Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “lead optimization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Tools of the Future: How Decision Tree Analysis Will Impact Mission Planning

The universe is infinitely complex; however, the human mind has a finite capacity. The multitude of possible variables, metrics, and procedures in mission planning are far too many to address exhaustively. This is unfortunate because, in general, considering more possibilities leads to more accurate and more powerful results. To compensate, we can get more insightful results by employing our greatest tool, the computer. The power of the computer will be utilized through a technology that considers every possibility, decision tree analysis. Although decision trees have been used in many other fields, this is innovative for space mission planning. Because this is a new strategy, no existing software is able to completely accommodate all of the requirements. This was determined through extensive research and testing of current technologies. It was necessary to create original software, for which a short-term model was finished this summer. The model was built into Microsoft Excel to take advantage of the familiar graphical interface for user input, computation, and viewing output. Macros were written to automate the process of tree construction, optimization, and presentation. The results are useful and promising. If this tool is successfully implemented in mission planning, our reliance on old-fashioned heuristics, an error-prone shortcut for handling complexity, will be reduced. The computer algorithms involved in decision trees will revolutionize mission planning. The planning will be faster and smarter, leading to optimized missions with the potential for more valuable data.

Otterstatter, Matthew R.↗
DOE OSTI2025

Optimizing semi-hydrogenation of unsaturated hydrocarbons by electrolyte engineering approach

Electrochemical hydrogenation of unsaturated hydrocarbons, when powered by renewables, represents a unique opportunity to substitute current energy-intensive synthetic routes. Modulation of adsorption energies of the organic substrate and key intermediates of the reaction is critical for fine tuning of the yield, selectivity and kinetics of the reaction. Interestingly, mounting evidence exists regarding the role of electrolyte composition in the outcome of semi-hydrogenation reactions. Nevertheless, electrolyte optimization is a complex task, owing to its hybrid nature. Indeed, it is composed of water serving as a proton source, an organic solvent necessary to dissolve the organic substrate and a conducting salt. Herein, we demonstrate that varying conducting salt and organic solvent has a dramatic impact on the outcomes of semi-hydrogenation of alkynes. By varying salt and water concentrations, we demonstrate that water does not serve as a proton source, and instead addition of an acid is necessary. While increasing the acid concentration increases the yield of the reaction, at too large concentrations the hydrogen evolution reaction becomes predominant. Furthermore, by combining electrochemical measurements with spectroscopic techniques including Fourier transform infrared (FTIR) spectroscopy and small angle X-ray spectroscopy (SAXS), we demonstrate that the electrolyte solvation structure dramatically impacts the yield of the reaction. Organic solvents weakly interacting with water, including acetonitrile, form aqueous nanoheterogeneities that prevent the organic substrate from accessing the catalyst interface and thus lead to limited yields. Instead, solvents such as dimethylformamide form homogeneous mixtures with which all reactants can access the interface, leading to yields greater than 80% for optimized compositions.

Zhang, Rongyu↗
DOE OSTI2020

Physical Security Model Development of an Electrochemical Facility

Nuclear facilities in the U.S. and around the world face increasing challenges in meeting evolving physical security requirements while keeping costs reasonable. The addition of security features after a facility has been designed and without attention to optimization (the approach of the past) can easily lead to cost overruns. Instead, security should be considered at the beginning of the design process in order to provide robust, yet efficient physical security designs. The purpose of this work is to demonstrate how modeling and simulation can be used to optimize the design of physical protection systems. A suite of tools, including Scribe3D and Blender, were used to model a generic electrochemical reprocessing facility. Physical protection elements such as sensors, portal monitors, barriers, and guard forces were added to the model based on best practices for physical security. Two theft scenarios (an outsider attack and insider diversion) as well as a sabotage scenario were examined in order to optimize the security design. Security metrics are presented. This work fits into a larger Virtual Facility Distributed Test Bed 2020 Milestone in the Material Protection, Accounting, and Control Technologies (MPACT) program through the Department of Energy (DOE). The purpose of the milestone is to demonstrate how a series of experimental and modeling capabilities across the DOE complex provide the capabilities to demonstrate complete Safeguards and Security by Design (SSBD) for nuclear facilities.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2004

