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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Magnetic resonance control of spin-correlated radical pair dynamics in vivo

Magnetic fields can influence reactions involving spin-correlated radical pairs (SCRPs). This provides a mechanism by which both static and time-varying magnetic fields can affect living systems at the biomolecular level. However, an engineered SCRP system conferring magnetic sensitivity to a non-native biochemical process in a multicellular organism has not yet been demonstrated. Here, in this study, we demonstrate control of SCRP dynamics using magnetic resonance in a live transgenic animal. We show that the emission of various red fluorescent proteins (RFPs), in the presence of a flavin cofactor, can be modified by a combination of static and radiofrequency magnetic fields applied near the electron spin resonance frequency. This effect was measured at room temperature both in vitro and in the nematode Caenorhabditis elegans, genetically modified to express the RFP mScarlet. These observations suggest that the magnetic field effects measured in RFP-flavin systems are due to quantum-correlated radical pairs with a coherence time larger than 4 ns. Our experiments demonstrate that radiofrequency magnetic fields can influence dynamics of reactions involving SCRPs in vivo, potentially enabling new methods for remotely controlling biomolecular processes, such as gene expression, and suggest broader potential for quantum tools in biology.

Burd, Shaun C. [Stanford Univ., CA (United States)↗
DOE OSTI2025

Protein–Protein Complex Stability Controls Substrate Scope in a β‐Ketoacyl‐ACP Reductase Specific for Medium Chains

Assembly-line enzymes carry out multistep synthesis of important metabolites by using acyl carrier proteins (ACPs) to shuttle intermediates along defined sequences of active sites. Despite longstanding interest in reprogramming these systems for metabolic engineering and biosynthetic chemistry, the mechanisms underlying their reaction order remain poorly understood and difficult to control. In this work, we describe a β-ketoacyl-ACP reductase from Pseudomonas putida (PpFabG4) with an unusual selectivity for medium chains and use it to explore the molecular basis of substrate specificity in enzymes that pull intermediates from fatty acid synthesis, a common route to specialized products. X-ray crystallography shows no obvious barriers to short-chain binding. Molecular simulations and supporting mutational analyses indicate that substrate preference arises instead from a weak enzyme–ACP interaction that is stabilized by medium acyl chains but not by short chains. Indeed, mutations that strengthen this interaction for PpFabG4 or weaken it for EcFabG, an Escherichia coli β-ketoacyl-ACP reductase with a broad substrate specificity, can enhance or reduce activity on short-chain substrates by over 100-fold. Our findings show how the stability of enzyme-ACP interactions can control substrate scope in promiscuous enzymes and guide the exchange of intermediates between (and within) assembly-line systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Elucidation of Marcus Relationships for Hydride Transfer Reactions Involving Transition Metal Hydrides

The rate of hydride transfer from three Ir hydride complexes of the type Cp*Ir( R bpy)H + (Cp* = C 5 Me 5 ; R bpy = 4,4′-R-2,2′-bipyridine, R = OMe, H, CO 2 Me) to six N-methylacridinium ( R Acr + ) acceptors with electronically different substituents in the 2- or 2,7-positions were measured. Using the thermodynamic hydricity of the donors and the hydride affinity of the acceptors the thermodynamic driving forces for hydride transfer were determined. Brønsted plots, which correlate kinetic and thermodynamic hydricity, demonstrate distinct linear free energy relationships for each complex, with different Brønsted α values. Thus, at the same driving force hydride transfer from Cp*Ir( OMe bpy)H + is faster than for Cp*Ir(bpy)H + or Cp*Ir( CO2Me bpy)H + . Experimental and computational analyses are consistent with a concerted hydride transfer mechanism for all Ir complexes. As the thermodynamic driving force increases an earlier transition state is observed and all transition states also include π-stacking interactions between the donor and acceptor, which likely contribute to the different α values. The experimental data fits well to the Marcus model, enabling the determination of reorganization energies (λ) that range from 58 to 69 kcal mol -1 . These are lower than λ values for hydride transfer reactions involving organic donors and acceptors. This work provides a rare example of the correlation of kinetic and thermodynamic hydricity using only experimental data and shows that hydride transfer reactions involving metal hydrides can follow Marcus theory. Furthermore, the findings offer insight into controlling metal-catalyzed hydride transfer reactions, which is valuable for designing improved systems for a range of transformations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Automation of Nanoparticle Synthesis Processes in a Plasma Environment Using LabVIEW

