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Modern Mars surface missions rely upon orbiting spacecraft to relay communications to and from Earth systems. An important component of this multi-mission relay process is the collection of relay performance statistics supporting strategic trend analysis and tactical anomaly identification and tracking.
Radio telescopes that employ arrays of many antennas are in operation, and ever larger ones are being designed and proposed. Signals from the antennas are combined by cross-correlation. While the cost of most components of the telescope is proportional to the number of antennas N, the cost and power consumption of cross-correlationare proportional to N2 and dominate at sufficiently large N. Here we report the design of an integrated circuit (IC) that performs digital cross-correlations for arbitrarily many antennas in a power-efficient way. It uses an intrinsically low-power architecture in which the movement of data between devices is minimized. In a large system, each IC performs correlations for all pairs of antennas but for a portion of the telescope's bandwidth (the so-called "FX" structure). In our design, the correlations are performed in an array of 4096 complex multiply-accumulate (CMAC) units. This is sufficient to perform all correlations in parallel for 64 signals (N=32 antennas with 2 opposite-polarization signals per antenna). When N is larger, the input data are buffered in an on-chipmemory and the CMACs are re-used as many times as needed to compute all correlations. The design has been synthesized and simulated so as to obtain accurate estimates of the IC's size and power consumption. It isintended for fabrication in a 32 nm silicon-on-insulator process, where it will require less than 12mm2 of silicon area and achieve an energy efficiency of 1.76 to 3.3 pJ per CMAC operation, depending on the number of antennas. Operation has been analyzed in detail up to N = 4096. The system-level energy efficiency, including board-levelI/O, power supplies, and controls, is expected to be 5 to 7 pJ per CMAC operation. Existing correlators for the JVLA (N = 32) and ALMA (N = 64) telescopes achieve about 5000 pJ and 1000 pJ respectively usingapplication-specific ICs in older technologies. To our knowledge, the largest-N existing correlator is LEDA atN = 256; it uses GPUs built in 28 nm technology and achieves about 1000 pJ. Correlators being designed for the SKA telescopes (N = 128 and N = 512) using FPGAs in 16nm technology are predicted to achieve about 100 pJ.
This manual documents the Aircraft NOise Prediction Program 2 (ANOPP2). ANOPP2 is a toolkit that includes a framework, noise prediction methods, and peripheral software to aid a user in predicting and understanding aircraft noise. This manual includes an explanation of the overall design and structure of ANOPP2, including a brief introduction to aircraft noise prediction and the ANOPP2 background, philosophy, and architecture. The concept of nested acoustic data surfaces and its application to a mixed-fidelity noise prediction are presented. The structure and usage of ANOPP2, which includes the communication between the user, the ANOPP2 framework, and noise prediction methods, are presented for two scenarios: wind-tunnel and flight. These scenarios serve to provide the user with guidance and documentation references for performing a noise prediction using ANOPP2.
Planning for future National Aeronautics and Space Administration (NASA) missions will encompass a variety of operational and engineering activities that involve a multitude of issues, constraints, and influences derived from the natural environment. This Technical Memorandum (TM) presents a definition of the natural environment, i.e., a description in engineering handbook format of models and data specifically selected to support the architecture development, engineering design, and technology development for NASA's Exploration Systems Development (ESD) initiatives.
A computational study of the Adaptive Deployable Entry and Placement Technology (ADEPT) Sounding Rocket (SR-1) Test is presented using the US3D flow solver. ADEPT SR-1 is intended, in part, to assess the dynamic stability of this entry vehicle architecture. Given that no dynamic stability data exists for the ADEPT geometry, a limited ballistic range campaign has been performed to characterize the vehicle's stability characteristics pre-flight for Mach numbers between 1.21 and 2.5. Here, this data is used to assess the accuracy of US3D's free-flight CFD capability. Computed trajectories from US3D and experimental data show that the flow solver compares well in vehicle oscillation frequency, downrange distance, and oscillatory amplitude during high Mach number flight (Mavg = 2.36). For Mach numbers below 1.5, the solver under predicts total angle-of-attack by an average of 16%, but compares well in oscillatory frequency and downrange distance. Additionally, a capability for simulating the trajectory of the flight article through the atmosphere using CFD is presented. This capability couples US3D's free-flight capability to an atmosphere model that accounts for changes in free-stream density and temperature as the vehicle descends. Two simulations for the purpose of demonstrating the capability and viability of this approach are applied to SR-1 flight article, and some unique challenges are discussed.
