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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Introduction to SIMRAND: Simulation of research and development project

SIMRAND: SIMulation of Research ANd Development Projects is a methodology developed to aid the engineering and management decision process in the selection of the optimal set of systems or tasks to be funded on a research and development project. A project may have a set of systems or tasks under consideration for which the total cost exceeds the allocated budget. Other factors such as personnel and facilities may also enter as constraints. Thus the project's management must select, from among the complete set of systems or tasks under consideration, a partial set that satisfies all project constraints. The SIMRAND methodology uses analytical techniques and probability theory, decision analysis of management science, and computer simulation, in the selection of this optimal partial set. The SIMRAND methodology is truly a management tool. It initially specifies the information that must be generated by the engineers, thus providing information for the management direction of the engineers, and it ranks the alternatives according to the preferences of the decision makers.

Miles, R. F., Jr.↗
DOE OSTI2025

Machine Learning-Based Technique for Automated Sensor Characterization

The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert s time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.

Zepeda, Cuevas [Chicago U., KICP]↗
NASA NTRS1991

Cost studies for commercial fuselage crown designs

Studies were conducted to evaluate the cost and weight potential of advanced composite design concepts in the crown region of a commercial transport. Two designs from each of three design families were developed using an integrated design-build team. A range of design concepts and manufacturing processes were included to allow isolation and comparison of cost centers. Detailed manufacturing/assembly plans were developed as the basis for cost estimates. Each of the six designs was found to have advantages over the 1995 aluminum benchmark in cost and weight trade studies. Large quadrant panels and cobonded frames were found to save significant assembly labor costs. Comparisons of high- and intermediate-performance fiber systems were made for skin and stringer applications. Advanced tow placement was found to be an efficient process for skin lay up. Further analysis revealed attractive processes for stringers and frames. Optimized designs were informally developed for each design family, combining the most attractive concepts and processes within that family. A single optimized design was selected as the most promising, and the potential for further optimization was estimated. Technical issues and barriers were identified.

Walker, T. H.↗
DOE OSTI2025

Automating Sensor Characterization with Bayesian Optimization

The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert's time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.

Cuevas-Zepeda, Julian [Chicago U., KICP; Chicago U↗
NASA NTRS1989

Selection of active member locations in adaptive structures

The effective use of multiple passive and active members in adaptive structures necessitates that these members be optimally distributed throughout the structure. In truss structures, the problem falls into the class of combinatorial optimization for which the solution becomes exceedingly intractable as the problem size increases. This is overcome by using the simulated annealing algorithm to obtain near optimal locations for passive and/or active members. The maximization of the rate of energy dissipation over a finite time period as the measure of optimality is adopted. The selection of optimal locations for both passive and active members is consistently treated through the use of the energy dissipation rate criterion within the simulated annealing algorithm. Numerical examples are used to illustrate the effectiveness of the methodology for large truss structures.

Chen, G.-S.↗
DOE OSTI2018

The MicroBooNE Search for Single Photon Events

This note describes and presents preliminary results for the MicroBooNE analysis being developed to search for neutrino-induced neutral current single photon events in the MicroBooNE detector. The analysis currently assumes neutrino neutral current Δ production followed by Δ radiative decay on argon (NC Δ → N γ) as the “signal model”; event reconstruction and selection have been developed and are being optimized in order to reduce cosmogenic and other beam-related backgrounds explicit to the NC Δ → N γ process. This note presents the current status of event reconstruction, selection, optimization and validation checks for the early stages of analysis on external (cosmogenic background) events. Also presented are the projected sensitivities for testing the standard model predicted rate for the NC Δ → N γ process and for testing the interpretation of the previously observed MiniBooNE low energy excess as NC Δ → N γ events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Advanced fuels from ethanol – a superstructure optimization approach

We develop a superstructure framework for the design of biorefineries for ethanol upgrading into advanced biofuels to replace gasoline, jet fuel or diesel. The framework integrates catalysis, process synthesis, and fuel property modelling towards the design of biorefineries producing fuels with specified properties. The proposed framework is applied to identify strategies for the upgrading of ethanol into one or more fuels with specific properties. We discuss the trade-off between profit and biorefinery complexity, as well as the relation among fuel property constraints, the optimal upgrading strategy selected, and process economics. Lastly, we show how to find the optimal biorefinery associated with a particular chemistry or catalyst. The results presented constitute the first systematic study of ethanol upgrading considering, simultaneously, fuel and process design.

