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At least 397 records · Page 22

Editorial: Advanced water splitting technologies development: Best practices and protocols

As the level of deployment and utilization of renewable energy sources, including wind and solar, continues to rise, large-scale, long-term energy storage technologies that could accommodate weekly and seasonal energy fluctuations will play a significant role in the overall deployment of renewable energies in the future. Harnessing and storing renewable energy resources via electrochemical, photoelectrochemical, or thermochemical processes by converting renewable energy into sustainable (energy storage) fuels have the potential to meet the long-term, terawatt scale energy storage challenge. Renewable hydrogen production is the cornerstone for sustainable fuel production and deep decarbonization of multiple sectors in our society. Cost-competitive clean hydrogen provides value to applications, such as 1) in the transportation sector for fuel cell vehicles, 2) in the electric grid sector for system stability and load balancing, and 3) in the industrial sector with metal refineries, cement production, and biomass upgrading (carbon-free fertilizer production). In addition, coupling clean renewable hydrogen with the carbon and nitrogen cycles enables known and well-established thermal-chemical processes to generate renewable hydrocarbon fuels and ammonia. The Advanced Water Splitting Technologies (AWST): low temperature electrolysis (LTE), high temperature electrolysis (HTE), photoelectrochemical (PEC) and solar thermo-chemical hydrogen (STCH) provide four unique and parallel approaches to produce low cost, low greenhouse gas (GHG) emission hydrogen at scale (Figure 1). Cost competitive clean hydrogen production using these four technologies is a current high priority focus for governments and industry. In June of 2022, the U.S. Department of Energy (DOE) launched the first in a series of Earthshot Initiatives. The Hydrogen Shot, “1 1 1” aims to reduce the cost of clean hydrogen by more than 80% to one dollar per one kilogram in 1 decade ($\$$1/kg H 2 ). The European Green Deal and the International Energy Agency (IEA) have implemented a strong focus on green hydrogen production for a clean and secure energy future.

benchmarking, low temperature electrolysis↗

Finite Element Simulation of Smart Structures

Finite element equations representing the behavior of piezoelectric materials when bounded to a typical structure and used as sensors and actuators were developed. Emphasis was placed on generating sensor output equations of piezoelectric sensors and responses of a typical structure bonded with piezoelectric sensors and actuators on the basis of finite element formulation. The model can predict not only structural responses due to both mechanical and electrical loading but also electrical potential due to mechanical or thermal effects. The resulted finite element equations were then used for simple control design and performance evaluation. In the control algorithm, voltages coming out from piezoelectric sensors, which are proportional to strains at sensing locations, are taken as input. The voltages applied to the piezoelectric actuators are used as output. The feasibility of integrating control algorithm with the element routine developed herein and FEAP was demonstrated. In particular, optimal independent modal space control was implemented in a software package on the basis of finite element formulation. A rudimentary finite element-control algorithm package was also developed to evaluate the performance of candidate control laws. A few numerical simulations using the software package developed herein were given. The integrated software package will provide a design tool to address issues such as how adaptive smart systems will scale to a full size aircraft, the amount of piezoelectric materials and the powers needed to actuate it for desired performance. It will also provide a viable new structural control design concept for practical applications in large flexible structures such as aerospace vehicles and aircraft.

Cui, Y. Lawrence↗

The Automated Mobility District Implementation Catalog: Insights from Ten Early-Stage Deployments

Major disruptive technologies are set to redefine the way in which people view travel, particularly in dense urban areas. Already, ride-hailing services have redefined mobility expectations of a new generation of urban dwellers in some places around the country. Over the next few decades, the proliferation of autonomous vehicles, which are generally referred to in the industry as “automated vehicles (AVs),” will be enhanced by the next generation of shared mobility. This combination of AV operations with on-demand service will provide convenience of mobility similar to that being exhibited in today’s transportation networking companies (TNCs). Shared, automated, public mobility resulting from the cross-hybridization of AVs with on-demand mobility service will bring economic and system efficiencies. Economic efficiencies may be realized by less vehicle ownership and more vehicle “usership.” Many companies are already exploring avenues for shared automated mobility through fleet operations as the wave of the future.