This work presents an automated control system for the synthesis of nanomaterials by plasma-enhanced chemical vapor deposition (PECVD), implemented using the LabVIEW software environment. The main objective of the study is to develop an integrated hardware-software platform that enables sequential control of the key stages of the PECVD process, including vacuum chamber preparation, pressure monitoring, working gas supply, plasma ignition, power matching, cyclic nanomaterial growth, and optical monitoring of nanoparticles in the plasma environment. The use of LabVIEW made it possible to integrate actuator control, experimental parameter acquisition, and realtime process visualization within a single automated system. The automated cycle begins with evacuation of the reaction chamber to a predefined base pressure. Transition to the next stage is permitted only after the specified pressure threshold has been reached, ensuring reproducible initial conditions for each experiment. The program then controls the supply of the working gas through mass flow controllers (MFCs). In this work, two gas-flow control modes were considered: analog control using a 0-5 V voltage signal and digital communication via RS-232 interface. It was shown that the analog approach requires accurate scaling of the control voltage, since applying 5 V corresponds to full-scale opening of the controller and results in the maximum gas flow. In contrast, the RS232 interface enables the gas flow rate to be specified directly in sccm, improving the accuracy, flexibility, and convenience of gas-environment control. After pressure stabilization, LabVIEW initiates RF plasma ignition and executes the RF matching algorithm aimed at minimizing reflected power and improving the stability of the plasma process. A separate software module implements the cyclic nanomaterial growth mode, in which the plasma-on time, plasma duration, and total number of synthesis cycles are predefined. This approach makes it possible to control material accumulation on the substrate and to correlate the process parameters with the morphological characteristics of the resulting nanostructures. The final module of the system is designed for optical monitoring of the nanoparticle cloud density in dusty plasma. For this purpose, the change in the intensity of laser radiation passing through the plasma region is recorded using a photodetector and a Keithley 2401 measuring unit connected to LabVIEW via RS-232 interface. The difference between the initial and modified optical signal intensity is used as a diagnostic parameter characterizing the formation and temporal evolution of nanoparticles. The developed system demonstrates that LabVIEW can be effectively applied not only for the automation of individual instruments, but also for the implementation of a complete digital control cycle for PECVD-based nanomaterial synthesis.

PECVD↗
DOE OSTI2022

Reactive Transport Modeling of Mineral Precipitation and Carbon Trapping in Discrete Fracture Networks

In this study we use numerical experiments to analyze reactive flow and transport behavior in discrete intersecting fracture networks, focusing on (a) how reaction-induced changes in physical and chemical properties affect flow connectivity and (b) how fracture networks developed in the Earth's critical zone contribute to carbon sequestration via mineral weathering reactions. In the first part of the study, we used two-dimensional reactive flow and transport simulations to analyze the impacts of mixing in a natural discrete fracture network. We concluded that reaction-induced changes can substantially alter the flow connectivity, especially at fracture intersections. The second set of simulations considered the problem of natural weathering of fractured mafic and ultramafic rocks in the partially saturated Earth's critical zone as a function of infiltration rates, fracture permeability, and partially saturated flow parameters. As a model system, we considered an incongruent reaction network with dissolution of forsterite and precipitation of magnesite. The behavior is complex in terms of the rate-controlling processes because of the multicomponent nature of the system as shown by the grid Peclet number: the CO 2 behavior is gas diffusion-controlled in the partially saturated zone, while the rate of water flow via the Damkӧhler number controls Mg 2+ transport through the fracture network. The amounts of carbon that can be trapped are modest, but the naturally fractured domain considered here provides a useful “base case” against which various engineered solutions can be compared.

