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All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗

DOE OSTI2022

Restoration Hydro: A Watershed Approach to Standard Modular New Hydropower

The objectives of FOA DE- FOA-0001836- “Standard Modular Hydropower” included designing a standardized, modular, and environmentally compatible hydropower schematic for implementation in greenfield sites that generate up to 10 MW of capacity. Utilizing funds competitively awarded under DOE’s Water Power Technologies Office, the Natel Energy team developed a concept for modular new stream reach (NSR) hydropower that incorporates multi species upstream and downstream fish passage, improved river channel connectivity, and recreational modules. The in-stream design of the collective modules minimized site specific design and maximized the opportunities for modularity. Financial data was also presented using actual costs from regional suppliers, with figures provided in 2022 dollars. While the project team did not address potential permitting process improvements, the site selection criteria did consider established barriers to hydropower development such as tribal and preserved lands, interconnection proximity, and endangered species to exclude or deprioritize. The project’s design schematic met the objectives of the FOA, and presented a unique solution that targets alluvial pockets as natural features for sustainable development. Natel’s concept also incorporated the company's fish-safe Restoration Hydro Turbine for safe downstream passage, while featuring a rock arch that integrates fish passage, water, recreation, and grade control modules (including sediment). Alignment with the Department of Energy Office of Energy Efficiency and Renewable Energy (EERE) “Innovative Design Concepts for Standard Modular Hydropower and Pumped-Storage Hydropower” Program: According to the Hydropower Vision (DOE, 2016), approximately 16 GW of hydropower growth is possible with the development of technology solutions that balance efficiency, economics, and environmental sustainability. The desired outcome of the SMH program is transformational innovation specifically in the site identification, conceptual, and detailed design phases of technology development lifecycles (DOE, 2018). In developing the SMH design schematic, the team aimed to address the opportunities outlined in the Vision through an inverted design philosophy; rather than singularly prioritizing efficiency and power production, the team focused on integrating hydropower with restoration of degraded streams to optimal ecosystem function and provision of exceptional recreation value as design criteria. To achieve this, Restoration Hydro incorporates the principles of nature-based engineering (WWAP, 2018) and biomimicry (Biomimicry NL.) to strategically deploy complementary combinations of permanent, semi-permanent, and ephemeral low-head structures - such as natural and engineered log jams - that harness geomorphological and hydrological processes at the landscape-scale. Primary applications of Restoration Hydro include: 1) restoration of degraded watersheds’ natural ecological function and enhancement of hydrological connectivity; and 2) creation of associated co-benefits to hydro production, including increased groundwater recharge, improved sediment transport and management, improved water security and water quality. Restoration Hydro projects build upon proven watershed restoration engineering techniques by integrating hydropower turbines into low-head structures using innovative and evolving civil works concepts that facilitate fish and sediment passage, and in some cases create additional revenue-generating recreational opportunities. Powering low-head structures creates a directly monetizable layer of economic value in the form of flexible, reliable, renewable energy on top of the already high-value water, environmental and recreational benefits of watershed and river restoration. The approach aims to create a virtuous, self-reinforcing cycle whereby Restoration Hydro projects support the scaling of ecosystem restoration activities, creating a water-energy-carbon multiplier effect that, through the principles of adaptive change management: 1) improves the resilience of landscapes and downstream population centers for changing hydrological cycles; 2) creates a reliable energy resource that facilitates the integration of intermittent renewable power sources into grids; and 3) supports climate change mitigation through grid decarbonization and enhanced ecosystem carbon capture and retention.

