Propulsion systems trends
Future technology trends in air breathing propulsion systems for aircrafts
Dugan, J. F., Jr.↗
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Echoes of the Past, Glimpses of the Future - Ongoing Trends in Assurance of EEE Parts for Spaceflight
This presentation will review some past NASA presentations that had identified challenges and future trends in the assurance of Electrical, Electronic and Electromechanical (EEE) parts for spaceflight applications. It will contrast the concerns expressed in those presentations with the challenges of today and identify trends that give insight into the future. Common themes include the use of commercial parts, limited technical and physical resources, the pace of technology change and the difficulties in handling and testing individual parts as they become smaller and more complex. Some options for dealing with these challenges will be suggested to accommodate increasing numbers of smallsats, cubesats, constellations and other disruptive concepts, as space becomes more and more commercialized.
NASA Electronic Parts and Packaging (NEPP) Program↗
Future Mission Trends and their Implications for the Deep Space Network
This viewgraph presentation discusses the direction of future missions and it's significance to the Deep Space Network. The topics include: 1) The Deep Space Network (DSN); 2) Past Missions Driving DSN Evolution; 3) The Changing Mission Paradigm; 4) Assessing Future Mission Needs; 5) Link Support Trends; 6) Downlink Rate Trends; 7) Uplink Rate Trends; 8) End-to-End Link Difficulty Trends; 9) Summary: Future Mission Trend Drivers; and 10) Conclusion: Implications for the DSN.
mission trends↗
Future Mission Trends and their Implications for the Deep Space Network
Planning for the upgrade and/or replacement of Deep Space Network (DSN) assets that typically operate for forty or more years necessitates understanding potential customer needs as far into the future as possible. This paper describes the methodology Deep Space Network (DSN) planners use to develop this understanding, some key future mission trends that have emerged from application of this methodology, and the implications of the trends for the DSN's future evolution. For NASA's current plans out to 2030, these trends suggest the need to accommodate: three times as many communication links, downlink rates two orders of magnitude greater than today's, uplink rates some four orders of magnitude greater, and end-to-end link difficulties two-to-three orders of magnitude greater. To meet these challenges, both DSN capacity and capability will need to increase.
future mission needs↗
Some trends in future launch vehicle systems technology.
Launch vehicle and spacecraft cost trends including larger vehicles, post Saturn phase, performance improvement by throttling, etc
SATURN LAUNCH VEHICLE↗
Future Intensity‐Duration‐Frequency Curves of Extreme Precipitation in the Midwest United States From Convection‐Permitting Modeling
Abstract During the last four decades, global warming has statistically significant intensified extreme precipitation events in the Midwestern United States (defined here as the region covering Illinois, Indiana, Ohio, and Kentucky), leading to increased risks to human life, property, and infrastructure. To enable climate change adaptation and resilience across various economic and social sectors in this region, updated information about future climate changes, specifically at finer spatial scales, is essential. Leveraging a new 150‐year dynamical downscaling data set at convection‐permitting resolution, this study introduces a framework to construct the projected future intensity‐duration‐frequency (IDF) curves of heavy precipitation, which are prominent tools for infrastructure design and water resources management. This framework generates IDF curves at both sub‐daily and multi‐day duration utilizing hourly in situ observations as well as quantile‐based statistical techniques in bias‐correction and return levels selection. The assumption of non‐stationarity in the distribution parameter fitting process is also implemented in this workflow. Compared to historical IDF curves for 1980–2022, future projected IDF curves for 2058–2100 under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios indicate an average intensity increase of approximately 15% and 25%, respectively, across 74 stations, considering both annual and seasonal timescales. Future projections suggest that extreme precipitation events may become more severe across six investigated return periods, with longer return periods showing a greater increase. The frequency of future extreme precipitation events in the Midwest region is also projected to double. Furthermore, current results reveal spatial heterogeneity of future trends across stations owing to the high‐resolution input data set. Plain Language Summary This study investigates the evolving nature of extreme precipitation events in the Midwestern United States under a changing climate. By leveraging a high‐resolution dynamical downscaling data set, we construct projected intensity‐duration‐frequency (IDF) curves for future extreme rainfall