Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “distributed infrastructure”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,387 records · Page 77

Cybersecurity for Electric Vehicle Charging Infrastructure

As the U.S. electrifies the transportation sector, cyberattacks targeting vehicle charging could impact several critical infrastructure sectors including power systems, manufacturing, medical services, and agriculture. This is a growing area of concern as charging stations increase power delivery capabilities and must communicate to authorize charging, sequence the charging process, and manage load (grid operators, vehicles, OEM vendors, charging network operators, etc.). The research challenges are numerous and complicated because there are many end users, stakeholders, and software and equipment vendors interests involved. Poorly implemented electric vehicle supply equipment (EVSE), electric vehicle (EV), or grid operator communication systems could be a significant risk to EV adoption because the political, social, and financial impact of cyberattacks — or public perception of such — would ripple across the industry and produce lasting effects. Unfortunately, there is currently no comprehensive EVSE cybersecurity approach and limited best practices have been adopted by the EV/EVSE industry. There is an incomplete industry understanding of the attack surface, interconnected assets, and unsecured inter faces. Comprehensive cybersecurity recommendations founded on sound research are necessary to secure EV charging infrastructure. This project provided the power, security, and automotive industry with a strong technical basis for securing this infrastructure by developing threat models, determining technology gaps, and identifying or developing effective countermeasures. Specifically, the team created a cybersecurity threat model and performed a technical risk assessment of EVSE assets across multiple manufacturers and vendors, so that automotive, charging, and utility stakeholders could better protect customers, vehicles, and power systems in the face of new cyber threats.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A Framework for Identifying Building Energy Models of Localized Utility Service Areas Using Smart Meter Data

Bottom-up load modeling of buildings offers a versatile approach to simulating baseline demand and scenarios of future technology evolution and adoption at the individual building level. This capability is essential to understanding how future load shapes may change with the adoption of electric equipment and vehicles, particularly as it relates to grid planning and infrastructure investments. Traditionally, grid planning techniques have used historical load data to predict future load and infrastructure needs. However, with the anticipated rise in adoption of electrification technologies such as heat pumps and electric vehicles, historical data become less reliable predictors of the future. By employing ResStock, a high-fidelity building stock modeling tool, we can fine-tune electrification scenarios and aggregate models to represent varying geographic resolutions of the grid system, while considering the underlying features of homes. This may enable a more accurate and responsive approach to anticipate and plan for the evolving landscape of energy demands. We present a new framework that leverages building stock energy modeling to identify building models that align with the load shapes and housing attributes of buildings with AMI data. This approach applies two model layers: (1) a classification step that identifies the presence of air conditioning, electric heating, and electric water heating, and (2) an optimization routine that identifies building energy models aligning with load profile data from advanced metering infrastructure meters. This report demonstrates one approach to deploying this framework, and presents results for three test cases that use both modeled and AMI data to assess performance. For a test case using AMI data in Fort Collins, Colorado, we observed a median monthly electricity load CV-RMSE of 16.6%, and a top ten daily heating and cooling median absolute percent error of 7.7% and 8.3%, respectively. For each AMI meter, we identify a set of potential energy models so that downstream use-cases can account for uncertainty driven by variability of baseline technologies and occupant behavior, which impact the response to electrification and energy efficiency scenarios. Our results indicate that ResStock has potential as a scalable solution for modeling residential energy demand at local grid resolutions. Its performance depends on location-specific factors, underlying building characteristics, and the level of aggregation, offering a path towards more precise and adaptive distribution grid planning for the evolving energy landscape.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Port Electrification Handbook: A reference to aid U.S. port energy transitions

Port electrification can take many forms, such as electrifying cargo handling equipment or deploying a microgrid to power critical port infrastructure. To help evaluate the growing challenge of increased electrification and its impacts on the system, Pacific Northwest National Laboratory developed this Port Electrification Handbook with support from the U.S. Department of Energy, Office of Electricity’s Microgrids R&D [research and development] program. The goals of this handbook are the following: (1) Help port operators and planners evaluate different electrification technologies; (2) Explain how these technologies could aid and impact ports and surrounding communities; and (3) Provide step-by-step considerations for port electrification.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Using Mapping Tools to Prioritize Electric Vehicle Charger Benefits to Underserved Communities