58 GEOSCIENCES↗
DOE OSTI2024

Cell-Free Systems Biology: Characterizing Central Metabolism of Clostridium thermocellum with a Three-Enzyme Cascade Reaction

Genetic approaches have been traditionally used to understand microbial metabolism, but this process can be slow in nonmodel organisms due to limited genetic tools. An alternative approach is to study metabolism directly in the cell lysate. This avoids the need for genetic tools and is routinely used to study individual enzymatic reactions but is not generally used to study systems-level properties of metabolism. Here we demonstrate a new approach that we call “cell-free systems biology”, where we use well-characterized enzymes and multienzyme cascades to serve as sources or sinks of intermediate metabolites. This allows us to isolate subnetworks within metabolism and study their systems-level properties. To demonstrate this, we worked with a threeenzyme cascade reaction that converts pyruvate to 2,3-butanediol. Although it has been previously used in cell-free systems, its pH dependence was not well characterized, limiting its utility as a sink for pyruvate. We showed that improved proton accounting allowed better prediction of pH changes and that active pH control allowed 2,3-butanediol titers of up to 2.1 M (189 g/L) from acetoin and 1.6 M (144 g/L) from pyruvate. The improved proton accounting provided a crucial insight that preventing the escape of CO 2 from the system largely eliminated the need for active pH control, dramatically simplifying our experimental setup. We then used this cascade reaction to understand limits to product formation in Clostridium thermocellum, an organism with potential applications for cellulosic biofuel production. We showed that the fate of pyruvate is largely controlled by electron availability and that reactions upstream of pyruvate limit overall product formation.

09 BIOMASS FUELS↗
DOE OSTI2022

Melting of the Fe–C–H System and Earth's Deep Carbon–Hydrogen Cycle

The occurrences and cycling of slab-originated carbon and hydrogen are considered to be controlled by their reactions with metallic iron from mantle disproportionation and slab serpentinization, to form Fe alloys containing carbon and hydrogen. In this work, we show experimental results on the phase relations and melting of the Fe-C-H system using laser-heated diamond anvil cell and X-ray diffraction techniques up to 72 GPa. The incorporation of hydrogen was found to lower the eutectic melting temperatures of Fe-C alloy by ~50–178 K at 20–70 GPa, facilitating the formation of metallic liquids in the deep mantle and thus enhancing the mobility and deep cycling of subducted carbon and hydrogen. Hydrogen also substitutes with carbon in Fe-C metal to form hydride and diamond at relatively high-temperature conditions (e.g., 42.6 GPa, >1885 K and 71.8 GPa, >1798 K). The hydrogen-carbon-enriched metallic liquids provide the necessary fluid environment for superdeep diamond growth.

58 GEOSCIENCES↗
DOE OSTI2021

Self-organization of nanoparticles and molecules in periodic Liesegang-type structures

Chemical organization in reaction-diffusion systems offers a strategy for the generation of materials with ordered morphologies and structural hierarchy. Periodic structures are formed by either molecules or nanoparticles. On the premise of new directing factors and materials, an emerging frontier is the design of systems in which the precipitation partners are nanoparticles and molecules. We show that solvent evaporation from a suspension of cellulose nanocrystals (CNCs) and L -(+)-tartaric acid [ L -(+)-TA] causes phase separation and precipitation, which, being coupled with a reaction/diffusion, results in rhythmic alternation of CNC-rich and L -(+)-TA–rich rings. The CNC-rich regions have a cholesteric structure, while the L -(+)-TA–rich bands are formed by radially aligned elongated bundles. The moving edge of the pattern propagates with a finite constant velocity, which enables control of periodicity by varying film preparation conditions. This work expands knowledge about self-organizing reaction-diffusion systems and offers a strategy for the design of self-organizing materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Nondestructive Photo-Cross-Linking of Microphase-Separated Diblock Polymers through Coumarin Dimerization

