Results for “information barrier” — Page 25

DOE OSTI2026

An Overview of Potential Future Aviation Energy Carriers

Global jet fuel demand is projected to grow to 165 billion gallons by 2050 and there is growing interest in the aviation industry to understand the capabilities of alternative energy carriers. This report considers several potential energy carriers, reviewing their production potential, operational considerations, and economic implications to inform potential next steps in aeronautics research. The following energy carriers (i.e., fuels) were evaluated in this study for future use in aviation: sustainable aviation fuel (SAF) - both biomass-based and power-to-liquids (PtL) - based SAF - cryogenic hydrogen (LH 2 ) - also known as liquid hydrogen - liquefied natural gas (LNG), liquefied ethane (LE), and Jet X. The barriers and opportunities for the evaluated energy carriers vary. SAF is a commercially available drop-in fuel compatible with existing aircraft and infrastructure. SAF is limited by both higher prices and slow commercialization of multiple pathways using different feedstocks necessary to significantly grow supply. Cryogenic fuels, in contrast, have very different thermal, physical, and chemical properties than SAF and Jet A, requiring the construction of new airport storage and dispensing infrastructure and the design of new aircraft energy systems capable of mitigating heat transfer and boil-off. Cryogenic fuels offer an opportunity to diversify aviation fuel carriers from domestic energy relevant for both energy security and resilience. This means cryogenic fuels are more of a longer-term solution for aircraft but are worth exploring for potential future cost savings and emissions benefits. Jet X refers to non-drop-in liquid hydrocarbon fuels in the early stages of investigation seeking favorable properties. There are substantial barriers to non-drop-in liquid aviation fuels that are not compatible with existing aircraft and fuel infrastructure.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2011

Composite Overwrapped Pressure Vessels, A Primer

Due to the extensive amount of detailed information that has been published on composite overwrapped pressure vessels (COPVs), this document has been written to serve as a primer for those who desire an elementary knowledge of COPVs and the factors affecting composite safety. In this application, the word "composite" simply refers to a matrix of continuous fibers contained within a resin and wrapped over a pressure barrier to form a vessel for gas or liquid containment. COPVs are currently used at NASA to contain high pressure fluids in propulsion, science experiments, and life support applications. They have a significant weight advantage over all metal vessels but require unique design, manufacturing, and test requirements. COPVs also involve a much more complex mechanical understanding due to the interplay between the composite overwrap and the inner liner. A metallic liner is typically used in a COPV as a fluid permeation barrier. The liner design concepts and requirements have been borrowed from all-metal vessels. However, application of metallic vessel design standards to a very thin liner is not straightforward. Different failure modes exist for COPVs than for all-metal vessels, and understanding of these failure modes is at a much more rudimentary level than for metal vessels.

McLaughlan, Pat B.↗

NASA NTRS2014

Satellite Data of Atmospheric Pollution for U.S. Air Quality Applications: Examples of Applications, Summary of Data End-user Resources, Answers to Faqs, and Common Mistakes to Avoid

Satellite data of atmospheric pollutants are becoming more widely used in the decision-making and environmental management activities of public, private sector and non-profit organizations. They are employed for estimating emissions, tracking pollutant plumes, supporting air quality forecasting activities, providing evidence for "exceptional event" declarations, monitoring regional long-term trends, and evaluating air quality model output. However, many air quality managers are not taking full advantage of the data for these applications nor has the full potential of satellite data for air quality applications been realized. A key barrier is the inherent difficulties associated with accessing, processing, and properly interpreting observational data. A degree of technical skill is required on the part of the data end-user, which is often problematic for air quality agencies with limited resources. Therefore, we 1) review the primary uses of satellite data for air quality applications, 2) provide some background information on satellite capabilities for measuring pollutants, 3) discuss the many resources available to the end-user for accessing, processing, and visualizing the data, and 4) provide answers to common questions in plain language.

remote sensing↗

NASA NTRS2014

Satellite Data of Atmospheric Pollution for U.S. Air Quality Applications: Examples of Applications, Summary of Data End-User Resources, Answers to FAQs, and Common Mistakes to Avoid