09 BIOMASS FUELS↗
NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗
NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗
DOE OSTI2019

Machine Learning Optimization of p-Type Transparent Conducting Films

p-Type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, photodetectors, displays, and flexible sensors. Cu–Zn–S thin films prepared by chemical bath deposition (CBD) can have both high transparency in the visible range (>80%) as well as excellent hole conductivity (>1000 S cm –1 ). However, the interplay between the deposition parameters in the CBD process (metal and sulfur precursor concentrations, temperature, pH, complexing agents, etc.) creates a multidimensional parameter space such that optimization for a specific application is challenging and time-consuming. Here we show that strategic design of experiment combined with machine learning (ML) allows for the efficient optimization of p-TCM performance. The approach is guided by a figure of merit (FOM) calculated from the film conductivity and optical transmission in the desired spectral range. A specific example is shown using two steps of optimization using a selected subset of four experimental CBD factors. The ML model is based on support vector regression employing a radial basis function as the kernel function. 10-fold cross-validation was performed to mitigate overfitting. After the first round of optimization, predicted areas in the parameter space with maximal FOMs were selected for a second round of optimization. Films with optimal FOMs were incorporated into heterojunction solar cells and transparent photodiodes. Here, the optimization approach shown here will be generally applicable to any materials synthesis process with multiple parameters.

14 SOLAR ENERGY↗
NASA NTRS2015

A Framework for Orbital Performance Evaluation in Distributed Space Missions for Earth Observation

Distributed Space Missions (DSMs) are gaining momentum in their application to earth science missions owing to their unique ability to increase observation sampling in spatial, spectral and temporal dimensions simultaneously. DSM architectures have a large number of design variables and since they are expected to increase mission flexibility, scalability, evolvability and robustness, their design is a complex problem with many variables and objectives affecting performance. There are very few open-access tools available to explore the tradespace of variables which allow performance assessment and are easy to plug into science goals, and therefore select the most optimal design. This paper presents a software tool developed on the MATLAB engine interfacing with STK, for DSM orbit design and selection. It is capable of generating thousands of homogeneous constellation or formation flight architectures based on pre-defined design variable ranges and sizing those architectures in terms of predefined performance metrics. The metrics can be input into observing system simulation experiments, as available from the science teams, allowing dynamic coupling of science and engineering designs. Design variables include but are not restricted to constellation type, formation flight type, FOV of instrument, altitude and inclination of chief orbits, differential orbital elements, leader satellites, latitudes or regions of interest, planes and satellite numbers. Intermediate performance metrics include angular coverage, number of accesses, revisit coverage, access deterioration over time at every point of the Earth's grid. The orbit design process can be streamlined and variables more bounded along the way, owing to the availability of low fidelity and low complexity models such as corrected HCW equations up to high precision STK models with J2 and drag. The tool can thus help any scientist or program manager select pre-Phase A, Pareto optimal DSM designs for a variety of science goals without having to delve into the details of the engineering design process.

Earth Science↗
DOE OSTI2020

Self-accelerated corrosion of nuclear waste forms at material interfaces

The US plan for high-level nuclear waste includes the immobilization of long-lived radionuclides in glass or ceramic waste forms in stainless-steel canisters for disposal in deep geological repositories. Here we report that, under simulated repository conditions, corrosion could be significantly accelerated at the interfaces of different barrier materials, which has not been considered in the current safety and performance assessment models. Severe localized corrosion was found at the interfaces between stainless steel and a model nuclear waste glass and between stainless steel and a ceramic waste form. The accelerated corrosion can be attributed to changes of solution chemistry and local acidity/alkalinity within a confined space, which significantly alter the corrosion of both the waste-form materials and the metallic canisters. The corrosion that is accelerated by the interface interaction between dissimilar materials could profoundly impact the service life of the nuclear waste packages, which, therefore, should be carefully considered when evaluating the performance of waste forms and their packages. Moreover, compatible barriers should be selected to further optimize the performance of the geological repository system.

