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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Integrating Carbon Nanomaterials with Metals for Bio-sensing Applications

Age structure in most developed countries is changing fast as the average lifespan is increasing significantly, calling for solutions to provide improved treatments for age-related neurological diseases and disorders. In order to address these problems, a reliable way of recording information about neurotransmitters from in vitro and in vivo applications is needed to better understand neurological diseases and disorders as well as currently used treatments. Likewise, recent developments in medicine, especially with the opioid crisis, are demanding a swift move to personalized medicine to administer patient needs rather than population-wide averages. In order to enable the so-called personalized medicine, it is necessary to be able to do measurements in vivo and in real time. These actions require sensitive and selective detection of different analytes from very demanding environments. Current state-of-the-art materials are unable to provide sensitive and selective detection of neurotransmitters as well as the required time resolution needed for drug molecules at a reasonable cost. To meet these challenges, we have utilized different metals to grow carbon nanomaterials and applied them for sensing applications showing that there are clear differences in their electrochemical properties based on the selected catalyst metal. Additionally, we have combined atomistic simulations to support optimizing materials for experiments and to gain further understanding of the atomistic level reactions between different analytes and the sensor surface. With carbon nanostructures grown from Ni and Al + Co + Fe hybrid, we can detect dopamine, ascorbic acid, and uric acid simultaneously. On the other hand, nanostructures grown from platinum provide a feasible platform for detection of H 2 O 2 making them suitable candidates for enzymatic biosensors for detection of glutamate, for example. Tetrahedral amorphous carbon electrodes have an ability to detect morphine, paracetamol, tramadol, and O-desmethyltramadol. With carbon nanomaterial-based sensors, it is possible to reach metal-like properties in sensing applications using only a fraction of the metal as seed for the material growth. We have also seen that by using nanodiamonds as growth catalyst for carbon nanofibers, it is not possible to detect dopamine and ascorbic acid simultaneously, although the morphology of the resulting nanofibers is similar to the ones grown using Ni. This further indicates the importance of the metal selection for specific applications. However, Ni as a continuous layer or as separate islands does not provide adequate performance. Thus, it appears that metal nanoparticles combined with fiber-like morphology are needed for optimized sensor performance for neurotransmitter detection. This opens up a new research approach of application-specific nanomaterials, where carefully selected metals are integrated with carbon nanomaterials to match the needs of the sensing application in question.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Green’s functions on a renormalized lattice: An improved method for the integer quantum Hall transition

Highlights: • Speed up of numerical calculations by exact renormalization group steps. • Practical approach to handle delicate scaling analysis. • Absence of evidence to prefer marginal corrections over power-law corrections. We introduce a performance-optimized method to simulate localization problems on bipartite tight-binding lattices. It combines an exact renormalization group step to reduce the sparseness of the original problem with the recursive Green’s function method. We apply this framework to investigate the critical behavior of the integer quantum Hall transition of a tight-binding Hamiltonian defined on a simple square lattice. In addition, we employ an improved scaling analysis that includes two irrelevant exponents to characterize the shift of the critical energy as well as the corrections to the dimensionless Lyapunov exponent. We compare our findings with the results of a conventional implementation of the recursive Green’s function method, and we put them into broader perspective in view of recent development in this field.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Process intensification approach to enhancing heat and mass transfer during drying: Ultrasonic (US) assisted drying of paper and board

Drying of paper and board is conventionally achieved through alternating conduction (steam-heated cylinders) and pocket convection (heated air over the paper web surface). These conventional drying systems rely heavily on steam from fossil fuels, resulting in inefficiencies, high energy usage, and thermal losses due to surface-driven mechanisms. Here, to address these challenges, an experimental system, with in-situ drying characteristics measurements, was developed to investigate process intensification using ultrasonic-based dewatering—a volumetric, pressure-driven acoustic energy system—integrated with conventional drying. The objectives of this study are to assess the impact of ultrasonics (US) on dewatering; compare performances to conventional drying systems; identify improvements in drying rate and energy use as a function of moisture content; and gain potential insights on heat and mass transfer mechanisms during US-assisted drying. US performance was evaluated across frequencies, power levels, pulp types, and basis weights. Results show that improvements to ultrasonic applications in conjunction with convection were 30-43% in drying rate and 20-35% in drying time over continuous and intermittent applications. When combined with conduction and convection, ultrasonics yielded up to 20% improvement in both rate and time and up to 20% reduction in energy consumption. Observations support a hypothesis of extension of the constant rate period due to improved capillary flow at higher moisture content and enhancing vapor diffusion and boundary layer disruption at lower moisture contents during falling rate period. These findings will inform future modeling, simulation, design and optimization of advanced drying systems.