13 HYDRO ENERGY↗

NASA NTRS2015

In-Space Propulsion, Logistics Reduction, and Evaluation of Steam Reformer Kinetics: Problems and Prospects

Human space missions generate waste materials. A 70-kg crewmember creates a waste stream of 1 kg per day, and a four-person crew on a deep space habitat for a 400+ day mission would create over 1600 kg of waste. Converted into methane, the carbon could be used as a fuel for propulsion or power. The NASA Advanced Exploration Systems (AES) Logistics Reduction and Repurposing (LRR) project is investing in space resource utilization with an emphasis on repurposing logistics materials for useful purposes and has selected steam reforming among many different competitive processes as the preferred method for repurposing organic waste into methane. Already demonstrated at the relevant processing rate of 5.4 kg of waste per day, high temperature oxygenated steam consumes waste and produces carbon dioxide, carbon monoxide, and hydrogen which can then be converted into methane catalytically. However, the steam reforming process has not been studied in microgravity. Data are critically needed to understand the mechanisms that allow use of steam reforming in a reduced gravity environment. This paper reviews the relevant literature, identifies gravity-dependent mechanisms within the steam gasification process, and describes an innovative experiment to acquire the crucial kinetic information in a small-scale reactor specifically designed to operate within the requirements of a reduced gravity aircraft flight. The experiment will determine if the steam reformer process is mass-transport limited, and if so, what level of forced convection will be needed to obtain performance comparable to that in 1-g.

Logistics↗

NASA NTRS2024

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become crucial across diverse economic sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot that specifically focuses on propulsion has been developed. Leveraging natural language processing (NLP) techniques, this chatbot simplifies the deployment of machine learning (ML) models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

AI Chatbot↗

DOE OSTI2021

Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning

Deep learning has shown great success in settings with massive amounts of data but has struggled when data is limited. Few-shot learning algorithms, which seek to address this limitation, are designed to generalize well to new tasks with limited data. Typically, models are evaluated on unseen classes and datasets that are defined by the same fundamental task as they are trained for (e.g. category membership). One can also ask how well a model can generalize to fundamentally different tasks within a fixed dataset (for example: moving from category membership to tasks that involve detecting object orientation or quantity). To formalize this kind of shift we define a notion of “independence of tasks” and identify three new sets of labels for established computer vision datasets that test a model's ability to generalize to tasks which draw on orthogonal attributes in the data. We use these datasets to investigate the failure modes of metric-based few-shot models. Based on our findings, we introduce a new few-shot model called Fuzzy Simplicial Networks (FSN) which leverages a construction from topology to more flexibly represent each class from limited data. In particular, FSN models can not only form multiple representations for a given class but can also begin to capture the low-dimensional structure which characterizes class manifolds in the encoded space of deep networks. We show that FSN outperforms state-of-the-art models on the challenging tasks we introduce in this paper while remaining competitive on standard few-shot benchmarks.

deep learning↗

DOE OSTI2022

Context matters: Natural tree mortality can lead to neighbor growth release or suppression

Where competition suppresses tree growth, mortality of adjacent trees can release the surviving individuals, leading to a growth increase. However, primary forests are complicated systems, where trees interact in both competitive and facilitative ways mediated by their size, species, and the broad ecological context in which they grow. Thus, the magnitude and even direction of growth responses to the mortality of nearby trees may vary, which has implications for our understanding of community- and ecosystem-level dynamics following mortality events. Unfortunately, although many studies focus on the impacts of light availability and general crowding on tree growth, we know relatively little about the effects of naturally occurring mortality events on the growth of neighboring trees. To address this issue, we used 40 years of data from 15 permanent forest-monitoring plots in Mt Rainier old-growth forests, comparing observed to expected radial growth of individual trees following the death of their nearest neighbor. Although we found evidence of a general growth-release response, this was not universal among all trees, with small trees in particular exhibiting growth suppression (rather than release) following neighboring tree mortality. In addition to small size, growth-suppression was more likely if the dead neighbor was the same species, consistent with facilitative effects as mediated through belowground networks. At the stand level, the average growth release after nearest neighbor mortality was greatest in low-density stands with large trees, with elevation and community composition also playing a role. Decades more monitoring could reveal how long growth release (or suppression) is sustained by individual trees following neighboring mortality events, as well as potential response lags and the role of species identity in determining whether interactions with neighbors are competitive or facilitative. Nonetheless, our results suggest that although mature trees have competitive effects on their larger neighbors, they also have an important role in supporting the ingrowth of small trees. More broadly, we demonstrate that the nature of interactions between individual neighboring trees is highly context dependent.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Final Scientific/Technical Report: Real Time Particle Size and Settling Velocity for In-Situ Monitoring of Deep-Sea Polymetallic Nodule Mining