Using High Resolution Design Spaces for Aerodynamic Shape Optimization Under Uncertainty

This paper explains why high resolution design spaces encourage traditional airfoil optimization algorithms to generate noisy shape modifications, which lead to inaccurate linear predictions of aerodynamic coefficients and potential failure of descent methods. By using auxiliary drag constraints for a simultaneous drag reduction at all design points and the least shape distortion to achieve the targeted drag reduction, an improved algorithm generates relatively smooth optimal airfoils with no severe off-design performance degradation over a range of flight conditions, in high resolution design spaces parameterized by cubic B-spline functions. Simulation results using FUN2D in Euler flows are included to show the capability of the robust aerodynamic shape optimization method over a range of flight conditions.

Li, Wu↗
DOE OSTI2021

The advanced tokamak path to a compact net electric fusion pilot plant

Abstract Physics-based simulations project a compact net electric fusion pilot plant with a nuclear testing mission is possible at modest scale based on the advanced tokamak concept, and identify key parameters for its optimization. These utilize a new integrated 1.5D core-edge approach for whole device modeling to predict performance by self-consistently applying transport, pedestal and current drive models to converge fully non-inductive stationary solutions, predicting profiles and energy confinement for a given density. This physics-based approach leads to new insights and understanding of reactor optimization. In particular, the levering role of high plasma density is identified, which raises fusion performance and self-driven ‘bootstrap currents’, to reduce current drive demands and enable high pressure with net electricity at a compact scale. Solutions at 6–7 T, ∼4 m radius and 200 MW net electricity are identified with margins and trade-offs possible between parameters. Current drive comes from neutral beam and ultra-high harmonic (helicon) fast wave, though other advanced approaches are not ruled out. The resulting low recirculating power in a double null configuration leads to a divertor heat flux challenge that is comparable to ITER, though reactor solutions may require more dissipation. Strong H-mode access (x2 margin over L–H transition scalings) and ITER-like heat fluxes are maintained with ∼20%–60% core radiation, though effects on confinement need further analysis. Neutron wall loadings appear tolerable. The approach would benefit from high temperature superconductors, as higher fields would increase performance margins while potential for demountability may facilitate nuclear testing. However, solutions are possible with conventional superconductors. An advanced load sharing and reactive bucking approach in the device centerpost region provides improved mechanical stress handling. The prospect of an affordable test device which could close the loop on net-electric production and conduct essential nuclear materials and breeding research is compelling, motivating research to validate the techniques and models employed here.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1994

A parametric sensitivity study for single-stage-to-orbit hypersonic vehicles using trajectory optimization

The class of hypersonic vehicle configurations with single stage-to-orbit (SSTO) capability reflect highly integrated airframe and propulsion systems. These designs are also known to exhibit a large degree of interaction between the airframe and engine dynamics. Consequently, even simplified hypersonic models are characterized by tightly coupled nonlinear equations of motion. In addition, hypersonic SSTO vehicles present a major system design challenge; the vehicle's overall mission performance is a function of its subsystem efficiencies including structural, aerodynamic, propulsive, and operational. Further, all subsystem efficiencies are interrelated, hence, independent optimization of the subsystems is not likely to lead to an optimum design. Thus, it is desired to know the effect of various subsystem efficiencies on overall mission performance. For the purposes of this analysis, mission performance will be measured in terms of the payload weight inserted into orbit. In this report, a trajectory optimization problem is formulated for a generic hypersonic lifting body for a specified orbit-injection mission. A solution method is outlined, and results are detailed for the generic vehicle, referred to as the baseline model. After evaluating the performance of the baseline model, a sensitivity study is presented to determine the effect of various subsystem efficiencies on mission performance. This consists of performing a parametric analysis of the basic design parameters, generating a matrix of configurations, and determining the mission performance of each configuration. Also, the performance loss due to constraining the total head load experienced by the vehicle is evaluated. The key results from this analysis include the formulation of the sizing problem for this vehicle class using trajectory optimization, characteristics of the optimal trajectories, and the subsystem design sensitivities.

