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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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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Rationally Engineered Vertically Aligned β ‐Ga 2− x W x O 3 Nanocomposites for Self‐Biased Solar‐Blind Ultraviolet Photodetectors with Ultrafast Response

Abstract With the astonishing advancement of present technology and increasing energy consumption, there is an ever‐increasing demand for energy‐efficient multifunctional sensors or transducers based on low‐cost, eco‐friendly material systems. In this context, self‐assembled vertically aligned β ‐Ga 2− x W x O 3 nanocomposite (GWO‐VAN) architecture‐assisted self‐biased solar‐blind UV photodetection on a silicon platform, which is the heart of traditional electronics is presented. Utilizing precisely controlled growth parameters, the formation of W‐enriched vertical β ‐Ga 2− x W x O 3 nanocolumns embedded into the W‐deficient β ‐Ga 2− x W x O 3 matrix is reached. Detailed structural and morphological analyses evidently confirm the presence of β ‐Ga 2− x W x O 3 nanocomposite with a high structural and chemical quality. Furthermore, absorption and photoluminescence spectroscopy explains photo‐absorption dynamics and the recombination through possible donor–acceptor energy states. The proposed GWO‐VAN framework facilitates evenly dispersed nanoregions with asymmetric donor energy state distribution and thus forms build‐in potential at the vertical β ‐Ga 2− x W x O 3 interfaces. As a result, the overall heterostructure evinces photovoltaic nature under the UV irradiation. A responsivity of ≈30 A/W is observed with an ultrafast response time (≈350 µs) under transient triggering conditions. Corresponding detectivity and external quantum efficiency are 7.9 × 10 12 Jones and 1.4 × 10 4 %, respectively. It is believed that, while this is the first report exploiting GWO‐VAN architecture to manifest self‐biased solar‐blind UV photodetection, the implication of the approach is enormous in designing electronics for extreme environment functionality and has immense potential to demonstrate drastic improvement in low‐cost UV photodetector technology.

Das, Debabrata↗
DOE OSTI2026

An AI-driven framework for evaluating local and state authorities’ permitting processes

The demand for new energy infrastructure is increasing across the United States, but heterogenous permitting processes and embedded requirements across different local jurisdictions can cause project delays, increase “soft costs,” and hinder developer expansion. This study analyzes the variability in local permitting requirements across the U.S. and develops a quantitative approach to describe their clarity and effectiveness in enabling infrastructure project development. By using an Energy Language Model (ELM), a large language model (LLM) for energy technologies, we systematically gathered permitting information from nearly 300 state-, county-, and city-level documents, creating a structured dataset of requirements and procedures on an unprecedented scale and speed. Our analysis revealed that local (city and county) permitting requirement documents are underrepresented compared to state-level guidance documents, which can impede timely and cost-effective installation of new electric infrastructure. Our validation process showed that the final database has an accuracy of approximately 95%. We, further, created a new quantitative method to score permitting requirements for clarity and efficiency, with electric vehicle supply equipment as an initial use case. The average local permitting document scored a 1.8 out of 5, which we interpret as meaning that half of the requirements developers face when installing electric infrastructure are ambiguous, increasing both cost and time. We also created a “Generalized Permit Process”, highlighting common procedural steps and identifying specific opportunities for municipalities to improve their documentation. This research establishes a systematic and scalable framework for evaluating the complexities of local infrastructure permitting processes by combining LLM-powered data collection and quantitative scoring. The framework enables policymakers and developers to identify and mitigate procedural bottlenecks, with the expectation that these improvements can accelerate application review and approval, reduce project costs, and expedite connection to utility distribution grids. As a foundational approach for streamlining local project development processes, this study’s methods are intended to be extended to a wide range of energy applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1971

Development of a collapsible reinforced cylindrical space observation window

Existing material technology was applied to the development of a collapsible transparent window suitable for manned spacecraft structures. The effort reported encompasses the evaluation of flame retardants intended for use in the window matrix polymer, evaluation of reinforcement angle which would allow for a twisting pantographing motion as the cylindrical window is mechanically collapsed upon itself, and evaluation of several reinforcement embedment methods. A fabrication technique was developed to produce a reinforced cylindrical space window of 45.7 cm diameter and 61.0 cm length. The basic technique involved the application of a clear film on a male-section mold; winding axial and girth reinforcements and vacuum casting the outer layer. The high-strength transparent window composite consisted of a polyether urethane matrix reinforced with an orthogonal pattern of black-coated carbon steel wire cable. A thin film of RTV silicone rubber was applied to both surfaces of the urethane. The flexibility, retraction system, and installation system are described.

