Results for “leverage points” — Page 12

NASA NTRS2018

Robust Exploration and Commercial Missions to the Moon Using Nuclear Thermal Rocket Propulsion and Lunar Liquid Oxygen Derived from FeO-Rich Pyroclasitc Deposits

The nuclear thermal rocket (NTR) has frequently been identified as a key space asset required for the human exploration of Mars. This proven technology can also provide the affordable access through cislunar space necessary for commercial development and sustained human presence on the Moon. It is a demonstrated technology capable of generating both high thrust and high specific impulse (I(sub sp) approx. 900 s) twice that of today's best chemical rockets. Nuclear lunar transfer vehicles-consisting of a propulsion stage using three approx. 16.5-klb(sub f) small nuclear rocket engines (SNREs), an in-line propellant tank, plus the payload-are reusable, enabling a variety of lunar missions. These include cargo delivery and crewed lunar landing missions. Even weeklong ''tourism'' missions carrying passengers into lunar orbit for a day of sightseeing and picture taking are possible. The NTR can play an important role in the next phase of lunar exploration and development by providing a robust in-space lunar transportation system (LTS) that can allow initial outposts to evolve into settlements supported by a variety of commercial activities such as in-situ propellant production used to supply strategically located propellant depots and transportation nodes. The use of lunar liquid oxygen (LLO2) derived from iron oxide (FeO)-rich volcanic glass beads, found in numerous pyroclastic deposits on the Moon, can significantly reduce the launch mass requirements from Earth by enabling reusable, surface-based lunar landing vehicles (LLVs)that use liquid oxygen and hydrogen (LO2/LH2) chemical rocket engines. Afterwards, a LO2/LH2 propellant depot can be established in lunar equatorial orbit to supply the LTS. At this point a modified version of the conventional NTR-called the LO2-augmented NTR, or LANTR-is introduced into the LTS allowing bipropellant operation and leveraging the mission benefits of refueling with lunar-derived propellants for Earth return. The bipropellant LANTR engine utilizes the large divergent section of its nozzle as an ''afterburner'' into which oxygen is injected and supersonically combusted with nuclear preheated hydrogen emerging from the engine's choked sonic throat-essentially ''scramjet propulsion in reverse.'' By varying the oxygen-to-hydrogen mixture ratio, LANTR engines can operate over a range of thrust and I(sub sp) values while the reactor core power level remains relatively constant. A LANTR-based LTS offers unique mission capabilities including short-transit-time crewed cargo transports. Even a ''commuter'' shuttle service may be possible allowing ''one-way'' trip times to and from the Moon on the order of 36 hours or less. If only 1% of the extracted LLO2 propellant from identified resource sites were available for use in lunar orbit, such a supply could support daily commuter flights to the Moon for many thousands of years! This report outlines an evolutionary architecture and examines a variety of mission types and transfer vehicle designs, along with the increasing demands on LLO2 production as mission complexity and velocity change delta V requirements increase. A comparison of vehicle features and engine operating characteristics, for both NTR and LANTR engines, is also provided along with a discussion of the propellant production and mining requirements associated with using FeO-rich volcanic glass as source material.

rocket↗

NASA NTRS2018

Robust Exploration and Commercial Missions to the Moon Using Nuclear Thermal Rocket Propulsion and In Situ Propellants Derived from Lunar Polar Ice Deposits

