Results for “scalability test” — Page 11

NASA NTRS2021

Adding Fresh Produce to the Space Diet

NASA’s Human Research Program has identified a Risk of inadequate nutrition and food stability as a risk of high concern for future long duration exploration missions such as a mission to Mars. One possible solution to help reduce this risk is the addition of in situ produced supplemental fresh produce to supplement and enhance the astronaut diet. A number of ground and ISS-based studies are being conducted which are helping to reduce this risk by filling the gaps in knowledge and technology associated with space crop production. These include studies with the Veggie vegetable production system on the International Space Station. Key focus areas include water and nutrient delivery, food safety and the crop microbiome, crop selection and testing, automation and robotics, radiation impacts, seed storage and handling, and scalability for different concepts and architectures. All of these areas have significant challenges that need to be addressed prior to successful integration of crop production into the crew diet. Data from these studies is feeding the development of new technologies and systems for space crop production. This research was funded by NASA Space Biology and NASA’s Human Research Program.

Gioia Massa↗

NASA NTRSDate not supplied

Adding Fresh Produce to the Space Diet

NASA’s Human Research Program has identified a Risk of inadequate nutrition and food stability as a risk of high concern for future long duration exploration missions such as a mission to Mars. One possible solution to help reduce this risk is the addition of in situ produced supplemental fresh produce to supplement and enhance the astronaut diet. A number of ground and ISS-based studies are being conducted which are helping to reduce this risk by filling the gaps in knowledge and technology associated with space crop production. These include studies with the Veggie vegetable production system on the International Space Station. Key focus areas include water and nutrient delivery, food safety and the crop microbiome, crop selection and testing, automation and robotics, radiation impacts, seed storage and handling, and scalability for different concepts and architectures. All of these areas have significant challenges that need to be addressed prior to successful integration of crop production into the crew diet. Data from these studies is feeding the development of new technologies and systems for space crop production. This research was funded by NASA Space Biology and NASA’s Human Research Program.

Space Crop Production↗

NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on current and forecast of traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. In this paper, a methodology using supervised learning is developed to build a predictive model for RCM decision-support from large volumes of historical data. Data from two full years (2018 and 2019) related to current and forecast weather, demand/capacity, etc. is collected, analyzed, and fused together. A variety of supervised learning algorithms are tested for predicting runway configuration and hyperparameter tuning is carried out to select the best performing model. The validation process involves two airports of low (Charlotte Douglas International Airport, CLT) and high (Denver International Airport, DEN) complexity of configuration decision-making. The results show significant promise for the two airports with test accuracy of 93% (CLT) and 73% (DEN). The methodology is scalable and generalizable to other airports across the U.S. National Airspace System.

air traffic management↗

NASA NTRSDate not supplied

Predicting Airport Runway Configuration for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on current and forecast of traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. In this paper, a methodology using supervised learning is developed to build a predictive model for RCM decision-support from large volumes of historical data. Data from two full years (2018 and 2019) related to current and forecast weather, demand/capacity, etc. is collected, analyzed, and fused together. A variety of supervised learning algorithms are tested for predicting runway configuration and hyperparameter tuning is carried out to select the best performing model. The validation process involves two airports of low (Charlotte Douglas International Airport, CLT) and high (Denver International Airport, DEN) complexity of configuration decision-making. The results show significant promise for the two airports with test accuracy of 93% (CLT) and 73% (DEN). The methodology is scalable and generalizable to other airports across the U.S. National Airspace System.

air traffic management↗

DOE OSTI2026

Benchmarking quantum trial wavefunctions for phaseless auxiliary-field quantum Monte Carlo

The phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) method is a stochastic imaginary-time projection technique for computing ground-state properties of strongly correlated quantum systems, with accuracy that depends critically on the choice of trial wavefunction. Here, we investigate ph-AFQMC with trial states prepared using parameterized quantum circuits. In this work, we present a comprehensive benchmarking study of quantum trial wavefunctions spanning unitary coupled-cluster, Hamiltonian-informed, Jastrow-inspired, and adaptively constructed ansatze. The benchmarking evaluates accuracy, expressibility, and scalability of these ansatze within the QC-AFQMC framework. We test these ansatze on linear hydrogen chains under bond stretching and find that several ansatz families produce chemically accurate ph-AFQMC energies across the dissociation curve. We have performed simulations using the CUDA-Q quantum development platform on the GPU partition of the Perlmutter supercomputer. When comparing ansatze at similar numbers of variational parameters, we find that different ansatz families yield comparable ph-AFQMC results despite exhibiting substantially different variational energies, optimization costs, and circuit depths. Our results indicate that the variational energy of an ansatz is not always a reliable indicator of its quality for ph-AFQMC and reveal instances of over-parameterization. In the strongly correlated regime, trial wavefunctions obtained from adaptive ansatze, exemplified here by ADAPT-VQE with the UCCSD operator pool, can outperform their fixed-ansatz counterparts (UCCSD) in terms of projected energies while using substantially more compact circuits, providing a flexible route to optimize quantum resources within the ph-AFQMC framework.

Rofougaran, Rod [LBNL, Berkeley; Columbia U.; PNL,↗

DOE OSTI2024

Next Generation Solid Oxide Fuel Cell Module Development

The overall objective of this project was to develop a transformative Solid Oxide Fuel Cell (SOFC) building block configuration comprised of multi-stack arrays that can be utilized in large-scale power plants. This transformative design signified the benefits of lower performance degradation coupled with improved reliability, low cost, smaller packaging for easier transport and installation, and improved maintenance and field serviceability characteristics. The project goal was to design and fabricate a scalable hot module for housing an array of SOFC stacks and to demonstrate the characteristics of the module gas distribution, insulation and instrumentation, and DC power take-off. The approach was to validate the design of a stack prototype scalable to megawatt (MW) class systems using FuelCell Energy’s Compact SOFC Architecture (CSA) stacks. The scope of work was intended to design, build, and test a compact and low-cost multi-stack sub-module with flexibility to house CSA stacks and scalable to 350 kW which could ultimately be deployed in construction of MW-class systems.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRSDate not supplied

Development Status of Powered Descent for High-Mass Mars Entry, Descent, and Landing Systems

Retropropulsion, initiated at supersonic conditions, provides both deceleration and control authority as an enabling capability for the delivery of human-scale payloads to the surface of Mars. There are no other presently viable approaches to the descent phase of flight for vehicles of such scales. NASA continues to dedicate investment in the maturation of powered descent in atmospheric environments, for the express application to large-scale entry, descent, and landing systems. Efforts focus on parametric, subscale, inert gas ground testing, rigorous validation of computational modeling approaches against these data, and the implementation of highly efficient, scalable simulation tools. This paper summarizes the current maturity of retropropulsion in a free-flight, atmospheric environment for vehicles with significant aerodynamic surface area, as well as the current status of efforts within NASA for ground testing, computational simulation, and flight testing.

Mars↗

NASA NTRSDate not supplied

Development Status of Powered Descent for High-Mass Mars Entry, Descent, and Landing Systems

Retropropulsion, initiated at supersonic conditions, provides both deceleration and control authority as an enabling capability for the delivery of human-scale payloads to the surface of Mars. There are no other presently viable approaches to the descent phase of flight for vehicles of such scales. NASA continues to dedicate investment in the maturation of powered descent in atmospheric environments, for the express application to large-scale entry, descent, and landing systems. Efforts focus on parametric, subscale, inert gas ground testing, rigorous validation of computational modeling approaches against these data, and the implementation of highly efficient, scalable simulation tools. This paper summarizes the current maturity of retropropulsion in a free-flight, atmospheric environment for vehicles with significant aerodynamic surface area, as well as the current status of efforts within NASA for ground testing, computational simulation, and flight testing.

Ashley Korzun↗

DOE OSTI2025

RE-INTEGRATE EMT Simulation Tool: Input Data Processing Layer for Bulk Power System

This paper introduces an advanced input data processing layer for EMT simulations of large-scale bulk power systems. The paper proposes two versions of the RE-INTEGRATE EMT simulation tool, RE-INTEGRATE Gen-0 and RE-INTEGRATE Gen-1, which are developed to enhance simulation generalizability, scalability, and accuracy. The framework leverages a generic class design for components to incorporate linear equations, which are generated by discretizing the Differential-Algebraic Equations (DAEs) that represent the dynamics of the components. In addition, the framework employs a parsing algorithm that parses a power system’s raw and dyr files to generate a connectivity graph which is then traversed to form the overall system’s dynamics. The proposed input data processing layer is used to simulate the IEEE 39-bus test system. The obtained results demonstrate the framework’s capability to achieve simulation scalability and accuracy. Further, the results indicate that EMT simulations performed using the proposed automations can effectively handle complex grid configurations.