Khan, A. Q.↗
DOE OSTI2024

HybridSystemsSimulations.jl - Solving the Merchant Collocated Facilities with JuMP

The development of new clean-generation technologies also leads to new plant-level architectures that combine several generation and storage assets behind the point of connection. These co-located generation resources (Hybrid Systems) primarily operate as merchant assets that employ automated market bidding models and internal Energy Management Systems (EMS) to comply with the operator's signals. Formulating an optimal bidding model requires embedding the EMS control model into the bidding algorithm, resulting in a bi-level optimization problem. In this presentation, we first showcase using JuMP to formulate and solve this problem effectively for multiple merchant systems and the bidding outcomes considering different model formulations. Second, the bidding outcomes are later integrated into a PowerSimulations.jl (also built with JuMP) simulation to study the system-level effects of the various merchant bidding and the interactions between market-clearing models and the embedded EMS model. We will showcase simulations conducted in the RTS system considering different levels of merchant hybrid systems participation. The presentation provides the following specific insights on JuMP usage: 1) the Formulation of specialized bi-level problems with custom cuts to solve the merchant hybrid system bidding problem; 2) the integration of a modular model within a complex simulation workflow supported by JuMP in PowerSimulations.jl; 3) Accelerating the solution of power systems operations simulation that employ agent optimization problems using JuMP.

energy markets↗
DOE OSTI2025

Performance Evaluation of Intelligent Solar Control Software Through Hardware-in-the-Loop (CRADA Final Report)

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been developed by Latimer Controls, Inc. to estimate the headroom of large PV plants for grid operation and control; however, these technologies lack comprehensive validation under real-world application scenarios. Latimer Controls, Inc. received two voucher awards for research at a national laboratory from the Department of Energy American Made Solar Prize Round 6. The National Renewable Energy Laboratory (NREL) was selected to collaborate with Latimer staff to conduct a performance evaluation of Latimer PV control software. The NREL team will develop a hardware-in-the-loop (HIL) testbed to perform testing and validation of the Latimer PV control technology in a de-risked yet realistic testbed environment. Latimer and NREL worked together to analyze the test data, draw conclusions from the results, and disseminate the resulting scientific findings. In this CRADA work, we propose to test and validate the real-world application of the Latimer Control solution in an HIL environment. We evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. In particular, a data-driven potential high limit (PHL) estimation is developed for large solar plants to accurately estimate their headroom so that they have fast and short-time regulation and control capability to participate in grid services and respond to grid signals in real time (e.g., AGC). This PHL estimation algorithm is embedded in a hardware power plant controller (PPC) and tested with an IEEE-39 bus system model developed in RTDS. To account for the varying cloud conditions and diverse inverter dispatches, we developed a 135-MW PV plant with detailed modeling of 27 individual PV modules and inverters using RTDS. The real-world communications used in such big plants, such as ModBus TCP/IP for inverter level and DNP3 for plant level, were developed to emulate the real-world applications in big PV plants. The ML-based PHL estimation method is tested under nine separate weather scenarios against the ‘reference-control’ solution, hereafter referred to as the baseline solution. The baseline method reserves a subset of inverters (reference group) to operate at their PHL at all times and dispatches only the remaining inverters (control group) at curtailed levels to fulfill the flexibility need. Despite being successfully piloted by NREL in California in 2017 and Chile in 2020, there exist two gaps in the state of the art to fully unlock the flexibility of PV plants: a. There is a trade-off between the PHL estimation accuracy and the flexibility range. b. There lacks granularity in the PHL estimation to capture the variation across inverters. The Latimer solution seeks to address these gaps by applying machine learning methods to improve PHL estimation accuracy while accounting for variability at every inverter. Performance metrics were taken from the 2023 Georgia Power CARES utility-scale RFP. The results demonstrate that the ML-based approach outperforms the traditional baseline method in PHL estimation accuracy for 7 of 9 scenarios. The average PHL error across the nine scenarios was 7.40% for the ML-based method, 2.06% less than the 9.46% PHL error average across scenarios that was exhibited by the baseline method. Additionally, the PHL error was below 5% for at least 95% of the testing interval for 3 of 9 tested intervals with the ML approach, whereas it did not achieve this metric for any of the baseline tests. Overall, simulation results indicate the superior performance of an ML-based approach compared to the conventional baseline reference-control approach, showcasing its potential to support grid stability and operational efficiency. This laboratory HIL testing using real PPC, representative power system simulation models in real-time with detailed PV plant and inverter models, and real-world communication protocols gives us confidence that this machine learning based PHL estimation algorithm works well in the hardware PPC and therefore de-risks future field commissioning. The end goal of this project is to advance grid technology to address the grid operation challenges brought by solar plant’s variability and uncertainties in power generation.