33 ADVANCED PROPULSION SYSTEMS↗

A Multi-Site Networked Hardware-in-Loop Platform for Evaluation of Interoperability and Distributed Intelligence at Grid-Edge

Electric power systems have experienced large increases in the number of intelligent, connected and controllable devices being deployed, leading to a high degree of distributed intelligence at the grid-edge. These devices, both utility-owned and consumer-owned, include but are not limited to: renewable generation sources, energy storage, remote switches, voltage regulators, and smart controllable loads such as electric vehicles. These new devices provide significant potential for increased operational flexibility that can be leveraged to achieve system reconfiguration, resiliency improvements, power quality improvements, and distribution system automation. However, there are two significant challenges that must be addressed before these assets can be leveraged for operations: interoperability and system level validation prior to deployment. Because of the complexity of distributed control systems, and their interactions with legacy centralized controls, a purely simulations-based approach for pre-deployment validation is not sufficient. It requires hardware-in-loop testing to emulate the operational hardware devices and evaluate their performance. Additionally, securely integrating multiple test facilities at utility operators and vendors might enable rapid scale-up of evaluation platforms, and remove the need for multiple expensive standalone installations. Presented in this paper, is the development of a multi-site evaluation platform that employs Advanced Distribution Management Systems (ADMS), distributed control devices, real-time hardware-in-loop assets, secure communication links, and protocol adapters. This platform uses standards-based approaches and open-source tools, and hence can serve as a template for other researchers and institutions to implement their multi-site evaluation frameworks for pre-deployment testing.

Essakiappan, Somasundaram↗

In-Space Propulsion Program Overview and Status

NASA's In-Space Propulsion (ISP) Program is designed to develop advanced propulsion technologies that can enable or greatly enhance near and mid-term NASA science missions by significantly reducing cost, mass, and/or travel times. These technologies include: Solar Electric Propulsion, Aerocapture, Solar Sails, Momentum Exchange Tethers, Plasma Sails and other technologies such as Advanced Chemical Propulsion. The ISP Program intends to develop cost-effective propulsion technologies that will provide a broad spectrum of mission possibilities, enabling NASA to send vehicles on longer, more useful voyages and in many cases to destinations that were previously unreachable using conventional means. The ISP approach to identifying and prioritizing these most promising technologies is to use mission and system analysis and subsequent peer review. The ISP program seeks to develop technologies under consideration to Technology Readiness Level (TRL) -6 for incorporation into mission planning within 3-5 years of initiation. The NASA TRL 6 represents a level where a technology is ready for system level demonstration in a relevant environment, usually a space environment. In addition, maximum use of open competition is encouraged to seek optimum solutions under ISP. Several NASA Research Announcements (NRA's) have been released asking industry, academia and other organizations to propose propulsion technologies designed to improve our ability to conduct scientific study of the outer planets and beyond. The ISP Program is managed by NASA Headquarters Office of Space Science and implemented by the Marshall Space Flight Center in Huntsville, Alabama.

Wercinski, Paul F.↗

Regional analysis of aluminum and steel flows into the American automotive industry

Aluminum and steel represent the two most dominant metals in light duty vehicles, yet the flows of these materials into the American automotive industry have not been well characterized. This study proposes and implements a method for analyzing the flow of these metals into the automotive industry. We create a framework for performing regionally linked, sector specific material flow analyses and use this framework to trace flows of aluminum and steel entering the American automotive industry, focusing on flows downstream from raw material production. We show that automotive aluminum sheet and extrusions are sourced primarily from the NPCC (23%), SERC (20%), MRO (18%), and RFC (13%) NERC regions and a spatially unresolved Local region within the U.S. and Canada (18%). We determine that primary aluminum is largely from Canada (70%), nearly all from Quebec (69%). Further upstream, alumina and bauxite originate mostly from Brazil, Australia, and Jamaica. We also show that finished automotive steel is sourced primarily from the RFC (63%) and SERC (20%) regions. The crude steel supply similarly originates mainly from the RFC (69%) and SERC (7%) regions. Upstream raw materials including coke, coking coal, iron ore, lime, and steel scrap are primarily sourced from the U.S. with only direct reduced iron and pig iron used in electric arc furnace steel production coming mostly from outside the U.S. The framework developed here allows for increased spatial resolution of material flows, which can be used to develop more specific life cycle impact factors for life cycle assessments.

