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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 OSTI2020

Probabilistic Forecasting of Generators Startups and Shutdowns in the MISO System Based on Random Forest

Solving security constrained unit commitment (SCUC) problems to plan an economical generation schedule for day-head electricity market has been an important research topic in recent years. Mixed integer programming method (MIP), the-state-of-art approach for solving SCUC problem, is known computationally hard when the number of binary status variables is large. In this paper, a machine learning-based algorithm - random forest (RF), was applied to forecast the startups (SU) and shutdowns (SD) hours of generators, based on historical hourly system condition observations in the Midcontinent Independent System Operator (MISO) system. The main purpose is to reduce the number of binary status variables, by fixing the SU/SD hours to a narrow range of high confidence. This would significantly reduce the size of the decision space, and therefore speed up SCUC solutions with reduced uncertainty.

Lin, Xinming↗
DOE OSTI2022

Statistical Estimation of Strain Using Spatial Correlation Functions

Ex-situ estimation of strains from deformed micrographs is not possible as there are no persistent features which can be tracked. Two point spatial statistics enable the rigorous quantification of spatial patterns in heterogeneous media. In this paper, we propose a novel method for estimating strains directly from dissimilar micrographs using a continuum mechanics approach. Rather than operating directly on images from sequential frames, as is done in digital image correlation, we operate on different microstructure realizations. This is made possible by comparing the spatial autocorrelation maps of deformed and undeformed micrographs rather than direct comparison of images. Additionally, a Bayesian framework is proposed for quantifying uncertainty. We first illustrate the efficacy of this method on speckle pattern images from digital image correlation experiments. Then, we demonstrate that the method is capable of operating on dissimilar micrographs using deformed synthetic binary microstructures. Finally, we present a case study on polycrystalline additively manufactured 316L deformed via tension. The proposed method works well and we discuss implications and limitations of the presented work.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Application of Convolutional and Feedforward Neural Networks for Fault Detection in Particle Accelerator Power Systems

High voltage converter modulators (HVCM) provide power to the accelerating cavities of the spallation neutron source (SNS) facility. HVCM experience catastrophic failures, which increase the downtime of the SNS and reduce beam time. The faults may occur due to different reasons including failures of the resonant capacitor, core saturation due to the magnetic flux, insulated-gate bipolar transistor (IGBT) failures, and others. We recently have setup a HVCM test stand to develop and test machine learning models for anomaly detection and fault prognostics. In this work, we propose binary classifiers and autoencoder architectures based on convolutional (CNN) and feedforward neural networks (FNN) to facilitate distinguishing normal from faulty waveforms coming from the HVCM during operation. The results indicate that the CNN binary classifier is the best model among the four showing very stable performance in the training and testing sets with impressive metrics of precision and recall reaching up to 99\% with a very small uncertainty. The FNN classifier shows the least performance with a large uncertainty in its metrics. The performances of the two autoencoders based on CNN and FNN were in between, showing very good performance nonetheless.

Radaideh, Majdi↗
DOE OSTI2021

Data-driven optimization of mixed-integer bi-level multi-follower integrated planning and scheduling problems under demand uncertainty

The coordination of interconnected elements across the different layers of the supply chain is essential for all industrial processes and the key to optimal decision-making. Yet, the modeling and optimization of such interdependent systems are still burdensome. Here we address the simultaneous modeling and optimization of medium-term planning and short-term scheduling problems under demand uncertainty using mixed-integer bi-level multi-follower programming and data-driven optimization. Bi-level multi-follower programs model the natural hierarchy between different layers of supply chain management holistically, while scenario analysis and data-driven optimization allow us to retrieve the guaranteed feasible solutions of the integrated formulation under various demand considerations. We address the data-driven optimization of this challenging class of problems using the DOMINO framework, which was initially developed to solve single-leader single-follower bi-level optimization problems to guaranteed feasibility. This framework is extended to solve single-leader multi-follower stochastic formulations and its performance is characterized by well-known single and multi-product process scheduling case studies. Through our data-driven algorithmic approach, we present guaranteed feasible solutions to linear and nonlinear mixed-integer bi-level formulations of simultaneous planning and scheduling problems and further characterize the effects of the scheduling level complexity on the solution performance, which spans over several hundred continuous and binary variables, and thousands of constraints.