42 ENGINEERING↗
DOE OSTI2023

Two-layer cathode architecture for high-energy density and high-power density solid state batteries

Solid state batteries with high-energy density and high-power density require the development of thick and energy dense cathodes. Structured cathode electrodes with a double-layer configuration were enabled using a freeze tape casting technique. A bottom dense layer was utilized to enhance the energy density whereas a top porous layer with vertically aligned walls was utilized to enhance the power density. The porous structure of the power layer was achieved by ice templating this layer on top of the densified energy layer of the cathode. This configuration was found to better utilize the active material of the cathodes and was optimized using numerical simulation and computer modeling. Cells with Li metal anode and LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) at approximately 5 and 20 mg/cm 2 were cycled at 70 °C at different C-rates. Poly(ethylene oxide) (PEO) with lithium bis-trifluoromethanesulfonimide (LiTFSI) was used for the catholyte and the solid-state electrolyte. The structured cathodes exhibited more than double capacity values as well as better Coulombic efficiency compared to non-structured (single-layer) thick cathodes. In conclusion, synchrotron X-ray tomography and scanning electron microscopy were used to characterize the microstructure of the cathodes.

25 ENERGY STORAGE↗
DOE OSTI2024

Adsorption Thermodynamics for Process Simulation

Adsorption has rapidly evolved in recent decades and is an established separation technology extensively practiced in gas separation industries and others. However, rigorous thermodynamic modeling of multicomponent adsorption equilibrium remains elusive, and industrial practitioners rely heavily on expensive and time-consuming trial-and-error pilot studies to develop adsorption units. Here, this article highlights the need for rigorous adsorption thermodynamic models and the limitations and deficiencies of existing models such as the extended Langmuir isotherm, dual-process Langmuir isotherm, and adsorbed solution theory. It further presents a series of recent advances in the generalization of the classical Langmuir isotherm of single-component adsorption by deriving an activity coefficient model to account for the adsorbed phase adsorbate–adsorbent interactions, substituting adsorbed phase adsorbate and vacant site concentrations with activities, and extending to multicomponent competitive adsorption equilibrium, both monolayer and multilayer. Requiring a minimum set of physically meaningful model parameters, the generalized Langmuir isotherm for monolayer adsorption and the generalized Brunauer–Emmett–Teller isotherm for multilayer adsorption address various thermodynamic modeling challenges including adsorbent surface heterogeneity, isosteric enthalpies of adsorption, BET surface areas, adsorbed phase nonideality, adsorption azeotrope formation, and multilayer adsorption. Also discussed is the importance of quality adsorption data that cover sufficient temperature, pressure, and composition ranges for reliable determination of the model parameters to support adsorption process simulation, design, and optimization.

09 BIOMASS FUELS↗
DOE OSTI2019

Understanding the Static Interfacial Polymer Layer by Exploring the Dispersion States of Nanocomposites