Mapping tools can play an important role in incorporating equity into planning, implementing, and evaluating investments in electric vehicle (EV) charging stations, also referred to as EV chargers or electric vehicle supply equipment (EVSE). Federal, state, and local organizations need methodologies for using mapping tools as they pursue equity-focused goals to ensure that the benefits of investments in EV chargers flow to energy and environmental justice (EEJ) underserved communities. This report provides examples of how to apply mapping tools to identify priority locations for installing EV chargers that may benefit EEJ underserved communities through four EV charger planning approaches: corridor charging, community charging, fleet electrification, and diversity in STEM and workforce development. It also explores various methodologies for calculating low-public EVSE density. Ensuring that the benefits of EV charger investments flow to underserved communities involves prioritizing locally identified needs and incorporating community input when choosing charging station locations. Installing EV chargers in a census tract identified as an EEJ underserved community does not inherently mean that those EV chargers provide benefits to residents of that community. In addition, representatives of historically disadvantaged communities or environmental justice communities have concerns that installing EV chargers in their communities could potentially exacerbate or propagate existing inequities. While the methodologies described in this report may help identify priority census tracts for equity-focused EV charger investment, additional community engagement and site evaluation are necessary to determine whether EV chargers are accessible, affordable, and convenient to EEJ underserved community residents and what benefits the local community is looking to realize with EV charger installations. This report is the culmination of many discussions with project leaders from DOE-funded projects deploying EV chargers in communities across the nation, organizations representing EEJ underserved communities, state agencies developing EV investment plans, utilities making major EV investments, and DOE national laboratory experts working in transportation electrification. The authors distributed a draft report for peer review, and reviewer comments are summarized in this report. These methodologies are likely to evolve as more EV charger funding programs are implemented and more real-world data is available to measure the effectiveness of strategies for incorporating equity in EV charger deployment projects. Continued efforts to document best practices and critically evaluate whether equity-focused programs achieve their goals are needed as transportation electrification proceeds at the local, regional, and national levels.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Enhanced Lighting Signals for Safety and Efficiency - Experiments with Addressable LEDs

For enhanced roadway safety, clear and immediate visual cues are essential for preventing accidents between drivers and pedestrians. At intersections, however, the line of sight to other roadway users may be obstructed by vehicles and infrastructure. Additionally, adverse conditions, including low visibility, poor weather, or inadequate lighting can increase the potential for collisions. Distracted drivers and pedestrians can further exacerbate the risk of accidents, particularly when using a smartphone, rather than focusing on roadway surroundings. These issues demonstrate the need for infrastructure upgrades that enhance visibility and awareness at crosswalks. A potential solution is through enhanced lighting signals integrated into the roadway infrastructure. One such example is the use of addressable LEDs, individually controllable lights that can change color and brightness instantaneously through programmable microcontrollers. They can be installed and integrated into crosswalks to maintain visibility in conditions where pedestrians may be difficult to see, while also offering peripheral cues to pedestrians who may be distracted by their phones or other objects rather than the road. Such a system (as one example) that is integrated into a traffic intersection digital twin that tracks all roadway users accurately, has the potential to enhance visibility of vulnerable road users, and thus enhance safety. This paper examines the potential implementation and feasibility of this technology, as well as the safety benefits it could provide. Laboratory experiments with addressable LEDs reveal the capabilities and challenges of this technology for roadway infrastructure safety. These findings could pave the way for more integrated lighting in infrastructure for vehicles and pedestrians at intersections, merge and diverge locations, and other areas where complex interactions present safety hazards. Such lighting solutions, enabled by modern computation and communications, could enhance safety and efficiency in our transportation system and improve overall mobility.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Leveraging Artificial Intelligence in Federal Projects

Artificial intelligence (AI) has the potential to transform grid operations. As energy demand rises, weather patterns shift, and foreign threats to critical infrastructure grow, it is essential to harness advanced technology to modernize the grid and increase overall resiliency. This paper examines the integration of AI in federally funded grid infrastructure projects. By analyzing project data, it evaluates the penetration and use cases of AI technologies within key funding initiatives and explores opportunities and challenges associated with their deployment. This analysis categorizes AI adoption in recent federal grid investments, establishing a baseline for measuring near-term impacts and identifying promising AI applications. The research found that 16% of recent federal energy projects included AI integration, and approximately 75% of these projects used AI for more than one primary application.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

Electric Vehicle Supply Equipment (EVSE) Study for Vernon County, Wisconsin [Slides]

