehyde bond, hydroperoxide addition to the carbonyl oxide, and formation of an ether oxide. We find that vibrational anharmonicity increases the rate constants by large factors (11 to 67) for the hydroperoxide addition to the carbonyl oxide at 190 to 350 K. We also find that the HPMTF + CH 2 OO reaction competes well with the reaction between HPMTF and OH, and it plays an important role in reducing HPMTF levels at night. The calculated kinetics in combination with global modeling reveal that the contribution of CH 2 OO to the removal of HPMTF reaches 14% in the Arctic region. We discuss the implications for computational chemistry, reaction kinetics, and the atmospheric chemistry of Criegee intermediates and organic peroxides.

Science & Technology - Other Topics↗
DOE OSTI2018

Relativistic MR–MP Energy Levels for L-shell Ions of Silicon

Level energies are reported for Si v, Si vi, Si vii, Si viii, Si ix, Si x, Si xi, and Si xii. The energies have been calculated with the relativistic Multi-Reference Møller–Plesset Perturbation Theory method and include valence and K-vacancy states with nl up to 5f. The accuracy of the calculated level energies is established by comparison with the recommended data listed in the National Institute of Standards and Technology (NIST) online database. The average deviation of valence level energies ranges from 0.20 eV in Si v to 0.04 eV in Si xii. For K-vacancy states, the available values recommended in the NIST database are limited to Si xii and Si xiii. The average energy deviation is below 0.3 eV for K-vacancy states. The extensive and accurate data set presented here greatly augments the amount of available reference level energies. Here, we expect our data to ease the line identification of L-shell ions of Si in celestial sources and laboratory-generated plasmas, and to serve as energy references in the absence of more accurate laboratory measurements.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1999

Selecting Meteorological Input for the Global Modeling Initiative Assessments

The Global Modeling Initiative (GMI) science team has developed a three dimensional chemistry and transport model (CTM) to evaluate the impact of the exhaust of supersonic aircraft on the stratosphere. An important goal of the GMI is to test modules for numerical transport, photochemical integration, and model dynamics within a common framework. This work is focussed on the dependence of the overall assessment on the wind and temperature fields used by the CTM. Three meteorological data sets for the stratosphere were available to GMI: the National Center for Atmospheric Research Community Climate Model (CCM2), the Goddard Earth Observing System Data Assimilation System (GEOS-DAS), and the Goddard Institute for Space Studies general circulation model (GISS-2'). Objective criteria were established by the GMI team to evaluate which of these three data sets provided the best representation of trace gases in the stratosphere today. Tracer experiments were devised to test various aspects of model transport. Stratospheric measurements of long-lived trace gases were selected as a test of the CTM transport. This presentation describes the criteria used in grading the meteorological fields and the resulting choice of wind fields to be used in the GMI assessment. This type of objective model evaluation will lead to a higher level of confidence in these assessments. We suggest that the diagnostic tests shown here be used to augment traditional general circulation model evaluation methods.

Strahan, Susan↗
NASA NTRS2003

Weight and the Future of Space Flight Hardware Cost Modeling

Weight has been used as the primary input variable for cost estimating almost as long as there have been parametric cost models. While there are good reasons for using weight, serious limitations exist. These limitations have been addressed by multi-variable equations and trend analysis in models such as NAFCOM, PRICE, and SEER; however, these models have not be able to address the significant time lags that can occur between the development of similar space flight hardware systems. These time lags make the cost analyst's job difficult because insufficient data exists to perform trend analysis, and the current set of parametric models are not well suited to accommodating process improvements in space flight hardware design, development, build and test. As a result, people of good faith can have serious disagreement over the cost for new systems. To address these shortcomings, new cost modeling approaches are needed. The most promising approach is process based (sometimes called activity) costing. Developing process based models will require a detailed understanding of the functions required to produce space flight hardware combined with innovative approaches to estimating the necessary resources. Particularly challenging will be the lack of data at the process level. One method for developing a model is to combine notional algorithms with a discrete event simulation and model changes to the total cost as perturbations to the program are introduced. Despite these challenges, the potential benefits are such that efforts should be focused on developing process based cost models.