The dynamic and static properties of the interfacial region between polymer and nanoparticles have wide-ranging consequences on performances of nanomaterials. The thickness and density of the static layer are particularly difficult to assess experimentally due to superimposing nanoparticle interactions. Here, we tune the dispersion of silica nanoparticles in nanocomposites by preadsorption of polymer layers in the precursor solutions, and by varying the molecular weight of the matrix chains. Nanocomposite structures ranging from ideal dispersion to repulsive order or various degrees of aggregation are generated and observed by small-angle scattering. Preadsorbed chains are found to promote ideal dispersion, before desorption in the late stages of nanocomposite formation. The microstructure of the interfacial polymer layer is characterized by detailed modeling of X-ray and neutron scattering. Only in ideally well-dispersed systems a static interfacial layer of reduced polymer density over a thickness of ca. 2 nm is evidenced based on the analysis with a form-free density profile optimized using numerical simulations. This interfacial gradient layer is found to be independent of the thickness of the initially adsorbed polymer, but appears to be generated by out-of-equilibrium packing and folding of the preadsorbed layer. The impact of annealing is investigated to study the approach of equilibrium, showing that initially ideally well-dispersed systems adopt a repulsive hard-sphere structure, while the static interfacial layer disappears. Furthermore, this study thus promotes the fundamental understanding of the interplay between effects which are decisive for macroscopic material properties: polymer-mediated interparticle interactions, and particle interfacial effects on the surrounding polymer.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Direct observation of coherence transfer and rotational-to-vibrational energy exchange in optically centrifuged CO2 super-rotors

Abstract Optical centrifuges are laser-based molecular traps that can rotationally accelerate molecules to energies rivalling or exceeding molecular bond energies. Here we report time and frequency-resolved ultrafast coherent Raman measurements of optically centrifuged CO 2 at 380 Torr spun to energies beyond its bond dissociation energy of 5.5 eV ( J max = 364, E rot = 6.14 eV, E rot / k B = 71, 200 K). The entire rotational ladder from J = 24 to J = 364 was resolved simultaneously which enabled a more accurate measurement of the centrifugal distortion constants for CO 2 . Remarkably, coherence transfer was directly observed, and time-resolved, during the field-free relaxation of the trap as rotational energy flowed into bending-mode vibrational excitation. Vibrationally excited CO 2 ( ν 2 > 3) was observed in the time-resolved spectra to populate after 3 mean collision times as a result of rotational-to-vibrational (R-V) energy transfer. Trajectory simulations show an optimal range of J for R-V energy transfer. Dephasing rates for molecules rotating up to 5.5 times during one collision were quantified. Very slow decays of the vibrational hot band rotational coherences suggest that they are sustained by coherence transfer and line mixing.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

A deep neural network regressor for phase constitution estimation in the high entropy alloy system Al-Co-Cr-Fe-Mn-Nb-Ni

High Entropy Alloys (HEAs) are composed of more than one principal element and constitute a major paradigm in metals research. The HEA space is vast and an exhaustive exploration is improbable. Therefore, a thorough estimation of the phases present in the HEA is of paramount importance for alloy design. Machine Learning presents a feasible and non-expensive method for predicting possible new HEAs on-the-fly. A deep neural network (DNN) model for the elemental system of: Mn, Ni, Fe, Al, Cr, Nb, and Co is developed using a dataset generated by high-throughput computational thermodynamic calculations using Thermo-Calc. The features list used for the neural network is developed based on literature and freely available databases. A feature significance analysis matches the reported HEAs phase constitution trends on elemental properties and further expands it by providing so far-overlooked features. The final regressor has a coefficient of determination ( r 2 ) greater than 0.96 for identifying the most recurrent phases and the functionality is tested by running optimization tasks that simulate those required in alloy design. The DNN developed constitutes an example of an emulator that can be used in fast, real-time materials discovery/design tasks.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Development of a Fast-Spectrum Self-Powered Neutron Detector for Molten Salt Experiments in the Versatile Test Reactor

The self-powered neutron detector (SPND) is a widely used flux monitor in thermal nuclear reactors. Although this is a mature technology, the current state of the art is tuned for a thermal neutron spectrum, so many of the devices currently in use lack sensitivity to fast neutrons. Because current in SPNDs is produced through nuclear reactions with the neutron flux inside a reactor, sensitivity in SPNDs is determined by the neutron cross section of the neutron-sensitive portion of the detector, termed the emitter. This neutron cross section drops by orders of magnitude between thermal and fast neutron energies for many emitters in currently used SPNDs, with a corresponding drop in current from the detector. This paper discusses efforts to develop a fast-spectrum self-powered neutron detector (FS-SPND) that is sensitive to neutrons with energies ranging from 0.025 eV up to 1 MeV. An in-depth analysis of Evaluated Nuclear Data File (ENDF)/B-VII.1 neutron-capture cross sections was performed, and four new materials were identified that are suitable emitter candidates for use in measuring fast neutrons. All four materials are stable mid-shell nuclei in the region between doubly magic 132Sn and 208Pb. Each candidate was simulated with the Geant4 Monte Carlo simulation toolkit to optimize overall detector efficiency.