42 ENGINEERING↗
DOE OSTI2022

Speed of Sound Measurements of Binary Mixtures of Hydrofluorocarbons [Pentafluoroethane (R-125), 1,1-Difluoroethane (R-152a), or 1,1,1,2,3,3,3-Heptafluoropropane (R-227ea)] with Hydrofluoroolefins [2,3,3,3-Tetrafluoropropene (R-1234yf) or trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E))]

In this article, speed of sound data measured using a dual-path pulse-echo instrument are reported for three binary refrigerant mixtures, R-125/1234yf, R-1234yf/152a, and R-1234ze(E)/227ea, at compositions of (0.33/0.67) and (0.67/0.33) mole fraction. The speed of sound was studied at temperatures ranging from 230 K to 345 K from pressures slightly above the bubble point curve up to 20 MPa for the mixtures containing R-1234yf and 49 MPa for the R-1234ze(E)/227ea mixtures. The relative combined expanded speed of sound uncertainty ranged from 0.039 % to 0.317 % with a mean uncertainty over all state points of less than 0.10 %. The reported data are compared to the most recent mixture models in REFPROP for each blend studied. Comparisons of available mixture models for the R-125/1234yf and R-1234yf/152a blends exhibit average absolute deviation values ranging from 0.10 % to 0.27 %, and the average absolute deviations for R-1234ze(E)/227ea blends range from 0.62 % to 0.94 %. The comparisons show that only minor adjustments are required to the R-125/1234yf and R-1234yf/152a mixture models to represent the speed of sound data within its uncertainty. However, significant adjustments are needed to improve the current mixture models for the R-1234ze(E)/227ea blend. Deficiencies with the R-1234ze(E)/227ea mixture model are not unexpected since it (1) utilizes a pure-fluid EOS R-1234ze(E) that has been shown to inaccurately represent R-1234ze(E) speed of sound values and (2) uses binary interaction parameters for the chemically similar mixture of R-1234yf/227ea.

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

Ab initio simulations on the pure Cr lattice stability at 0K: Verification with the Fe-Cr and Ni-Cr binary systems

Significant discrepancies have been observed and discussed on the lattice stability of Cr between the predictions from the ab initio calculations and the CALPHAD approach. In the current work, we carefully examined the possible structures for pure Cr and reviewed the history back from how Kaufman originally determined the Gibbs energy of FCC-Cr in the 1970s. The reliability of Cr lattice stability derived by the CALPHAD and ab initio approaches was systematically discussed. It is concluded that the Cr lattice stability based on the CALPHAD approach has large uncertainty. Meanwhile, we cannot claim that the ab initio H FCC-Cr is error-free as FCC-Cr is an unstable phase under ambient conditions. The present work shows that the ab initio HFCC-Cr can be a viable scientific approach. As both approaches have their limitations, the present work propose to integrate the ab initio results into the CALPHAD platform for the development of the next generation CALPHAD database. As a result, the Fe-Cr and Ni-Cr binary systems were chosen as two case studies demonstrating the capability to adopt the ab initio Cr lattice stability directly into the current CALPHAD database framework.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modeling Kilonova Light Curves: Dependence on Nuclear Inputs

The mergers of binary neutron stars, as well as black hole–neutron star systems, are expected to produce an electromagnetic counterpart that can be analyzed to infer the element synthesis that occurred in these events. We investigate one source of uncertainties pertinent to lanthanide-rich outflows: the nuclear inputs to rapid neutron capture nucleosynthesis calculations. We begin by examining 32 different combinations of nuclear inputs: eight mass models, two types of spontaneous fission rates, and two types of fission daughter product distributions. We find that such nuclear physics uncertainties typically generate at least one order of magnitude uncertainty in key quantities such as the nuclear heating (one and a half orders of magnitude at 1 day post-merger), the bolometric luminosity (one order of magnitude at 5 days post-merger), and the inferred mass of material from the bolometric luminosity (factor of 8 when considering the 8–10 day region). Since particular nuclear processes are critical for determining the electromagnetic signal, we provide tables of key nuclei undergoing β-decay, α-decay, and spontaneous fission important for heating at different times, identifying decays that are common among the many nuclear input combinations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

