SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Machine learning methods were explored in to answer the following question: Which nuclear data are likely related to bias in simulating the effective neutron multiplication factor, k eff , of ICSBEP critical assemblies?
Dimensional tolerances for high-speed-machined aluminum products continue to tighten due to the demand for automated assembly of complex monolithic parts in aerospace and other industries. Understanding the contribution of inherent residual stress in wrought Al 7050-T7451 plate, common in aircraft manufacture, to distortion of high-aspect-ratio machined parts is critical but remains problematic due to the alloy's low residual stress magnitude over large geometries. Prior investigations into residual stress effects on machined part distortion suffer inadequate characterizations of the wrought material stress field, either because of low fidelity due to “slitting” methods, confounding effects in machined-layer removal methods, or small sample size when using neutron diffraction (ND). In this work, inherent residual stress is measured via ND at 860 locations in a 90.5 mm thick Al 7050-T7451 plate having dimensions 399 mm in the rolling direction and 335 mm in the transverse direction. Unlike prior studies, the ND residual stress is reconstructed using an iterative algorithm to ensure fully compatible, equilibrated 3D field prior to examining its effect on distortion. Further, the findings from simulations and experiments show that inherent residual stress alone could distort a high-aspect-ratio part beyond aerospace industry requirements, that slitting measurements may not sufficiently characterize residual stress for predicted distortion, and that parts machined from different plate thickness locations could exhibit reversed distortion patterns. Thus, research into distortion prediction that considers machining should carefully characterize and reconstruct inherent residual stress so that the coupled machining effects are accurately modeled.
Researchers at NASA/GSFC evaluated various automated inspection systems (AIS) technologies using test boards with known defects in surface mount solder joints. These boards were complex and included almost every type of surface mount device typical of critical assemblies used for space flight applications: X-ray radiography; X-ray laminography; Ultrasonic Imaging; Optical Imaging; Laser Imaging; and Infrared Inspection. Vendors, representative of the different technologies, inspected the test boards with their particular machine. The results of the evaluation showed limitations of AIS. Furthermore, none of the AIS technologies evaluated proved to meet all of the inspection criteria for use in high-reliability applications. It was found that certain inspection systems could supplement but not replace manual inspection for low-volume, high-reliability, surface mount solder joints.
The Cray XK7 Titan was the top supercomputer system in the world for a long time and remained critically important throughout its nearly seven year life. It was an interesting machine from a reliability viewpoint as most of its power came from 18,688 GPUs whose operation was forced to execute three rework cycles, two on the GPU mechanical assembly and one on the GPU circuitboards. We write about the last rework cycle and a reliability analysis of over 100,000 years of GPU lifetimes during Titan’s 6-year-long productive period. Using time between failures analysis and statistical survival analysis techniques, we find that GPU reliability is dependent on heat dissipation to an extent that strongly correlates with detailed nuances of the cooling architecture and job scheduling. We describe the history, data collection, cleaning, and analysis and give recommendations for future supercomputing systems. We make the data and our analysis codes publicly available.
When embarking on a retrofit of a building envelope, it is critical to understand the composition of its assembly. This practice is currently done by destructive and invasive material testing or demolition, which is sometimes not possible when in historic or protected buildings. To address this problem, in-situ thermal testing can be utilized along with machine learning classification algorithms to infer the composition of an assembly. In this paper, a proof-of-concept K-nearest neighbors classification model is developed to classify assembly composition from effective thermal resistance, effective thermal mass, and assembly cladding. This model was trained and tested utilizing a synthetic dataset producing an F1-score of 94.6%. This model was also validated with experimental data from a 100-year old wall assembly, confirming the model’s real-world validity. The paper presents a framework for inferring as-built envelope assemblies, all without having to damage or disturb the building and its occupants.