The effect of long-wavelength ultraviolet photocrosslinking on microphase-separated coumarin-containing block polymers was studied by photo-rheometry and small-angle X-ray scattering. This model system consisted of three photocrosslinkable diblock polymers of poly(methoxyethyl acrylate)-b-poly(hexyl methacrylate-co-coumarin methacrylate) with different volume fractions of the crosslinkable coumarin-containing block, which microphase separated into lamellar and cylindrical morphologies. All polymers stiffened upon exposure to 365 nm light, with much greater relative increases in moduli recorded for lamellae-forming polymers (ca. 3200% increase) compared to the cylinder-forming polymer (ca. 550% increase). Disordering transitions that were evident in uncrosslinked samples were no longer observed after crosslinking in the ordered state, and domain sizes were found to remain stable to heating. The photocrosslinking reaction only proceeded under active irradiation, indicating a high degree of spatiotemporal control over curing in this system. Finally, at constant concentration of couamarin within the crosslinkable block, the cure rate was largely independent of polymer composition, suggesting a constant local concentration of coumarin moieties within the segregated crosslinkable domains. Lastly, these findings establish a set of specific structure-property relationships governing the phase-selective photocrosslinking of diblock polymers that can guide the design of robust nanostructured materials.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Fault diagnosis and fault tolerant control for T-S fuzzy stochastic distribution systems subject to sensor and actuator faults

The problem of fault diagnosis (FD) and fault tolerant control (FTC) for a class of Takagi-Sugeno (T-S) fuzzy stochastic distribution control (SDC) systems subject to sensor and actuator faults is discussed in this paper. First, fuzzy logic models are used to approximate the output probability density function (PDF). Next, an adaptive augmented state/fault diagnosis observer is proposed to estimate the system state, sensor and the actuator faults simultaneously. New expected weights based on the sensor fault estimation information and a PI-type fuzzy feedback fault tolerant (FT) controller are designed to compensate the effect of sensor fault and actuator fault simultaneously. When the sensor fault occurs, the expected objective is redesigned to compensate the sensor fault. Meanwhile, the PI controller can compensate the effect of actuator fault, and the output PDF of the system can still track the desired PDF after the fault occurs. Finally, an example of quality distribution control in chemical reaction process is given to confirm the effectiveness of the algorithm.

42 ENGINEERING↗
DOE OSTI2022

Implementation of a Model Predictive Control Strategy to Regulate Temperature Inside Plug-Flow Solar Reactor With Countercurrent Flow

Abstract Solar-driven thermochemical energy storage systems are proven to be promising energy carriers (solar fuels) to utilize solar energy by using reactive solid-state pellets. However, the production of solar fuel requires a quasi-steady-state process temperature, which represents the main challenge due to the transient nature of solar power. In this work, an adaptive model predictive controller (MPC) is presented to regulate the temperature inside a tubular solar reactor to produce solid-state solar fuel for long-term thermal storage systems. The solar reactor system consists of a vertical tube heated circumferentially over a segment of its length by concentrated solar power, and the reactive pellets (MgMn2O4) are fed from the top end and flow downwards through the heated tube. A countercurrent flowing gas supplied from the lower end interacts with flowing pellets to reduce it thermochemically at a temperature range of 1000—1500 °C. A low-order physical model was developed to simulate the dynamics of the solar reactor including the reaction kinetics, and the proposed model was validated numerically by using a 7-kW electric furnace. The numerical model then was utilized to design the MPC controller, where the control system consists of an MPC code linked to an adaptive system identification code that updates system parameters online to ensure system robustness against external disturbances (sudden change in the flow inside the reactor), model mismatches, and uncertainty. The MPC controller parameters are tuned to enhance the system performance with minimum steady-state error and overshoot. The controller is tested to track different temperature ranges between 500 °C and 1400 °C with different particles/gas mass flowrates and ramping temperature profiles. Results show that the MPC controller successfully regulated the reactor temperature within ± 1 °C of its setpoint and maintained robust performance with minimum input effort when subjected to sudden changes in the amount of flowing media and the presence of chemical reaction.