Mishra, Rahul [ORNL] (ORCID:0000000328205932)↗

NASA NTRS2009

Freezable Radiator Coupon Testing and Full Scale Radiator Design

Freezable radiators offer an attractive solution to the issue of thermal control system scalability. As thermal environments change, a freezable radiator will effectively scale the total heat rejection it is capable of as a function of the thermal environment and flow rate through the radiator. Scalable thermal control systems are a critical technology for spacecraft that will endure missions with widely varying thermal requirements. These changing requirements are a result of the space craft s surroundings and because of different thermal loads during different mission phases. However, freezing and thawing (recovering) a radiator is a process that has historically proven very difficult to predict through modeling, resulting in highly inaccurate predictions of recovery time. This paper summarizes tests on three test articles that were performed to further empirically quantify the behavior of a simple freezable radiator, and the culmination of those tests into a full scale design. Each test article explored the bounds of freezing and recovery behavior, as well as providing thermo-physical data of the working fluid, a 50-50 mixture of DowFrost HD and water. These results were then used as a tool for developing correlated thermal model in Thermal Desktop which could be used for modeling the behavior of a full scale thermal control system for a lunar mission. The final design of a thermal control system for a lunar mission is also documented in this paper.

Lillibridge, Sean T.↗

DOE OSTI2024

Mechanochemical in Situ Encapsulation of Palladium in Covalent Organic Frameworks

Palladium-encapsulated covalent organic frameworks (Pd/COFs) have garnered enormous attention in heterogeneous catalysis. However, the dominant ex situ encapsulation synthesis is tedious (multistep), time-consuming (typically 4 days or more), and involves the use of noxious solvents. Here we develop a mechanochemical in situ encapsulation strategy that enables the one-step, time-efficient, and environmentally benign synthesis of Pd/COFs. By ball milling COF precursors along with palladium acetate (Pd(OAc) 2 ) in one pot under air at room temperature, Pd/COF hybrids were readily synthesized within an hour, exhibiting high crystallinity, uniform Pd dispersion, and superb scalability up to gram scale. Moreover, this versatile strategy can be extended to the synthesis of three Pd/COFs. Remarkably, the resulting Pd/DMTP-TPB showcases extraordinary activity (96-99% yield in 1 h at room temperature) and broad substrate scope (>10 functionalized biaryls) for the Suzuki-Miyaura coupling reaction of aryl bromides and arylboronic acids. Furthermore, the heterogeneity of Pd/DMTP-TPB is verified by recycling and leaching tests. Finally, the mechanochemical in situ encapsulation strategy disclosed herein paves a facile, rapid, scalable, and environmentally benign avenue to access metal/COF catalysts for efficient heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2023

Procedure Automation Rating Matrix

The National Aeronautics and Space Administration (NASA) Advanced Air Mobility (AAM) National Campaign (NC) is researching the means by which future Urban Air Mobility (UAM) aircraft will operate safely in an integrated and scalable airspace architecture. Consistent with this objective, the NASA NC Airspace Procedures team designed a matrix to evaluate UAM instrument flight procedure design, flyability and interoperability of candidate departure, enroute, and approach architectures in live flight or simulation. The Procedure Automation Rating Matrix (PARM) is a multi-dimensional rating scale designed to provide direct feedback from test pilots and operators to airspace procedure designers developing airspace constructs for the integration and scalability of AAM operations in the National Airspace System (NAS). The PARM is assessed using a hierarchical decision tree that guides the operator through a ten-point alpha-numeric rating scale initiated either with or without the use of automation.