In the coming decade, it is anticipated that deep-sea mining activities will commence throughout the deep ocean, at depths around 4000-6000m where vast deposits of baseball-sized rocks called polymetallic nodules lay on the seabed. These nodules contain resources such as cobalt, copper and nickel which are needed to produce batteries for electric vehicles. Collector machines driving on the seabed and gathering these nodules will stir up clouds of sediment, and there is concern about how the resulting concentration of sediment in the near-bottom ocean water will affect deep and mid-ocean biology. There is currently no technology capable of comprehensively monitoring the concentrations and properties of stirred up sediment in the deep. This project addressed this critical gap by developing a deep-sea particle measurement system (Real-Time Size and Settling Velocity, RTSSV) that uses a multi-camera high-resolution video imaging system, with accompanying image processing, to measure the concentration, size distribution and settling speed of sediment. Importantly, these measurements are made in-situ, so that delicate sediment aggregates will not be disturbed, as is the case when collecting samples at depth to be measured in the lab. The instrument can be mounted on a variety of established deep-sea platforms such as moorings, AUVs and ROVs, providing data in near real time, where the key measurements need to be made—from the source of the sediment disturbance and throughout the resulting plume—vital for determining if deep-sea mining can be done in an environmentally responsible manner. The developed system (RTSSV) will possess the only such capability worldwide, giving a US-based group a substantial competitive advantage as this newly evolving global industry develops. As added value, the technology will also have widespread application to the offshore oil and gas industry, dredging industry, and to sediment science research.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Transport Cost for Carbon Removal Projects With Biomass and CO2 Storage

Strategies to remove carbon from the atmosphere are needed to meet global climate goals. Promising strategies include the conversion of waste biomass to hydrogen, methane, liquid fuels, or electricity coupled with CO 2 capture and storage (CCS). A key challenge for these projects is the need to connect geographically dispersed biomass supplies with geologic storage sites by either transporting biomass or CO 2 . We assess the cost of transport for biomass conversion projects with CCS using publicly available cost data for trucking, rail, and CO 2 pipelines in the United States. We find that for large projects (order of 1 Mt/yr CO 2 or greater), CO 2 by pipeline is the lowest cost option. However, for projects that send most of the biomass carbon to storage, such as gasification to hydrogen or electricity production, biomass by rail is a competitive option. For smaller projects and lower fractions of carbon sent to storage, such as for pyrolysis to liquid fuels, CO 2 by rail is the lowest cost option. Assessing three plausible example projects in the United States, we estimate that total transport costs range from $24/t-CO 2 stored for a gasification to hydrogen project traversing 670 km to $\$ 36$/t for a gasification to renewable natural gas project traversing 530 km. In general, if developers have flexibility in choosing transport mode and project type, biomass sources and storage sites can be connected across hundreds of kilometers for transport costs in the range of $20-40/t-CO 2 stored. Truck and rail are often viable modes when pipelines cannot be constructed. Distances of 1,000 km or more can be connected in the same cost range when shared CO 2 pipelines are employed.

09 BIOMASS FUELS↗

DOE OSTI2023

Dynamic integration of GCAM-Demeter and CLUBS-Select: Datasets on urban extensification effects on land cover, crop yields, net primary production, and CO2 emissions

The datasets provide background data from McManamay et al. (2024) Nature Communications Earth and Environment. We integrate projected dynamic non-urban lands from GCAM-Demeter with projected dynamic urban lands from SELECT from 2015 to 2100 at 1-km resolution to examine stress to the land system, compromised crop production, and losses in Net Primary Production (NPP), that can arise from the competition over land resources. These datasets include changes to non-urban land use and land cover, estimated losses in crop yields, losses in NPP, and increases in CO2 emissions.