Guo, Xiaolei↗
DOE OSTI2022

Component-wise reduced-order model design optimization such as for lattice design optimization

Systems and methods for optimizing a lattice structure design are disclosed herein. In some embodiments, a method for optimizing a lattice structure design can include (i) modeling the lattice structure with a component-wise reduced-order model (CWROM) and (ii) optimizing the CWROM based on a selected criterion using a topology optimization algorithm for lattice design. The selected criterion can include a boundary condition and a load applied to the lattice structure. By modeling the lattice structure as a CWROM, the optimization process can be very fast while still permitting the accurate computation of physical quantities of the lattice structure.

Choi, Youngsoo↗
DOE OSTI2021

Optimizing Active Sites for High CO Selectivity during CO 2 Hydrogenation over Supported Nickel Catalysts

The undesired formation of CH 4 during the hydrogenation of CO 2 remains a great challenge with direct impact on selectivity towards CO or CH 3 OH. In this study, the selectivity of a supported Ni catalyst prepared by traditional impregnation method was found to change after a first CO 2 hydrogenation reaction cycle from 100 to 800 °C. The usually high CH 4 formation was suppressed leading to full selectivity towards CO. This behavior was also observed after the catalyst was treated under methane or propane atmospheres at elevated temperatures. In-situ spectroscopic studies revealed that the accumulation of carbon species on the catalyst surface at high temperature leads to a nickel carbide-like phase. The catalyst regains its high selectivity to CH 4 production after carbon depletion from the surface of the Ni particles by oxidation. However, the selectivity readily shifts back towards CO formation after a new temperature programmed CO 2 hydrogenation cycle. The fraction of weakly adsorbed CO species increases on the carbide-like surface when compared to a clean nickel surface, explaining the higher selectivity towards CO formation. This easy protocol of changing the surface of a common Ni catalyst to gain selectivity represents an important step for the commercial use of CO 2 hydrogenation to CO process together with Fischer-Tropsch applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1985

An expert system for choosing the best combination of options in a general-purpose program for automated design synthesis

An expert system was developed to aid a user of the Automated Design Synthesis (ADS) general-purpose optimization computer program in selecting the best combination of strategy, optimizer, and one-dimensional search options for solving a problem. There are approximately 100 such combinations available in ADS. The knowledge base contains over 200 rules, and is divided into three categories: constrained problems, unconstrained problems, and constrained problems treated as unconstrained problems. The inference engine is written in LISP and is available on DEC-VAX and IBM PC/XT computers.

Rogers, J. L., Jr.↗
NASA NTRS2010

Optimizing Flight Departure Delay and Route Selection Under En Route Convective Weather

This paper presents a linear Integer Programming model for managing air traffic flow in the United States. The decision variables in the model are departure delays and predeparture reroutes of aircraft whose trajectories are predicted to cross weather-impacted regions of the National Airspace System. The model assigns delays to a set of flights while ensuring their trajectories are free of any conflicts with weather. In a deterministic setting, there is no airborne holding due to unexpected weather incursion in a flight s path. The model is applied to solve a large-scale traffic flow management problem with realistic weather data and flight schedules. Experimental results indicate that allowing rerouting can reduce departure delays by nearly 57%, but it is associated with an increase in total airborne time due to longer routes flown by aircraft. The computation times to solve this problem were significantly lower than those reported in the earlier studies.

Mukherjee, Avijit↗
NASA NTRS2006

Postoptimality Analysis in the Selection of Technology Portfolios

This slide presentation reviews a process of postoptimally analysing the selection of technology portfolios. The rationale for the analysis stems from the need for consistent, transparent and auditable decision making processes and tools. The methodology is used to assure that project investments are selected through an optimization of net mission value. The main intent of the analysis is to gauge the degree of confidence in the optimal solution and to provide the decision maker with an array of viable selection alternatives which take into account input uncertainties and possibly satisfy non-technical constraints. A few examples of the analysis are reviewed. The goal of the postoptimality study is to enhance and improve the decision-making process by providing additional qualifications and substitutes to the optimal solution.

prioritization↗