Engineering↗
DOE OSTI2026

Transient Catalytic Reaction Analysis Through Signal Defragmentation

The Temporal Analysis of Products (TAP) pulse response technique provides valuable insights into catalytic function and reaction kinetics. However, complex fragmentation patterns in the TAP mass spectrometry signals can complicate precise quantification, particularly when analyzing transient gas flux data typical of TAP experiments. This work demonstrates a standard defragmentation method that deconvolves transient TAP signals while maintaining the temporal resolution of the experiment. First, the integrals of calibration gas fluxes are used to determine the fingerprint fragmentation pattern and construct a fragmentation matrix. This matrix is then used to defragment experimental flux data at each recorded time point via a non-negative least squares regression. The effectiveness of this method is demonstrated using virtual data and control experiments with a TAP reactor system. The defragmentation is then applied to the more complex propane dehydrogenation reaction on a chromia/alumina catalyst, which can contain up to ten significant gas species in the reactor outlet. Initial propane pulsing reveals an induction period during which propane is fully oxidized to CO2, followed by partial reduction to CO. Afterwards, there is a transition in chemistries towards coking and propylene production. Our example illustrates a practical method for the accurate determination of the time-dependent reactant/product concentrations and rates for a thorough analysis of the propane dehydrogenation kinetics. This approach can be broadly applied to any transient mass spectrometry experiment for a better understanding of catalyst-reaction dynamics.

36 - MATERIALS SCIENCE↗
DOE OSTI2020

Determining the Reaction Time for Triggering Supportive Control Actions to Guarantee Adequate Frequency Response in Smart Grids

The progressive integration of renewable energy resources in the modern power grid can result in unsatisfactory frequency responses. To address this problem, wind turbine generators (WTGs), in particular, can be employed to support the power grid. Most works, however, only consider a simple step disturbance. In this paper, a new class of worst-case disturbances is introduced and their nefarious impact on renewable-penetrated power systems is investigated. The worst-case disturbances are derived using optimal control theory. More importantly, the proposed mechanism allows to determine the reaction time to trigger supportive control actions of WTGs to ensure satisfactory frequency response. Numerical results are provided and the effects of the worst-case disturbance on the reaction time are presented.

Luo, Jiecai↗
DOE OSTI2020

River Dynamics Control Transit Time Distributions and Biogeochemical Reactions in a Dam-Regulated River Corridor

Residence Time Distributions (RTDs) exert an important control on biogeochemical translation in the hydrological systems. Previous tracer studies have revealed that RTDs often followed time-invariant exponential, lognormal, or heavy-tailed RTDs that have power-law behavior for long tails in headwater or low-order streams. However, there is a recognition that RTDs can be more complicated and time-variable in response to dynamic hydrological forcing. Here, we use particle tracking to estimate RTDs along the Hanford Reach of the Columbia River and to quantify the influences of river stage fluctuations. Particle tracking is conducted using the velocity field from high-resolution 3D groundwater flow simulations. The effects of hydrological forcing on the residence time distribution were evaluated by varying river flow boundary conditions and releasing particles in different time windows. Our results revealed that dynamic stage fluctuations created rapidly changing losing-gaining conditions in the river and led to highly transient RTDs, which contributes to multiple modes of RTDs. Dam-induced high-frequency (sub-daily) flow variation contributes to the short-time (sub-daily) component of the RTDs. Deviation of the reactant consumption under the single-mode assumption compared to the multimodal RTDs is relatively small (~5%) and appears when the Damköhler number was close to one.More specifically, high-frequency (daily) dam-induced stage variations have higher impacts on biogeochemical reactions with faster reaction rate, and can potentially increase the reactant consumption by 26.68%. Based on these findings, we suggest that current river basin models could be improved by including bank storage and more complex RTDs influenced by both short and long term river stage fluctuations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Mechanistic Insights into Dinitrogen Reduction to Ammonia in Light-Controlled Nanocrystal:Nitrogenase Complexes