Viroqua is a town in Wisconsin with a population of approximately 4,500. The Vernon County Energy district is a local nonprofit in Viroqua that provides energy education, individualized energy consulting and coaching to residents and businesses. They have a very good relationship with our county government and wish to support their efforts. The Vernon County government is currently working on a comprehensive plan and would like to include planning for electric vehicle (EV) charging infrastructure in the plan. They are seeking guidance on logical phases for implementation and identifying the best locations for Level 2 and Level 3 EV chargers. NREL provided technical input on strategic planning for electric vehicle (EV) adoption and electric vehicle supply equipment (EVSE) expansion in Vernon County through the Clean Energy to Communities (C2C) Expert Match technical assistance program. NREL provided strategic planning support for EV expansion planning in Vernon County and siting considerations for locating EVSE.

24 POWER TRANSMISSION AND DISTRIBUTION↗

EDOS Initiatives to Decrease Latency of NRT Data for LANCE

NASA's EOS Data and Operations System (EDOS) is the primary supplier of NRT (near real-time) data to the NASA NRT user community known as the Land, Atmosphere NRT Capability for EOS (LANCE). EDOS provides NRT data for various instruments on the EOS missions Terra, Aqua, Aura, as well as for the NOAA missions Suomi NPP and NOAA-20. This poster describes an overview of the EDOS multi-mission system with emphasis on the NRT products distributed for LANCE elements: AIRS, MISR, MLS, MODIS, MOPITT, OMPS, OMI and VIIRS. Remote EDOS high-rate data capture systems are deployed at NASA ground stations which provide data-driven capture of high-rate science for EOS missions. The remote EDOS components transfer the science data via high-rate WANs to the centralized EDOS Level-zero processing systems located at Goddard Space Flight Center. EDOS produces session-based data sets especially for LANCE NRT use from a single ground station contact; this data is sent to dual LANCE destinations as part of the standard redundancy requirement for LANCE elements. EDOS has implemented various latency improvements with the ultimate goal to have EDOS processing of NRT data keep up with the spacecraft data downlink. EDOS enhancements have included implementation of priority-based QoS, expanded network architecture to include open networks, and use of a delay-tolerant protocol. EDOS has streamlined its systems and infrastructure to minimize latency for NRT data delivery for LANCE. EDOS begins to transfer the NRT data to the LANCE elements within minutes of the end of the contact session with an average packet latency from instrument observation to Level 0 product delivery to each LANCE element of just over one hour.

Michael, Karen↗

A Multi-Model Framework for Assessing Long- and Short-Term Climate Influences on the Electric Grid

Climate change influences many aspects of the electric grid, but prior work and industry practices often ignore the potential effects of changing climate, or they only consider a single effect or individual effects in isolation. Challenges vary with each grid and include adapting to long-term trends such as changing temperature and precipitation or shorter-term events such as drought or storms that could increase in frequency or intensity. Here we present a multi-model framework designed to analyze the effects of long and short-term climate impacts in combination. This framework couples capacity expansion and production cost models with hydrologic models and future climate scenario data to analyze alternative climate and energy futures at high spatial, temporal, and process resolutions. Furthermore, we constructed and evaluated the results of a suite of simulated scenarios exploring climate impacts on capacity investment and stress-tested the resulting future infrastructures using hourly dispatch modeling under alternative drought and load conditions. We demonstrate the approach through a case study of the U.S. Western Interconnection, where climate impacts depend on interactions between temperature-induced load, water availability for hydropower, technology competitiveness, and demand flexibility. Changes in 2038 generating capacity range from -8.5-16.6 GW, and changes in 2038 transmission capacity range from -1-2 GW. Capacity increases are driven by higher load from higher temperatures, while capacity reductions can be achieved in scenarios with higher future hydropower availability and increased demand flexibility. Scenarios requiring additional capacity cost an additional $\$5$-$\$17$ billion (discounted) from 2018 to 2038; however, scenarios with capacity reductions cost $\$1$-$\$18$ billion less. Stress tests on four 2038 infrastructures demonstrated that the identified systems were able to serve at least 99.999% of load and 99.96% of reserves. However, drought and unexpected high-load conditions can result in reduced capacity to respond to contingency events we did not model. Although these results are system and scenario specific, they highlight the importance of considering multiple climate change impacts simultaneously in long-term planning efforts and demonstrate a multi-model, multiscale approach that can be flexibly applied to any system and set of climate change concerns.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. Furthermore, these quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

Artificial intelligence↗

ComStock™ 2024 Release 1 [SWR-19-33 and SWR-20-32]

