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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Community Solar Subscription Credit Considerations and Case Study

Together New Orleans (TNO) requested technical assistance through the US Department of Energy's (DOE's) National Community Solar Partnership (NCSP). The National Community Solar Partnership is a coalition of community solar stakeholders working to expand access to affordable community solar to every U.S. household and enable subscribers and their communities to realize meaningful benefits, such as reduced energy burden, increased resilience, community ownership, and equitable workforce development. TNO asked for a subject matter expert from NCSP to review the Entergy New Orleans (ENO) proposed revised Rate Schedule for Community Solar Generating Facilities. TNO requested that the proposed methodology for subscription credits for applicable residential and non-residential rate schedules be reviewed to determine the expected credit rate. The aim behind the analysis is to provide TNO and engaged stakeholders with an informed understanding of the proposed rate schedule before making decisions on the appropriate rate design for community solar (CS) subscriptions. This report is an exploration of CS subscription credit rate calculation considerations using the CS program in New Orleans as a case study. The report provides a framework for modeling CS credit rates in addition to topics that may be helpful to address when undertaking program design or rule making.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗
NASA NTRS1999

Automated Testing Experience of the Linear Aerospike SR-71 Experiment (LASRE) Controller

System controllers must be fail-safe, low cost, flexible to software changes, able to output health and status words, and permit rapid retest qualification. The system controller designed and tested for the aerospike engine program was an attempt to meet these requirements. This paper describes (1) the aerospike controller design, (2) the automated simulation testing techniques, and (3) the real time monitoring data visualization structure. Controller cost was minimized by design of a single-string system that used an off-the-shelf 486 central processing unit (CPU). A linked-list architecture, with states (nodes) defined in a user-friendly state table, accomplished software changes to the controller. Proven to be fail-safe, this system reported the abort cause and automatically reverted to a safe condition for any first failure. A real time simulation and test system automated the software checkout and retest requirements. A program requirement to decode all abort causes in real time during all ground and flight tests assured the safety of flight decisions and the proper execution of mission rules. The design also included health and status words, and provided a real time analysis interpretation for all health and status data.

Larson, Richard R.↗
NASA NTRS1993

Shuttle landing facility cloud cover study: Climatological analysis and two tenths cloud cover rule evaluation

The two-tenths cloud cover rule in effect for all End Of Mission (EOM) STS landings at the Kennedy Space Center (KSC) states: 'for scattered cloud layers below 10,000 feet, cloud cover must be observed to be less than or equal to 0.2 at the de-orbit burn go/no-go decision time (approximately 90 minutes before landing time)'. This rule was designed to protect against a ceiling (below 10,000 feet) developing unexpectedly within the next 90 minutes (i.e., after the de-orbit burn decision and before landing). The Applied Meteorological Unit (AMU) developed and analyzed a database of cloud cover amounts and weather conditions at the Shuttle Landing Facility for a five-year (1986-1990) period. The data indicate the best time to land the shuttle at KSC is during the summer while the worst time is during the winter. The analysis also shows the highest frequency of landing opportunities occurs for the 0100-0600 UTC and 1300-1600 UTC time periods. The worst time of the day to land a shuttle is near sunrise and during the afternoon. An evaluation of the two-tenths cloud cover rule for most data categorizations has shown that there is a significant difference in the proportions of weather violations one and two hours subsequent to initial conditions of 0.2 and 0.3 cloud cover. However, for May, Oct., 700 mb northerly wind category, 1500 UTC category, and 1600 UTC category there is some evidence that the 0.2 cloud cover rule may be overly conservative. This possibility requires further investigation. As a result of these analyses, the AMU developed nomograms to help the Spaceflight Meteorological Group (SMG) and the Cape Canaveral Forecast Facility (CCFF) forecast cloud cover for EOM and Return to Launch Site (RTLS) at KSC. Future work will include updating the two tenths database, further analysis of the data for several categorizations, and developing a proof of concept artificial neural network to provide forecast guidance of weather constraint violations for shuttle landings.

Atchison, Michael K.↗
DOE OSTI2020

Unexpected crystalline homogeneity from the disordered bond network in La(Cr 0.2 Mn 0.2 Fe 0.2 Co 0.2 Ni 0.2 )O 3 films

Designing and understanding functional electronic and magnetic properties in perovskite oxides requires controlling and tuning the underlying crystal lattice. In this work, we report the structure, including oxygen and cation positions, of a single-crystal, entropy stabilized perovskite oxide film of La(Cr 0.2 Mn 0.2 Fe 0.2 Co 0.2 Ni 0.2 )O 3 grown on SrTiO3 (001). The parent materials range from orthorhombic (LaCrO 3 , LaMnO 3 , and LaFeO 3 ) to rhombohedral (LaCoO 3 and LaNiO 3 ), and first principles calculations indicate that these structural motifs are nearly degenerate in energy and should be highly distorted site to site. Despite this extraordinary local configurational disorder on the B-site sublattice, we find a structure with unexpected macroscopic crystalline homogeneity with a clear orthorhombic unit cell, whose orientation is demonstrated to be controlled by the strain and crystal structure of the substrate for films grown on (La 0.3 Sr 0.7 )(Al 0.65 Ta 0.35 )O 3 and NdGaO 3 (110). Furthermore, quantification of the atom positions within the unit cell reveals that the orthorhombic distortions are small, close to LaCrO 3, which may be driven by a combination of disorder averaging and the average ionic radii. This is a step toward understanding the rules for designing crystal motifs and tuning functional properties through controlled configurational complexity.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