25-Pin metallic fuel performance benchmark case based on the EBR-II X430 experiments series

A metallic fuel benchmark case was developed for the fuel performance code BISON based on 25 uranium-zirconium and uranium-plutonium-zirconium pins of the Experimental Breeder Reactor II X430 experiment series. Results of the benchmarks were compared with measurements and calculations made at the time of the experiment as well as subsequent measurements reported in 2019. The comparisons were used to quantify the accuracy of the BISON predictions and to identify patterns in the BISON differences.BISON predicted burnup, plenum pressure, and fission gas release fractions accurately. BISON temperature predictions were somewhat cooler than the temperatures determined at the time of the experiment but appeared to be reasonably accurate considering uncertainties in the legacy temperature calculations, uncertainties in the legacy linear heat rate calculations, and the high sensitivities of the BISON-predicted temperatures to BISON inputs. Fuel axial elongation predictions had errors correlated to fuel composition; BISON tended to underpredict the elongation of binary fuels and overpredict the elongation of ternary fuels. BISON cladding radial dilation predictions were also significantly lower than legacy PIE measurements.Recommendations were made to improve the BISON fuel gaseous swelling model to account for fuel composition, to add additional capabilities to the coolant channel temperature model to ease benchmark development, and to continue developing benchmark cases based on a wide range of experiments in several reactors. Once a wide array of benchmarks is developed, an attempt can be made to enhance or calibrate BISON models to improve the cladding dilation predictions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Machine learning elastic constants of multi-component alloys

The present manuscript explores application of machine learning methods for determining elastic constants and other derived mechanical properties of multi-component alloys. Here, a number of machine learning models, including linear regression, neural network and random forest based models, are trained and tested on a dataset of binary alloys generated using density functional theory (DFT) calculations and spanning over a large number of elemental species in the periodic table. Starting with a wide range of simple and easily accessible compositionally-averaged elemental features, a correlation-based feature selection strategy was used to systematically down-select a set of most relevant features towards the prediction of the elasticity tensor components. The true predictive performance and the associated uncertainties of the models were established by testing on unseen data and bootstrapping, respectively. A single and pair-wise feature partial dependence analysis was performed to visualize the average property trends in the multi-dimensional feature space in order to further understand the achieved predictive performance. The utility of the trained model is further demonstrated by obtaining sufficiently accurate yet highly efficient approximations for bulk modulus, Young’s modulus, shear modulus and Poisson’s ratio for alloys beyond the binary space (i.e., two-component alloys) on which the model was originally trained. More importantly, we test and validate the predictive performance of the developed model directly against the experimentally measured elastic constants of technologically relevant multi-component alloys (such as, Ni- and Ti-based alloys). Finally, utility of such a data-enabled route is demonstrated by predicting the possible range of various elastic properties for vast composition space available within the five component Ni-Cr-Fe-Mo-W alloy system in a high-throughput manner.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The SDSS-HET Survey of Kepler Eclipsing Binaries. A Sample of Four Benchmark Binaries

The purpose of this work is to extend a sample of accurately modeled, benchmark-grade eclipsing binaries (EBs) with accurately determined masses and radii. We select four "well-behaved" Kepler binaries, KIC 2306740, KIC 4076952, KIC 5193386 and KIC 5288543, each with at least eight double-lined spectra from the Apache Point Observatory Galactic Evolution Experiment instrument that is part of the Sloan Digital Sky Surveys III and IV, and from the Hobby–Eberly High Resolution Spectrograph. We obtain masses and radii with uncertainties of 2.5% or less for all four systems. Three of these systems have orbital periods longer than 9 days, and thus populate an undersampled region of the parameter space for extremely well-characterized detached EBs. We compare the derived masses and radii against MESA MIST isochrones to determine the ages of the systems. All systems were found to be coeval, showing that the results are consistent across MESA MIST and PHOEBE .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