The nuclear thermal rocket (NTR) has frequently been identified as a key space asset required for the human exploration of Mars. This proven technology can also provide the affordable “access through cislunar space” necessary for commercial development and sustained human presence on the Moon. It is a demonstrated technology capable of generating both high thrust and high specific impulse (I(sub sp) ~900 s)— twice that of today’s best chemical rockets. Nuclear lunar transfer vehicles—consisting of a propulsion stage using three ~16.5-klb(sub f) small nuclear rocket engines (SNREs), an in-line propellant tank, plus the payload—can enable a variety of reusable lunar missions. These include cargo delivery and crewed lunar landing missions. Even weeklong “tourism” missions carrying passengers into lunar orbit for a day of sightseeing and picture taking are possible. The NTR can play an important role in the next phase of lunar exploration and development by providing a robust in-space lunar transportation system (LTS) that can allow initial outposts to evolve into settlements supported by a variety of commercial activities such as in situ propellant production used to supply strategically located propellant depots and transportation nodes. The processing of lunar polar ice (LPI) deposits (estimated to be ~2 billion metric tons) for propellant production—specifically liquid oxygen (LO(sub 2)) and hydrogen (LH(sub 2))—can significantly reduce the launch mass requirements from Earth and can enable reusable, surface-based lunar landing vehicles (LLVs) using LO(sub 2)/LH(sub 2) chemical rocket engines. Afterwards, LO(sub 2)/LH(sub 2) propellant depots can be established in lunar polar and equatorial orbits to supply the LTS. At this point a modified version of the conventional NTR called the LO(sub 2)-augmented NTR, or LANTR, would be introduced into the LTS, allowing bipropellant operation and leveraging the mission benefits of refueling with lunar-derived propellants (LDPs) for Earth return. The bipropellant LANTR engine utilizes the large divergent section of its nozzle as an “afterburner” into which oxygen is injected and supersonically combusted with nuclear preheated hydrogen emerging from the engine’s choked sonic throat—essentially “scramjet propulsion in reverse.” By varying the oxygen-to-hydrogen mixture ratio, LANTR engines can operate over a range of thrust and I(sub sp) values while the reactor core power level remains relatively constant. A LANTR-based LTS offers unique mission capabilities including short transit time crewed cargo transports. Even a “commuter” shuttle service may be possible, allowing “one-way” trip times to and from the Moon on the order of 36 hr or less. If only 1% of the postulated trapped water ice were available for use in lunar orbit, such a supply could support routine commuter flights to the Moon for many thousands of years. This report outlines an evolving LTS architecture that uses propellants derived from LPI and examines a variety of mission types and transfer vehicle designs along with their operating characteristics and increasing demands on LDP production as mission complexity and velocity change ΔV requirements increase. A comparison of the LDP production and mining requirements using LPI and volcanic glass to produce lunar-derived liquid oxygen (LUNOX) via the hydrogen reduction process is included, and the synergy with an evolving helium-3 mining industry is also discussed.

Borowski, Stanley K.↗

NASA NTRS2021

Dual Quaternion Visual Servo Control

This paper focuses on a dual quaternion-based estimation and control approach for position-based visual servoing (PBVS). The pose estimation of the camera is achieved using a dual quaternion-based Extended Kalman Filter (EKF), which estimates the position and orientation of the camera based on feature points acquired through a sequence of camera images. Based on the estimation, a dual quaternion control law is developed to regulate the camera to the desired pose. Leveraging the local exponential stability of the EKF and the global exponential stability of the designed controller, a nonlinear separation principle is used to prove the stability of the joint estimation and control for PBVS. The method is distinguished from other PBVS methods in the sense that a compact representation of dual quaternion is used to represent the pose, and a joint stability of estimator and controller for PBVS in dual quaternion space is presented. The proposed dual quaternion PBVS method is validated using a simulation.

Quaternions↗

DOE OSTI2024

Chemical randomness, lattice distortion and the wide distributions in the atomic level properties in high entropy alloys