Experiments and training with critical assemblies and fissionable material (at or near the critical state) that explore reactivity phenomena are central to a number of national security challenges. From fission energy to nuclear weapons to a broad suite of scientific challenges, it is clear that additional capacity and capability are needed. The National Criticality Experiments Research Center (NCERC) marked 10 years of operations in 2021. This anniversary was an opportune time to celebrate our successes and progress, and to evaluate the remaining and emergent challenges. Against this backdrop, a workshop of approximately 140 national and international leaders in nuclear research was convened in Los Alamos, New Mexico to explore “NCERC Futures.” Therefore, the present workshop focused specifically on needed capabilities and tools to meet the research challenges in eight topical mission areas served by NCERC. As each Topical Group summarized their discussions in the out brief, it was recognized that key challenges could be met through enabling infrastructure investments and new critical assemblies. Enabling infrastructure includes staffing, additional space/buildings, developing an agile bounding safety basis, the ability to keep pace with technological advances in detectors and data acquisition systems (allowing use of those with Bluetooth™ and similar technologies), an expanded set of materials options (especially plutonium), a “Plug and Play” design and implementation mindset, and a facility that enabled free-field measurements. The new critical assemblies that were identified as having the most impact were a bare plutonium (Pu) Critical Assembly, a Horizontal Split Table (HST), a Super Comet, and a Uranium Solution Burst Assembly. NECRC is a unique, one-of-a-kind facility in the United States. If all the improvements were to be made, NCERC would enable the United States and its partners to address many important research questions related to criticality. These include but are not limited to: (1) Covering the entire neutron energy spectrum for both highly enriched uranium (HEU) and Pu in configurations for virtually all conceivable applications; (2) Performing multi-physics solution experiments and irradiations with a Uranium Solution Burst Assembly, which more closely resembles actual criticality accidents; (3) Conducting free-field experiments to make basic fission physics measurements and much cleaner benchmarks with various experimental observables; and (4) Providing more training classes and more experiments annually at greater cost efficiency enabled by additional buildings and machines and an agile, bounding, risk-balanced Safety Basis. In the end, workshop attendees enthusiastically concluded that NCERC Futures are bright and the workshop helped to identify a roadmap of capability gaps that need to be addressed. This report documents the results of those efforts.
Refueling systems for fast reactors are designed to handle fresh and used core assemblies (fuel, reflector, and shield core assemblies) within the reactor vessel in an opaque coolant environment without visual reference. These refueling machines are designed to work in a sodium (or other fast reactor coolant) and argon vapor space environment and are engineered with the rotatable plug system to allow for the movement of fresh and spent fuel into and out of the reactor core. The refueling machines are a critical component in any reactor and thus need to undergo extensive testing in a prototypic environment to ensure that they will meet all of the system functions and requirements. Argonne has developed an innovative compact refueling system design for the Advanced Fast Reactor-100 that is based upon some mechanisms used in previous reactor designs, such as the U.K.’s Prototype Fast Reactor (PFR) and some mechanisms that have not been used in sodium. This compact refueling machine supports the reduction in size of the AFR-100’s reactor vessel, and if fully developed, would support and inform the development of the in-vessel refueling machines for such commercial reactors as the GEH PRISM reactor plant, the ARC Clean Energy’s ARC-100 reactor, and the Natrium reactor, among others. This refueling system is a vital component of a fast reactor that supports reducing the cost of the reactor and increasing its reliability. During the development of the compact fuel handling machine conceptual design for the AFR-100, a lack of testing data for many mechanical components in sodium under typical in-reactor loads and conditions was discovered. The reduction in lifetime of the various mechanical components in the liquid sodium environment needs to be quantified versus the calculated component lifetimes under normal conditions in the testing while they are subjected to typical loading profiles experienced in the past. The Gripper Test Assembly discussed here includes a full-size gripper device with appropriate mechanical features that will be tested in sodium to provide this testing data. The Gripper Test Assembly is used to test various mechanical fuel handling components submerged in high temperature liquid sodium. These mechanisms are gears, bearings, gripper jaws and head, universal joints and shafts, ball screws, among others. These components will be tested under the typical sodium environmental conditions experienced during refueling operations with appropriate loading conditions that simulate the removal and insertion of core assemblies into a fast reactor grid plate structure. This Gripper Test Assembly is the second in a series of refueling system mechanisms developed for testing in sodium. The first test assembly is the Gear Test Assembly (GTA) which was used to test the performance of gears and bearings operating in sodium. Because of the successful testing conducted with the GTA, it was decided to continue with the development of the Gripper Test Assembly which uses the same gears and bearings tested in GTA. Using the data collected during operation of this gripper test assembly, lifetime reduction factors of the various mechanical components can be calculated for the material combinations selected. These lifetime reduction factors can be used in the design of future mechanical systems which operate in these environments to accurately predict component end of life. In addition, an understanding of the ability of these components and mechanisms to operate under-sodium with the chosen materials will be accomplished. Again, this Gripper Test Assembly is the follow-on test article to the Gear Test Assembly which was testing the ability of gears and bearings operating in a sodium environment. Once fabricated and qualified, it will be tested in the Mechanism Engineering Test Loop (METL) facility located in Building 308 at Argonne National Laboratory.