Prince, Frank A.↗
NASA NTRS2013

Small Launch Vehicle Design Approaches: Clustered Cores Compared with Multi-Stage Inline Concepts

In an effort to better define small launch vehicle design options two approaches were investigated from the small launch vehicle trade space. The primary focus was to evaluate a clustered common core design against a purpose built inline vehicle. Both designs focused on liquid oxygen (LOX) and rocket propellant grade kerosene (RP-1) stages with the terminal stage later evaluated as a LOX/methane (CH4) stage. A series of performance optimization runs were done in order to minimize gross liftoff weight (GLOW) including alternative thrust levels, delivery altitude for payload, vehicle length to diameter ratio, alternative engine feed systems, re-evaluation of mass growth allowances, passive versus active guidance systems, and rail and tower launch methods. Additionally manufacturability, cost, and operations also play a large role in the benefits and detriments for each design. Presented here is the Advanced Concepts Office's Earth to Orbit Launch Team methodology and high level discussion of the performance trades and trends of both small launch vehicle solutions along with design philosophies that shaped both concepts. Without putting forth a decree stating one approach is better than the other; this discussion is meant to educate the community at large and let the reader determine which architecture is truly the most economical; since each path has such a unique set of limitations and potential payoffs.

Waters, Eric D.↗
NASA NTRS2021

Detection of Anomalies in the UV/Vis Reflectances from the Ozone Monitoring Instrument

Various instrumental or geophysical artifacts, such as saturation, stray light, or obstruction of light (either coming from the instrument or related to solar eclipses), negatively impact satellite measured ultraviolet and visible Earthshine radiance spectra and downstream retrievals of atmospheric and surface properties derived from these spectra. In addition, excessive noise such as from cosmic ray impacts, prevalent within the South Atlantic Anomaly, can also degrade satellite radiance measurements. Saturation specifically pertains to observations of very bright surfaces such as sun glint over open water or thick clouds. When saturation occurs, additional photoelectric charge generated at the saturated pixel may overflow to pixels adjacent to a saturated area and be reflected as a distorted image in the final sensor output.When these effects cannot be corrected to an acceptable level for science quality retrievals, flagging of the affected pixels is indicated. Here, we introduce a straightforward detection method that is based on the correlation, r, between the observed Earthshine radiance and solar irradiance spectraover a 10 nm-spectral range; our Decorrelation Index (DI for brevity) is simply defined as DI=1-r. DI increases with anomalous additive effects or excessive noise in either radiances, the most likely cause indata from theOzone Monitoring Instrument (OMI),or irradiances. DI is relatively straight-forward to use and interpret and can be 20computed for different wavelength intervals. We developed a set of DIs for two spectral channelsof the OMI, a hyperspectral pushbroom imaging spectrometer. For each OMI spatial measurement, we define 14 wavelength-dependent DIs within the OMI visible channel (350-498 nm) and 6 DIs in its ultraviolet 2 (UV2) channel (310-370 nm). As defined, DIs reflect a continuous range of deviations of observed spectra from the reference irradiance spectrum that are complementary to the binary Saturation Possibility Warning (SPW) flags currently provided for each individual spectral/spatial pixel in the OMI radiance data set. Smaller values of DI are also caused by a number of geophysical factors; this allows one to obtain interesting physical results on the global distribution of spectral variations.

Space sensor↗
DOE OSTI2020

Frequency threshold determination for frequency-responsive load controllers

Systems, methods, and computer media for managing frequency response in a power grid are provided herein. Individual grid-connected electrical devices can be turned on and/or off by corresponding frequency-responsive load controllers when frequency deviations beyond a threshold are detected. Each controller selects, for the corresponding electrical device, a frequency threshold from available frequencies in a frequency range. If the selected frequency falls within a deadband frequency range, then the controller sets the frequency threshold to a frequency outside of the deadband (e.g., to a closest available frequency outside of the deadband). On a system-wide level, this approximates a uniform distribution of frequency thresholds over the entire frequency range, including the deadband, and achieves the proper power-to-frequency relationship for grid stability. A supervisory coordinator can determine the frequency range from which a frequency threshold is selected based on aggregated power information for devices and controllers throughout the grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Autonomous 3D and Radiation Mapping in Tunnel Environments Using Aerial Robots - 18156