High entropy alloys (HEAs) consist of multiple elements present in large proportions that are randomly distributed on a crystal lattice. On the one hand, the presence of multiple elements engenders wide ranges of atomic radii, electronegativities, electron valences and magnetic moments, whereas on the other, the presence of chemical randomness creates unique nearest neighbor environments among the lattice sites. As a result, the symmetry of the energy landscape is broken essentially at each lattice site thereby resulting in highly distorted energy landscapes. At the atomistic level, the lattice distortion has been widely observed in the form of varying bond lengths. At the electronic level, a range of charge transfers result in the charge density distortion. Collectively, the distorted landscapes cause large quantitative variations of the atomic level properties; in this review, we highlight the effect of lattice distortion on point defect energetics, stacking fault energies, and dislocation mobility. Besides the well- known large HEAs phase space, the enormity of the distorted energy landscape that scales with the atomic configurations is a new consideration; understanding this coupling between composition, lattice distortion and properties’ variations thus becomes an exciting but challenging area within the field of HEAs. Furthermore, this coupling is expected to open a new door for materials design, where the materials properties could be tuned via leveraging the lattice distortion, which is essentially absent in dilute/ordered alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. We employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2D materials↗

DOE OSTI2022

Machine Learning for Automated Metadata Assignment in Buildings: Cooperative Research and Development (Final Report, CRADA Number CRD-18-00767)

RealTerm Energy and NREL have identified a shared vision to evaluate opportunities to facilitate the organization and assignment of metadata to building control system (BCS) data via industry-informed machine learning (ML). Manual metadata assignment is labor intensive and costly, slowing down any Energy Management and Information System (EMIS) deployment in the building space. This project aims to develop methodologies to accurately assign this metadata and significantly decrease the level of effort associated with deploying EMIS. The objective of this project is to identify/design methodologies to assign metadata to HVAC control points automatically. The identified methodologies will be programmed in analytics algorithms so they can ingest a list of points and produce a detailed tagging following the Haystack classification nomenclature. To validate the efficacy of each methodology, tagging results will be compared utilizing a list of points extracted from RealTerm's building database-as well as data extracted from the NREL campus via the Intelligent Campus program-enabling testing against large datasets with real world challenges. The developed methodologies may leverage building manager/operator input on a limited basis to add context to the classifying algorithms. The partnership aims to advance global efforts in areas related to the DOE missions through improving operational performance of commercial buildings. It is well documented that buildings fall out of commission after they are occupied, wasting significant energy and incurring associated costs simply due to poor operational performance. Emerging EMIS technologies that perform continuous commissioning help to address this issue, yet integration of these systems can be labor intensive both for the technology vendor and the building owner/operator. This project will enable more efficient and cost-effective analytics for buildings, enabling improvement in building operations at lower cost points.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2d materials↗

DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.

2D materials↗

DOE OSTI2024

Bayesian mixture model approach to quantifying the empirical nuclear saturation point

The equation of state (EOS) in the limit of infinite symmetric nuclear matter exhibits an equilibrium density, $n_0 \approx 0.16 \, \mathrm{fm}^{-3}$, at which the pressure vanishes and the energy per particle attains its minimum, $E_0 \approx -16 \, \mathrm{MeV}$. Although not directly measurable, the nuclear saturation point $(n_0,E_0)$ can be extrapolated by density functional theory (DFT), providing tight constraints for microscopic interactions derived from chiral effective field theory (EFT). However, when considering several DFT predictions for $(n_0,E_0)$ from Skyrme and Relativistic Mean Field (RMF) models together, a discrepancy between these model classes emerges at high confidence levels that each model prediction's uncertainty cannot explain. How can we leverage these DFT constraints to rigorously benchmark nuclear saturation properties of chiral interactions? To address this question, we present a Bayesian mixture model that combines multiple DFT predictions for $(n_0,E_0)$ using an efficient conjugate prior approach. The inferred posterior distribution for the saturation point's mean and covariance matrix follows a Normal-inverse-Wishart class, resulting in posterior predictives in the form of correlated, bivariate $t$-distributions. The DFT uncertainty reports are then used to mix these posteriors using an ordinary Monte Carlo approach. At the 95\% credibility level, we estimate $n_0 \approx 0.157 \pm 0.010 \, \mathrm{fm}^{-3}$ and $E_0 \approx -15.97 \pm 0.40 \, \mathrm{MeV}$ for the marginal (univariate) $t$-distributions. Combined with chiral EFT calculations of the pure neutron matter EOS, we obtain bivariate normal distributions for the nuclear symmetry energy and its slope parameter evaluated at $n_0$: $S_v \approx 32.0 \pm 1.1 \, \mathrm{MeV}$ and $L\approx 52.6\pm 8.1 \, \mathrm{MeV}$ (95\%), respectively. Furthermore, our Bayesian framework is publicly available, so practitioners can readily use and extend our results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Systems Innovation: Modernization & Efficiencies for ESH&Q Reviews