NASA’s current baseline plan for a crewed Mars mission anticipates a transit time of up to three hundred days in microgravity and 3-14 days on the Martian surface for gravity acclimation before the crew can safely perform their first Extra-Vehicular Activity (EVA). While there are multiple options for how initial surface operations will be performed, all current designs involve acclimation on the surface, and the impacts on the mission schedule, required supplies, and crew lander systems are significant. This paper proposes an alternative option utilizing artificial gravity, which offers benefits in terms of mission scope, mass savings, crew health, and long-term strategic vision. By moving the acclimation requirement to the orbiting habitat’s existing systems, rather than adding redundant systems to the lander, the Mars Descent Vehicle (MDV) can be a much smaller, simpler, and lighter design. Rather than the lander being designed to support crew for days, it would be mere hours. While ambitious, the concept of pre-acclimation in orbit can be not only safe and feasible, but done with fairly minimal changes to the planned architecture and overall mass requirements. The data used draws on decades of established research and demonstrates how this capability can be not only used for pre-acclimation, but also to support crew during early orbital-only missions, surface abort contingency scenarios, return-to-orbit abort scenarios, and as an early proof of capability into larger and more ambitious artificial gravity designs needed for extended exploration missions in the future.
In preparation for long duration manned space flight, numerous technology development efforts are ongoing in the area of environmental control and life support (ECLS). In cooperation with international, industry, and academic partners, NASA seeks to leverage the International Space Station as a testbed for technologies targeted for Exploration-class missions. In recent years, Equivalent Systems Mass (ESM) analyses have been conducted to evaluate the relative breakeven points and to compare technologies as part of ECLS architectural trades. While these studies have provided important data pertaining to key engineering metrics, additional considerations are important to more fully understand the potential impacts and costs associated with selecting a specific architecture. A tool, called the Life Support Multi-Dimensional Assessment Criteria (LSMAC), was recently proposed by Sierra Nevada Corporation in an attempt to incorporate influences of these additional considerations including Maintainability, Risk Analysis, Technology Readiness Level, Radiation Impacts, Manufacturing Costs, Reliability, Human Factors, and Un-Crewed Operations. As a first step toward evaluating and implementing this tool, LSMAC was used to revisit the ISS oxygen recovery trade from the 1990’s wherein Sabatier was selected over Bosch technology. Second, the tool was used to compare oxygen recovery developmental technologies currently in work. The results of these studies as well as a comparison with standalone ESM analyses are reported. Further, a discussion of the potential application of the tool across the ECLS portfolio and its potential use in future technology selection for ISS flight demonstrations is provided.
International civil space agencies around the world are working together in the Interagency Operation Advisory Group (IOAG) and the Consultative Committee for Space Data Systems (CCSDS) to develop interoperability architectures and standards for space communications. Within CCSDS, there is a working group dedicated on developing recommendations and standards for optical communications. These standards include recommendations for the physical layer, coding and synchronization layer, and best practices for measuring and monitoring atmospheric conditions and operating optical links. The working group has developed standards for both Near Earth and deep space robotic and hum exploration missions. The standards generally address both free space links between spacecraft and free space links between spacecraft and ground. This paper will provide an overview and update on the set of standards the CCSDS Optical Communications Working Group has developed
As envisioned, Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) will introduce new vehicles and operations within the national airspace, moving people and cargo safely and efficiently at a much larger scale than today. Driven by transformative technology and revolutionary aircraft, this movement must still manage technical, regulatory, operational, and policy challenges. NASA’s work in support of AAM and UAM includes, but is not limited to tools, technologies, and architectures for distributed sensing of aircraft, data & reasoning services exchange, Human-Autonomy Teaming (HAT), contingency management, and vehicle health management. This paper builds upon these concepts and evaluates the use of distributed sensing and infrastructure assistance towards health management and mission assurance of UAM vehicles in specific operational scenarios. Through analysis of these example missions, aided by the data produced by the conceptual distributed sensing and reasoning infrastructure, we define opportunities for state estimation, diagnosis, and key decision points affecting the health state of the vehicle and the airspace volume. As a result, we define a number of measurable health state parameters providing relevant information to drive decision-making in contingency situations or feed automation tools in support of operators and managers.
