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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An experimental study on dehumidification and regeneration performance of a new nonporous membrane-based heat and mass exchanger using an ionic liquid desiccant

As a promising alternative to inefficient vapor-compression-based air conditioning, liquid desiccant dehumidification uses a liquid desiccant in contact with the humid air absorbing the moisture. However, it has not gained much market share due to the issues related to the carryover, corrosion, fouling, and crystallization of liquid desiccant. The research has developed a new membrane-based exchanger, which uses a non-corrosive ionic liquid desiccant and nonporous tubular membranes to address these issues. As the second generation of the prototype, the new exchanger was tested at various operating conditions. According to the experimental data, when it is used in the dehumidification loop, the new membrane-based exchanger achieves a specific vapor transportation rate of 778.6 g/(h–m 2 ) and an average water vapor flux of 0.316 g/(h-m 2 -Pa), which is a significant improvement compared with the previous designs using nonporous membranes. It also achieved a comparable or even better dehumidification performance compared with the dehumidifiers that use porous membrane and conventional liquid desiccants. However, the regeneration performance is not as good as its dehumidification. It is mainly caused by the high operating temperature required in the regeneration loop. The experimental data and findings provide first-hand experimental data and enhance the understanding of advanced membrane-based ionic liquid desiccant systems.

42 ENGINEERING↗
DOE OSTI2023

Life-cycle analysis of recycling of post-use plastic to plastic via pyrolysis

Advanced recycling enables the application of post-use plastics (PUP) to produce valuable industrial chemicals and develop markets for recycled feedstocks. Pyrolysis is one of the most common advanced recycling technologies undergoing industrial-scale implementation for converting PUP. This paper presents a life cycle analysis (LCA) to assess greenhouse gas (GHG) emissions, fossil energy, water consumption, and solid waste impacts of converting PUP into new plastics such as high-density and low-density polyethylene (HDPE and LDPE, respectively). Data was collected from eight plastic pyrolysis companies. This study addresses the impacts of pyrolysis plant size and maturity; two substitution rate (SR) cases of pyrolysis oil with fossil-derived feedstocks in steam crackers (5% and a 20% of pyrolysis oil SR); and potentially avoided emissions from traditional end-of-life (EOL) management. Because the conventional feedstock slate of steam crackers in the Unites States is comprised of 94% gases (a mix of ethane, propane, and butane) and 6% naphtha, the 5% SR case looked at polyethylene (PE) derived from 5% pyrolysis oil, 1% naphtha, and 94% gases; while the 20% SR looked at PE derived from 20% pyrolysis oil and 80% gases. Moreover, the results are presented from two perspectives: 1) steam crackers' and 2) plastic recyclers'. In the recyclers' perspective, the results for the 5% SR showed for each kg of PUP used there was a 23% and 18% decrease in GHG emissions for HDPE and LDPE respectively, while the 20% SR showed a 4% and 3% reduction in GHG emissions for HDPE and LDPE respectively compared to virgin plastic. The 20% SR has lower GHG emissions reductions because there is an added step of hydrotreating the pyrolysis oil to remove chlorine concentrations that is not included in the 5% SR scenario. Furthermore, the 5% SR removes most of the naphtha, a more carbon intense feedstock, and replaces it with PUP-based pyrolysis oil, a less carbon intense feedstock. GHG emissions for PUP pyrolysis could be further reduced by 50% and 131% in the United States and European Union respectively if the GHG emissions of current PUP incineration practices were considered as emission reductions credits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Assessment of Cloud-Based Applications Enabling a Scalable Risk-Informed Predictive Maintenance Strategy Across the Nuclear Fleet