Goetz, Callie↗
DOE OSTI2023

A minimum assumption approach to MEG sensor array design

Objective. Our objective is to formulate the problem of the magnetoencephalographic (MEG) sensor array design as a well-posed engineering problem of accurately measuring the neuronal magnetic fields. This is in contrast to the traditional approach that formulates the sensor array design problem in terms of neurobiological interpretability the sensor array measurements. Approach. We use the vector spherical harmonics (VSH) formalism to define a figure-of-merit for an MEG sensor array. We start with an observation that, under certain reasonable assumptions, any array of m perfectly noiseless sensors will attain exactly the same performance, regardless of the sensors' locations and orientations (with the exception of a negligible set of singularly bad sensor configurations). We proceed to the conclusion that under the aforementioned assumptions, the only difference between different array configurations is the effect of (sensor) noise on their performance. We then propose a figure-of-merit that quantifies, with a single number, how much the sensor array in question amplifies the sensor noise. Main results. We derive a formula for intuitively meaningful, yet mathematically rigorous figure-of-merit that summarizes how desirable a particular sensor array design is. We demonstrate that this figure-of-merit is well-behaved enough to be used as a cost function for a general-purpose nonlinear optimization methods such as simulated annealing. We also show that sensor array configurations obtained by such optimizations exhibit properties that are typically expected of 'high-quality' MEG sensor arrays, e.g. high channel information capacity. Significance. Our work paves the way toward designing better MEG sensor arrays by isolating the engineering problem of measuring the neuromagnetic fields out of the bigger problem of studying brain function through neuromagnetic measurements.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Performance Evaluation of District Energy Microgrids Planning Tool for Non-Technical Users

Community Microgrids are increasingly gaining popularity worldwide for their efficiency, cost-effectiveness, and local resilience improvement. Microgrid planning tools play a crucial role in their deployment. In the process, tentative designs of the microgrid are simulated, analyzed, and optimized. Due to the complexity of the problem, planning tools must carefully balance computational efficiency while seeking the most optimal solutions. This paper investigates the impact of algorithm selection on CPU and memory utilization of a Community Microgrid planning tool that is specifically designed for non-technical users. We compare two version of the code with two alternatives for Community Microgrid planning tools: a Greedy Algorithm, and a Linear Programming Approach. This comparison examines scenarios spanning from 2 to 50 buildings. Our findings revealed a significant difference in performance between the two algorithms, underscoring the critical role of algorithm selection in optimizing the efficiency of Community Microgrid Planning Tools.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning

We report building a sustainable burn platform in inertial confinement fusion (ICF) requires an understanding of the complex coupling of physical processes and the effects that key experimental design changes have on implosion performance. While simulation codes are used to model ICF implosions, incomplete physics and the need for approximations deteriorate their predictive capability. Identification of relationships between controllable design inputs and measurable outcomes can help guide the future design of experiments and development of simulation codes, which can potentially improve the accuracy of the computational models used to simulate ICF implosions. In this article, we leverage developments in machine learning (ML) and methods for ML feature importance/sensitivity analysis to identify complex relationships in ways that are difficult to process using expert judgment alone. We present work using random forest (RF) regression for prediction of yield, velocity, and other experimental outcomes given a suite of design parameters, along with an assessment of important relationships and uncertainties in the prediction model. We show that RF models are capable of learning and predicting on ICF experimental data with high accuracy, and we extract feature importance metrics that provide insight into the physical significance of different controllable design inputs for various ICF design configurations. These results can be used to augment expert intuition and simulation results for optimal design of future ICF experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Multivariate Testing of Sampling Techniques to Address Class Imbalance in Building Use Type Classification