matsim-agents v1.0

matsim-agents is a multi-agent AI framework for atomistic materials simulation and discovery. It orchestrates large language models (LLMs), machine-learned interatomic potentials (MLIPs), and DFT codes into a single agentic loop running on laptops and DOE leadership-class supercomputers. MULTI-AGENT ORCHESTRATION A LangGraph state machine with three nodes: a Planner that converts a natural-language research objective into structured tasks; an Executor that dispatches atomistic tools and loops until the queue is empty; and an Analyst that summarizes results into a human-readable report. State is checkpointed after every step and human-in-the-loop gates can be inserted at any edge. HYPOTHESIS-DRIVEN DISCOVERY CHAT An interactive REPL (matsim-agents chat) that couples LLM dialogue with atomistic simulation. Chemical formulas are automatically detected in conversation turns and trigger a full crystal-phase exploration: structure generation → relaxation → stability scoring → result injection back into the conversation, creating a closed hypothesis-refinement loop. CRYSTAL PHASE ENUMERATION Given a composition, the phase explorer enumerates prototypes by stoichiometry: elemental (fcc/bcc/hcp/sc/diamond), binary 1:1 (rocksalt/CsCl/zincblende/ wurtzite/fluorite/rutile), ternary 1:1:3 (cubic perovskite), ternary 1:2:4 (perovskite + spinel), quaternary 1:1:2:6 (Fm-3m double perovskite). 2-D prototypes (graphene, h-BN, MoS2 2H/1T) and multilayer stacking are also supported via --include-2d and --num-layers. SUPERCELL GENERATION AND SITE DECORATION Auto-tiling to a minimum atom count (--min-atoms), explicit NxNxN tiling (--supercell), symmetry-distinct site decorations (--n-orderings), and isotropic lattice-scale sweeps (--lattice-scales) for volume bracketing. MLFF RELAXATION AND STABILITY SCORING HydraGNN (multi-headed GNN) drives structure relaxation via ASE with FIRE, BFGS, or BFGSLineSearch. Stability output: delta-E/atom ranking across phases and a max-residual-force dynamical-stability proxy. Other MLIPs (MACE, NequIP, Orb) can be plugged in through the same interface. DFT BACKENDS Quantum ESPRESSO pw.x and VASP 6.6 are first-class labellers. Both have validated GPU builds and SLURM/PBS launchers for three DOE platforms: Frontier (AMD MI250X, ROCm), Aurora (Intel PVC, oneAPI), Perlmutter (NVIDIA A100, CUDA). QE produces ~100 binaries (pw.x, ph.x, epw.x, ...). VASP supports scf, relax, vc-relax, and vc-relax-shape run types. ACTIVE-LEARNING LOOP matsim-agents al run CONFIG.yaml drives an iterative HydraGNN-DFT loop: MD generates candidates → ensemble/MC-dropout uncertainty selects the most informative → DFT labels them in parallel inside one allocation → dataset grows → HydraGNN retrains → repeat. DFT backend is a single YAML toggle (dft.backend: vasp | qe). LLM-generated seed structures are supported (no curated POSCAR library needed). Config uses ${VAR}, ${VAR:-default}, ${VAR:?msg} shell-style substitution for cross-user/cross-site portability. LLM BACKENDS Ollama (local, default), vLLM (HPC multi-GPU serving), OpenAI, Anthropic, HuggingFace Transformers+Accelerate. Selected at runtime via flag or env var with no code changes. HPC PORTABILITY Same Python entry points run on Frontier (ROCm 7.2), Aurora (oneAPI), and Perlmutter (CUDA 12). DFT and ML stacks are never co-loaded in the same shell; they couple through the scheduler and filesystem. Advanced multi-node launchers (serve, discovery-chat, single-relaxation, active-learning, QE warm-start) are provided for all three platforms. CODABENCH COMPETITION BUNDLE A self-contained benchmark: 159 atomistic test structures across 11 material classes, 5 tasks (formation energy, forces, ML relaxation, AI-DFT relaxation, phase stability ranking), public/private leaderboard split (30/70), and four ready-to-run baselines: MACE-MP-0, HydraGNN, UMA, AllScAIP.