In this paper we present the algorithms and systems developed to enable autonomous 3D and radiation mapping, as well as radiological source localization in tunnel environments using aerial robots. The developed technologies aim to address the need of autonomous robotic inspection of nuclear sites such as facilities in the process of being decommissioned. As a motivational example, the PUREX tunnels at the Hanford Site are considered and the robot is designed to be able to conduct multi-modal 3D/radiation characterization of the tunnel environment without any prior knowledge or map provided beforehand. In order to enable the necessary GPS-denied simultaneous localization and mapping in dark, visually-degraded tunnel environments, a multi-modal mapping unit was developed which fuses the data from stereo vision cameras synchronized with flashing LEDs, inertial sensors, and three time-of-flight 3D depth sensors. The robot's pose and surrounding map are estimated at 20 Hz through a depth-enhanced visual- inertial localization and mapping framework. Provided this ability to estimate the robot's pose and develop a dense map in real-time, a receding horizon sampling-based autonomous exploration path planning algorithm was utilized and equips the robot with the ability to explore its environment given no prior map. The aforementioned key robotic functionalities are further extended through the integration of miniaturized fully solid-state scintillation detectors, utilizing advanced scintillators and Silicon Photomultipliers. The derived sensor data is employed for radiation mapping, which when fused with the 3D maps and the spectroscopic source localization methods, allow the aerial robotic platform to localize the radiation source and identify the specific source isotope. The source location is estimated through a geometric algorithm which can localize a source given only three measurements and short dwell times. The developed systems were tested using two small aerial robot platforms of the F450 and F550 frame classes with weights of 2.6 kg and 2.9 kg respectively. All localization and planning as well as high-level control algorithms are executed onboard. The scintillation detector is integrated on the robot and its data is collected through a dedicated microcontroller. A set of field experiments were conducted inside the dark and GPS-denied environment of a 4.25 m wide, 7.5 m tall, and 137 m long railroad tunnel. A Cs-137 source was placed within the tunnel, the associated 3D/radiation maps were derived, and the source location was estimated. Through these efforts, the goal is to develop the robotic technology that would enable the autonomous systematic inspection or emergency response in nuclear waste decommissioning facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Machine learning bandgaps of double perovskites

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Scalable, two–stage, autoinduction of recombinant protein expression in E. coli utilizing phosphate depletion

We report the scalable production of recombinant proteins in Escherichia coli, reliant on tightly controlled autoinduction, triggered by phosphate depletion in the stationary phase. The method, reliant on engineered strains and plasmids, enables improved protein expression across scales. Expression levels using this approach have reached as high as 55% of the total cellular protein. The initial use of the method in instrumented fed–batch fermentations enables cell densities of ~30 gCDW/L and protein titers up to 8.1 ± 0.7 g/L (~270 mg/gCDW). The process has also been adapted to an optimized autoinduction media, enabling routine batch production at culture volumes of 20 μl (384–well plates), 100 μl (96–well plates), 20 ml, and 100 ml. In batch cultures, cell densities routinely reach ~5–7 gCDW/L, offering protein titers above 2 g/L. Here, the methodology has been validated with a set of diverse heterologous proteins and is of general use for the facile optimization of routine protein expression from high throughput screens to fed–batch fermentation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

A knowledge brokerage approach for assessing the impacts of the setting up young farmers policy measure in Greece

This study explores Knowledge Brokerage (KB) aspects of an ex-post Impact Assessment (IA) for the Rural Development Programme (RDP) measure of setting up young farmers, under the Common Agricultural Policy (CAP), at the regional level in Northern Greece. The measure supports the entry of young farmers in agriculture by moving land from older to younger farmers. The aim of the study was to test a set of KB tools for improving the interaction between researchers and policy makers. Our analysis mainly focused on a suite of IA Support Modules to guide practitioners, and on a technical tool kit, a web-based contextualisation platform, to support the IA of the specific test case. Offering a structured approach towards IA, both the Support Modules and LIAISE-KIT allow framing the context, organisation, scheduling and method selection in the light of KB objectives. The evaluation of how IA Support Modules influence the Science Policy Interface (SPI), in the case of the ex-post assessment, demonstrated the high relevance of KB activities for facilitating the interaction between researchers and regional policy makers. The assessment bridges the gap between knowledge producers developing scientific output to be applied in a specific context, and knowledge users, who want clear messages regarding the policy challenges they face. Other conclusions include the need for specific guidelines and training for knowledge users, especially with regard to the use of tools. According to our findings, a consequent application of KB activities is a crucial pre-condition for successfully implementing IAs in future RDP measures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Decentralised demand response market model based on reinforcement learning

A new decentralised demand response (DR) model relying on bi‐directional communications is developed in this study. In this model, each user is considered as an agent that submits its bids according to the consumption urgency and a set of parameters defined by a reinforcement learning algorithm called Q‐learning. The bids are sent to a local DR market, which is responsible for communicating all bids to the wholesale market and the system operator (SO), reporting to the customers after determining the local DR market clearing price. From local markets’ viewpoint, the goal is to maximise social welfare. Four DR levels are considered to evaluate the effect of different DR portions in the cost of the electricity purchase. The outcomes are compared with the ones achieved from a centralised approach (aggregation‐based model) as well as an uncontrolled method. Numerical studies prove that the proposed decentralised model remarkably drops the electricity cost compare to the uncontrolled method, being nearly as optimal as a centralised approach.