The current light water reactor fleet uses time-based or failure-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging technologies in terms of artificial intelligence (AI) and cloud computing to enable a cost-effective, predictive maintenance strategy. This report examines the feasibility of cloud computing for the nuclear industry’s needs in terms of the cloud’s computing capabilities, feasibility, and regulatory concerns. The technical viability of cloud computing was analyzed using one year worth of data from a boiling water reactor’s safety relief valve. Models were hosted on a local desktop, Idaho National Laboratory’s high-performance computer, and Microsoft Azure. Data was loaded, processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was used to determined that cloud computing has adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, there is a benefit as the plant no longer needs manage their own servers, software, cybersecurity, and IT support staff. Many of these features can be offloaded on to the cloud provider. A comprehensive analysis was completed that showed the current annual cost of operating is more expensive than using cloud computing resources. Lastly, the regulatory framework does not explicitly address AI or autonomous control. Currently, the NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than new regulations to address the use of AI and ML. But since many of the AI applications are focused on non-safety related applications, such as balance-of-plant components, they will likely have little or no regulatory restrictions or necessary approvals. Demonstrating how AI can improve maintenance and operation of these non-safety related systems seems like the likely path forward for implementing AI and cloud computing resources inside nuclear power plants (NPPs).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Eddy Flux and Meteorology over Deciduous Forest, Prairie, and Soybean Ecosystems in Missouri, USA, during the Total Solar Eclipse of 2017

Eddy fluxes and meteorology data are reported at high temporal resolution for three flux tower sites in Mid-Missouri before, during, and after the Total Solar Eclipse of August 21, 2017. Mid-Missouri experienced up to 2 minutes and 40 seconds of totality at around solar noon during the eclipse (Fig. 1). The sites are located in deciduous oak-hickory forest, native prairie, and soybean ecosystems (Table 1 and Fig. 3). Eddy fluxes were computed using a wavelet-based approach that permitted the calculation of two-minute mean fluxes without losing low frequency flux contributions (Fig. 2). During the eclipse, standard meteorological variables were sampled and recorded at 5 second intervals. The two-minute means of fluxes and meteorology are reported. This data product includes three data files in NetCDF (*.nc) format, three data dictionary files and a file-level metadata file in comma separate (*.csv) format, and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Packages of Distributed Energy Technologies Demonstrating Demand Flexibility at Community Scale

The combination of increased electric load growth across all sectors, deferred electrical infrastructure investment, and other factors resulting in variable electric power supply, has created technical challenges to maintaining a resilient and reliable grid. Many federal, regional, and local efforts are in play to modernize the electric grid, including advancing building technologies and distributed energy resources (DERs) that are utilizing smarter controls to become responsive to both occupant and grid needs. This report reviews ten pilot projects demonstrating how groups of buildings combined with behind-the-meter (BTM) DERs such as electric vehicle (EV) charging, battery storage, flexible HVAC and domestic hot water systems, and photovoltaic systems can reliably and cost effectively provide grid services. Each of the ten pilot projects aim to deliver both energy efficiency and demand flexibility (DF) while supporting load growth. The ten demonstration teams are piloting flexible DER packages across diverse communities of residential and commercial buildings to address a variety of regional grid needs. The outcomes of these pilot projects will be used to inform future scaling through utility program development. This paper characterizes the ten teams, showcasing the decision-making process used by each group to develop their packages (Section 2), the grid services they plan to deliver (Section 3), the types of DER packages selected for deployment within building sectors (Section 4) and trends between building sector, DER types, and grid services In order to achieve community scale benefits, the pilot projects must utilize aggregated control mechanisms for coordinating buildings and DERs together. Several types of coordinated control architectures have evolved amongst the teams, influenced by use type, existing market conditions, and integration type. Three coordinated controls architectures have been characterized, highlighting their use cases, benefits, challenges, and tradeoffs in their design. These insights can aid utilities, control vendors, and developers in scaling community-level energy systems (Paul, 2024). Ultimately, the technology packages selected by the ten teams will be coordinated to provide power system services, also known as grid services. Insights from these demonstrations will be useful for grid operators, regulators, aggregators and other stakeholders as they look to deploy demand flexible resources as grid services in the future. The grid services that each team is targeting for demonstration are described in Section 3 and Section 4. Methods for evaluating the grid services have been described in the paper Metrics for Evaluating Grid Service Provision from Communities of Grid-interactive and Efficient Buildings and other DER (MacDonald, 2023). To identify technology packages for demonstration, Section 2 shows that project teams used a range of analysis approaches, including building energy modeling, AMI data analysis, cost-benefit frameworks, and utility pilot data. Some teams emphasized technical modeling to quantify grid impacts and demand reduction potential, while others prioritized economic evaluations, stakeholder input, or exploratory pilots to inform deployment decisions. This diversity reflects the need to tailor selection methods to project goals, available data, and organizational context. Section 5 discusses trends between the DER technologies deployed and the grid service provisions from each team. Residential buildings (multifamily and single family) lean towards technologies that enhance energy efficiency (e.g. weatherization upgrades, smart thermostats) and onsite power generation integration (e.g. solar PV). Commercial building demonstrations prioritize technologies that ensure operational reliability (e.g. battery storage) and centralized energy management systems and optimization solutions. Teams that are deploying controllable storage-based technologies are more likely to provide grid services that require a near real-time response. Teams incorporating load shifting technologies like smart thermostats with HEMs are likely to include energy markets participation and customer bill management offerings. Campus demonstrations are adopting diverse sets of DERs to emphasize renewable generation, paired with centralized control. This section also describes technologies that were considered during project planning but ultimately excluded from final deployment. These demonstrations reveal that effective DER package design should be tailored to building type, customer segment, and construction vintage. Multifamily buildings benefit from centralized HVAC upgrades and supervisory controls, while single-family homes are well-suited for individualized technologies like solar, storage, and smart home energy monitors. Commercial and campus settings prioritize EMIS integration and load optimization. New construction enables cost-effective integration of DER-ready infrastructure, whereas retrofits require deployments aligned with owner and tenant value streams. For utility program planners, early coordination with developers and building owners, paired with segmented and modular program offerings, can improve adoption, scalability, and grid impact.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Community Energy Planning: Best Practices and Lessons Learned in NREL's Work with Communities