As the International Space Station (ISS) travels 17,500 miles per hour, normal is having a constant sensation of free-falling. Normal is no rain, but an extreme amount of shine.with temperatures reaching 250 F when facing the Sun. Thanks to a number of advanced control systems onboard the ISS, however, the interior of the station remains a cool, comfortable, normal environment where astronauts can live and work for extended periods of time. There are two main control systems on the ISS that make it possible for humans to survive in space: the Thermal Control System (TCS) and the Environmental Control and Life Support system. These intricate assemblies work together to supply water and oxygen, regulate temperature and pressure, maintain air quality, and manage waste. Through artificial means, these systems create a habitable environment for the space station s crew. The TCS constantly works to regulate the temperature not only for astronauts, but for the critical instruments and machines inside the spacecraft as well. To do its job, the TCS encompasses several components and systems both inside and outside of the ISS. Inside the spacecraft, a liquid heat-exchange process mechanically pumps fluids in closed-loop circuits to collect, transport, and reject heat. Outside the ISS, an external system circulates anhydrous ammonia to transport heat and cool equipment, and radiators release the heat into space. Over the years, NASA has worked with a variety of partners.public and private, national and international. to develop and refine the most complex thermal control systems ever built for spacecraft, including the one on the ISS.
Understanding how solvent properties influence the solution-to-film assembly of conjugated polymers remains a critical challenge due to the complex and intertwined nature of polymer–solvent interactions. In this study, we integrate a data-driven framework with experimental validation to identify key parameters influencing the assembly and performance of poly[2,5-(2-octyldodecyl)-3,6-diketopyrrolopyrrole-alt-5,5-(2,5-di(thien-2-yl)thieno[3,2-b]thiophene)] (DPP-DTT) in organic field-effect transistors (OFETs). A machine learning (ML) approach identified the normalized Reichardt polarity parameter (E T N ) as a significant descriptor correlated with DPP-DTT hole mobility (μ). Systematic DPP-DTT devices fabricated using solvents across a wide E T N range revealed that higher E T N solvents yield enhanced μ. To elucidate the structural origins of high μ, we conducted comprehensive analyses using UV–vis–NIR spectroscopy and grazing incidence wide angle X-ray scattering (GIWAXS) measurements. The results revealed that films processed from high E T N solvents exhibit reduced paracrystallinity. By analyzing the solution-state behavior using optical microscopy and solution WAXS, we revealed polymer solubility differences in the various solvents and associated distinct polymer assembly pathways, elucidating why the high E T N solvent produces long-range ordered films. Notably, the high E T N solvent shows a pronounced preference for liquid-crystal (LC)-mediated assembly, providing a mechanistic explanation for the enhanced structural order. Therefore, these results demonstrate that solvent polarity, as evaluated by E T N , serves as an important parameter that plays a significant role in the DPP-DTT assembly pathway and resultant solid-state morphology. This work provides a strategy for integrating data science with experiments to identify critical parameters associated with complex polymer systems and helps guide rational process design for high-performance organic electronics.
A family of algorithms was outlined which would appear to be particularly well-suited for implementation in a parallel environment. This is due to the fact that for any partition of the mesh each subdomain in the partition can be processed over a time step simultaneously and independently of the rest. The method eliminates the need for assembling and factorizing large global arrays while retaining the unconditional stability properties of the algorithms used at the local level. To critically appraise the proposed methodology, two limiting cases were considered: element-by-element mesh partitions, and coarse mesh partitions. It was concluded that while the proposed methodology can be useful in sequential machines, it would appear to be promising as it bears on computation. It should also be emphasized that extensions of the method to nonlinear problems are possible.