Environmental compliance reviews at INL have traditionally been managed through fragmented systems, relying on multiple spreadsheets and manual processes. This inefficiency led to time-consuming status updates and redundant tasks, such as manually sending reminder emails and transferring data from Excel to the Environmental Review Process (ERP). Initial attempts to streamline these processes using Power Automate and Excel revealed significant limitations, necessitating a more comprehensive solution. To address these immediate inefficiencies, automated workflows were developed using Power Automate. These workflows were designed to send scheduled status update reminders and capture responses through standardized forms, with submitted data flowing directly into centralized Excel trackers. This automation reduced the administrative burden, improved data accuracy, and enabled faster, more consistent reporting. Specifically, email automation achieved a 65% efficiency gain, while data integration saw a 48% improvement, resulting in 91% of project statuses being updated within two months. Despite the improvements brought by Power Automate, the fragmented nature of the review processes persisted. To further enhance efficiency and accuracy, the Integrated Review Tool (IRT) was developed. The IRT aims to centralize review initiation and connect team systems, creating an interconnected data infrastructure that preserves team autonomy while enhancing overall efficiency. This tool automates email reminders, centralizes reviews, and streamlines data integration, significantly improving the accuracy and efficiency of environmental compliance reviews. The design and development of the IRT involved advanced systems methodology, process mapping, project management, and collaboration with subject matter experts. The minimum viable product design is 100% complete, and system development is currently underway, with expected outcomes including a centralized entry point for all ESH&Q reviews, automated routing, real-time tracking and analytics, AI integration, and a user-friendly interface. This project demonstrates the potential of leveraging automation and integrated systems to enhance efficiency, accuracy, and decision-making in environmental reporting and compliance processes at INL.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

Real-time Simulation of Coordinated Sub-Transmission Volt-Var Control Tool under High Distributed PV Penetration

With the rapid penetration of intermittent solar photovoltaic (PV) and other distributed energy resources (DER) into the grid, and subsequent changes in power flow patterns in both distribution and sub-transmission, voltage regulation is becoming a major challenge. It is prudent to leverage PV and DER to provide ancillary services to the grid, such as voltage regulation. Recently, a quasi-static, coordinated real-time sub transmission volt-var control algorithm (CReST-VCT) was developed for voltage regulation under high PV penetration by dispatching the reactive power settings of the shunt devices and PV inverters. The algorithm was validated offline, on a quasistatic study. Such quasi-static dispatching algorithms cannot guarantee its performance for highly nonlinear dynamical power systems. In this paper the performance of the algorithm is validated with a real time nonlinear dynamic simulation of a modified IEEE 118 bus system in Opal-RT. The real time simulation is used to emulate the actual system operation, providing a more realistic testing environment for CReST-VCT. The dispatch and control signals are communicated between the power system (Opal solver) and the control center (GAMS solver) in real-time with a MODBUS bridge. The results demonstrate i) system is stable with the new dispatch points ii) significant improvement in system-wide voltage profiles compared to an uncontrolled scenario. Another significant contribution of the work is developing a framework for dispatchable, long duration dynamic simulations that can be leveraged for market/dispatch studies.