events. These curves serve as vital tools for infrastructure planning and water resource management. Our analysis reveals a significant increase in both the intensity and frequency of extreme precipitation events in the region. Future projected IDF curves for the late century indicate an average intensity increase of approximately 15%–25% compared to historical values. Moreover, the frequency of such events is expected to double. Spatial heterogeneity in future trends is observed across different stations within the Midwest, highlighting the importance of high‐resolution modeling in capturing localized climate variability. These findings underscore the urgent need for climate adaptation strategies to mitigate the increasing risks associated with extreme precipitation events in the region. Key Points This study introduces a workflow to construct future intensity‐duration‐frequency (IDF) curves over the Midwest United States using a new convection‐permitting modeling data set The current IDF construction workflow reproduces well the historical observed IDF 30 curves in summer months with median relative errors of 2.4% among 74 stations and 6 investigated durations The projected IDF curves show diverse future trends of extreme precipitation across stations, with intensity increases of approximately 15% and 25% under RCP4.5 and RCP8.5 climate scenarios, respectively, and a doubling of frequency on average
Nguyen, Trung↗
Precipitation trends determine future occurrences of compound hot–dry events
Compound hot–dry events—co-occurring hot and dry extremes—frequently cause damages to human and natural systems, often exceeding separate impacts from heatwaves and droughts. Strong increases in the occurrence of these events are projected with warming, but associated uncertainties remain large and poorly understood. Here, using climate model large ensembles, we show that mean precipitation trends exclusively modulate the future occurrence of compound hot–dry events over land. This occurs because local warming will be large enough that future droughts will always coincide with at least moderately hot extremes, even in a 2°C warmer world. By contrast, precipitation trends are often weak and equivocal in sign, depending on the model, region and internal climate variability. Therefore, constraining regional precipitation trends will also constrain future compound hot–dry events. These results help to assess future frequencies of other compound extremes characterized by strongly different trends in the drivers.
54 ENVIRONMENTAL SCIENCES↗
Fire in the Arctic: Current Trends and Future Pathways
Wildland fires in the Arctic are expected to become more frequent and more severe. In recent years, extreme fire seasons have been documented across the Pan-Arctic and boreal in five of the last seven years – including large wildfires in Greenland in 2017 and 2019 over tundra and high carbon soil landscapes and an earlier start of extreme fire seasons in 2023. Future Arctic and boreal fire regimes will experience increased fire risk though the end of this century (McCarty et al., 2021, Senande-Rivera et al., 2022). The main factors affecting the severity and frequency of wildland fires are fuels (vegetated biomass and type of biomass) and fuel condition (dryness), fire weather conditions (relative humidity, drought, precipitation), and ignition (human-caused, lightning). Climate change directly influences all of these drivers, and indirectly also some human-caused ignitions.
Fire↗
A review of machine learning in building load prediction
The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.
Liang, Zhang↗
Impact of digital systems technology on man-vehicle systems research
The present study, based on a NASA technology assessment, examines the effect of new technologies on trends in crew-systems design and their implications from the vantage point of man-vehicle systems research. Those technologies that are most relevant to future trends in crew-systems design are considered along with problems associated with the introduction of rapidly changing technologies and systems concepts from a human-factors point of view. The technologies discussed include information processing, displays and controls, flight and propulsion control, flight and systems management, air traffic control, training and simulation, and flight and resource management. The historical evolution of cockpit systems design is used to illustrate past and possible future trends in man-vehicle systems research.
Bretoi, R. N.↗
A Well Connected Future: Networking Trends
It is clear networks of various types are beginning to have a substantial impact on every aspect of our lives, our jobs, society and the way we perceive and use information. Commerce is moving onto the Internet; doctors are performing surgery and diagnosis with the aid of high performance networks and satellites; and political revolutions and coups are reported through the use of networks. This is only the first glimpse of what is yet to come. This paper is a brief survey of technology research in networking, with emphasis on the use of satellites present and future as a component of a global information mesh.