The pressing need for novel materials that can serve rising demands in solar cell and optoelectronic technologies makes the nexus of halide perovskites, high-throughput computations, and machine learning, very promising. Ever increasing amounts of data on the structure, fundamental properties, and device performance of halide perovskites provide opportunities for learning chemical rules and design principles that make these materials attractive, and applying them across wide chemical spaces. In this work, we show that impurity properties of halide perovskites computed using density functional theory (DFT) can be combined with machine learning (ML) to deliver predictive models and quick identification of optoelectronically active impurity atoms. Our computation lead to the largest reported dataset of the formation energies and charge transition levels of Pb-site impurities in methylammonium lead halide (MAPbX 3 ) perovskites. Descriptors are defined to uniquely represent any impurity atom in any MAPbX 3 compound and mapped to the computed impurity properties using regression techniques such as Gaussian process regression, neural networks, and random forests. We use the best optimized predictive models to make predictions for hundreds of impurities across 9 MAPbX 3 compounds and create lists of dominating impurities, that is, impurities that can shift the equilibrium Fermi level in the perovskite as determined by native point defects. Finally, this accelerated screening powered by computations and machine learning can guide the identification of problematic impurities that may cause undesired recombination of charge carriers, as well as impurities that can be deliberately introduced to tune the perovskite conductivity and resulting photovoltaic absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Methanol Synthesis from CO 2 Hydrogenation over a Potassium-Promoted Cu x O/Cu(111) ( x ≤ 2) Model Surface: Rationalizing the Potential of Potassium in Catalysis

Alkalis have been reported as a promotor in the heterogeneous catalysis, being able to enhance the activity and selectivity of catalysts. The effective utilization of alkalis in catalyst optimization requires the fundamental understanding of the underlying mechanism. In this work, we take a potassium (K)-modified Cu x O/Cu(111) (x ≤ 2) model surface as a case study to rationalize the nature of K during the carbon dioxide hydrogenation using combined density functional theory (DFT) calculation and the kinetic Monte Carlo (KMC) simulation. Our result demonstrates the significant tuning of selectivity from carbon monoxide to methanol on going from Cu(111) to K-modified Cu x O/Cu(111). The deposited K + stabilizes the Cu x O thin film under the reducing condition of carbon dioxide hydrogenation. More importantly, our study reveals that K + acts as an active center for selective tuning in the binding, an accelerator for charge transfer, and a mediator for the electron tunneling. As a result, the K-modified Cu x O/Cu(111) opens a methanediol [H 2 C(OH) 2 ]-mediated formate pathway to facilitate the selective conversion of carbon dioxide to methanol. Our study develops the intrinsic rules of design to tune the catalytic performance using alkali metals.

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

First-principles studies of substituent effects on squaraine dyes

Squaraine dyes are candidates for DNA-templated excitonic interactions. This work presents substituent effects on the electronic and photophysicalproperties of squaraine dyes and a correlation between empirical Hammettconstant and those properties.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Differentiable Quantum Programming with Unbounded Loops

The emergence of variational quantum applications has led to the development of automatic differentiation techniques in quantum computing. Existing work has formulated differentiable quantum programming with bounded loops, providing a framework for scalable gradient calculation by quantum means for training quantum variational applications. However, promising parameterized quantum applications, e.g., quantum walk and unitary implementation, cannot be trained in the existing framework due to the natural involvement of unbounded loops. To fill in the gap, we provide the first differentiable quantum programming framework with unbounded loops, including a newly designed differentiation rule, code transformation, and their correctness proof. Technically, we introduce a randomized estimator for derivatives to deal with the infinite sum in the differentiation of unbounded loops, whose applicability in classical and probabilistic programming is also discussed. We implement our framework with Python and Q# and demonstrate a reasonable sample efficiency. Through extensive case studies, we showcase an exciting application of our framework in automatically identifying close-to-optimal parameters for several parameterized quantum applications.