14 SOLAR ENERGY↗
DOE OSTI2024

Comparative life cycle assessment of woody biomass processing: air classification, drying, and size reduction powered by bioelectricity versus grid electricity

Sulfur accumulation during biofuel production is pollutive and toxic to conversion catalysts and causes the premature breakdown of processing equipment. Air classification is an effective preprocessing technology for ash and sulfur reduction from biomass feedstocks. Here, a life cycle assessment (LCA) sought to understand the environmental impact of implementing air classification as a sulfur-mitigation technique to improve feedstock quality for pine residues using a grid electricity scenario (GES) versus a bioelectricity scenario (BES). Global warming potential (GWP) for preprocessing was simulated using inventory databases embedded in SimaPro and the Argonne National Laboratory’s GREET model, specifically focusing on comparing the GWP of a GES versus a BES. Overall, the GES had a GWP impact over seven times that of the BES (136 versus 18 kg CO 2 equivalent per tonne of usable feedstock), with steam generation during rotary drying accounting for 57% of the GES’s GWP. Air classification represents 0.4% and 1.6% of the total GWP impact for the GES and BES, respectively. Therefore, air classification can facilitate a 30% reduction in feedstock sulfur content to improve feedstock quality for biofuel conversion and lessen corrosion of equipment while contributing minimal GWP impact during preprocessing.

Air classification↗
DOE OSTI2023

Role of initial conditions in plasma-current coupling of gas-puff Z-pinches

Azimuthal magnetic field measurements obtained during the implosion phase of an oxygen gas-puff Z-pinch on a 500 kA peak current and 180 ns rise time linear transformer driver are presented. While a fraction of the driver current was measured within the imploding plasma, key initial conditions were found to significantly impact the delivery of current to the plasma load. The electrode geometry was modified to assist the initial dielectric breakdown and resulted in improved shot reproducibility. Optimization of the gas injection plenum pressure and timing resulted in an increase in the current coupling parameter, defined as the ratio of the measured value of $B$ $θ$ to the expected value, from 50% to 75%. Furthermore, the degree of radial expansion of the gas puff in the load region, which is suspected to lead to the observed current loss during the implosion, was reduced by shortening the valve opening duration. Additionally, a pre-embedded axial magnetic field of up to 0.2 T was found to have no significant impact on the plasma-current coupling of the oxygen implosions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Integrate FARM with PID controllers: IES Simulation Ecosystem Control System Development

The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM was designed to support the HERON software module in the solution of the optimal dispatch problem for IES units. As the result of HERON-FARM dispatch simulation, the set-point trajectories are optimized to meet constraints on both the production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and the process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.) at a coarse time resolution (every 10 or 100 seconds) over long time horizons (several days or weeks). In case the operational constraints need to be met at finer time resolution, the computational burden of HERON-FARM would linearly increase with the sampling rate, and sub-optimal solutions might be obtained. System responses characterized by overshoots and damped oscillations temporarily violating the imposed constraints might occur during abrupt power transients. In this report, a hierarchical control system architecture for the operation of the Dynamic Energy Technology and Integration Laboratory (DETAIL) facility constructed at INL was proposed. First, the preliminary studies on the proposed control strategy for operating the facility and the designed PI controllers were reviewed. In particular, the current approach for generating the set-point trajectories was studied, and its limits were identified. To this aim, the inclusion of a Supervisory Control layer embedding a modified version of the FARM algorithm for preserving the system safe operation over both long and real-time horizons was proposed. In this way, FARM would be applied twice, i.e., the original version (“FARM Validator”) aiding the solution of the power dispatch problem, and the modified version (“FARM Supervisory” coordinating the PI controllers to address the real-time control tasks. Despite the kernel of the two modules is the same algorithm, their roles, tasks, and capabilities are quite different. A detailed description of the role of FARM at addressing low-level control tasks is provided, along with tentative operational procedures for training the models embedded into the algorithm by using the collected experimental data.