Satellite data of atmospheric pollutants are becoming more widely used in the decision-making and environmental management activities of public, private sector and non-profit organizations. They are employed for estimating emissions, tracking pollutant plumes, supporting air quality forecasting activities, providing evidence for "exceptional event" declarations, monitoring regional long-term trends, and evaluating air quality model output. However, many air quality managers are not taking full advantage of the data for these applications nor has the full potential of satellite data for air quality applications been realized. A key barrier is the inherent difficulties associated with accessing, processing, and properly interpreting observational data. A degree of technical skill is required on the part of the data end-user, which is often problematic for air quality agencies with limited resources. Therefore, we 1) review the primary uses of satellite data for air quality applications, 2) provide some background information on satellite capabilities for measuring pollutants, 3) discuss the many resources available to the end-user for accessing, processing, and visualizing the data, and 4) provide answers to common questions in plain language.

Duncan, Bryan↗

NASA NTRSDate not supplied

Human-system Interaction Issues and Proposed Solutions To Promote Successful Maturation of the UTM System

Over five years, NASA, together with partnering organizations, has been developing and successfully demonstrating the maturing capabilities of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) system and its ability to support communication and coordination among small UAS operations through a series of flight tests. During these flight tests, human-system interaction (HSI) elements were also explored in order to identify the barriers to implementation as human operators transitionally fulfill roles that will be ultimately tasked to future automation. Throughout the tests, similar issues were regularly documented and are expected to persist if not formally addressed by consistent procedures or regulation. Documented here, along with suggested mitigations, are the most frequently noted HSI items, which include operator training, data standardization, and information quantity.

UTM↗

NASA NTRSDate not supplied

Human-system interaction issues and proposed solutions to promote successful maturation of the UTM system

Over five years, NASA, together with partnering organizations, has been developing and successfully demonstrating the maturing capabilities of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) system and its ability to support communication and coordination among small UAS operations through a series of flight tests. During these flight tests, human-system interaction (HSI) elements were also explored in order to identify the barriers to implementation as human operators transitionally fulfill roles that will be ultimately tasked to future automation. Throughout the tests, similar issues were regularly documented and are expected to persist if not formally addressed by consistent procedures or regulation. Documented here, along with suggested mitigations, are the most frequently noted HSI items, which include operator training, data standardization, and information quantity.

UTM↗

NASA NTRSDate not supplied

Studying Human-System Interaction within the UTM System

Over five years, NASA, together with partnering organizations, has been developing and successfully demonstrating the maturing capabilities of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) system and its ability to support communication and coordination among small UAS operations through a series of flight tests. During these flight tests, human-system interaction (HSI) elements were also explored in order to identify the barriers to implementation as human operators transitionally fulfill roles that will be ultimately tasked to future automation. Throughout the tests, similar issues were regularly documented and are expected to persist if not formally addressed by consistent procedures or regulation. Documented here, along with suggested mitigations, are the most frequently noted HSI items, which include operator training, data standardization, and information quantity. .

UTM↗

NASA NTRSDate not supplied

Studying Human-System Interaction with the UTM System

Over five years, NASA, together with partnering organizations, has been developing and successfully demonstrating the maturing capabilities of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) system and its ability to support communication and coordination among small UAS operations through a series of flight tests. During these flight tests, human-system interaction (HSI) elements were also explored in order to identify the barriers to implementation as human operators transitionally fulfill roles that will be ultimately tasked to future automation. Throughout the tests, similar issues were regularly documented and are expected to persist if not formally addressed by consistent procedures or regulation. Documented here, along with suggested mitigations, are the most frequently noted HSI items, which include operator training, data standardization, and information quantity.

UTM↗

DOE OSTI2025

A machine learning method of modern urban building energy modeling: A case study of Chicago

Urban-scale building energy modeling is vital for urban planning. However, it can be challenging to assimilate reliable non-geometry building data for urban-scale modeling without extensive investment. Here, this study introduces a novel approach to developing modern urban-scale building energy stock data using geographic information systems and machine learning algorithms without necessarily requiring pre-supplied non-geometric metadata. The proposed framework integrates building footprint and height data to estimate gross floor areas, and matches each building to a pool of candidate records from ComStock or ResStock—filtered to the same county and ranked by geometric similarity—demonstrate a proof-of-concept case study in Chicago for predicting energy use intensity (EUI) using scalable datasets. The model achieved a mean bias error (MBE) of 0.08 kWh/m² and root mean square error (RMSE) of 14.84 kWh/m² under full metadata input for EUI prediction. With only location inputs, the model captured 69.2 % of EUI within predicted ranges. These results demonstrate the model’s potential to support early-stage urban planning, identify candidates for energy-efficient retrofits. By removing the dependency on detailed pre-surveys or extensive building metadata, the approach overcomes a key barrier in traditional urban-scale building energy modeling, illustrating a pathway toward broader and more cost-effective application, though further multi-city validation and improved treatment of pre-1925 buildings are needed.