St.Johns, Michael↗
Materials processing in space: Future technology trends
NASA's materials processing in space- (MPS) program involves both ground and space-based research and looks to frequent and cost effective access to the space environment for necessary progress. The first generation payloads for research are under active design and development. They will be hosted by the Space Shuttle/Spacelab on Earth orbital flights in the early 1980's. hese missions will focus on the acquisition of materials behavior research data, the potential enhancement of Earth based technology, and the implementation of space based processing for specialized, high value materials. Some materials to be studied in these payloads may provide future breakthroughs for stronger alloys, ultrapure glasses, superior electronic components, and new or better chemicals. An operational 25 kW power system is expected to be operational to support sustained, systematic space processing activity beyond shuttle capability for second generation payload systems for SPACELAB and free flyer missions to study solidification and crystal growth and to process metal/alloys, glasses/ceramics, and chemicals and biologicals.
Barter, N. J.↗
Wind turbine drivetrains: state-of-the-art technologies and future development trends
Abstract. This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the system that converts kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. Offshore development and digitalization are also a focal point in this study. Drivetrain in this context includes the whole power conversion system: main bearing, shafts, gearbox, generator and power converter. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps. The main challenges in drivetrain research identified in this paper include drivetrain dynamic responses in large or floating turbines, aerodynamic and farm control effects, use of rare-earth material in generators, improving reliability through prognostics, and use of advances in digitalization. These challenges illustrate the multidisciplinary aspect of wind turbine drivetrains, which emphasizes the need for more interdisciplinary research and collaboration.
17 WIND ENERGY↗
Lignin-based membranes for health, food safety, environmental, and energy applications: current trends and future directions
Not Available
Acurio-Cerda, Karen↗
Heterostructured stainless steel: Properties, current trends, and future perspectives
Not provided.
Materials Science↗
New Era Towards Autonomous Additive Manufacturing: A Review of Recent Trends and Future Perspectives
Abstract The Additive Manufacturing (AM) landscape has significantly transformed in alignment with Industry 4.0 principles, primarily driven by the integration of Artificial Intelligence (AI) and Digital Twin (DT). However, current Intelligent Additive Manufacturing (IAM) systems face limitations such as fragmented AI tool usage and suboptimal human-machine interaction (HMI). This paper reviews existing IAM solutions, emphasizing control, monitoring, process autonomy, and end-to-end integration, and identifies key limitations, such as the absence of a high-level controller for global decision-making. To address these gaps, we propose a transition from IAM to Autonomous Additive Manufacturing (AAM), featuring a hierarchical framework with four integrated layers: knowledge, generative solution, operational, and cognitive. In the cognitive layer, AI agents notably enable machines to independently observe, analyze, plan, and execute operations that traditionally require human intervention. These capabilities streamline production processes and expand the possibilities for innovation, particularly in sectors like in-space manufacturing (ISM). Additionally, this paper discusses the role of AI in self-optimization and lifelong learning, positing that the future of AM will be characterized by a symbiotic relationship between human expertise and advanced autonomy, fostering a more adaptive, resilient manufacturing ecosystem.
Fan, Haolin↗
Economic and environmental bottlenecks in the industrial‐scale production of lipid‐derived biofuels from oleaginous yeasts: A review of the current trends and future prospects
Abstract Concerns about climate change and the reliance on fossil fuel reserves have motivated researchers to identify new renewable sources of energy. Biomass holds the potential to replace fossil‐derived products with biofuels and bio‐based chemicals. Plant‐derived lipids are promising sources of biofuels; however, the production of plant oil often leads to the release of massive amounts of carbon dioxide due to deforestation and land‐use change. The production of biofuels via plant oils (such as soybean) also competes with food production and in turn, impacts biodiversity. To mitigate these issues, the production of lipids from oleaginous yeasts could be an excellent alternative by incorporating these microbes into biorefineries utilizing agricultural or forest residues. Eventually, these microbial lipids could be potential sources for producing lipid‐derived biofuels. However, the current conventional methods for the production and recovery of lipids from oleaginous microbes suffer from economic and ecological challenges that affect its industrial‐scale expansion. This review highlights the major economic and environmental bottlenecks for the production of lipid‐derived biofuels from oleaginous yeasts. It also provides perspectives on the strategies that could be adapted on economic and ecological fronts to assist the expansion of the production of microbial lipid‐derived biofuels at an industrial scale.
Agriculture↗