Whether driven by local goals and actions, external market forces, or both, the clean energy transition is accelerating. The associated increase in clean energy deployment occurs on the ground in communities. As a result, communities increasingly need technical expertise and assistance in planning for and managing the energy transition. Building on decades of work with state, local, and tribal jurisdictions, NREL's work providing modeling, analysis, and technical expertise to enable more data-driven community energy planning is expanding. To inform and enhance NREL's capabilities in community energy planning and provide a resource for others working in this space, NREL developed this best-practices document through interviews with seasoned NREL practitioners and a review of the literature on equitable community planning. Findings include five best practices for community energy planning that NREL practitioners can apply to increase the impact of their work.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Estimating the electric vehicle charging demand of multi-unit dwelling residents in the United States

Abstract Early battery electric vehicle (EV) adopters can access home chargers for reliable charging. As the EV market grows, residents of multi-unit dwellings (MUDs) may face barriers in owning EVs and charging them without garage or parking availability. To investigate the mechanisms that can bridge existing disparities in home charging and station deployment, we characterized the travel behavior of MUD residents and estimated their EV residential charging demand. This study classifies the travel patterns of MUD residents by fusing trip diary data from the National Household Travel Survey and housing features from the American Housing Survey. A hierarchical agglomerative clustering method was used to cluster apartment complex residents’ travel profiles, considering attributes such as dwell time, daily vehicle miles traveled (VMT), income, and their residences’ US census division. We propose a charging decision model to determine the charging station placement demand in MUDs and the charging energy volume expected to be consumed, assuming that MUD drivers universally operate EVs in urban communities. Numerical experiments were conducted to gain insight into the charging demand of MUD residents in the US. We found that charging availability is indispensable for households that set out to meet 80% state of charge by the end of the day. When maintaining a 20% comfortable state of charge the entire day, the higher the VMT are, the greater the share of charging demand and the greater the energy use in MUD chargers. The upper-income group requires a greater share of MUD charging and greater daily kWh charged because of more VMT.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Demonstration of a modeling toolkit for the design of building electrical distribution systems