Energy Use Intensity↗

DOE OSTI2024

Fine structure of current noise spectra in nanoelectromechanical resonators

Here, we study the frequency-dependent noise of a suspended carbon nanotube quantum dot nanoelectromechanical resonator induced by electron-vibration coupling. Using a rigorous Keldysh diagrammatic technique, we establish a formal framework connecting the vibrational properties to electrical measurements. We find that the noise power spectrum exhibits a narrow resonant peak at the frequency of the vibrational modes. However, this fine structure tends to disappear due to a coherent cancellation effect when the tunneling barriers are tuned to a symmetric point. Notably, measuring the electrical current noise spectra provides a sensitive alternative method for detecting the damping and dephasing of quantum vibrational modes.

Electric measurements↗

DOE OSTI2026

SEED: Semantic Energy Exploration and Discovery

The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.

Pan, Meiyu (Melrose) [Oak Ridge National Laborator↗

NASA NTRS2015

Comparing and Contrasting Detectors: JWST NIR vs HST WFC3

In many ways, WFC3s IR channel is a good indicator for what to expect with JWST. There are some differences, most of which should be beneficial in JWST- JWSTs lower operating temperature will freeze out charge traps that would affect WFC3. Benefits should include lower dark current, lower persistence, and better reciprocity- JWSTs more recent HgCdTe process has lower defect density. The benefits are as described above- JWST uses better indium barriers. The benefits should include fewer RC type pixels. One area where more study might be beneficial is stability. The detector electronics play a significant role in determining how stable a detector system is(v.s. bias drifts and photometry). JWSTs SIDECARs are completely WFC3s Ball electronics- Studies comparing the bias and photometric stability of WFC3 and JWST might be useful to informing data acquisition and calibration strategies for JWST.

NIR↗

NASA NTRS2015

Application of Data Cubes for Improving Detection of Water Cycle Extreme Events

As part of an ongoing NASA-funded project to remove a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series), for the hydrology and other point-time series-oriented communities, "data cubes" are created from which time series files (aka "data rods") are generated on-the-fly and made available as Web services from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Data cubes are data as archived rearranged into spatio-temporal matrices, which allow for easy access to the data, both spatially and temporally. A data cube is a specific case of the general optimal strategy of reorganizing data to match the desired means of access. The gain from such reorganization is greater the larger the data set. As a use case of our project, we are leveraging existing software to explore the application of the data cubes concept to machine learning, for the purpose of detecting water cycle extreme events, a specific case of anomaly detection, requiring time series data. We investigate the use of support vector machines (SVM) for anomaly classification. We show an example of detection of water cycle extreme events, using data from the Tropical Rainfall Measuring Mission (TRMM).

water cycle extreme events↗

DOE OSTI2020

Safety issues of defective lithium-ion batteries: identification and risk evaluation

Lithium-ion batteries inevitably suffer minor damage or defects caused by external mechanical abusive loading, e.g. , penetration, deformation, and scratch without triggering a hard/major short circuit. The replacement of cells becomes a dilemma if the safety risk of the defective batteries remains unknown. Herein, by introducing a representative defect form, i.e. , screw indentation, we demonstrate the safety characteristics of defective batteries. We prove that defective batteries have a significantly increased thermal risk and deteriorated mechanical integrity, but can go undetected due to prompt voltage recovery and insignificant local temperature increase. We discover that the voltage curve within the first few cycles contains sufficient information to identify defective batteries from otherwise good ones and propose methodologies to monitor the cells. Capacity loss and current leakage are two characteristics that can be estimated using the voltage curve. According to the defect size and position, the capacity loss could be 1 to 10 2 mA h and the leakage current could be 5–50 mA. Results remove the barriers for defective battery safety risk evaluation, enabling identification, monitoring, and early warning of minor damaged batteries.