42 ENGINEERING↗
NASA NTRS1990

Active vibration control activities at the LaRC - Present and future

The NASA Controls-Structures-Interaction (CSI) program is presented with a description of the ground testing element objectives and approach. The goal of the CSI program is to develop and validate the technology required to design, verify and operate space systems in which the structure and the controls interact beneficially to meet the needs of future NASA missions. The operational Mini-Mast ground testbed and some sample active vibration control experimental results are discussed along with a description of the CSI Evolutionary Model testbed presently under development. Initial results indicate that embedded sensors and actuators are effective in controlling a large truss/reflector structure.

Newsom, J. R.↗
NASA NTRS2014

Evolution of Software-Only-Simulation at NASA IV and V

Software-Only-Simulations have been an emerging but quickly developing field of study throughout NASA. The NASA Independent Verification Validation (IVV) Independent Test Capability (ITC) team has been rapidly building a collection of simulators for a wide range of NASA missions. ITC specializes in full end-to-end simulations that enable developers, VV personnel, and operators to test-as-you-fly. In four years, the team has delivered a wide variety of spacecraft simulations that have ranged from low complexity science missions such as the Global Precipitation Management (GPM) satellite and the Deep Space Climate Observatory (DSCOVR), to the extremely complex missions such as the James Webb Space Telescope (JWST) and Space Launch System (SLS).This paper describes the evolution of ITCs technologies and processes that have been utilized to design, implement, and deploy end-to-end simulation environments for various NASA missions. A comparison of mission simulators are discussed with focus on technology and lessons learned in complexity, hardware modeling, and continuous integration. The paper also describes the methods for executing the missions unmodified flight software binaries (not cross-compiled) for verification and validation activities.

Embedded↗
NASA NTRSDate not supplied

Delay Tolerant, Radio Frequency Identification (RFID )-enabled Sensing

Radio Frequency Identification (RFID) technology offers a completely passive method to transmit fixed data sequences from an RFID tag, which typically doesn’t have its own power supply, to an interrogator. Radio frequency (RF) energy harvested from the interrogator is rectified by the tag and used to charge an integrated circuit (IC). The IC then modulates the received signal with the data stored on the tag and reflects the energy back to the interrogator. RFID has seen great proliferation in terrestrial inventory management applications, and it has recently made the jump to spaceflight applications onboard the International Space Station, augmenting an existing optical bar‐code infrastructure for tracking supplies. A number of advanced automated logistics management (ALM) concepts employing RFID are currently being developed and evaluated, including so‐called “smart” shelves, cubbies, and trash receptacles using low‐power, embeddable RFID interrogators. An infrastructure where both crew members and robotic assistants, such as autonomous free flyers, are similarly equipped with small RFID interrogators seems likely. It therefore behooves us to consider extending this infrastructure beyond ALM to applications such as low power, embedded sensing. Typically, data on an RFID tag can only be written by an RFID interrogator, using interrogator energy. In recent years, however, a few efforts have focused on using that energy to drive data acquisition from the tag IC, allowing the tag to modify its stored data sequence with sensor data before replying to an interrogator. In this way, the tag can act as a completely passive sensing device. One problem exists with this approach, however: the tag cannot gather data when an interrogator is not present. Thus, strictly passive RFID sensing tags cannot gather data at regular intervals, in the manner of a typical wireless sensor network, without careful, and impractical, planning of mobile interrogator movements. To address this shortcoming, we look to a recent advance in RFID technology which allows an external microprocessor to power the tag IC and write directly into its RFID memory using a wired serial interface. In this paradigm, data gathering is driven by a small, on‐board power supply (using batteries or harvested energy), and data transfer is provided passively through the RFID interrogation service. Since communication typically consumes the lion’s share of power in WSNs, such a technique has the potential to enable extremely long‐lived, embedded wireless sensing when used with extremely low‐current microcontrollers. Since the communication channel is only open when an interrogator is present and actively interrogating the RFID sensing tag, transport of periodically‐sampled sensor data presents itself as a delay/disruption‐tolerant networking (DTN) problem. In this paper, we present the design of a DTN‐like overlay on the common EPC Global, Class 1, Generation 2 RFID standard. This overlay allows seamless, guaranteed data transfer using the EPC Global protocol, supporting extremely low‐power, embedded sensing using an infrastructure likely to be already in place for ALM applications. We evaluate a prototype implementation of a complete end‐to‐end system using a robotic RFID interrogation agent, and we present future directions for the development of this sensing technique.