A simulation of NASA Ames Research Center was developed to provide a common testbed for multiple areas of research within the Intelligent Systems Division, primarily related to verification and validation of autonomous technologies, machine learning, and digital twin systems. AmesSim corresponds a physical rover that is capable of navigation in the real-world environment; in this way, the same experiments can be run in both settings, with the same software and hardware stacks in the loop. The simulation is built in Unreal Engine 4 and uses the AirSim plugin for API convenience. Several custom modifications allow deterministic, faster-than-realtime execution, which enables consistent testing of on-line algorithms and large-scale data collection. This paper describes the architecture and capabilities of the simulation and discusses development challenge.
Automation has become critical for ground systems, improving efficiency and reliability while reducing costs across mission operations. The Goddard Mission Services Evolution Center (GMSEC) software suite has played a significant role in enabling this automation, leveraging its publish/subscribe paradigm through a message bus architecture to facilitate seamless communication and data flow. Historically, the GMSEC suite, through components like Criteria Action Table (CAT) has been pivotal in automating ground system capabilities. However, as technology advances, limitations in automation with CAT have emerged, creating an opportunity to enhance ground system automation through the introduction of GEMU. This new GMSEC component brings new capabilities and addresses specific automation constraints that CAT could not overcome, allowing for more sophisticated, flexible, and efficient message processing. GEMU, at its core, is designed to accelerate the development of custom GMSEC-compliant applications. It enables users to construct automated message processing pipelines quickly, supporting both drag-and-drop web-based configuration and scripting through a simple domain-specific language. This advancement not only simplifies the process but also reduces the time needed for implementing automated solutions. This presentation will outline GEMU’s potential value in improving mission operations automation. It will highlight the benefits of transitioning from CAT to GEMU and offer insights into how GEMU can drive operational efficiencies. We will also provide an overview of the automation capabilities of GEMU and its potential impact on mission operations centers (MOCs).
Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.
NASA’s Intelligent Systems Technology Program (IST) is driving a paradigm shift in Earth science through the development of Earth System Digital Twins (ESDT). These integrated information systems create a dynamic "digital replica" of the Earth by harmonizing continuous, multi-source observations with high-fidelity models and state-of-the-art artificial intelligence (AI) that enable “What now?”, “What next?”, and “What if?” scenario building. These scenarios are reflected in NASA IST’s series of ESDTs, from the Coastal Zone Digital Twin that integrates complex data on the current state of the Chesapeake Bay to the Terrestrial Environmental Rapid-Replication and Assimilation Hydrometeorological (TerraHydro) AI-based ESDT that forecasts water movement across Earth’s surface, to the Agriculture Land Information System (AgLIS) which can be used to assess optimal planting dates and crop yield estimates. By bridging the gap between vast data archives and actionable insights, these projects enable a system-of-systems approach to understanding complex, interacting Earth processes. This poster will highlight recent innovations and future directions from NASA’s ESDT initiatives: Continuous Data Assimilation & Multi-Source Fusion. A core requirement of the ESDT work is the transition from static models to dynamic "living" replicas. This involves creating frameworks for the continual assimilation of near-real-time data from uncoordinated, heterogeneous sources, including satellite observations and airborne assets, and ground-based Internet of Things (IoT) sensors. These systems link design, operational status, and environmental data, ensuring the digital twin accurately reflects the current state of the physical Earth system. High-Fidelity Hybrid Modeling & Computational Acceleration to enable interactive "what-if" explorations, programs are moving beyond traditional, slow physical solvers by developing fast surrogate machine learning models and Deep Generative Models (DGMs). These hybrid approaches use neural networks to emulate complex physics, such as cloud feedback or ocean dynamics, at a fraction of the original computing cost, often leveraging advanced hardware like Graphics Processing Units (GPUs) to achieve the necessary scale. Federated Ecosystems & Interoperable Frameworks rather than building isolated tools, NASA IST is moving toward federated ESDTs and reusable analytic collaborative frameworks. This theme focuses on interoperability standards and common ontologies that allow specialized digital twins to interact and share data. This system-of-systems architecture supports multi-discipline investigations, such as analyzing how upstream watershed changes impact downstream urban flooding or how wildfire emissions affect regional air quality. By leveraging these advancements, ESDTs empower researchers and decision-makers to conduct real-time analysis and run complex hypothetical scenarios, ultimately improving our understanding of Earth’s evolving systems and informing critical real-world applications.