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
DOE OSTI2023

Photometry of Outer Solar System Objects from the Dark Energy Survey. I. Photometric Methods, Light-curve Distributions, and Trans-Neptunian Binaries

We report the methods of and initial scientific inferences from the extraction of precision photometric information for the >800 trans-Neptunian objects (TNOs) discovered in the images of the Dark Energy Survey (DES). Scene-modeling photometry is used to obtain shot-noise-limited flux measures for each exposure of each TNO, with background sources subtracted. Comparison of double-source fits to the pixel data with single-source fits are used to identify and characterize two binary TNO systems. A Markov Chain Monte Carlo method samples the joint likelihood of the intrinsic colors of each source as well as the amplitude of its flux variation, given the time series of multiband flux measurements and their uncertainties. A catalog of these colors and light-curve amplitudes A is included with this publication. We show how to assign a likelihood to the distribution q(A) of light-curve amplitudes in any subpopulation. Using this method, we find decisive evidence (i.e., evidence ratio <0.01) that cold classical (CC) TNOs with absolute magnitude 6 < H$_{r}$ < 8.2 are more variable than the hot classical (HC) population of the same H$_{r}$, reinforcing theories that the former form in situ and the latter arise from a different physical population. Resonant and scattering TNOs in this H$_{r}$ range have variability consistent with either the HCs or CCs. DES TNOs with H$_{r}$ < 6 are seen to be decisively less variable than higher-H$_{r}$ members of any dynamical group, as expected. More surprising is that detached TNOs are decisively less variable than scattering TNOs, which requires them to have distinct source regions or some subsequent differential processing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Low-metallicity Nova Explosions: A Site for Weak rp -process Nucleosynthesis

Classical novae are common cataclysmic events involving a binary system of a white dwarf and a main-sequence or red giant companion star. In metal-poor environments, these explosions produce ejecta differently from their solar counterparts due to the accretion of subsolar-metallicity material onto the white dwarf. In particular, it has been suggested that the nucleosynthesis flow in such low-metallicity nova explosions extends up to the Cu–Zn region, much beyond the expected endpoint, around Ca, predicted for solar-metallicity classical novae. This behavior resembles a weak rp-process, and such nuclear activity has never been observed in accreting white dwarf binaries with typical accretion flows. In this work, we study the characteristics of the weak rp-process for four nova models with metallicities Z = 2 × 10 −9 , 10 −7 , 2 × 10 −6 , and 2 × 10 −5 , and explore the impact of the nuclear physics uncertainties via a Monte Carlo sensitivity study. We identify nuclear reactions whose uncertainties affect the production of intermediate-mass nuclei under these conditions. These reactions and relevant nuclear quantities are targets for measurements at stable or radioactive beam facilities to reduce their rate uncertainties.

Classical novae↗
DOE OSTI2021

Measurement of hybrid rocket solid fuel regression rate for a slab burner using deep learning

This study presents an imaging-based deep learning tool to measure the fuel regression rate in a 2D slab burner experiment for hybrid rocket fuels. The slab burner experiment is designed to verify mechanistic models of reacting boundary layer combustion in hybrid rockets by the measurement of fuel regression rates. A DSLR camera with a high intensity flash is used to capture images throughout the burn and the images are then used to find the fuel boundary to calculate the regression rate. A U-net convolutional neural network architecture is explored to segment the fuel from the experimental images. Here, a Monte-Carlo Dropout process is used to quantify the regression rate uncertainty produced from the network. The U-net computed regression rates are compared with values from other techniques from literature and show error less than 10%. An oxidizer flux dependency study is performed and shows the U-net predictions of regression rates are accurate and independent of the oxidizer flux, when the images in the training set are not over-saturated. Training with monochrome images is explored and is not successful at predicting the fuel regression rate from images with high noise. The network is superior at filtering out noise introduced by soot, pitting, and wax deposition on the chamber glass as well as the flame when compared to traditional image processing techniques, such as threshold binary conversion and spatial filtering. U-net consistently provides low error image segmentations to allow accurate computation of the regression rate of the fuel.

42 ENGINEERING↗
DOE OSTI2023

Precision cosmology with primordial GW backgrounds in presence of astrophysical foregrounds