Components that handle millimeter and submillimeter wavelengths fabricated conveniently. Micromachining rectangular waveguide involves standard steps of masking, etching, and deposition of metal on silicon. Parts made assembled into half-waveguide and finally into full waveguide. Silicon-micromachining approach enables simultaneous fabrication of several versions of waveguide, with variations in critical parameter, on single wafer of silicon. Performances of versions compared and optimized more quickly and at lower cost than is possible if different versions are fabricated sequentially, by conventional machining techniques.
In the consumer electronics industry, precision processing technology is the basis for enhancing product functions and for minimizing components and end products. Throughout Japan, manufacturing technology is seen as critical to the production and assembly of advanced products. While its population has increased less than 30 percent over twenty-five years, Japan's gross national product has increase thirtyfold; this growth has resulted in large part from rapid replacement of manual operations with innovative, high-speed, large-scale, continuously running, complex machines that process a growing number of miniaturized components. The JTEC panel found that introduction of next-generation electronics products in Japan goes hand-in-hand with introduction of new and improved production equipment. In the panel's judgment, Japan's advanced process technologies and equipment development and its highly automated factories are crucial elements of its domination of the consumer electronics marketplace - and Japan's expertise in manufacturing consumer electronics products gives it potentially unapproachable process expertise in all electronics markets.
In this work, we demonstrate how machine learning (ML) techniques can be used as an auxiliary tool for nuclear data validation analysis. The output of the ML analysis can inform evaluators and validators on the quality, or lack thereof, of specific nuclear data and benchmark experiments with respect to simulating these experimental benchmark values. To this end, measured and simulated effective neutron multiplication factors, k eff , of 875 selected ICSBEP critical assemblies and the latter's sensitivities with respect to nuclear data as well as benchmarks' features (e.g., material nuclide, core geometry) are used as input for a random forest (RF) regression model. The RF is used to encode the complex inter-dependencies between thousands of nuclear data features (i.e., sensitivity profiles and aspects describing the measurements) and 875 simulated and experimental k eff values in order to understand what nuclear data features are most informative for predicting bias. The complexity of relationships and high-dimensional space is difficult-to-impossible to search fully using simply expert judgment. As a first proof-of-concept—step, it is demonstrated that this technique is able to correctly trace large discrepancies between simulated and experimental k eff back to fabricated shortcomings in nuclear data that were perturbed to simulated k eff values. In a second, real-case scenario, step, the RF algorithm is used to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data. One case is showcased where the chosen ML algorithms highlighted nuclear data (the 19 F(n,inl) cross section from 0.4–0.9 MeV) that are shown to be problematic by comparing them to associated differential experimental data and nuclear data from other libraries. In addition to that, it is shown that the RF results point towards poor benchmark experiments and associated underestimated uncertainties (e.g., the PU-SOL-THERM-028 series). However, using the RF algorithm for validating nuclear data with respect to k eff is currently limited to pinpointing groups of questionable nuclear data due to the inherent correlations between features introduced by the nuclear data themselves and how k eff is simulated. Due to this, we recommend that the ML methods presented be used to augment—rather than replace—the expert knowledge of evaluators and validators.
Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.
Nuclear criticality experiments are conducted to investigate specific nuclear data important for safe handling and storage of fissile materials, reactor design and operation, and the validation of radiation transport codes. Incorrect or uncertain nuclear data can prohibitively impact operational safety limits, reactor licensing, and predictive simulation capability; therefore, integral measurements from criticality experiments are necessary and should be performed frequently. To maximize the impact of the integral measurements, it is important to consider experiment geometry, material selection, and component dimensions. When taking these considerations into account, the experiment design process becomes iterative and very time intensive. This work utilizes a genetic algorithm to efficiently explore potential nuclear criticality experiment designs for the Laboratory Directed Research & Development project PARADIGM (PARallel Approach of Differential and InteGral Measurements) at Los Alamos National Laboratory. In this paper, the building blocks of the genetic algorithm are discussed in detail, the genetic algorithm methodology is verified, and the genetic algorithm is used to produce three candidate experiment models for the final PARADIGM design. The three candidate models produced by the genetic algorithm consist of copper-reflected assemblies containing 14 repeating units of alumina, graphite, boron, and plutonium plates. Furthermore, in addition to the optimization results, final design considerations are also discussed for designs with a height and/or weight very close to or slightly above assembly machine operational limits.