aluminum↗

Study of Hydrogen Fuel Cell Technology for Freight Rail Propulsion and Review of Relevant Industry Standards.

Alternatives to conventional diesel electric propulsion are currently of interest to rail operators. In the U.S., smaller railroads have implemented natural gas and other railroads are exploring hydrogen technology as a cleaner alternative to diesel. Diesel, battery, hydrogen fuel cell, or track electrification all have trade-offs for operations, economics, safety, and public acceptability. A framework to compare different technologies for specific applications is useful to optimize the desired results. Standards from the Association of American Railroads (AAR) and other industry best practices were reviewed for applicability with hydrogen fuel cell technology. Some technical gaps relate to the physical properties of hydrogen, such as embrittlement of metals, invisible flames, and low liquid temperatures. A reassessment of material selection, leak/flame detection, and thermal insulation methods is required. Hydrogen is less dense and diffuses more easily than natural gas, and liquid hydrogen is colder than liquefied natural gas. Different densities between natural gas and hydrogen require modifications to tank designs and flow rates. Leaked hydrogen will rise rather than pool on the ground like diesel, requiring a modification to the location of hydrogen tanks on rolling stock. Finally, the vibration and shock experienced in the rail environment is higher than light-duty vehicles and stationary applications for which current fuel cell technology has been developed, requiring a modification in tank design requirements and testing.

08 HYDROGEN↗

Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.

33 ADVANCED PROPULSION SYSTEMS↗

Positively Verifying Mating of Previously Unverifiable Flight Connectors

Current practice is to uniquely key the connectors, which, when mated, could not be verified by ground tests such as those used in explosive or non-explosive initiators and pyro valves. However, this practice does not assure 100-percent correct mating. This problem could be overcome by the following approach. Errors in mating of interchangeable connectors can result in degraded or failed space mission. Mating of all flight connectors considered not verifiable via ground tests can be verified electrically by the following approach. It requires two additional wires going through the connector of interest, a few resistors, and a voltage source. The test-point voltage V(sub tp) when the connector is not mated will be the same as the input voltage, which gets attenuated by the resistor R(sub 1) when the female (F) and male (M) connectors are mated correctly and properly. The voltage at the test point will be a function of R(sub 1) and R(sub 2). Monitoring of the test point could be done on ground support equipment (GSE) only, or it can be a telemetry point. For implementation on multiple connector pairs, a different value for R(sub 1) or R(sub 2) or both can be selected for each pair of connectors that would result in a unique test point voltage for each connector pair. Each test point voltage is unique, and correct test point voltage is read only when the correct pair is mated correctly together. Thus, this design approach can be used to verify positively the correct mating of the connector pairs. This design approach can be applied to any number of connectors on the flight vehicle.

Pandipati R. K. Chetty↗

Coal as Value-Added for Lithium Battery Anodes (Final Report)