ComStock™ is an NREL model of the U.S. commercial building stock. The model takes some building characteristics from the U.S. Department of Energy's (DOE's) Commercial Prototype Building Models and Commercial Reference Building. However, unlike many other building stock models, ComStock also combines these with a variety of additional public- and private-sector data sets. Collectively, this information provides high-fidelity building stock representation with a realistic diversity of building characteristics. This repository contains the source code used to build and execute ComStock models, including upgrade scenarios. In addition, the sampling of buildings characteristics used for the initial ComStock (V1.0) release is provided. The ComStock model is under active calibration and development, which is publicly visible on this repository. Execution of the ComStock workflow is managed through the buildstockbatch repository, a shared asset of ResStock™ and ComStock™ , specifically developed to scale to execution of tens of millions of simulations through multiple infrastructure providers. The dataset output from the initial ComStock (V1.0) release can be found at the accompanying ComStock data viewer website and additional information about ComStock found on the NREL Buildings Website. For more details about ongoing model development please consult the End Use Load Profiles website. ComStock is a direct result of the NREL residential stock modeling tool ResStock™ (recipient of a R&D100 award) and was inspired by the high-fidelity solar & storage adoption model dGen™. Additionally, this tool would not be possible without the decades of work undertaken by the OpenStudio® and EnergyPlus® visionaries and contributors, significant funding, feedback and support from the Los Angeles Department of Water and Power, and the Department of Energy's Building Technology Office ongoing support of and investment in building energy modeling software. is an analytic methodology for modeling the energy usage of the commercial building stock within the United States of America. The commercial building stock is represented through a sampling of complex probabilistic distributions of various features of interest for modeling energy usage within commercial buildings. Each sample from these distributions is converted into a building energy model based on the features of that specific sample. Each building energy model can be simulated as is, but additional changes can be made to the model through addition of energy conservation measures, component faults, or other desired alterations. The results of the simulations are then processed to provide insights for various stakeholders, including but not limited to policy makers, engineers, and marketers.

Horsey, Henry↗

Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI-XFC)

GEMINI-XFC will use first-of-a-kind integrated high-fidelity grid and transport modeling to identify effective pathways for widespread electrification, to design and evaluate integrated vehicle-grid control schemes, and to optimize electric vehicle integration at a full regional scale with individual customer resolution. Control variables will include: Charging station design and planning (where and what kind of charging stations); EV route scheduling considering grid "status"; and Dispatch of behind-the-meter energy storage and legacy voltage control actuators (on-load tap changes, voltage regulators, capacitors).

DIRECT ENERGY CONVERSION,POWER TRANSMISSION AND DI↗

Framework for Assessing Impact of Wave-Powered Desalination on Resilience of Coastal Communities

Coastal communities face unique challenges in maintaining continuous service from critical infrastructure. This research advances capabilities for evaluating the impact of using wave energy to desalinate water on the resilience of coastal communities. The study focuses on the feasibility of using wave energy conversion to provide drinking water to communities in need and applying resilience metrics to quantify its impact on the community. To assess the feasibility of wave-powered desalination, this research couples the open-source software Wave Energy Converter SIMulator (WEC-Sim) and Water Network Tool for Resilience (WNTR). This research explores variations in both the wave resource (location, seasonality, and duration) and the ability to maintain drinking water service during a disruption scenario by applying the simulation framework to three case studies, which are based on communities in Puerto Rico. The simulation framework provides a contextualized assessment of the ability of wave-powered desalination to improve the resilience of coastal communities, which can serve as a methodology for future studies seeking the integration of wave-powered desalination with water distribution systems.

16 TIDAL AND WAVE POWER↗

Milwaukee Urban Development: Assessing the Drivers of Urban Flood Vulnerability in Milwaukee using the Integrated Valuation of Ecosystem Services and Tradeoffs Urban Flood Risk Mitigation Model (InVEST)

Milwaukee County has experienced an increase in flooding due to climate change and urbanization. The frequency and severity of flooding vary spatially due to differences in land cover, surface permeability, and infrastructure. Marginalized communities tend to experience disproportionately high flooding and damage due to infrastructural inequalities and limited access to resources. To quantify these differences, we used the Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model to calculate and create maps of runoff retention, nominal flood depth, and economic damage to buildings in Milwaukee. Our model inputs included land cover, surface permeability, and rainfall. To inform our precipitation inputs, we used NASA’s Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) and National Weather Service (NWS) data. We assessed the relationship between flood risk and social and environmental spatial data including redlining, racial demographics, greenspace, and community resilience. The data demonstrate that flood risk is higher in historically redlined neighborhoods, majority Hispanic and Black census block groups, areas that lack parks and trees, and areas of low community resilience as measured by the Census Bureau’s Community Resilience Estimates (CRE). These findings will support our partners, Groundwork Milwaukee and Groundwork USA, in their efforts to promote the equitable distribution of resources and support environmental health in urban spaces. The end products of this project provide our partners with tools to assess urban flooding vulnerability, guide future intervention projects, quantify the effects of environmental injustice, and improve stakeholder access to data.