NASA Glenn Research Center is developing a 1.4 MW high-efficiency electric machine for future electrified aircraft to reduce energy consumption, emissions, and noise. This wound-field, synchronous machine employs a self-cooled, superconducting rotor to achieve excellent specific power and efficiency. This paper presents the progress made over the past year toward a critical design of the superconducting rotor, including refined analysis and risk reduction testing on prototype superconducting coils. A 3D finite element model of the rotor is developed to simulate the combined loading imposed by the initial cool down of the rotor from room temperature to cryogenic temperature and the subsequent rotation of the rotor up to the design speed. The model is evaluated to better predict the stress state in the critical components and the reduction in physical gap between the stator and rotor due to the resulting radial deflections. This model includes an enhanced model of the superconducting coil that considers the coil as an assembly of four individual coil layers. An improved coil fabrication process is described in detail. Two sub-scale test coils are fabricated and then tested under repeated thermal cycling between room temperature and liquid nitrogen temperature (77 K). It is demonstrated that the proposed coil fabrication procedure can produce no-insulation, high temperature superconducting coils that can survive several thermal cycles without apparent degradation.
The partitioning of trace elements between metal and silicate melts serves as the foundation for understanding the differentiation of a planetary body into a metallic core and silicate mantle. Element activity influences metal-silicate partitioning behavior. Activity coefficients are directly dependent on composition and temperature, and can be indirectly dependent on oxygen fugacity and pressure. Distinguishing the effect of pressure from other variables on the activity coefficients and partitioning is important for understanding the chemical evolution of different planetary bodies during differentiation. In this study, we investigated the influence of pressure on the activity coefficients of Cu, Mo, Pd, Pt, As, Sb, and Bi in Fe-Si metallic liquids. All of these elements exhibit moderate to high activity coefficients in Fe-Si liquids at low pressure, which significantly controls their metal-silicate partitioning behavior. Identifying whether this strong dependence persists at higher pressures is critical to modeling and understanding the chemical consequences of core formation. New experiments at 10 GPa were used to derive activity coefficients for these metals which can be compared to activity coefficients determined at 1 GPa. Experiments were conducted at 10 GPa and 2373K using a 10/5 assembly in the 880-ton multi anvil press at NASA Johnson Space Center. The standard 10/5 COMPRES assembly was slightly modified to accommodate a sample capsule machined from single-crystal MgO that minimizes melt percolation out of the sample volume during the experiment. Experiment starting materials were comprised of 70 wt.% Knippa basalt and 30 wt.% metal. The metal mixture (~85 wt.% Fe) was created by adding the elements of interest (Cu, Mo, Pd, Pt, As, Sb, Bi) to Fe metal powder. Varied amounts of Si metal (0-10 wt.% Si) were added to the metal-silicate mixtures to generate a systematic series of starting materials. For each element, an epsilon interaction parameter in Fe-Si liquid was derived from the results of our 0-10 wt.% Si metal series. To investigate whether pressure influences the trace element activity coefficients in the 1-10 GPa pressure range, we compared our results at 10 GPa to those at 1-4 GPa [1-3]. Our results can also be directly compared to interaction parameters for Au, P, V, Mn, Ga, Zn, Cd, Sn, W, Pb, and Nb previously determined at 10 GPa and 2373K following the same methods [4]. Combined, this suite of interaction parameters will directly inform metal- silicate partitioning between 1 and 10 GPa, and assess whether these values can be extrapolated to modeling differentiation processes at pressures >10 GPa. [1] Righter et al. (2018) GCA 232, 101-123 [2] Righter et al. (2019) MaPS 54, 1379-1394 [3] Steenstra et al. (2020) Icarus, 335, 113391 [4] Righter et al. (2020) Geochem. Persp. Let. 15, 44-49