Lovell, T. Alan↗
NASA NTRS2015

An Automated DAKOTA and VULCAN-CFD Framework with Application to Supersonic Facility Nozzle Flowpath Optimization

Removing human interaction from design processes by using automation may lead to gains in both productivity and design precision. This memorandum describes efforts to incorporate high fidelity numerical analysis tools into an automated framework and applying that framework to applications of practical interest. The purpose of this effort was to integrate VULCAN-CFD into an automated, DAKOTA-enabled framework with a proof-of-concept application being the optimization of supersonic test facility nozzles. It was shown that the optimization framework could be deployed on a high performance computing cluster with the flow of information handled effectively to guide the optimization process. Furthermore, the application of the framework to supersonic test facility nozzle flowpath design and optimization was demonstrated using multiple optimization algorithms.

Axdahl, Erik L.↗
NASA NTRS2019

Performance Optimization of Plate Airfoils for Martian Rotor Applications Using a Genetic Algorithm

The Mars Helicopter Technology Demonstrator will be flying on the NASA Mars 2020 rover mission scheduled to launch in July of 2020. The goal is to demonstrate the viability and potential of heavier-than-air vehicles in the Martian atmosphere. Research is performed at the Jet Propulsion Laboratory and NASA Ames Research Center to extend these capabilities and develop the Mars Science Helicopter as the next possible step for Martian rotorcraft. The Mars Science Helicopter mass is scaled up to the 5 to 20 kg range, allowing a greater payload (approximately 0.5 to 2.0 kg), and greater range (approximately 3 km). Key to achieving these targets is careful aerodynamic rotor design. The Martian atmosphere’s low density and the small helicopter rotors result in very low chord-based Reynolds number flows, which reduces rotor performance. A continuous genetic algorithm is developed to optimize airfoil shapes at representative conditions for the Martian atmosphere. Previous research indicates that sharp leading edges and plate-like airfoils can out-perform conventional airfoil shapes. The present optimization allows for camber and thickness variation of curved and polygonal thin airfoils with sharp leading edges. The airfoil performance is evaluated at the highest attainable liftto- drag ratio near a moderate lift coefficient at compressible Mach numbers, as expected for Martian rotor application. Increases between 16% and 29% in airfoil lift-to-drag ratio at fixed lift coefficients are observed when compared with the Mars Helicopter Technology Demonstrator airfoils. Improvements in hover figure of merit are estimated to be between 4% and 10%, when applied to the Mars Helicopter Technology Demonstrator.

Koning, Witold J. F.↗
NASA NTRS2010

Aircraft Trajectory Optimization and Contrails Avoidance in the Presence of Winds

There are indications that persistent contrails can lead to adverse climate change, although the complete effect on climate forcing is still uncertain. A flight trajectory optimization algorithm with fuel and contrails models, which develops alternative flight paths, provides policy makers the necessary data to make tradeoffs between persistent contrails mitigation and aircraft fuel consumption. This study develops an algorithm that calculates wind-optimal trajectories for cruising aircraft while avoiding the regions of airspace prone to persistent contrails formation. The optimal trajectories are developed by solving a non-linear optimal control problem with path constraints. The regions of airspace favorable to persistent contrails formation are modeled as penalty areas that aircraft should avoid and are adjustable. The tradeoff between persistent contrails formation and additional fuel consumption is investigated, with and without altitude optimization, for 12 city-pairs in the continental United States. Without altitude optimization, the reduction in contrail travel times is gradual with increase in total fuel consumption. When altitude is optimized, a two percent increase in total fuel consumption can reduce the total travel times through contrail regions by more than six times. Allowing further increase in fuel consumption does not seem to result in proportionate decrease in contrail travel times.

Ng, Hok K.↗
DOE OSTI2022

Water confinement in small polycyclic aromatic hydrocarbons

The confinement of water molecules is vital in fields from biology to nanotechnology. The conditions allowing confinement in small finite polycyclic aromatic hydrocarbons (PAHs) are unclear, yet are crucial for understanding confinement in larger systems. Here, we report a computational study of water cluster confinement within PAHs dimers. Our results serve as a model for larger carbon allotropes and for understanding molecular interactions in confined systems. We identified size and structural motifs allowing confinement and demonstrated the motifs in various PAHs systems. Here we show that optimal OH∙∙∙π interactions between water clusters and the PAH dimer permit optimal confinement to occur. However, the lack of such interactions leads to the formation of CH∙∙∙O interactions, resulting in less ideal confinement. Confinement of layered clusters is also possible, provided that the optimal OH∙∙∙π interactions are conserved.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Regulating Anionic Redox via Mg Substitution in Mn-Rich Layered Oxide Cathodes Enabling High Electrochemical Stability for Sodium-Ion Batteries