Graphite is a basic material used for energy storage in lithium-ion (Li-ion) batteries. More than 70% of worldwide graphite production and nearly 50% of the annual graphite supply for the United States comes from China. Potential economic and national security concerns exist because Li-ion batteries are used not only for the fast-growing electrical vehicle market (15.6% compound annual growth rate [CAGR] from 2018 to 2028 in North America), but also for defense applications including unmanned aerial and ground vehicles. Development of an alternative formulation for Li-ion batteries is key. Building upon previous research confirming the ability of silicon alloys to improve the energy density of anodes, Semplastics developed a novel material based on our X-MAT® polymer-derived ceramic (PDC) technology. The X-MAT anode material is a composite of chemically tailored silicon oxycarbide (SiOC) and domestically sourced coal powder, designed to be a drop-in replacement for graphite within Li-ion battery anodes. Preliminary tests of this material showed more than twice the reversible capacity of graphite anodes (1,000 mAh/g vs. 372 mAh/g), with excellent stability and capacity retention, almost 100% coulombic efficiency, low voltage potential, and low thermal expansion. At full adoption in 2024, we expect the coal utilization to reach 27,300 tons. Through this project, Semplastics proposed to complete development and begin commercialization of this material. The objective was to determine the best formulation for technical performance and economic viability. At the end of the project, the X-MAT anode material is ready for implementation into existing battery manufacturing processes and can have a significant impact on the utilization of coal, with positive effects for the mining sector and the mitigation of carbon dioxide (CO 2 ) emissions.

01 COAL, LIGNITE, AND PEAT↗

Dataset for Blueprinting Electrified Transit System Implementation

This dataset contains the figures and tabulated results generated from a system-level optimization study of transit fleet electrification planning. The dataset does not include executable modeling code required to reproduce the optimization. The dataset includes results for optimized charging infrastructure deployment by location and power level and service block assignments by fuel type, battery capacity selections, and distributed energy resource sizing. It also contains aggregated financial results, capital expenditures, operating cost summaries, net present cost comparisons across scenarios, and quantified air quality impacts. Results are structured to reflect multiple planning scenarios, including heuristic electrification plans, system-optimized configurations, and sensitivity cases with alternative objective weightings. The modeling was developed using publicly available General Transit Feed Specification data from Omnitrans and standardized modeling assumptions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Dataset for Blueprinting Electrified Transit System Implementation

This dataset contains the figures and tabulated results generated from a system-level optimization study of transit fleet electrification planning. The dataset does not include executable modeling code required to reproduce the optimization. The dataset includes results for optimized charging infrastructure deployment by location and power level and service block assignments by fuel type, battery capacity selections, and distributed energy resource sizing. It also contains aggregated financial results, capital expenditures, operating cost summaries, net present cost comparisons across scenarios, and quantified air quality impacts. Results are structured to reflect multiple planning scenarios, including heuristic electrification plans, system-optimized configurations, and sensitivity cases with alternative objective weightings. The modeling was developed using publicly available General Transit Feed Specification data from Omnitrans and standardized modeling assumptions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Systems Health Management and Prognostics Approaches for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously published information.

Systems Health Managent↗

Health Monitoring and Prognostics for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously approved and published information.

Systems Health Managent↗

Preliminary Statistical Maneuver Analysis for A Low-Thrust NRHO to DRO Transfer

To understand practical implications of implementing transfers that leverage multibody dynamics, a statistical analysis is performed on an Earth-Moon Near Rectilinear Halo Orbit (NRHO) to distant retrograde orbit (DRO) transfer using solar electric propulsion. The statistical analysis includes perturbations due to maneu- ver execution and orbit determination error to understand operational character- istics of the transfer such as statistical ∆V and statistical state targeting errors at the final DRO. We explore the need for low-thrust trajectory correction maneuver (TCM) opportunities as well as the delivery accuracy to the final target. Finally, we explore options for long-term-stable target DROs.

low thrust↗

Experimental Verification of Electric Drive Technologies Based on Artificial Intelligence Tools

In this report, a fully integrated prototype of a flight servo control system is successfully developed and implemented using brushless dc motors. The control system is developed by the fuzzy logic theory, and implemented with a multilayer neural network. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. The network structure and the parameter learning are performed simultaneously and online in the fuzzy-neural network system. The structure learning is based on the partition of input space. The parameter learning is based on the supervised gradient decent method, using a delta adaptation law. Using experimental setup, the performance of the proposed control system is evaluated under various operating conditions. Test results are presented and discussed in the report. The proposed learning control system has several advantages, namely, simple structure and learning capability, robustness and high tracking performance and few nodes at hidden layers. In comparison with the PI controller, the proposed fuzzy-neural network system can yield a better dynamic performance with shorter settling time, and without overshoot. Experimental results have shown that the proposed control system is adaptive and robust in responding to a wide range of operating conditions. In summary, the goal of this study is to design and implement-advanced servosystems to actuate control surfaces for flight vehicles, namely, aircraft and helicopters, missiles and interceptors, and mini- and micro-air vehicles.