Computer Science↗
DOE OSTI2022

Decision Science for Machine Learning (DeSciML)

The increasing use of machine learning (ML) models to support high-consequence decision making drives a need to increase the rigor of ML-based decision making. Critical problems ranging from climate change to nonproliferation monitoring rely on machine learning for aspects of their analyses. Likewise, future technologies, such as incorporation of data-driven methods into the stockpile surveillance and predictive failure analysis for weapons components, will all rely on decision-making that incorporates the output of machine learning models. In this project, our main focus was the development of decision scientific methods that combine uncertainty estimates for machine learning predictions, with a domain-specific model of error costs. Other focus areas include uncertainty measurement in ML predictions, designing decision rules using multiobjecive optimization, the value of uncertainty reduction, and decision-tailored uncertainty quantification for probability estimates. By laying foundations for rigorous decision making based on the predictions of machine learning models, these approaches are directly relevant to every national security mission that applies, or will apply, machine learning to data, most of which entail some decision context.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Modified 316H constitutive model updated with high temperature stress relaxation test data

This report describes the calibration of a new high temperature constitutive model for 316H stainless steel, suitable for use with the ASME Boiler & Pressure Vessel Section III, Division 5, Class A rules for design by inelastic analysis. The model retains the same mathematical form used by the reference model included in Nonmandatory Appendix Z of the Code, but refits the model to an expanded dataset including all the data used to fit the original model plus seven new stress relaxation tests. The addition of these high temperature stress relaxation tests improves the model's accuracy in predicting relaxation at temperatures greater than 700 ⁰C, without compromising the accuracy of the model versus the original calibration data.

36 MATERIALS SCIENCE↗
NASA NTRS1973

Study 2.6 operations analysis mission characterization

An analysis of the current operations concepts of NASA and DoD is presented to determine if alternatives exist which may improve the utilization of resources. The final product is intended to show how sensitive these ground rules and design approaches are relative to the total cost of doing business. The results are comparative in nature, and assess one concept against another as opposed to establishing an absolute cost value for program requirements. An assessment of the mission characteristics is explained to clarify the intent, scope, and direction of this effort to improve the understanding of what is to be accomplished. The characterization of missions is oriented toward grouping missions which may offer potential economic benefits by reducing overall program costs. Program costs include design, development, testing, and engineering, recurring unit costs for logistic vehicles, payload costs. and direct operating costs.

Wolfe, R. R.↗
NASA NTRS1978

Standardized development of computer software. Part 2: Standards

This monograph contains standards for software development and engineering. The book sets forth rules for design, specification, coding, testing, documentation, and quality assurance audits of software; it also contains detailed outlines for the documentation to be produced.

Tausworthe, R. C.↗
NASA NTRS1985

Integrated digital/electric aircraft concepts study

The integrated digital/electrical aircraft (IDEA) is an aircraft concept which employs all electric secondary power systems and advanced digital flight control systems. After trade analysis, preferred systems were applied to the baseline configuration. An additional configuration, the alternate IDEA, was also considered. For this concept the design ground rules were relaxed in order to quantify additional synergistic benefits. It was proposed that an IDEA configuration and technical risks associated with the IDEA systems concepts be defined and the research and development required activities to reduce these risks be identified. The selected subsystems include: power generation, power distribution, actuators, environmental control system and flight controls systems. When the aircraft was resized, block fuel was predicted to decrease by 11.3 percent, with 7.9 percent decrease in direct operating cost. The alternate IDEA shows a further 3.4 percent reduction in block fuel and 3.1 percent reduction in direct operating cost.

Cronin, M. J.↗
NASA NTRS1986

A comparison of methods for DPLL loop filter design

Four design methodologies for loop filters for a class of digital phase-locked loops (DPLLs) are presented. The first design maps an optimum analog filter into the digital domain; the second approach designs a filter that minimizes in discrete time weighted combination of the variance of the phase error due to noise and the sum square of the deterministic phase error component; the third method uses Kalman filter estimation theory to design a filter composed of a least squares fading memory estimator and a predictor. The last design relies on classical theory, including rules for the design of compensators. Linear analysis is used throughout the article to compare different designs, and includes stability, steady state performance and transient behavior of the loops. Design methodology is not critical when the loop update rate can be made high relative to loop bandwidth, as the performance approaches that of continuous time. For low update rates, however, the miminization method is significantly superior to the other methods.

Aguirre, S.↗
NASA NTRS1992

A combined Earth scanner and momentum wheel for attitude determination and control of small spacecraft

In order to satisfy the stringent cost and power requirements of small satellites, an advanced SCANWHEEL was designed, built, and qualified by ITHACO, Inc. The T-SCANWHEEL is a modular momentum/reaction wheel with an integral conical Earth scanner. The momentum wheel provides momentum bias and control torques about the pitch axis of a spacecraft. An angled scan mirror coupled to the rotating shaft of the momentum wheel provides a conical scan of the field-of-view of an infrared sensor to provide pitch-and-roll attitude information. By using the same motor and bearings for the momentum wheel and Earth scanner, the overall power consumption is reduced and the system reliability is enhanced. The evolution of the T-SCANWHEEL is presented, including design ground rules, tradeoff analyses, and performance results.

Bialke, Bill↗