Raymond S. Wagner↗
DOE OSTI2025

High‐Speed Embedded Ink Writing of Anatomic‐Size Organ Constructs

Embedded ink writing (EIW) is an emerging 3D printing technique that fabricates complex 3D structures from various biomaterial inks but is limited to a printing speed of ∼10 mm s −1 due to suboptimal rheological properties of particulate-dominated yield-stress fluids when used as liquid baths. In this work, a particle-hydrogel interactive system to design advanced baths with enhanced yield stress and extended thixotropic response time for realizing high-speed EIW is developed. In this system, the interactions between particle additive and three representative polymeric hydrogels enable the resulting nanocomposites to demonstrate different rheological behaviors. Accordingly, the interaction models for the nanocomposites are established, which are subsequently validated by macroscale rheological measurements and advanced microstructure characterization techniques. Filament formation mechanisms in the particle-hydrogel interactive baths are comprehensively investigated at high printing speeds. To demonstrate the effectiveness of the proposed high-speed EIW method, an anatomic-size human kidney construct is successfully printed at 110 mm s −1 , which only takes ∼4 h. This work breaks the printing speed barrier in current EIW and propels the maximum printing speed by at least 10 times, providing an efficient and promising solution for organ reconstruction in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bimetallic NiCo boride nanoparticles confined in a MXene network enable efficient ambient ammonia electrosynthesis

Ambient electrocatalytic nitrogen fixation is an emerging technology for green ammonia synthesis, but the absence of optimized, stable and performant catalysts can render its practical application challenging. Herein, bimetallic NiCo boride nanoparticles confined in MXene are shown to accomplish high-performance nitrogen reduction electrolysis. Taking advantage of the synergistic effect in specific compositions with unique electronic d and p orbits and typical architecture of rich nanosized particles embedded in the interconnected conductive network, the synthesized MXene@NiCoB composite demonstrates extensive improvements in nitrogen molecule chemisorption, active area exposure and charge transport. As a result, optimal NH 3 yield rate of 38.7 μg h -1 mgcat. -1 and Faradaic efficiency of 6.92% are acquired in 0.1 M Na 2 SO 4 electrolyte. Moreover, the great catalytic performance can be almost entirely maintained in the cases of repeatedly-cycled and long-term electrolysis. Theoretical investigations reveal that the nitrogen reduction reaction on MXene@NiCoB catalyst proceeds according to the distal pathway, with a distinctly-reduced energy barrier relative to the Co 2 B counterpart. In conclusion, this work may inspire a new route towards the rational catalyst design for the nitrogen reduction reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Ion-capture electrodialysis using multifunctional adsorptive membranes

Technologies that can efficiently purify nontraditional water sources are needed to meet rising global demand for clean water. Water treatment plants typically require a series of costly separation units to achieve desalination and the removal of toxic trace contaminants such as heavy metals and boron. We report a series of robust, selective, and tunable adsorptive membranes that feature porous aromatic framework nanoparticles embedded within ion exchange polymers and demonstrate their use in an efficient, one-step separation strategy termed ion-capture electrodialysis. This process uses electrodialysis configurations with adsorptive membranes to simultaneously desalinate complex water sources and capture diverse target solutes with negligible capture of competing ions. Our methods are applicable to the development of efficient and selective multifunctional separations that use adsorptive membranes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2019