This study addresses the challenges inherent in building use type classification, particularly focusing on the issue of class imbalance in the training datasets for machine learning classifiers. We comprehensively analyze the efficacy of various class-balancing sampling techniques. Employing Monte Carlo simulations and Bayesian optimization, we evaluated the performance of multiple sampling methods, including Random Oversampling, Random Undersampling, SMOTE, Borderline-SMOTE, and ADASYN, across a dataset encompassing nine southeastern coastal states of the United States. Our findings reveal that simple random over- and undersampling techniques outperform more sophisticated methods. Additionally, we show inherent value in creating an imbalance in training data to effectively train a machine learning classifier for distinguishing between residential and nonresidential buildings. This study provides valuable guidance for future research on building use type classification research and lays essential groundwork for developing attribute-rich building stock datasets.

Adams, Daniel↗
DOE OSTI2021

Analysis of pump-to-signal noise transfer in two-stage ultra-broadband optical parametric chirped-pulse amplification

In optical parametric chirped-pulse amplification (OPCPA), pump temporal intensity modulation is transferred to the chirped-signal spectrum via instantaneous parametric gain and results in contrast degradation of the recompressed signal. We investigate, for the first time to our knowledge, the pump-to-signal noise transfer in a two-stage ultra-broadband OPCPA pumped by a single laser and show the dependence of pump-induced signal noise, characterized both before and after pulse compression, on the difference in pump-seed delay in the two stages. We demonstrate an up-to-15-dB reduction of the pump-induced contrast degradation via pump-seed delay optimization. Experiments and simulations show that, even when parametric amplifiers are operated in saturation, the pump-seed delay can be used to minimize the pump-induced contrast degradation that is attributed largely to the noises from the unsaturated edges of the pulse and that of the beam.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

High Precision Integrated Beam-Position and Emittance Monitor

The purpose of this project was to design and build a set of resonant cavity beam diagnostics including a monopole cavity, two dipole cavities, and a quadrupole mode cavity. An array of such diagnostics placed in an accelerator FODO lattice can be used to reconstruct beam emittance and envelope information on a pulse-by-pulse basis. In this project a more efficient quadrupole cavity shape was discovered which could provide the required sensitivity with just two cells vs. nine cells as originally envisioned. This new design was studied and optimized with RF simulation programs, followed by mechanical specifications and drawings, and fabrication of the diagnostics. The device was tested with a relativistic electron beam at Brookhaven National Laboratory ATF accelerator.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

GentenMPI: Distributed Memory Sparse Tensor Decomposition

GentenMPl is a toolkit of sparse canonical polyadic (CP) tensor decomposition algorithms that is designed to run effectively on distributed-memory high-performance computers. Its use of distributed-memory parallelism enables it to efficiently decompose tensors that are too large for a single compute node's memory. GentenMPl leverages Sandia's decades-long investment in the Trilinos solver framework for much of its parallel-computation capability. Trilinos contains numerical algorithms and linear algebra classes that have been optimized for parallel simulation of complex physical phenomena. This work applies these tools to the data science problem of sparse tensor decomposition. In this report, we describe the use of Trilinos in GentenMPl, extensions needed for sparse tensor decomposition, and implementations of the CP-ALS (CP via alternating least squares) and GCP-SGD (generalized CP via stochastic gradient descent) sparse tensor decomposition algorithms. We show that GentenMPl can decompose sparse tensors of extreme size, e.g., a 12.6-terabyte tensor on 8192 computer cores. We demonstrate that the Trilinos backbone provides good strong and weak scaling of the tensor decomposition algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Tunable Laser-Plasma Amplifier (Final Report)