CReST-VCT, Opal-RT, Volt/Var Control, renewable in↗

DOE OSTI2022

Photometric redshift estimation with convolutional neural networks and galaxy images: Case study of resolving biases in data-driven methods

Deep-learning models have been increasingly exploited in astrophysical studies, but these data-driven algorithms are prone to producing biased outputs that are detrimental for subsequent analyses. In this work, we investigate two main forms of biases: class-dependent residuals, and mode collapse. We do this in a case study, in which we estimate photometric redshift as a classification problem using convolutional neural networks (CNNs) trained with galaxy images and associated spectroscopic redshifts. We focus on point estimates and propose a set of consecutive steps for resolving the two biases based on CNN models, involving representation learning with multichannel outputs, balancing the training data, and leveraging soft labels. The residuals can be viewed as a function of spectroscopic redshift or photometric redshift, and the biases with respect to these two definitions are incompatible and should be treated individually. We suggest that a prerequisite for resolving biases in photometric space is resolving biases in spectroscopic space. Experiments show that our methods can better control biases than benchmark methods, and they are robust in various implementing and training conditions with high-quality data. Our methods hold promises for future cosmological surveys that require a good constraint of biases, and they may be applied to regression problems and other studies that make use of data-driven models. Nonetheless, the bias-variance tradeoff and the requirement of sufficient statistics suggest that we need better methods and optimized data usage strategies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Development of in-use engine speed/torque heat maps across multiple heavy-duty commercial vehicle vocations

The U.S. Department of Energy (DOE) established the SuperTruck program with the goal of achieving brake thermal efficiency (BTE) greater than or equal to 55% as demonstrated in an operational heavy-duty (HD) diesel engine at a 65-miles-per-hour (mph) cruise point. Beyond the line-haul application, HD engines operate in a wide range of speed and torque conditions that are unlikely to yield the same efficiency under real-world operation. Thereby, the in-use engine heat maps described in this paper are a valuable tool to illustrate whether the engine-efficiency “sweet spot” matches the most frequent operating conditions. In this study, NREL developed engine heat maps to quantify the important operating points for various vocations using our Fleet DNA database of commercial fleet vehicle operations data. These heat maps clearly show that high-frequency operating points vary significantly according to vehicle vocation, while only a few of them match the sweet spot. Beyond the illustration, engine in-use heat maps can also be leveraged to build up reduced-order engine-efficiency models, needed by many rapid powertrain simulations. As case studies, nine reduced-order models – including line-haul truck, transfer truck, transit bus, transit bus with compressed natural gas (CNG) engine, drayage, refuse pickup, local delivery, utility truck, and school bus with CNG engine – using a trust-region reflective algorithm to fit the on-road data extracted based on the engine in-use heat maps.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2019

Google Dataset Search and CMR

Searching for earth science data from scratch has, traditionally, been a non-trivial process. Using commercial search engines a starting point, it has not been trivial to locate information and services related to a science users needs. The advent of Google Dataset Search has improved that process. CMR has leveraged the schema.org dataset feature to provide semantic markup on our collection landing pages that are used by Google Dataset Search to provide results that map directly to a scientist's needs and provide an intuitive gateway in to the NASA Earthdata environment.

CMR↗

NASA NTRS2019

Google Dataset Search and CMR

Searching for earth science data from scratch has, traditionally, been a non-trivial process. Using commercial search engines a starting point, it has not been trivial to locate information and services related to a science users needs. The advent of Google Dataset Search has improved that process. CMR has leveraged the schema.org dataset feature to provide semantic markup on our collection landing pages that are used by Google Dataset Search to provide results that map directly to a scientist's needs and provide an intuitive gateway in to the NASA Earthdata environment.