Artificial Intelligence: Powering Human Exploration of the Moon and Mars

Artificial Intelligence (AI) is a growing field of computa- tional science techniques designed to mimic functions per- formed by people. Advancements in autonomy will depend on a portfolio of AI technologies. Automated planning and scheduling is a venerable field of study in AI, and is needed for a variety of mission planning functions. Plan execution technology is less well studied, but important for auton- omy and robotics. Specialized forms of automated reason- ing and machine learning are key technologies to enable fault management. Over the past decade, the NASA Au- tonomous Systems and Operations (ASO) project has devel- oped and demonstrated numerous autonomy enabling tech- nologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission op- erations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission oper-ations functions. Vehicle System Management uses AI tech- niques to turn the astronaut's spacecraft into a robot, allow- ing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. When these capabilities are used to enable astro- nauts to operate autonomously, they must be integrated with user interfaces, introducing numerous human factors con- siderations; when these capabilities are used to enable vehi- cle system management, they must be integrated with flight software, and run on embedded processors under the control of real-time operating systems.We first describe human spaceflight mission operations capabilities. The remainder of the paper will describe the ASO project, and the development and demonstration per- formed by ASO since 2011. We will describe the AI tech- niques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learn- ing based approaches. Finally, we conclude with an assess- ment of future development needs for AI to enable NASA's future Exploration missions.

Mission Operations↗
NASA NTRSDate not supplied

Atmospheric Entry Heat Flux Sensor Calibration and Flight Data Analysis

- Recent NASA missions have included total heat flux sensors (THFS) embedded in the thermal protection system (TPS) to measure the combined convective and radiative heating during atmospheric entry. These measurements are key to fundamental entry science and mission design. - The THFSs for the Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2) sensor suite on the Mars 2020 entry vehicle and the Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) technology demonstration mission were passively cooled Schmidt–Boelter gauges. - As presented at IPPW 2021, THFS calibration is notoriously difficult and includes several sources of measurement uncertainty [1]. - Recent work has been conducted to understand and quantify the measurement uncertainty in the MEDLI2 and LOFTID Schmidt-Boelter THFSs.

R. A. Miller↗
DOE OSTI2021

Transport and Assembly of Magnetic Surface Rotors**

This minireview focuses on recent advances with surface magnetic rotors, namely field-responsive spherical or anisotropic microparticles that translate close to, or are embedded in a confining surface. The application of external magnetic modulations allows these microscopic wheels to be remotely spun and steered while also tuning their interactions and inducing assembly from a collection of disordered, moving units. With optical microscopy one can observe and characterize the complex collective phenomena that emerge in dissipative colloidal systems driven far from equilibrium by external fields. From a technological point of view, magnetic surface rotors envisage implementation into microfluidic devices, and can be used for drug delivery, mixing as well as a model system for biological active matter.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Design optimization of integrated cooling inserts in modular Fischer-Tropsch reactors

Sustainable production of liquid fuels and feedstocks from atmospheric CO 2 through carbon recycling technologies is necessary to broaden decarbonization efforts and further reduce global emissions. Fischer–Tropsch (FT) reactors can address this challenge by providing storable, high-value liquid hydrocarbon fuels and feedstocks from syngas generated through reductive CO 2 utilization. FT technology, however, is most cost effective at large-scale, fixed-site plants and does not effectively address the smaller, globally distributed CO 2 point sources. Alternatively, smaller, modular reactors can be composed together to the specific scale of the emission source, yielding a flexible solution that enables more widespread deployment. In these modular reactors, thermal management using a finned cooling insert embedded within the catalyst matrix is critical to maintaining the performance and viability. Thus, in this work, topology optimization is used to determine the cooling insert geometry that maximizes reactor productivity while preventing auto-thermal runaway. Optimal designs are generated for varying number of constituent fins and over a range of maximum operating temperatures. Constraints including minimum feature length-scales and prescribed cooling insert material are imposed on the designs to enhance manufacturability. The impact of design features such as insert tapering and increased length scale hierarchy is automatically revealed by the systematic design framework employed. Here, this approach thus generates novel optimal geometries while automating, accelerating, and enhancing the design process compared to traditional heuristic approaches for cooling insert design in modular FT reactors.

30 DIRECT ENERGY CONVERSION↗