Exploration at the laser intensity frontier has always offered new avenues for physics and reaching beyond this frontier is a grand challenge. Present-day petawatt-class lasers provide focused intensities on target of 10 22 W/cm 2 , corresponding to electric fields of 200 TV/m, while laser-plasma amplification opens a route for focused intensities well above 10 23 W/cm 2 . Intensities in this range provide the ability to test quantum electrodynamics in the unexplored low-energy, strong-field regime where signatures for new physics may arise. The behavior of matter under such extraordinary conditions is a rich and fascinating subject, not only in its own right in fundamental plasma physics, but for the many potential applications that promise to enrich the natural sciences in the future, including compact electron-beam, ion-beam particle accelerators, and ultra-bright X-ray sources. Although laser systems are now under construction internationally to access intensities of 10 23 W/cm 2 , the current technologies used appear to be fundamentally limited to these intensities. The realization of intensities beyond 10 23 W/cm 2 using parametric amplification in plasmas promises a breakthrough in high-energy density physics. Parametric amplification using Raman scattering in a plasma could provide the enabling technology for the generation of ultra-high-power laser pulses, but a more complete understanding of the nonlinear optics of plasmas is required. There is a significant gap in well-diagnosed laser-plasma instability studies of nonlinear plasma-wave phenomena, which are critical to understand for future laser-plasma devices. To achieve an efficient laser-plasma amplifier, plasma waves must be driven to large amplitude where significant energy can be rapidly transferred from the pump to the seed over the pulse duration of the seed. Simulations suggest that this nonlinear pump depletion regime can be achieved in the “pi-pulse” amplification regime. While simulations show this optimal regime with efficient amplification, it has remained elusive in experiments and there is a growing consensus within the community that thermal effects and pump beam limitations prevent laser-plasma amplifiers from progressing through the linear regime into the nonlinear pump depletion regime. Previous experiments have been significantly limited by the laser power available at the necessary wavelengths for the seed laser; therefore, the amplification is required to start in the linear regime where it is sensitive to many deleterious effects. The enabling technology (currently unique to plasma-wave amplification in the world) at the University of Rochester is the ability to provide a seed pulse with sufficient power (4 mJ/100 fs seed) to immediately drive nonlinear plasma waves into the pi-pulse regime and to tune its wavelength to optimize the efficiency of energy transfer. This in combination with the state-of-the-art OMEGA heater beams providing multiple kilojoules in a nanosecond to sufficiently heat the plasma make this system distinct from previous studies. These heater beams will provide, for the first time in Raman amplification studies, a homogeneous electron temperature high enough to prevent pump beam propagation issues that have plagued previous experiments. These systems will provide a platform for driving electron-plasma waves into the nonlinear regime where pump depletion and pulse shortening are predicted to lead to high amplification efficiencies (>30%). The Team has made significant progress through prior support from DOE Fusion Energy Sciences [DOE Office of Science Award Number DE-SC0016253 (2016-2022)]. This includes twenty-two peer-reviewed manuscripts, one patent, ten contributed talks presented at international conferences, and research that was highlighted as invited talks at fifteen international conferences. The broader impacts of this research are evident in the support of early career scientists, two Ph.D. theses, four current graduate students, a Masters Project, two undergraduate researchers, and an underrepresented minority student hired through the California Alliance for Minority Participation who now works as a Research Engineer in the group. This research met all of the funded research objectives and the highlights from primary Raman amplification thrust of this work are discussed below and form the foundation for the proposed research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Samoa Updater: An Application of the Levenberg-Marquardt Method to Update DELFIC Predictions Using Field Measurements

The US Department of Energy (DOE) Forensics Operations (DFO) is a member of the Ground Collections Task Force (GCTF), which is responsible for sample collection of radiological debris for attribution should a nuclear detonation ever occur in the United States. The DFO runs the Defense Land Fallout Interpretive Code (DELFIC) Fallout Planning Tool to predict the deposition of fallout from a nuclear detonation. This prediction is refined using the DELFIC Updater tool, which takes ground measurements and adjusts DELFIC inputs to minimize the difference between prediction and observation, yielding improved predictions of fallout in locations both measured and not yet measured. Samoa, a framework for uncertainty analysis and optimization, is used to improve DELFIC predictive fallout modeling. This new capability using Samoa, dubbed “Samoa Updater,” is compared with the current DELFIC Updater, a brute-force sampling approach. Samoa Updater uses the Levenberg– Marquardt (LM) method, a gradient-based nonlinear least squares approach that uses the functional shape of the input space to increase optimization speed. In simulated test cases Samoa Updater yields faster and more accurate solutions than the current Updater.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