Madeleine Tango↗

Milwaukee Urban Development: Assessing the Drivers of Urban Flooding Vulnerability in Milwaukee Using NASA Earth Observations

Milwaukee County has experienced an increase in flooding due to climate change and urbanization. The frequency and severity of flooding vary spatially due to differences in land cover, surface permeability, and infrastructure. Marginalized communities tend to experience disproportionately high flooding and damage due to infrastructural inequalities and limited access to resources. To quantify these differences, we used the Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model to calculate and create maps of runoff retention, nominal flood depth, and economic damage to buildings in Milwaukee. Our model inputs included land cover, surface permeability, and rainfall. To inform our precipitation inputs, we used NASA’s Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) and National Weather Service (NWS) data. We assessed the relationship between flood risk and social and environmental spatial data including redlining, racial demographics, greenspace, and community resilience. The data demonstrate that flood risk is higher in historically redlined neighborhoods, majority Hispanic and Black census block groups, areas that lack parks and trees, and areas of low community resilience as measured by the Census Bureau’s Community Resilience Estimates (CRE). These findings will support our partners, Groundwork Milwaukee and Groundwork USA, in their efforts to promote the equitable distribution of resources and support environmental health in urban spaces. The end products of this project provide our partners with tools to assess urban flooding vulnerability, guide future intervention projects, quantify the effects of environmental injustice, and improve stakeholder access to data.

Madeleine Tango↗

Measuring Societal Infrastructure Service Burden

Social Infrastructure Service Burden (abbr. Social Burden) is defined as the burden to a population for attaining services needed from infrastructure. Infrastructure services represent opportunities to acquire things that people need, such as food, water, healthcare, financial services, etc. Accessing services requires effort, disruption to schedules, expenditure of money, etc. Social Burden represents the relative hardship people experience in the process of acquiring needed services. Social Burden is comprised of several components. One component is the effort associated with travel to a facility that provides a needed service. Another component of burden is the financial impact of acquiring resources once at the providing location. We are applying Social Burden as a resilience metric by quantifying it following a major disruption to infrastructure. Specifically, we are most interested in quantifying this metric for events in which energy systems are a major component of the disruption. We do not believe this is the only such use of the Social Burden metric, and therefore we will also be exploring its use to describe blue-sky conditions of a society in the future. Furthermore, while the construct can be applied to a dynamically changing situation, we are applying it statically, directly following a disruption. This notably ignores recovery dynamics that are a key capability of resilient systems. This too will be explored in future research.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Innovating Distributed Embedded Energy Prize (InDEEP): A Lessons Learned Report