ATCA/{mu}TCA platforms are attractive because of the modern serial link architecture, high availability features and many packaging options. Less-demanding availability applications can be met economically by scaling back speed and redundancy. The ATCA specification was originally targeted for the Telecom industry but has gained recently a much wider user audience. The purpose of this paper is to report on present hardware and software R and D efforts where ATCA and {mu}TCA are planned, already being used or in development using selected examples for accelerator and detectors in the Physics community. It will present also the status of a proposal for physics extensions to ATCA/{mu}TCA specifications to promote inter-operability of laboratory and industry designs for physics.
The industrial or manufacturing sector is a foundational component to all economic activity. In addition to being a large direct consumer of energy, the manufacturing sector also produces materials, products, and technologies that influence the energy use of other economic sectors. For example, the manufacturing of a lighter-weight vehicle component affects the energy required to ship that component as well as the fuel efficiency of the assembled vehicle. Many energy efficiency opportunities exist to improve manufacturing energy consumption, however comparisons of manufacturing sector energy efficiency investment opportunities tend to exclude any impacts that occur once the product leaves the factory. Expanding the scope of analysis to include energy impacts across different stages of product life-cycle can highlight less obvious opportunities and inform actions that create the greatest economy-wide benefits. We present a methodology and associated analysis tool (LIGHTEnUP Lifecycle Industry GHgas, Technology and Energy through the Use Phase) that aims to capture both the manufacturing sector energy consumption and product life-cycle energy consumption implications of manufacturing innovation measures. The tool architecture incorporates U.S. national energy use data associated with manufacturing, building operations, and transportation. Inputs for technology assessment, both direct energy saving to the manufacturing sector, and indirect energy impacts to additional sectors are estimated through extensive literature review and engineering methods. The result is a transparent and uniform system of comparing manufacturing and use-phase impacts of technologies.
Although recording of usage data is common in scholarly information services, its exploitation for the creation of value-added services remains limited due to concerns regarding, among others, user privacy, data validity, and the lack of accepted standards for the representation, sharing and aggregation of usage data. A technical, standards-based architecture for sharing usage information is presented. In this architecture, OpenURL-compliant linking servers aggregate usage information of a specific user community as it navigates the distributed information environment that it has access to. This usage information is made OAI-PMH harvestable so that usage information exposed by many linking servers can be aggregated to facilitate the creation of value-added services with a reach beyond that of a single community or a single information service.
Resilient monitoring systems are sensor networks that degrade gracefully under malicious attacks on their sensors, causing them to project misleading information. The goal of this paper is to design, analyze, and evaluate the performance of a resilient monitoring system intended to monitor plant conditions (normal or anomalous). The architecture developed consists of four layers: data quality assessment, process variable assessment, plant condition assessment, and sensor network adaptation. Each of these layers is analyzed by either analytical or numerical tools, and the performance of the overall system is evaluated using simulations. The measure of resiliency of the resulting system is evaluated using Kullback Leibler divergence, and is shown to be sufficiently high in all scenarios considered.
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.