Discovery↗

NASA NTRSDate not supplied

Production of Flight Instruments for Multi-Satellite Constellations

Recent National Academy of Science Decadal Surveys in space and Earth-science have called for simultaneous, distributed multi-point measurements in and from space, requiring constellations of small spacecraft. Small satellites have demonstrated their utility for enabling high-quality science measurements and observations. NASA missions have leveraged advances in sensor miniaturization, technology innovations, and new small satellite mission architectures to enable meaningful measurement-based scientific investigations that operate on small satellites and that are responsive to science objectives described in National Academy of Science Decadal Surveys. The advent of high capability small spacecraft enables consideration of science missions involving multiple small spacecraft, constellations of a few or many for simultaneous distributed in situ observations or remote observations from a variety of viewpoints. The central challenge for fielding instrumented space-flight constellations is to provide the required multiple sets of fully verified and calibrated instrument hardware, software, and operational processes from within a one-off project-based scientific space flight culture. Although there are numerous commercial entities providing “off-the shelf” spacecraft and avionics, the challenge lies in the multi-unit production of the uniquely targeted instrumentation necessary to perform the specific measurements required for a particular science investigation. Traditionally, the cost of such instrumentation has represented a significant portion of the hardware cost for a mission and posed the highest risk area for implementation. A shift in paradigm from large science platforms to constellations of smaller satellites drives the challenge to build instruments in a quasi-production environment. This paper describes key aspects of, and challenges encountered in the development program for the successful production of instruments for the Fast-Plasma Investigation (FPI) instrument suite of 64 flight plasma spectrometers and supporting electronics on the NASA Magnetospheric Multiscale (MMS) 4 satellite constellation mission. Although the MMS mission was not composed of small satellites, there are key aspects of the instrument production that apply directly to constellations of small satellites.

Arthur D Jacques↗

NASA NTRSDate not supplied

Production of Flight Instruments for Multi-Satellite Constellations

Recent National Academy of Science Decadal Surveys in space and Earth-science have called for simultaneous, distributed multi-point measurements in and from space, requiring constellations of small spacecraft. Small satellites have demonstrated their utility for enabling high-quality science measurements and observations. NASA missions have leveraged advances in sensor miniaturization, technology innovations, and new small satellite mission architectures to enable meaningful measurement-based scientific investigations that operate on small satellites and that are responsive to science objectives described in National Academy of Science Decadal Surveys. The advent of high capability small spacecraft enables consideration of science missions involving multiple small spacecraft, constellations of a few or many for simultaneous distributed in situ observations or remote observations from a variety of viewpoints. The central challenge for fielding instrumented space-flight constellations is to provide the required multiple sets of fully verified and calibrated instrument hardware, software, and operational processes from within a one-off project-based scientific space flight culture. Although there are numerous commercial entities providing “off-the shelf” spacecraft and avionics, the challenge lies in the multi-unit production of the uniquely targeted instrumentation necessary to perform the specific measurements required for a particular science investigation. Traditionally, the cost of such instrumentation has represented a significant portion of the hardware cost for a mission and posed the highest risk area for implementation. A shift in paradigm from large science platforms to constellations of smaller satellites drives the challenge to build instruments in a quasi-production environment. This paper describes key aspects of, and challenges encountered in the development program for the successful production of instruments for the Fast-Plasma Investigation (FPI) instrument suite of 64 flight plasma spectrometers and supporting electronics on the NASA Magnetospheric Multiscale (MMS) 4 satellite constellation mission. Although the MMS mission was not composed of small satellites, there are key aspects of the instrument production that apply directly to constellations of small satellites.

Arthur D Jacques↗

DOE OSTI2023

A Reference Implementation for a Quantum Message Passing Interface

Practical applications of quantum computing are currently limited by the number of qubits that can be set with reasonable fidelities for each system. Therefore, a distributed quantum computing system with multiple quantum computers coherently connected is highly demanding. To realize the internode communication of quantum information, the software interface, Quantum Message Passing Interface (QMPI), leveraging the framework built for classical MPI but taking advantage of quantum teleportation to communicate between different quantum nodes was proposed. In this project, we develop the QMPI with point-to-point and collective operations in Qiskit and characterize its performance by demonstrating the application implementations. Moreover, we developed a new technique for optimizing collective communication of the distributed quantum programs with Multi-Controlled Toffoli gates. This technique beats the state-of-the-art in terms of fidelity and the number of remote EPR pairs consumed in both simulations and experiments.

Shi, Yue↗