The U.S. Department of Energy's Water Power Technologies Office (WPTO) launched the Innovating Distributed Embedded Energy Prize (InDEEP) in March 2023 to accelerate innovation in Distributed Embedded Energy Conversion Technologies (DEEC-Tec) for ocean wave energy. Administered by the National Laboratory of the Rockies (NLR) with technical support from Sandia National Laboratories (SNL), InDEEP focused on the development of small, distributed, and embeddable energy converters (DEECs) and their integration into scalable DEEC-Tec metamaterials for marine renewable energy applications. Spanning three phases over two years, InDEEP awarded approximately $2.3 million to teams from academia, industry, and startups. Phase I emphasized conceptual design. Phase II moved into the prototyping of individual DEECs. Phase III required integration into functional DEEC-Tec metamaterial prototypes. Across 60 submissions, teams explored a wide range of energy conversion mechanisms - including piezoelectric, variable-capacitance, ionic, and inductive methods. Note, the prize did not include the design nor demonstration of ocean wave energy conversion systems. Rather, the prize only required participants to design and demonstrate individual DEECs and corresponding DEEC-Tec metamaterials. This prize utilized a mix of novel and proven techniques to attract participants from outside marine energy, including an engagement leaderboard, robust recruitment, technical expert mentorship, and a suite of technical trainings. Key insights from the competition emphasized that DEEC-Tec metamaterials must be intentionally designed to produce beneficial emergent behaviors - advantages that go beyond simply combining multiple DEEC units. Top-performing teams showed that thoughtful design of system architecture, coordinated deformation, and systems adaptabilities could unlock meaningful performance gains both at the DEEC system level and DEEC-Tec metamaterial system level. A critical realization was that many DEEC-Tec metamaterials could benefit from being designed to accept lower-frequency energy inputs and shift those into higher-frequencies per each DEEC making up the respective DEEC-Tec metamaterial. Other important takeaways included the need for rigorous and quantitative performance testing, effective integration of power conditioning electronics, and the pivotal role of material science in enabling innovative, adaptive DEEC-Tec-based energy conversion designs. InDEEP also helped establish a growing DEEC-Tec community of practitioners, attracting participants from beyond traditional marine energy sectors. Through a strong support infrastructure, InDEEP fostered early-stage innovation and laid a foundation for future DEEC-Tec-based ocean wave energy conversion solutions - positioning DEEC-Tec as a promising pathway toward scalable, resilient ocean wave energy conversion. Through focused R&D of individual DEECs and their integration into DEEC-Tec metamaterials, alongside a growing, multidisciplinary community catalyzed by InDEEP, there is a strong opportunity to drive a disruptive shift in ocean wave energy conversion design and development. This convergence of novel architectures, emergent behaviors, and collaborative innovation positions DEEC-Tec as a transformative approach, moving the field from rigid, centralized energy conversion-based designs to resilient, modular systems highly adaptable for real-world ocean wave energy conversion applications.

16 TIDAL AND WAVE POWER↗

Network-Capable Application Process and Wireless Intelligent Sensors for ISHM

Intelligent sensor technology and systems are increasingly becoming attractive means to serve as frameworks for intelligent rocket test facilities with embedded intelligent sensor elements, distributed data acquisition elements, and onboard data acquisition elements. Networked intelligent processors enable users and systems integrators to automatically configure their measurement automation systems for analog sensors. NASA and leading sensor vendors are working together to apply the IEEE 1451 standard for adding plug-and-play capabilities for wireless analog transducers through the use of a Transducer Electronic Data Sheet (TEDS) in order to simplify sensor setup, use, and maintenance, to automatically obtain calibration data, and to eliminate manual data entry and error. A TEDS contains the critical information needed by an instrument or measurement system to identify, characterize, interface, and properly use the signal from an analog sensor. A TEDS is deployed for a sensor in one of two ways. First, the TEDS can reside in embedded, nonvolatile memory (typically flash memory) within the intelligent processor. Second, a virtual TEDS can exist as a separate file, downloadable from the Internet. This concept of virtual TEDS extends the benefits of the standardized TEDS to legacy sensors and applications where the embedded memory is not available. An HTML-based user interface provides a visual tool to interface with those distributed sensors that a TEDS is associated with, to automate the sensor management process. Implementing and deploying the IEEE 1451.1-based Network-Capable Application Process (NCAP) can achieve support for intelligent process in Integrated Systems Health Management (ISHM) for the purpose of monitoring, detection of anomalies, diagnosis of causes of anomalies, prediction of future anomalies, mitigation to maintain operability, and integrated awareness of system health by the operator. It can also support local data collection and storage. This invention enables wide-area sensing and employs numerous globally distributed sensing devices that observe the physical world through the existing sensor network. This innovation enables distributed storage, distributed processing, distributed intelligence, and the availability of DiaK (Data, Information, and Knowledge) to any element as needed. It also enables the simultaneous execution of multiple processes, and represents models that contribute to the determination of the condition and health of each element in the system. The NCAP (intelligent process) can configure data-collection and filtering processes in reaction to sensed data, allowing it to decide when and how to adapt collection and processing with regard to sophisticated analysis of data derived from multiple sensors. The user will be able to view the sensing device network as a single unit that supports a high-level query language. Each query would be able to operate over data collected from across the global sensor network just as a search query encompasses millions of Web pages. The sensor web can preserve ubiquitous information access between the querier and the queried data. Pervasive monitoring of the physical world raises significant data and privacy concerns. This innovation enables different authorities to control portions of the sensing infrastructure, and sensor service authors may wish to compose services across authority boundaries.

Figueroa, Fernando↗