25 ENERGY STORAGE↗

NASA NTRS1992

Computer modeling of solar wind interaction with Venus and Mars

The fundamental ideas, successes, and shortcomings of fluid models based on MHD theory for the interaction of the solar wind and a planetary magnetic field and/or atmosphere are reviewed, with special attention to applications to Venus and Mars. An account, including new quantitative results, is given of a number of enhancements of a basic gasdynamic convected field approximation to the magnetohydrodynamic model that incorporate features of solar wind flow past a planet not treated previously. These permit solutions to be calculated for 3D, as opposed to axisymmetric, obstacle shapes, the effects of a magnetic barrier region just exterior to the ionopause, the complete MHD equations for the special case in which the interplanetary magnetic field is aligned with the flow, the effects of mass loading by pickup of ions newly created by photoionization of atmospheric neutrals, and for the unsteady effects associated with the passage of an interplanetary shock wave. Particular emphasis is placed on the information furnished by these models that can be compared with observations, and conversely on the observational data that provide critical tests of the models.

Spreiter, John R.↗

NASA NTRS1976

Electric utility companies and geothermal power

The requirements of the electric utility industry as the primary potential market for geothermal energy are analyzed, based on a series of structured interviews with utility companies and financial institution executives. The interviews were designed to determine what information and technologies would be required before utilities would make investment decisions in favor of geothermal energy, the time frame in which the information and technologies would have to be available, and the influence of the governmental politics. The paper describes the geothermal resources, electric utility industry, its structure, the forces influencing utility companies, and their relationship to geothermal energy. A strategy for federal stimulation of utility investment in geothermal energy is suggested. Possibilities are discussed for stimulating utility investment through financial incentives, amelioration of institutional barriers, and technological improvements.

Pivirotto, D. S.↗

DOE OSTI2022

Assessing the New Home Market Opportunity: Case Study and Cost Modeling for Solar and Storage in 2030

Residential solar and storage markets are growing in the United States. With approximately 1 million new homes constructed every year, this represents a significant opportunity for solar and storage installations. Some homebuilders have begun to build new homes with solar and storage included as a standard offering. It is not clear how solar and storage is incorporated into the new construction process and at what cost. Further, it is unclear what barriers or opportunities exist to scale this model nationwide. To fill this gap in the literature, this research conducts a case study of Mandalay Homes' new solar and storage community in Arizona to gather lessons learned. From this foundation, we further generate a set of pathways to reduce install costs and expand solar and storage market penetration in this sector. To model existing and 2030 solar and storage costs, we use the National Renewable Energy Laboratory's (NREL's) bottom-up cost model. This modeling is further informed by 12 interviews conducted with new home builders, solar contractors, and other subject matter expert organizations. Our case study analysis generated three key considerations for other homebuilders including: 1. Educating local permitting, inspection, and in some cases utility officials on solar and storage products, designs, and code compliant building practices may be required. The need for education may decline as more local governments and utilities review and approve solar and storage projects. 2. Incorporating solar and storage systems into the homebuilding process can add complexity and related coordination challenges. This does not need to result in home construction delays, but can result in costly contractor "dry runs" to construction sites. 3. Deploying solar and storage at the time of new construction has significant economies of scale, which can improve the value proposition of the systems. The case study, extant literature, and interviews were used to model both existing and future solar and storage installation costs at time of new construction. Here, we find three key cost reduction opportunities relating to solar and battery storage hardware, customer acquisition, and overhead. If future contractors can maximize the cost reduction opportunities outlined here, residential new construction costs could decline by 8 - 25% by 2030, depending on the modeled scenario. Though we expect costs to decline through 2030, it is unclear which of these scenarios may ultimately appear. Interviewees further identified a variety of barriers across each cost category that could temper the savings shown here. At the same time, interviewees described several pathways to scale the new construction solar and storage market, beyond installation cost savings. Interviewees confirmed that changes in finance, rate design, resilience policies, deployment mandates, and DER aggregation could all support more market adoption than seen today. These findings suggest that there are significant opportunities to expand new construction markets and this research can serve as a baseline to assess progress in this segment through 2030.

14 SOLAR ENERGY↗

DOE OSTI2023

Differentiable modelling to unify machine learning and physical models for geosciences

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. Here, in this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

58 GEOSCIENCES↗