The deployment of building equipment (e.g., lighting, security) and miscellaneous electrical loads that fundamentally require DC power for operation is increasing. Powering these DC loads has traditionally required an AC/DC converter, but the installation of photovoltaic (PV) and battery energy storage systems that primarily produce DC power eliminates the need for AC/DC converters at each end-device. However, analyzing system energy efficiency and cost for different electrical distribution architectures can be challenging as software tools that support this are not readily available. This paper presents preliminary results from a laboratory verification of the Building Electrical Efficiency Analysis Model (BEEAM) toolkit that was developed to address this gap. Three different eight-luminaire lighting systems comprised of market-available products were designed and modeled: one that used traditional AC distribution, a second that used a hybrid AC -to- centralized DC electrical distribution architecture, and a third that used a hybrid AC -to- distributed DC architecture. Notably, AC/DC conversions are required in all three systems. BEEAM models for LED drivers as well as Power-over-Ethernet (PoE) switches were created using laboratory characterization data. The lighting systems were simulated in Modelica, and the results were compared with each other and physics-based expectations. Simulation results show that PoE system energy efficiency is highly dependent on both device specification (e.g., LED driver and PoE switch efficiency) and system architecture (e.g., PoE switch loading) choices, as expected. For the products and system architectures selected for this study, the two DC systems were found to be less efficient than the AC system over the course of typical operation. In future work, simulation results will be compared with laboratory measurements to validate the usefulness of this software for design, and lighting systems with integrated PV and battery energy storage will be simulated to quantify the energy performance improvements that result from the elimination of some AC/DC converters.

Waghale, Anay S.↗
DOE OSTI2022

H2@Scale Program Multi-Party Cooperative Research and Development Agreement: California Hydrogen Infrastructure Research Consortium Task (Cooperative Research and Development Final Report, CRADA Number CRD-18-00754)

Many stakeholders are working on hydrogen and fuel cell products, markets, requirements, mandates, and policies. California has been leading the way for hydrogen infrastructure and fuel cell electric vehicle (FCEV) deployment. This leadership has advanced a hydrogen network that is not duplicated anywhere in the United States and is unique in the world for its focus on providing a retail fueling experience. In addition, the advancements have identified many lessons learned for hydrogen infrastructure development, deployment, and operation. Other interested states and countries are using California's experience as a model case, making success in California paramount to enabling market acceleration and uptake in the United States. The technical research capability of the national laboratories can be used to assist California in decisions and evaluations, as well as to verify solutions to problems impacting the industry. Because these challenges cannot be addressed by one agency or one laboratory, a hydrogen research consortium has been organized to combine and collaborate. The collaboration aims to ensure that data are available to evaluate projects and inform decision makers, independently verify and validate component solutions, provide experimental results for future hydrogen infrastructure, increase the availability of technical experts for quick-need issues for California hydrogen infrastructure development, deployment, operation, and technology advances. The proposed tasks include data collection from operational stations, component failure fix verification (i.e. nozzle freeze lock), new fueling methods for medium and heavy duty applications, and ensuring hydrogen quality is maintained. U.S. leadership for hydrogen technologies is rooted in California, a location for implementing many H2@Scale pathways such as reducing curtailment and stranded resources, reducing petroleum use and emissions, and developing and creating jobs.

08 HYDROGEN↗
DOE OSTI2025

Agricultural crop and residue production by county - near-term scenario

This dataset contains data on agricultural crop and residue production by county in 2030. The agricultural crops in this dataset include barley, corn, cotton, grain sorghum, hay, oats, rice, soybeans, and wheat. The agricultural residues include barley straw, corn stover, oats straw, sorghum stubble, and wheat straw. The dataset was obtained from the database of the BT23 (Davis et al.,2024) for the near-term scenario with biomass market prices of up to $70 per dry ton.

agricultural crop↗
DOE OSTI2020

Knowledge spillovers between PV installers can reduce the cost of installing solar PV