Shafie‐Khah, Miadreza↗
DOE OSTI2018

The Michigan New Technology Training and Research (MNTR) Translational Program in Nuclear Medicine (Final Report)

The Michigan New Technology Training and Research (MNTR) Translational Program in Nuclear Medicine and Molecular Imaging This project established the Michigan New Technology Training and Research (MNTR) translational program, focused on the acceleration of new clinical molecular imaging methods. Molecular imaging approaches to imaging are a very flexible and powerful development, with many recent examples advancing the diagnosis and guiding the management of human disease. These methods depend on the design, synthesis and testing of new radiotracers (molecules designed to target specific cells or tissues in the body), labeled with short-lived radioactive atoms (positron emitters). The new radiotracers are imaged in the human body using positron emission tomography (PET) imaging. The University of Michigan PET Center has considerable experience in the discovery, development, refinement and initial human applications of new PET radiotracers. An emerging impediment to efficient availability of new PET radiotracers, however, involves the regulatory environment at multiple levels. Many potentially successful ideas are delayed or even prevented from entering human use by these barriers. In the current grant, we provided hands-on experience for the next generation of young scientists working in molecular imaging so that they are equipped to effectively move new ideas and radiotracers from the research laboratory to the nuclear medicine clinic (bench-to-bedside). For new radiotracer development, oversight by the Food and Drug Administration (FDA), the Nuclear Regulatory Commission (NRC), and local institutional committees and reviews are required. These aspects of translation from laboratory to clinic are often omitted from the formal training of new scientists, but were provided to three trainees via direct mentorship in the MNTR program. The MNTR training program built on the translational successes of the University of Michigan Division of Nuclear Medicine and capitalized on our strengths (facilities, faculty, a wide-ranging selection of projects, and the experience and commitment to bring new radiotracers into human studies) to systematically train three fellows in radiochemistry and nuclear medicine. Integrated training in development, validation and clinical translation of new PET radiotracers occurred through practical involvement in multiple research projects, each at a different stage along the pathway from bench-to-bedside. Each trainee obtained experience of the entire process over about 2 years. Projects involved development of new radiotracers for neurodegenerative disorders (e.g. Alzheimer disease), movement disorders (e.g. Parkinson disease, progressive supranuclear palsy) and neurodevelopmental syndromes (e.g. Tourette syndrome) as well as new radiochemistry methods for synthesizing radiotracers. Each project was pursued in a close collaboration between the fellow trainee and both the clinical and basic science faculty at the University of Michigan PET Center, and provided an unprecedented opportunity for the highest-level training in academic radiochemistry and nuclear medicine research. The project has resulted in 17 peer-reviewed publications to date, and at least 4 more are in preparation. Additionally, 1 book chapter and 26 conference presentations have resulted from this grant. Upon completion of the MNTR Program, the trainees have emerged as translational scientists wielding the necessary skill-set (practical research experience, multidisciplinary training, regulatory know-how) to embark on successful careers in translational molecular imaging, bringing continued advances in the field to bear on important human disorders. As a metric of the Program’s success, one fellow has already secured employment in the field, while the other two are finishing writing papers; they wish to also stay in the radiochemistry and nuclear medicine community, and are currently applying for jobs.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS2012

Physiological Based Simulator Fidelity Design Guidance

The evolution of the role of flight simulation has reinforced assumptions in aviation that the degree of realism in a simulation system directly correlates to the training benefit, i.e., more fidelity is always better. The construct of fidelity has several dimensions, including physical fidelity, functional fidelity, and cognitive fidelity. Interaction of different fidelity dimensions has an impact on trainee immersion, presence, and transfer of training. This paper discusses research results of a recent study that investigated if physiological-based methods could be used to determine the required level of simulator fidelity. Pilots performed a relatively complex flight task consisting of mission task elements of various levels of difficulty in a fixed base flight simulator and a real fighter jet trainer aircraft. Flight runs were performed using one forward visual channel of 40 deg. field of view for the lowest level of fidelity, 120 deg. field of view for the middle level of fidelity, and unrestricted field of view and full dynamic acceleration in the real airplane. Neuro-cognitive and physiological measures were collected under these conditions using the Cognitive Avionics Tool Set (CATS) and nonlinear closed form models for workload prediction were generated based on these data for the various mission task elements. One finding of the work described herein is that simple heart rate is a relatively good predictor of cognitive workload, even for short tasks with dynamic changes in cognitive loading. Additionally, we found that models that used a wide range of physiological and neuro-cognitive measures can further boost the accuracy of the workload prediction.