Here, with the limited resources and high cost of lithium-ion batteries (LIBs) and the ever-increasing market demands, sodium-ion batteries (SIBs) gain much interest due to their economical sustainability, and similar chemistry and manufacturing processes to LIBs. As cathodes play a vital role in determining the energy density of SIBs, Mn-based layered oxides are promising cathodes due to their low cost, environmental friendliness, and high theoretical capacity. However, the main challenge is structural instability upon cycling at high voltage. Herein, Mg is introduced into the P2-type Na 0.62 Ni 0.25 Mn 0.75 O 2 cathode to enhance electrochemical stability. By combining electrochemical testing and material characterizations, it is found that substituting 10 mol% Mg can effectively alleviate the P2–O2 phase transition, Jahn-Teller distortion, and irreversible oxygen redox. Moreover, structural integrity is greatly improved. These lead to enhanced electrochemical performances. With the optimized sample, a remarkable capacity retention of 92% in the half cell after 100 cycles and 95% in the full cell after 170 cycles can be achieved. Altogether, this work provides an alternative way to stabilize P2-type Mn-based layer oxide cathodes, which in turn, put forward the development of this material for the next-generation SIBs.

25 ENERGY STORAGE↗
DOE OSTI2020

Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

Physics-informed neural networks (PINNs) were recently proposed in [18] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution, while a PDE-induced NN is coupled to the solution NN, and all differential operators are treated using automatic differentiation. Here, we first employ the standard PINN and a stochastic version, sPINN, to solve forward and inverse problems governed by a non-linear advection–diffusion–reaction (ADR) equation, assuming we have some sparse measurements of the concentration field at random or pre-selected locations. Subsequently, we attempt to optimise the hyper-parameters of sPINN by using the Bayesian optimisation method (meta-learning) and compare the results with the empirically selected hyper-parameters of sPINN. In particular, for the first part in solving the inverse deterministic ADR, we assume that we only have a few high-fidelity measurements, whereas the rest of the data is of lower fidelity. Hence, the PINN is trained using a composite multi-fidelity network, first introduced in [12], that learns the correlations between the multi-fidelity data and predicts the unknown values of diffusivity, transport velocity and two reaction constants as well as the concentration field. For the stochastic ADR, we employ a Karhunen–Loève (KL) expansion to represent the stochastic diffusivity, and arbitrary polynomial chaos (aPC) to represent the stochastic solution. Correspondingly, we design multiple NNs to represent the mean of the solution and learn each aPC mode separately, whereas we employ a separate NN to represent the mean of diffusivity and another NN to learn all modes of the KL expansion. For the inverse problem, in addition to stochastic diffusivity and concentration fields, we also aim to obtain the (unknown) deterministic values of transport velocity and reaction constants. The available data correspond to 7spatial points for the diffusivity and 20 space–time points for the solution, both sampled 2000 times. We obtain good accuracy for the deterministic parameters of the order of 1–2% and excellent accuracy for the mean and variance of the stochastic fields, better than three digits of accuracy. In the second part, we consider the previous stochastic inverse problem, and we use Bayesian optimisation to find five hyper-parameters of sPINN, namely the width, depth and learning rate of two NNs for learning the modes. Here, we obtain much deeper and wider optimal NNs compared to the manual tuning, leading to even better accuracy, i.e., errors less than 1% for the deterministic values, and about an order of magnitude less for the stochastic fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Deactivation of Mo/H-ZSM-5 in Microwave-Assisted and Thermal-Driven Methane Dehydroaromatization