We analyze pricing in PV systems using data from 2008-2014 to identify the effects of knowledge spillovers in reducing the installed cost of PV. This paper estimates the size of the effects of learning by doing and knowledge spillovers using multiple formulations of spillover related variables. We found knowledge spillovers between firms within a county to be a significant and substantial factor in reducing the costs of PV. However, these spillovers reduce costs only for firms over a certain size threshold, and no cost-reducing spillovers were found for smaller installers. Geographic spillovers, within a firm from one county to another were also significant although not as large as the local between-firm effects. We ran 43 specifications on the data and generally found these main results to be robust, although not in every specification. One implication of these results is that policies that subsidize demand for PV are leading to the creation of new knowledge that would not exist at lower levels of demand. Furthermore, that the spillovers become stronger at higher levels of experience suggests that these subsidies would need to be substantial, particularly in new markets with many small firms.

14 SOLAR ENERGY↗
DOE OSTI2025

Heating Ventilation Air-Conditioning (HVAC) System Analysis and Optimization for Revit with the OpenStudio Analytical Framework (CRADA Final Report)

This project proposes to integrate the NREL-developed OpenStudio (OS) and its accompanying Analytical Framework (OSAF) into Autodesk’s (ADSK’s) market leading suite of building information modeling (BIM) tools. As a result, BIM users will gain access to advanced (but user-friendly) building energy modeling (BEM), calibration, and design optimization capabilities. Following integration, the BIM software will automatically generate a consistent OS-based BEM model, allowing users to develop projects in an integrated manner, moving back and forth between architectural and engineering perspectives without redundancy or data loss, saving time and effort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Community Energy Planning: Best Practices and Lessons Learned in NREL's Work with Communities

The clean energy transition is accelerating due to local, state, and national actions combined with external market forces. Regardless of where goals are set and decisions are made, clean energy deployment occurs on the ground in communities. As a result, communities increasingly need technical expertise and assistance with planning for and managing the energy transition. Building on decades of work with state, local, and tribal jurisdictions, the National Renewable Energy Laboratory (NREL) increasingly provides community-driven modeling, analysis, and technical assistance to enable more data-driven and equitable community energy planning. To inform and enhance NREL's capabilities in community energy planning and provide a resource for others working in this space, the Joint Institute for Strategic Energy Analysis (JISEA) Sustainable Communities Catalyzer supported this best-practices presentation based on interviews with seasoned NREL practitioners and a literature review on equitable community energy planning. Findings include five best practices for community energy planning that NREL practitioners and others can apply to strengthen their impact: 1) do your homework in preparation for community interactions; 2) be humble, authentic, and honest in your interactions with community members; 3) respect community agency in every step of the process; 4) meet the community where they are; 5) democratize participation.