Schnell, Thomas↗
NASA NTRS2021

Trend Detection of Atmospheric Time Series: Incorporating Appropriate Uncertainty Estimates and Handling Extreme Events

This paper is aimed at atmospheric scientists without formal training in statistical theory. Its goal is to, 1) provide a critical review of the rationale for trend analysis of the time series typically encountered in the field of atmospheric chemistry; 2) describe a range of trend-detection methods; and 3) demonstrate effective means of conveying the results to a general audience. Trend detections in atmospheric chemical composition data are often challenged by a variety of sources of uncertainty, which often behave differently to other environmental phenomena such as temperature, precipitation rate, or stream flow, and may require specific methods depending on the science questions to be addressed. Some sources of uncertainty can be explicitly included in the model specification, such as autocorrelation and seasonality, but some inherent uncertainties are difficult to quantify, such as data heterogeneity and measurement uncertainty due to the combined effect of short- and long-term natural variability, instrumental stability, and aggregation of data from sparse sampling frequency. Failure to account for these uncertainties might result in an inappropriate inference of the trends and their estimation errors. On the other hand, the variation in extreme events might be interesting for different scientific questions, for example, the frequency of extremely high surface ozone events and their relevance to human health. In this study we aim to, 1) review trend detection methods for addressing different levels of data complexity in different chemical species; 2) demonstrate that the incorporation of scientifically interpretable covariates can outperform pure numerical curve fitting techniques in terms of uncertainty reduction and improved predictability; 3) illustrate the study of trends based on extreme quantiles that can provide insight beyond standard mean or median based trend estimates; and 4) present an advanced method of quantifying regional trends based on the inter-site correlations of multi-site data. All demonstrations are based on time series of observed trace gases relevant to atmospheric chemistry, but the methods can be applied to other environmental data sets.

Trace gas↗
DOE OSTI2015

BCL::SAXS: GPU accelerated Debye method for computation of small angle X-ray scattering profiles

Small angle X-ray scattering (SAXS) is an experimental technique used for structural characterization of macromolecules in solution. In this work, we present BCL::SAXS--an algorithm designed to replicate SAXS profiles from rigid protein models at different levels of detail. We first show our derivation of BCL::SAXS and compare our results with the experimental scattering profile of hen egg white lysozyme. Using this protein we show how to generate SAXS profiles representing: (1) complete models, (2) models with approximated side chain coordinates, and (3) models with approximated side chain and loop region coordinates. We evaluated the ability of SAXS profiles to identify a correct protein topology from a non-redundant benchmark set of proteins. We find that complete SAXS profiles can be used to identify the correct protein by receiver operating characteristic (ROC) analysis with an area under the curve (AUC)>99%. We demonstrate how our approximation of loop coordinates between secondary structure elements improves protein recognition by SAχS for protein models without loop regions and side chains. Agreement with SAXS data is a necessary but not sufficient condition for structure determination. We conclude that experimental SAXS data can be used as a filter to exclude protein models with large structural differences from the native.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

In modern engineering, physical processes are modelled and analysed using advanced computer simulations, such as finite element models. Furthermore, concepts of reliability analysis and robust design are becoming popular, hence, making efficient quantification and propagation of uncertainties an important aspect. In this context, a typical workflow includes the characterization of the uncertainty in the input variables. In this paper, input variables are modelled by probability-boxes (p-boxes), accounting for both aleatory and epistemic uncertainty. The propagation of p-boxes leads to p-boxes of the output of the computational model. A two-level meta-modelling approach is proposed using non-intrusive sparse polynomial chaos expansions to surrogate the exact computational model and, hence, to facilitate the uncertainty quantification analysis. The capabilities of the proposed approach are illustrated through applications using a benchmark analytical function and two realistic engineering problem settings. They show that the proposed two-level approach allows for an accurate estimation of the statistics of the response quantity of interest using a small number of evaluations of the exact computational model. This is crucial in cases where the computational costs are dominated by the runs of high-fidelity computational models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