A better understanding of catalyst deactivation is needed to improve catalyst design and performance in microwave-enhanced methane dehydroaromatization (MDA). Here, this study investigates the deactivation of a molybdenum supported H-ZSM-5 zeolite (Mo/H-ZSM-5) catalyst in MDA under microwave-heated conditions, comparing its performance to that of the same catalyst under conventional heating. While the microwave-assisted (MW) process achieved higher benzene yields, the catalyst experienced faster deactivation due to the selective and rapid deposition of coke within the pores of the zeolite, as confirmed through Brunauer–Emmett–Teller (BET), X-ray diffraction (XRD), ammonia-temperature programmed desorption (NH 3 -TPD), thermal gravimetric analysis (TGA), temperature programmed oxidation (TPO), and X-ray photoelectron spectroscopy (XPS) analyses. The quantification of total coke content via TGA/TPO and surface carbon (XPS) revealed that nearly twice as much coke was deposited on the catalyst under MW conditions compared to that on the conventionally heated material, and the coke exhibited a more conductive and graphitic nature. The accelerated deactivation rates were attributed to the formation of hot spots in the MW system, leading to enhanced coupling with coke formed in situ during the reaction and resulting in increased Mo reduction. Observations indicated that the CO activation used to carburize the catalyst prior to the reaction is not advantageous in the MW heating environment. The presence of large amounts of Mo oxides at elevated temperatures (through hot spots) exposed to methane leads to instability under the reaction conditions. Optimizing the activation environment and improvement of the Mo dispersion within the pores are potential strategies to improve catalyst stability.

Mo/H-ZSM-5 zeolite↗
DOE OSTI2022

Hydrogen Dissociation Controls 1-Hexyne Selective Hydrogenation on Dilute Pd-in-Au Catalysts

Increasing the selectivity of the catalytic hydrogenation of alkynes to alkenes is of major importance for the processing of petrochemicals and the production of fine chemicals. Achieving high selectivity for alkene formation at high conversions, however, remains a long-standing challenge in heterogeneous catalysis. Here, the mechanism and origin of the high selectivity of dilute Pd-in-Au catalysts has been studied by a combination of first-principles calculations, microkinetic simulations, and isotopic exchange hydrogenation experiments using Pd 0.04 Au 0.96 nanoparticles embedded in raspberry colloid-templated silica. The Pd is predominantly in the form of isolated atoms, only surrounded by Au atoms, based on prior studies. The simulations indicate that the rate-limiting process for 1-hexyne hydrogenation on Pd monomers in Au(111) is H 2 dissociation, which has a large free energy barrier of 0.86 eV at 363 K and 0.2 bar of H 2 . The C–H bond formation steps, on the other hand, proceed with lower barriers, which contrasts with previous studies of extended Pd catalysts. The microkinetic simulations identify the sizable H 2 dissociation barrier and the small barrier for the hydrogenation of 1-hexyne as key factors that lead to a high selectivity for the production of 1-hexene from 1-hexyne, even at high conversion. The unconventional H 2 dissociation limiting process in combination with the low coverage of weakly bound hydrocarbon intermediates explains the near-zero order of 1-hexyne found experimentally. Furthermore, the partial hydrogenation of 1-hexyne to form 1-hexene is shown to be an irreversible process from our isotopic exchange hydrogenation experiments and is explained by the strongly exothermic nature of the reaction. Diluting active species, like Pd, in a less active host metal, like Au, hence appears promising as a means of tuning the binding energy of reactants and altering reaction profiles, leading to distinct kinetic behavior for an optimal catalytic activity and selectivity. Furthermore, the combination of microkinetic modeling, density functional theory calculations, and isotopic exchange experiments is thus demonstrated to be an effective approach to modeling important catalytic phenomena.

1-hexyne↗
DOE OSTI2021

Iridium metallene oxide for acidic oxygen evolution catalysis

Exploring new materials is essential in the field of material science. Especially, searching for optimal materials with utmost atomic utilization, ideal activities and desirable stability for catalytic applications requires smart design of materials’ structures. Herein, we report iridium metallene oxide: 1 T phase-iridium dioxide (IrO 2 ) by a synthetic strategy combining mechanochemistry and thermal treatment in a strong alkaline medium. This material demonstrates high activity for oxygen evolution reaction with a low overpotential of 197 millivolt in acidic electrolyte at 10 milliamperes per geometric square centimeter (mA cm geo –2 ). Together, it achieves high turnover frequencies of 4.2 s UPD –1 (3.0 s BET –1 ) at 1.50 V vs. reversible hydrogen electrode. Furthermore, 1T-IrO 2 also shows little degradation after 126 hours chronopotentiometry measurement under the high current density of 250 mA cm geo –2 in proton exchange membrane device. Theoretical calculations reveal that the active site of Ir in 1T-IrO 2 provides an optimal free energy uphill in *OH formation, leading to the enhanced performance. The discovery of this 1T-metallene oxide material will provide new opportunities for catalysis and other applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Defect filtering for thermal expansion induced dislocations in III–V lasers on silicon