Developing systems that can efficiently capture photon energy and convert this energy into fuels and chemicals requires understanding how to assemble molecular components with diverse functions into complete systems possessing selectivity and efficiency in directing charge carriers to catalytic reactions. There are many challenges to achieving this goal. One promising approach is the development of hybrid systems that combine semiconductor nanocrystals (NCs) for light capture and enzymes as efficient catalysts. Such biohybrid systems capitalize on the tunable electronic and optical properties of NCs while leveraging the unmatched specificity and efficiency of enzymes in catalyzing chemical reactions, thereby offering opportunities to surpass the limitations of each component alone. Here, we focus on recent progress in developing a biohybrid system that combines CdS NCs for photon capture with the enzyme nitrogenase to accomplish light-driven dinitrogen (N 2 ) reduction to ammonia (NH 3 ). Integrating light-harvesting materials with biological catalysts requires a deep understanding of NC properties, protein stability, and electron transfer (ET), making it an inherently multidisciplinary problem. The reduction of N 2 to NH 3 is a challenging reaction, with a high demand in both agriculture and industrial chemical production. This reaction is intrinsically energy intensive, due to the need to activate the N≡N triple bond. The current standard industrial approach to N 2 reduction, the Haber−Bosch reaction, obtains the necessary energy input from fossil fuels, whereas biological systems capable of N 2 reduction utilize the hydrolysis of ATP as their energy source. Replacing these costly, energy-intensive inputs with renewable light energy represents a critical step toward sustainable NH 3 production. Recent progress has demonstrated that semiconductor CdS NCs can be coupled to the catalytic component of nitrogenase, the MoFe protein, to form a biohybrid CdS NC:MoFe protein complex, enabling light-driven N 2 reduction rather than energy input from fossil fuels or ATP. This illustrates how inorganic NCs can functionally replace the natural Fe protein partner, yielding a biohybrid catalyst that enables controlled electron delivery and provides not only light-driven NH 3 production but also new approaches for probing enzyme catalytic function. The CdS NC:MoFe protein biohybrid system enables light-initiated electron delivery at ambient temperature, as well as temperatures below freezing, allowing for stabilization and spectroscopic characterization of key reaction intermediates. These findings highlight how photochemical biohybrids can serve as both functional catalysts and mechanistic probes. Beyond studies of the nitrogenase mechanism, studies of the CdS NC:MoFe system reveal how variables such as NC size, electrostatic binding interactions, and sacrificial electron donors (SEDs) govern complex stability, charge transfer efficiency, and catalytic performance. In addition, studies of nitrogenase and the high activation barrier for N 2 reduction are enabling investigations of new and interesting questions regarding the properties and limitations of NC biocatalysis. In this Account, we describe the key features of CdS NC:MoFe protein biohybrids and the parameters for optimal light-driven N 2 reduction, and how controlling ET with light illuminates the path to new insights into the nitrogenase mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗
DOE OSTI2022

RAFT (Response Amplitudes of Floating Turbines) [SWR-22-40]

RAFT is a Python code for frequency-domain analysis of floating wind turbines. RAFT works in the frequency domain to provide efficient computations of a floating wind system's response spectra accounting for platform hydrodynamics, quasi-static mooring reactions, rotor aerodynamics, and linearized control. It can be used to calculate response amplitude operators (RAO's), power spectral densities, mean/static properties, and response metrics based on mean and root-mean-square values of each output. RAFT serves as the Level 1 model in the WEIS (Wind Energy with Integrated Servo-control) toolkit, and features an OpenMDAO wrapper.

Hall, Matthew↗