Earth Systems↗

NASA NTRS1978

An analysis of short haul air passenger demand, volume 2

Several demand models for short haul air travel are proposed and calibrated on pooled data. The models are designed to predict demand and analyze some of the motivating phenomena behind demand generation. In particular, an attempt is made to include the effects of competing modes and of alternate destinations. The results support three conclusions: (1) the auto mode is the air mode's major competitor; (2) trip time is an overriding factor in intermodal competition, with air fare at its present level appearing unimportant to the typical short haul air traveler; and (3) distance appears to underly several demand generating phenomena, and therefore, must be considered very carefully to any intercity demand model. It may be the cause of the wide range of fare elasticities reported by researchers over the past 15 years. A behavioral demand model is proposed and calibrated. It combines the travel generating effects of income and population, the effects of modal split, the sensitivity of travel to price and time, and the effect of alternative destinations satisfying the trip purpose.

Blumer, T. P.↗

NASA NTRS1991

The business case for connectivity

Information systems that provide competitive advantages to organizations can be broadly classified into those that improve the effectiveness of a business function and those that improve the reach of information in the organization. The latter, organizational connectivity systems, can be categorized as intraorganizational and interorganizational systems. Intraorganization systems provide connectivity to function areas within the business, while interorganizational systems support the exchange of business data between independent business units. These system are not confined to a single entity but span organizational boundaries which can be national or international in scope. A series of case studies was undertaken in an effort to better understand the issues and problems associated with providing an increased flow of information within and outside of an organization. Ten issues emerged from this study. In summary, it is necessary for firms to first consider how effective their internal communications systems are before launching projects that tie the organization to external systems.

Adams, Dennis↗

NASA NTRSDate not supplied

Analysis of Pyrolysis Products from Ablative Thermal Protection Systems

NASA’s state-of-the-art ablative materials are composed of a three-dimensional network of carbon fibers impregnated with polymers that dissipate thermal energy through pyrolysis. A fundamental understanding of the decomposition mechanisms and pyrolysis product distributions of various classes of polymers is instrumental in the design of new ablative materials. Furthermore, innovative experiments are essential to the continuous modernization of material response models by providing high-fidelity data. Thus, an apparatus has been designed to measure pyrolysis products from polymers and composite materials by implementing in-situ mass spectrometric techniques. Initial results from experiments performed on siloxane resins and a common phenolic resin will be discussed. Both classes of polymers exhibit heating-rate-dependent decomposition mechanisms. At the onset of heating, phenolic polymers decompose through competitive reactions to form gaseous products and a carbonaceous char. Gas phase products of phenolic resins are typically composed of molecular hydrogen, water, and aromatic hydrocarbons. Pyrolysis products from siloxane polymers include molecular hydrogen, small molecules, and cyclic oligomers from the polymer backbone.

Pyrolysis↗

NASA NTRSDate not supplied

Analysis of Pyrolysis Products from Ablative TPS

Pyrolysis↗

DOE OSTI2022

Performance Evaluations of Noisy Approximate Quantum Fourier Arithmetic

The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers, and offers a potential route towards a numerical quantum-computational paradigm. In this paper, we utilize efficient techniques to implement QFT-based integer addition and multiplications. These operations are fundamental to various quantum applications including Shor’s algorithm, weighted sum optimization problems in data processing and machine learning and quantum algorithms requiring inner products. We carry out performance evaluations of these implementations based on IBM’s superconducting qubit architecture using different compatible noise models. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, and the number of the arithmetic operands’ superposed integer states. We analyze performance, and identify the most effective approximation depths for quantum add and quantum multiply within the given context. We observe significant dependency of the optimal approximation depth on the degree of machine noise and the number of superposed states in certain performance regimes. Finally, we elaborate on the algorithmic challenges - relevant to signed, unsigned, modular and non-modular versions - that could also be applied to current implementations of QFT-based subtraction, division, exponentiation, and their potential tensor extensions. Here, we analyze performance trends in our results and speculate on possible future development within this computational paradigm.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1992

Use of the VLBI delay observable for orbit determination of Earth-orbiting VLBI satellites