Epitaxially integrated III–V semiconductor lasers for silicon photonics have the potential to dramatically transform information networks, but currently, dislocations limit performance and reliability even in defect-tolerant InAs quantum dot (QD)-based lasers. Despite being below the critical thickness, QD layers in these devices contain previously unexplained misfit dislocations, which facilitate non-radiative recombination. We demonstrate here that these misfit dislocations form during post-growth cooldown due to the combined effects of (1) thermal-expansion mismatch between the III–V layers and silicon and (2) mechanical hardening in the active region. By incorporating an additional sub-critical thickness, indium-alloyed “misfit dislocation trapping layer,” we leverage these mechanical hardening effects to our advantage, displacing 95% of misfit dislocations from the QD layer in model structures. Unlike conventional dislocation mitigation strategies, the trapping layer reduces neither the number of threading dislocations nor the number of misfit dislocations. It simply shifts the position of misfit dislocations away from the QD layer, reducing the defects' impact on luminescence. In full lasers, adding a misfit dislocation trapping layer both above and below the QD active region displaces misfit dislocations and substantially improves performance: we measure a twofold reduction in lasing threshold currents and a greater than threefold increase in output power. Overall, our results suggest that devices employing both traditional threading dislocation reduction techniques and optimized misfit dislocation trapping layers may finally lead to fully integrated, commercially viable silicon-based photonic integrated circuits.

36 MATERIALS SCIENCE↗
DOE OSTI2024

When in-memory computing meets spiking neural networks—A perspective on device-circuit-system-and-algorithm co-design

This review explores the intersection of bio-plausible artificial intelligence in the form of spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain, highlighting their collective potential for low-power edge computing environments. Through detailed investigation at the device, circuit, and system levels, we highlight the pivotal synergies between SNNs and IMC architectures. Additionally, we emphasize the critical need for comprehensive system-level analyses, considering the inter-dependencies among algorithms, devices, circuit, and system parameters, crucial for optimal performance. An in-depth analysis leads to the identification of key system-level bottlenecks arising from device limitations, which can be addressed using SNN-specific algorithm–hardware co-design techniques. This review underscores the imperative for holistic device to system design-space co-exploration, highlighting the critical aspects of hardware and algorithm research endeavors for low-power neuromorphic solutions.

Physics↗
DOE OSTI2021

Design and optimization of the RF input-coupler for a low-frequency, split-coaxial RFQ

The Isotope Decay-At-Rest experiment (IsoDAR) is a proposed underground experiment which is expected to be a definitive search for sterile neutrinos. In order to be decisive within 5 years, high rates of neutrinos must be produced, by impinging a 10 mA continuous wave proton beam at 60 MeV on a high power target. Due to space restrictions, a compact cyclotron was chosen as an accelerator to produce this driver beam. To overcome space charge limitations during injection, H 2 + ions are accelerated and later stripped into protons by means of a carbon foil. IsoDAR uses an especially designed low-frequency (32.8 MHz) split-coaxial Radio-Frequency Quadrupole (RFQ) to effectively bunch H 2 + ions before injecting them into this cyclotron. The RFQ will be embedded vertically in the cyclotron yoke, facilitating a very compact design. This puts stringent limits on RFQ size, type, and accessibility. Here, we present the design and optimization of the low-frequency (32.8 MHz) RF input-coupler for the IsoDAR RFQ. The design is challenging due to the necessarily small diameter of the RFQ (28 cm) and the split-coaxial type, as well as limited access to the RFQ. We have determined the optimal position and shape for the coupler, leading to a low power consumption of < 6 kW for an inter-vane voltage of 22 kV. Finally, the highest calculated fields are safely below the Kilpatrick limit for this structure.

43 PARTICLE ACCELERATORS↗