The Artemis era of human lunar exploration is nearing take-off as NASA’s new super heavy-lift launch vehicle, the Space Launch System (SLS), begins stack-ing and integration operations in mid-2020 at Kennedy Space Center (KSC) in Florida. With a planned upgrade path to progressively more powerful vehicles and availability in crew and cargo configurations, SLS provides a unique and flexible launch solution to send crew, large-scale infrastructure and robotic probes to deep space. The SLS Block 1 vehicle, the initial variant to fly, is optimized for lunar missions with a proven propulsion system consisting of four liquid hydrogen (LH2)/liquid oxygen (LOX)-fed RS-25 engines and twin five-segment solid rocket boosters (SRBs). The Block 1 vehicle can also be outfitted with an industry-standard 5 m-class payload fairing (the “cargo” configuration) and will launch at least 27 metric tons (t) of mass to trans-lunar injection (TLI). SLS is the backbone of NASA’s Artemis program, which will return the agency’s human spaceflight program to the Moon for the first time since 1972. For the Artemis I mission, SLS will send an uncrewed Orion spacecraft to TLI, where it will enter a distant retrograde lunar orbit and fly 38,000 nmi past the Moon – farther than any spacecraft built for humans has ever traveled. The SLS Block 1 vehicle for Artemis I completed manufacturing in 2019. Several elements, including the upper stage, have been delivered to the Exploration Ground Systems (EGS) program at KSC and are being prepped for integration and stack-ing. The five-segment solid rocket boosters – the largest and most powerful ever built for flight – are also complete. The booster motor segments for the Artemis I flight are scheduled to ship from prime contractor Northrop Grumman’s Utah facilities and begin stacking and integration at KSC in June 2020. The SLS core stage is the largest rocket stage NASA has ever built in terms of volume and height, and includes the avionics and the tanks that feed cryogenic propellant to the four RS-25s (formerly Space Shuttle Main Engines [SSMEs]). They have been modified with an updated controller and nozzle insulation to protect them from the hotter launch environment. The SLS core stage is currently being test-ed at NASA’s Stennis Space Center (SSC) in a series of “green run” tests to verify it meets design and performance requirements. Following the green run test series, which is scheduled to culminate with a full-duration hot-fire of the four RS-25 engines, the core stage will ship to KSC and be stacked between the sol-id rocket boosters in the Vehicle Assembly Building (VAB). Integration of the vehicle will continue with the upper stage, known as the Interim Cryogenic Propulsion Stage (ICPS) and the Launch Vehicle Stage Adapter (LVSA) on the core stage. Another adapter, the Orion Stage Adapter (OSA), connects SLS to Orion and provides housing for 13 6U CubeSat payloads manifested on Artemis I. The CubeSats will be released in deep space after Orion separates from the vehicle, and the flight marks the first ride share opportunity for independent small-sats to deep space. The second major SLS variant to come online, Block 1B, replaces the single-engine ICPS with a four-engine LH2/LOX Exploration Upper Stage (EUS). This more powerful upper stage, along with other vehicle up-grades, will enable the Block 1B vehicle to launch 38-42 t to TLI, depending on crew or cargo configuration. The final evolution of the vehicle, Block 2, will onramp evolved solid rocket boosters to increase mass to TLI to 43-46 t, de-pending on crew or cargo configuration. The Block 1B/Block 2 vehicles can be outfitted with an 8.4 m-diameter payload fairing in 19.1 m or 27.4 m lengths, to provide unprecedented volume for payloads. With the initial Block 1 vehicle completely manufactured and the core stage in final testing before shipping to KSC, the SLS Program and its industry partners have made significant progress manufacturing subsequent vehicles. For the second Block 1 vehicle, the solid rocket motor segments are complete, as are the RS-25 engines with controllers. All five major components of the Artemis II core stage – the forward skirt, LOX and LH2 tanks, intertank and engine section – are manufactured and technicians are installing subsystems at NASA’s rocket factory, Michoud Assembly Facility. The RL-10 engine for the Artemis II ICPS is complete and panels have been machined for its LH2 tank. In addition, panels are machined for the vehicle’s two adapters, with welding scheduled to begin in summer 2020. Flight hard-ware is also in production for the third SLS vehicle, with several booster motor segments cast. The pace of development on the EUS has increased, with the goal to complete Critical Design Review (CDR) in December 2020. Several EUS test rings have been machined at Michoud. The EUS is designed to exe-cute a variety of missions – human spaceflight, deployment of deep-space infra-structure, or high-C3 missions to the outer solar system – with crew and cargo configurations available beginning in the mid-2020s. The near-term goal for the nation’s powerful new space exploration asset, however, is to launch the Arte-mis program, and send the first woman and the next man to the lunar surface. At the Astrodynamics Specialist Conference, the SLS program will update the community on the progress of the initial Block 1 vehicle in final green run test-ing, integration and stacking. In addition, this paper will provide an update to the community on the manufacturing status of subsequent Block 1 and Block 1B vehicles.