The era of Gravitational-Wave (GW) astronomy will grant the detection of the astrophysical GW background from unresolved mergers of binary black holes, and the prospect of probing the presence of primordial GW backgrounds. In particular, the low-frequency tail of the GW spectrum for causally-generated primordial signals (like a phase transition) offers an excellent opportunity to measure unambiguously cosmological parameters as the equation of state of the universe, or free-streaming particles at epochs well before recombination. We discuss whether this programme is jeopardised by the uncertainties on the astrophysical GW foregrounds that coexist with a primordial background. We detail the motivated assumptions under which the astrophysical foregrounds can be assumed to be known in shape, and only uncertain in their normalisation. In this case, the sensitivity to a primordial signal can be computed by a simple and numerically agile procedure, where the optimal filter function subtracts the components of the astrophysical foreground that are close in spectral shape to the signal. We show that the degradation of the sensitivity to the signal in presence of astrophysical foregrounds is limited to a factor of a few, and only around the frequencies where the signal is closer to the foregrounds. Our results highlight the importance of modelling the contributions of eccentric or intermediate-mass black hole binaries to the GW background, to consolidate the prospects to perform precision cosmology with primordial GW backgrounds.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

High-Order Multipole and Binary Love Number Universal Relations

Using a data set of approximately 2 million phenomenological equations of state consistent with observational constraints, we construct new equation-of-state-insensitive universal relations that exist between the multipolar tidal deformability parameters of neutron stars, Λ l , for several high-order multipoles (l = 5,6,7,8), and we consider finite-size effects of these high-order multipoles in waveform modeling. We also confirm the existence of a universal relation between the radius of the 1.4M ⊙ NS, R 1.4 and the reduced tidal parameter of the binary, Λ˜, and the chirp mass. We extend this relation to a large number of chirp masses and to the radii of isolated NSs of different mass M, R M . We find that there is an optimal value of M for every M such that the uncertainty in the estimate of R M is minimized when using the relation. We discuss the utility and implications of these relations for the upcoming LIGO O4 run and third-generation detectors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Monte-Carlo Neutrino Transport in Neutron Star Merger Simulations

Gravitational waves and electromagnetic signals from merging neutron star binaries provide valuable information about the the properties of dense matter, the formation of heavy elements, and high-energy astrophysics. To fully leverage observations of these systems, we need numerical simulations that provide reliable predictions for the properties of the matter unbound in these mergers. An important limitation of current simulations is the use of approximate methods for neutrino transport that do not converge to a solution of the transport equations as numerical resolution increases, and thus have errors that are impossible to quantify. Here, we report on a first simulation of a binary neutron star merger that directly solves the transport equations using Monte-Carlo techniques. We demonstrate that a computationally cheap Monte-Carlo transport can be implemented without ill effects from sampling noise. We apply Monte-Carlo transport to an unequal mass neutron star binary merger up to 5ms past merger, and report on the properties of the matter and neutrino outflows. Finally, we compare our results to the output of our best approximate `M1' transport scheme, demonstrating that an M1 scheme that carefully approximates the neutrino energy spectrum only leads to ~10% uncertainty in the composition and velocity of the ejecta, and ~20% uncertainty in the electron (anti)neutrino luminosities and energies. Here, the most significant disagreement found between M1 and Monte-Carlo results is a factor of ~2 difference in the luminosity of heavy-lepton neutrinos.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Observations with the Differential Speckle Survey Instrument. X. Preliminary Orbits of K-dwarf Binaries and Other Stars

This paper details speckle observations of binary stars taken at the Lowell Discovery Telescope, the WIYN telescope, and the Gemini telescopes between 2016 January and 2019 September. The observations taken at Gemini and Lowell were done with the Differential Speckle Survey Instrument (DSSI), and those done at WIYN were taken with the successor instrument to DSSI at that site, the NN-EXPLORE Exoplanet Star and Speckle Imager (NESSI). In total, we present 378 observations of 178 systems, and we show that the uncertainty in the measurement precision for the combined data set is ∼2 mas in separation, ∼1°–2° in position angle depending on the separation, and ∼0.1 mag in magnitude difference. Together with data already in the literature, these new results permit 25 visual orbits and one spectroscopic-visual orbit to be calculated for the first time. In the case of the spectroscopic-visual analysis, which is done on the ternary star HD 173093, we calculate masses with a precision of better than 1% for all three stars in that system. Twenty-one of the visual orbits calculated have a K dwarf as the primary star; we add these to the known orbits of K-dwarf primary stars and discuss the basic orbital properties of these stars at this stage. Although incomplete, the data that exist so far indicate that binaries with K-dwarf primaries tend not to have low-eccentricity orbits at separations of one to a few tens of astronomical units, that is, on solar system scales.

47 OTHER INSTRUMENTATION↗