Rubaai, Ahmed↗

Prediction and Control of Atomic Ordering in Electrodeposited Binary Alloy Films: Direct Synthesis of L10 Magnetic Phases

Permanent magnet materials are essential components in renewable energy technology, underlying, electric vehicles, hybrids, wind turbines and more. Today commercial permanent magnets based on Nd-Fe-B exhibit an energy product of ~ 400 kJ/m 3 , capable to run wind turbines efficiently. Unfortunately, the supplies of rare-earth elements are centralized, and their availability may see volatility over time. This state of affairs stimulates the search for the search for rare-earth-free permanent magnetic materials. The L1 0 crystal structure underlines an important class of chemical ordered alloys that exhibit uniaxial magnetocrystalline anisotropy. This material has been considered an excellent candidate for rare-earth-free permanent magnets. In this program, a series of systematic experiments were performed and analysed in order to understand the effect of each. In order to synthesize L 10-Fe-Ni, one has to circumvent the sluggish kinetic and the limited driving force resulted from the low order-disorder temperature (~320°C) of the system. A strategy was found to trigger a martensitic transformation from the FCC phase to one of the non-cubic phases by high-strain methods. The body-centered tetragonal Fe-Ni obtained by this strategy was considered as a precursor of L10 Fe-Ni. The formation of BCT Fe-Ni via a displacive process was also implemented at the nanoscale by using tailored AuNi@FeNi core@shell.

36 MATERIALS SCIENCE↗

Harnessing Ocean Thermal Gradients Using Thermoelectric Based Submersibles for Ocean Power Applications

The urgent need for energy solutions in marine environments has accelerated the development of innovative technologies capable of leveraging natural resources for power generation. This study introduces a buoyancy-driven submersible system designed to harness ocean thermal gradients using thermoelectric generators (TEGs) and phase change materials (PCMs). The technology aims to provide autonomous power to offshore aquaculture farms, unmanned underwater vehicles (UUVs), offshore platform illumination, and ocean sensors, significantly reducing dependence on fossil fuels. Ocean thermal gradients, especially prevalent in mid-latitude regions, exhibit temperature differences between surface and deep waters ranging from 7 degrees Celsius to 30 degrees Celsius depending on seasonal variations. The proposed submersible technology utilizes TEGs to convert thermal energy from these gradients into electrical power, generating between 0.2 and 0.5 watts, while PCMs are employed to store and regulate this energy, ensuring a stable and continuous power supply. The buoyancy-driven mechanism of the submersible enhances its capability to navigate through varying depths, optimizing its exposure to different thermal gradients and maximizing energy harvesting. The performance of this submersible system is analyzed through detailed thermodynamic assessments and computational fluid dynamics (CFD) modeling focused on heat transfer. These analyzes consider real-world ocean temperature profiles and seek to refine the interaction between TEGs and PCMs to optimize energy extraction. The evaluation encompasses several key performance metrics, including power output and energy efficiency. Results confirm the potential of this innovative technology to provide a continuous and reliable power source for marine applications. By demonstrating the feasibility of using ocean thermal gradients for energy generation, this study contributes to the broader efforts of innovation in energy technologies for harsh, remote marine environments. The implementation of such promises is significant advancements in the autonomy of marine operations. The ongoing research will further investigate scalability ensuring that the system can be effectively adapted to various marine settings and operational demands.

16 TIDAL AND WAVE POWER↗