National Campaign↗

DOE OSTI2022

Space-Time Block Preconditioning for Incompressible Flow

Parallel-in-time methods have become increasingly popular in the simulation of time-dependent numerical PDEs, allowing for the efficient use of additional message passing interface processes when spatial parallelism saturates. Most methods treat the solution and parallelism in space and time separately. In contrast, all-at-once methods solve the full space-time system directly, largely treating time as simply another spatial dimension. All-at-once methods offer a number of benefits over separate treatment of space and time, most notably significantly increased parallelism and faster time to solution (when applicable). However, the development of fast, scalable all-at-once methods has largely been limited to time-dependent (advection-)diffusion problems. This paper introduces the concept of space-time block preconditioning for the all-at-once solution of incompressible flow. By extending well-known concepts of spatial block preconditioning to the space-time setting, we develop a block preconditioner whose application requires the solution of a space-time (advection-)diffusion equation in the velocity block, coupled with a pressure Schur complement approximation consisting of independent spatial solves at each time-step, and a space-time matrix-vector multiplication. The new method is tested on four classical models in incompressible flow. Finally, the results indicate perfect scalability in refinement of spatial and temporal mesh spacing, perfect scalability in nonlinear Picard iteration count when applied to a nonlinear Navier--Stokes problem, and minimal overhead in terms of number of preconditioner applications compared with sequential time-stepping.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Dynamic in-context learning with conversational models for data extraction and materials property prediction

The advent of natural language processing and large language models (LLMs) has revolutionized the extraction of data from unstructured scholarly papers. However, ensuring data trustworthiness remains a significant challenge. In this paper, we introduce PropertyExtractor, an open-source tool that leverages advanced conversational LLMs such as Google gemini-pro and OpenAI gpt-4, blends zero-shot with few-shot in-context learning, and employs engineered prompts for the dynamic refinement of structured information hierarchies—enabling autonomous, efficient, scalable, and accurate identification, extraction, and verification of material property data. Our tests on material data demonstrate precision and recall that exceed 95% with an error rate of ∼9%, highlighting the effectiveness and versatility of the toolkit. Finally, databases for 2D material thicknesses, a critical parameter for device integration, and energy bandgap values are developed using PropertyExtractor. In particular, for the thickness database, the rapid evolution of the field has outpaced both experimental measurements and computational methods, creating a significant data gap. Our work addresses this gap and showcases the potential of PropertyExtractor as a reliable and efficient tool for the autonomous generation of various material property databases, advancing the field.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗

DOE OSTI2022

Trust-Region Approximation of Extreme Trajectories in Power System Dynamics

In this work we present a novel technique, based on a trust-region optimization algorithm and second-order trajectory sensitivities, to compute the extreme trajectories of power system dynamic simulations given a bounded set that represents parametric uncertainty. Furthermore, we show how this method, while remaining computationally efficient compared with sampling-based techniques, overcomes the limitations of previous sensitivity-based techniques to approximate the bounds of the trajectories when the local approximation loses validity because of the nonlinearity. We present several numerical experiments that showcase the accuracy and scalability of the technique, including a demonstration on the IEEE New England test system.

42 ENGINEERING↗

DOE OSTI2020

Hybrid Li-S pouch cell with a reinforced sulfide glass solid-state electrolyte film separator

Here, in this paper, sulfide glass electrolyte powder with a nominal composition of (Li 2 S) 60 (SiS 2 ) 28 (P 2 S 5 ) 12 is hot pressed at a 330°C and 12 MPa into separator films using non-woven fiberglass reinforcement. The reinforced separator films are 93% dense, bendable, and have an ionic conductivity of 0.7 mS/cm at room temperature. When integrated into hybrid Li-S coin cells with liquid electrolyte, these reinforced glass separators blocked the parasitic polysulfide shuttle sufficiently to enable reversible cycling without LiNO 3 co-salt. Furthermore, the scalability of the reinforced glass separator technology is demonstrated by assembling and testing a hybrid single-layer pouch cell.

25 ENERGY STORAGE↗

DOE OSTI2024

Carbon-Storing Structural Material Based on Wastewater-Cultivated Chlorella Sorokiniana

Microalgae cultivation is a promising technology for carbon sequestration and wastewater remediation. It is economically and logistically critical to find viable applications for the large quantities of produced biomass. In current research, a structural material is developed by using nearly 100% Chlorella sorokiniana. The otherwise non-cohesive microalgae cells are first surface-activated and then compacted under a relatively high pressure. The resulting material is a dense solid, stronger than typical steel-reinforced concrete in flexural tests. The processing procedure is simple and fast, and the setup is scalable. This technique may be useful for next-generation green construction and provide a use-case for sustainable microalgae biomass.

Yi, Haozhe↗

DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