best practices↗
DOE OSTI2021

Quantifying the Solar Energy Resource for Puerto Rico

After Hurricane Maria, multiple U.S. Department of Energy laboratories studied the state of the electric grid in Puerto Rico and analyzed grid resilience and grid integration of renewable energy. As part of the work done at the National Renewable Energy Laboratory, researchers created new solar resource data, conducted a technical potential and supply curve analysis, and studied the interannual variability of the solar resource. A new methodology was developed to downscale solar resource data from the National Solar Radiation Data Base (NSRDB) from a 4-km x 4-km spatial and 30-minute temporal resolution to a 2 km x 2 km and 5-minute resolution. This methodology primarily used simple physical principles to develop high-resolution cloud properties which were then used to compute solar radiation. The high-resolution datasets were validated against ground measurements and the error metrics were found to be similar to the original lower resolution dataset. Using 20 years of downscaled data from the NSRDB multi-year capacity factors for photovoltaics (PV) were developed for both single-axis tracking and fixed latitude-tilt configurations. Use of the multi-year data provides the ability to understand variability in capacity factors due to variability in weather over a long period of time. For Puerto Rico the coastal regions were found to have significant higher capacity factors than inland. Using land-use and terrain information a technical potential analysis was conducted for Puerto Rico. This analysis restricted single PV plant development to a maximum of 100 MW nameplate capacity. The nameplate capacity for each municipality were then determined. Based on our assumptions, 56 of the 78 total municipalities of Puerto Rico contain some level of solar capacity. Most of the interior municipalities did not have any capacity because of the geographic exclusions used in this study. The lowest capacity for a PV plant observed in a municipality was 10 MW. The maximum capacity within a county was 2,000 MW. Further a supply curve analysis was conducted by taking the results of the technical potential and quantifying system and transmission costs. The levelized cost of energy (LCOE) was calculated for each theoretical PV plant site, and the levelized cost of transmission was added to the LCOE to produce a total cost estimate for each site. The results of the supply curve analysis allow for a relative comparison of the cost for integrating new PV capacity into the grid. This analysis indicates that cheaper total LCOE sites tend to be larger in capacity. The total capacity in this study was found to be far beyond the maximum peak load for the island. However, this study does not consider the economic and market potential for development. The cumulative capacity presented in this study assumes that the best locations are developed first and ignores the complex decision paths for new power plant development. Therefore, this analysis can only be treated as illustrative. Finally this study investigates the impact of inter-annual variability of resource using a variety of metrices including probability of exceedance and variation in capacity factor and LCOE. This study demonstrates that the capacity factor or LCOE could vary by over 10% year to year. This clearly indicates the risks involved in using any particular year of data and clearly points to the use of multi-year data to reduce some of the risks related to variability in weather.

14 SOLAR ENERGY↗
DOE OSTI2022

Extremum-Seeking-Based Ultra-local Model Predictive Control and Its Application to Electric Motor Speed Regulation

Electric vehicle (EV) market is rapidly expanding. As a critical component of EV, an electric motor needs to accurately follow a reference speed signal while respecting the electrical current constraint for safety. Those requirements are usually formulated as a model predictive control (MPC) problem. However, the performance of traditional model-based MPC depends on the accuracy of the system model, which may not always be guaranteed in reality. Therefore, we utilize a data-driven, model-free predictive control strategy, called ultra-local MPC (ULMPC), to control the speed of an electric motor. To further enhance the control performance of ULMPC, we employ the extremum-seeking control (ESC) to tune the control gain of the ULMPC online. Simulation and hardware experiments demonstrate the enhancement of the extremum-seeking-based ULMPC over a constant-gain ULMPC.

Zhou, Yujing↗
DOE OSTI2023

Signal Decomposition for Intrusion Detection in Reliability Assessment in Cyber Resilience (Summary Report)

The complexity of assuring cyber resilience for physical process interactions in connected systems such as energy grids increases dramatically as the coupling between processes becomes more direct and responsive. An example of this growing complexity is provided by Integrated Energy Systems (IES), in which various processes such as nuclear heat generation and commodity production are being directly coupled for increased responsiveness to highly variable signals such as market pricing or electricity demand. As such, the potential attack surface of the coupled processes is larger than the two processes independently. Securing these complex systems requires two-fold monitoring: cybersecure monitoring for potential malicious incursion, and physics monitoring for system tampering. Physics monitoring includes analyzing the behavior of the signals within the system for anomalous behavior. This analysis has been shown to be insufficient if approached by only data-driven machine learning and artificial intelligence (MLAI) techniques or only low-level model comparison. Previous efforts at Purdue University suggested combining high-fidelity models with MLAI algorithms as a basis for a software tool for detecting anomalies in physical processes. This work built on that suggestion, developing an advanced library for signal decomposition and analysis using both MLAI and high-fidelity physics algorithms for greatly improved anomaly detection, especially false data injection. This software can be used as part of a secure imbedded intelligence (SEI) system designed under Consequence-driven Cyber-informed Engineering (CCE) for complex coupled systems. This library established a foundation for online and posteriori analysis of digital signals for the purpose of detecting potential malicious tampering in digital signals representing physical processes. Demonstrations carried out throughout the development highlight the effective use of characterization algorithms to detect signal perturbations, particularly triangle attack-style perturbations, in three wide-ranging applications: seismic monitoring, nuclear thermal hydraulics system simulation, and custom manufacturing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Catalytic Upgrading of Pyrolysis Products for the Production of Sustainable Aviation Fuel