Very long-baseline interferometry (VLBI) observations using a radio telescope in Earth orbit were performed first in the 1980s. Two spacecraft dedicated to VLBI are scheduled for launch in 1995; the primary scientific goals of these missions will be astrophysical in nature. This article addresses the use of space VLBI delay data for the additional purpose of improving the orbit determination of the Earth-orbiting spacecraft. In an idealized case of quasi-simultaneous observations of three radio sources in orthogonal directions, analytical expressions are found for the instantaneous spacecraft position and its error. The typical position error is at least as large as the distance corresponding to the delay measurement accuracy but can be much greater for some geometries. A number of practical considerations, such as system noise and imperfect calibrations, set bounds on the orbit-determination accuracy realistically achievable using space VLBI delay data. These effects limit the spacecraft position accuracy to at least 35 cm (and probably 3 m or more) for the first generation of dedicated space VLBI experiments. Even a 35-cm orbital accuracy would fail to provide global VLBI astrometry as accurate as ground-only VLBI. Recommended charges in future space VLBI missions are unlikely to make space VLBI competitive with ground-only VLBI in global astrometric measurements.

Ulvestad, J. S.↗

NASA NTRS2016

Application of a Full Reynolds Stress Model to High Lift Flows

A recently developed second-moment Reynolds stress model was applied to two challenging high-lift flows: (1) transonic flow over the ONERA M6 wing, and (2) subsonic flow over the DLR-F11 wing-body configuration from the second AIAA High Lift Prediction Workshop. In this study, the Reynolds stress model results were contrasted with those obtained from one- and two{equation turbulence models, and were found to be competitive in terms of the prediction of shock location and separation. For an ONERA M6 case, results from multiple codes, grids, and models were compared, with the Reynolds stress model tending to yield a slightly smaller shock-induced separation bubble near the wing tip than the simpler models, but all models were fairly close to the limited experimental surface pressure data. For a series of high-lift DLR{F11 cases, the range of results was more limited, but there was indication that the Reynolds stress model yielded less-separated results than the one-equation model near maximum lift. These less-separated results were similar to results from the one-equation model with a quadratic constitutive relation. Additional computations need to be performed before a more definitive assessment of the Reynolds stress model can be made.

Lee-Rausch, E. M.↗

NASA NTRS1984

Creating competitive rotorcraft noise technology

Rotorcraft noise includes turbofan engine noise components, as well as noise from the main and tail rotors that is conditioned by the aircraft's various operational modes. Both of the rotors generate loading noise and broadband noise. Another noise contributor is blade/vortex interaction noise, which results when shed vortices are encountered by a following blade, releasing impulsive acoustic energy. Attention is presently given to the experimental and developmental initiatives to be made by a NASA/industry five-year program that began in 1983. Aeroacoustic data acquired from experiments conducted in NASA facilities can be used in the development of empirical noise prediction methods, in the improvement of existing noise prediction methodology, in the evaluation of proposed reduced noise designs, and in the establishment of useful scaling relationships for selected noise-generating mechanisms.

Raney, J. P.↗

DOE OSTI2025

Performance evaluations of signed and unsigned noisy approximate quantum Fourier arithmetic

The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers, and offers a potential route toward a numerical quantum-computational paradigm. In this paper, we utilize efficient techniques to implement QFT-based integer addition and multiplications. These operations are fundamental to various quantum applications including Shor’s algorithm, weighted-sum optimization problems in data processing and machine learning, and quantum algorithms requiring inner products. We carry out performance evaluations of these implementations based on IBM’s superconducting-qubit architecture using different compatible noise models. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, and the number of the arithmetic operands’ superposed integer states. We analyze performance and identify the most effective approximation depths for unsigned quantum addition and quantum multiplication within the given context. We then perform a similar analysis of signed addition and compare to the unsigned results. We observe significant dependency of the optimal approximation depth on the degree of machine noise and the number of superposed states in certain performance regimes. Finally, we elaborate on the algorithmic challenges—relevant to signed, unsigned, modular and non-modular versions—that could also be applied to current implementations of QFT-based subtraction, division, exponentiation, and their potential tensor extensions. Here, we analyze the performance trends in our results and speculate on possible future developments within this computational paradigm.

Computational models↗