The objective of this project is advance the state-of-technology for a catalytic fast pyrolysis (CFP) + hydrotreating (HT) process to produce sustainable aviation fuel and other biogenic products. Our approach focuses on performing integrated experiments using realistic biomass feedstocks and non-noble metal technical catalyst formulations. CFP is performed in an ex-situ configuration using a fluidized bed reactor without co-fed hydrogen. Research advancements over the past two years include establishing benchmark yield structures and compositional data for each step of the biomass-to-SAF process, demonstrating the ability to produce a cycloalkane-rich SAF product that meets key ASTM 4054 guidelines, generating benchmark characterization data for technical catalyst formulations with an emphasis on determining the unique composition and combustion properties of biogenic coke, and establishing bio-oil critical material attributes to mitigate the risk of plugging during down-stream hydroprocessing. Other impacts from this project include generation of broadly enabling scientific knowledge (12 publications/12 presentations since 2021), engagement with industry partners (Johnson Matthey, ExxonMobil, Phillips 66), and identification of a promising pathway to market that addresses emerging demands for biogenic refinery feedstocks.

biomass↗
DOE OSTI2021

Tailored Fiber Placement for Complex Preforms

Tailored Fiber Placement (TFP) offers a novel approach to optimize fiber architecture for the fabrication of complex, structural parts not traditionally suitable for advanced composites. This technology not only offers new routes for weight reduction via metal substitution, it also offers cost reduction through minimization of material scrap and reduced labor. This reduction in component weight leads to increased fuel efficiency, and reduced production energy consumption, thereby, helping to achieve the stated IACMI technical goals. This technology leverages centuries of manufacturing development in support of the textile and embroidery industry. One major drawback to this technology is the lack of commercial or non- proprietary structural performance data and robust analytical tools used to optimize fiber architecture and predict performance. This project was structured to utilize common sub-element features to validate analytical performance tools, generate performance data, and gather cost and performance data on components of interest. This project was designed to give industry sponsors the confidence and ability to take full advantage of TFP to fabricate primary, highly loaded structure and integrate features such as metallic fasteners. The project focused principally on the use of high strength carbon fiber, such as T700, and the use of aerospace epoxy resin matrix to primarily support development of new composite applications in vehicle, aerospace, and industrial markets. This project applied previously developed analytical tools to predict the performance of TFP produced parts. This work focused on developing the pipeline to characterize material in order to accurately predict component performance when modifying the TFP print paths and stitch density. This focused on experimental characterization via standardized ASTM testing, alongside experimental testing of more representative service components by testing curved beam strength, beam shear performance, a large scale TFP lug, and ultimately designing a fully TFP clip bracket that reduced weight and cost compared to a traditional metallic component. The new knowledge gained from this program included: 1) development and demonstration of novel analytical tools applied to analysis of TFP preforms; 2) development and demonstration of a building block approach using coupons and sub-elements to optimize the design of a more complex component; 3) demonstration that optimized fiber orientation using TFP can exceed performance of conventional textile composite materials and can open new applications currently limited to metallic components; 4) Demonstration of performance and cost benefits of the TFP process as compared to metallic and conventional textile composites. Recommendations for follow-on work include development of design allowables to assess the impact of high temperature/moisture exposure or saturation during loading, tracking the impact of stitching needle wear on the performance of parts and ability to stitch thicker preforms, using TFP preforms as local reinforcement at areas of bearing or complex loading, and topology optimization of components by tow steering. The expertise developed during the course of this project can be leveraged to provide commercial engineering design and fabrication services using TFP. UDRI is in the process of formalizing their partnership with Spintech, who will serve as the commercialization partner for this technology and provide molding services and deliver finished components to the end user. UDRI will continue to produce the preforms until the economics allow Spintech to procure its own TFP equipment or lease UDRI equipment, at which point UDRI will step away from manufacture and serve as the engineering and design lead on product development.

36 MATERIALS SCIENCE↗