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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

New reflective symmetry design capability in the JPL-IDEAS Structure Optimization Program

The JPL-IDEAS antenna structure analysis and design optimization computer program was modified to process half structure models of symmetric structures subjected to arbitrary external static loads, synthesize the performance, and optimize the design of the full structure. Significant savings in computation time and cost (more than 50%) were achieved compared to the cost of full model computer runs. The addition of the new reflective symmetry analysis design capabilities to the IDEAS program allows processing of structure models whose size would otherwise prevent automated design optimization. The new program produced synthesized full model iterative design results identical to those of actual full model program executions at substantially reduced cost, time, and computer storage.

Strain, D.↗
NASA NTRS2006

Development and Characterization of Improved NiTiPd High-Temperature Shape-Memory Alloys by Solid-Solution Strengthening and Thermomechanical Processing

The need for compact, solid-state actuation systems for use in the aerospace, automotive, and other transportation industries is currently motivating research in high-temperature shape-memory alloys (HTSMA) with transformation temperatures greater than 100 C. One of the basic high-temperature alloys investigated to fill this need is Ni(19.5)Ti(50.5)Pd30. Initial testing has indicated that this alloy, while having acceptable work characteristics, suffers from significant permanent deformation (or ratcheting) during thermal cycling under load. In an effort to overcome this deficiency, various solid-solution alloying and thermomechanical processing schemes were investigated. Solid-solution strengthening was achieved by substituting 5at% gold or platinum for palladium in Ni(19.5)Ti(50.5)Pd30, the so-called baseline alloy, to strengthen the martensite and austenite phases against slip processes and improve thermomechanical behavior. Tensile properties, work behavior, and dimensional stability during repeated thermal cycling under load for the ternary and quaternary alloys were compared. The relative difference in yield strength between the martensite and austenite phases and the dimensional stability of the alloy were improved by the quaternary additions, while work output was only minimally impacted. The three alloys were also thermomechanically processed by cycling repeatedly through the transformation range under a constant stress. This so-called training process dramatically improved the dimensional stability in these samples and also recovered the slight decrease in work output caused by quaternary alloying. An added benefit of the solid-solution strengthening was maintenance of enhanced dimensional stability of the trained material to higher temperatures compared to the baseline alloy, providing a greater measure of over-temperature capability.

Bigelow, Glen↗
NASA NTRS1989

STRUTEX: A prototype knowledge-based system for initially configuring a structure to support point loads in two dimensions

Only recently have engineers begun making use of Artificial Intelligence (AI) tools in the area of conceptual design. To continue filling this void in the design process, a prototype knowledge-based system, called STRUTEX has been developed to initially configure a structure to support point loads in two dimensions. This prototype was developed for testing the application of AI tools to conceptual design as opposed to being a testbed for new methods for improving structural analysis and optimization. This system combines numerical and symbolic processing by the computer with interactive problem solving aided by the vision of the user. How the system is constructed to interact with the user is described. Of special interest is the information flow between the knowledge base and the data base under control of the algorithmic main program. Examples of computed and refined structures are presented during the explanation of the system.

Robers, James L.↗
DOE OSTI2024

An iterative bidirectional gradient boosting approach for CVR baseline estimation

Here this paper presents a novel Iterative Bidirectional Gradient Boosting Model (IBi-GBM) for estimating the baseline of Conservation Voltage Reduction (CVR) programs. In contrast to many existing methods, we treat CVR baseline estimation as a missing data retrieval problem. The approach involves dividing the load and its corresponding temperature profiles into three periods: pre-CVR, CVR, and post-CVR. To restore the missing load profile during the CVR period, the method employs a three-step process. First, a forward-pass GBM is executed using data from the pre-CVR period as inputs. Subsequently, a backward-pass GBM is applied using data from the post-CVR period. The two restored load profiles are reconciled, considering pre-calculated weights derived from forecasting accuracy, and only the leftmost and rightmost points are retained. The newly restored points are then included as inputs for the subsequent iteration. This iterative procedure continues until the original load data in the CVR period is fully restored. We develop IBi-GBM using actual smart meter and Supervisory Control and Data Acquisition (SCADA) data. Our results demonstrate that IBi-GBM exhibits robust performance across various data resolutions and in different seasons and outperforms existing methods by achieving a 1-2% reduction in normalized Root Mean Square Error (nRMSE).

42 ENGINEERING↗
NASA NTRS1990

Queueing models for token and slotted ring networks

Currently the end-to-end delay characteristics of very high speed local area networks are not well understood. The transmission speed of computer networks is increasing, and local area networks especially are finding increasing use in real time systems. Ring networks operation is generally well understood for both token rings and slotted rings. There is, however, a severe lack of queueing models for high layer operation. There are several factors which contribute to the processing delay of a packet, as opposed to the transmission delay, e.g., packet priority, its length, the user load, the processor load, the use of priority preemption, the use of preemption at packet reception, the number of processors, the number of protocol processing layers, the speed of each processor, and queue length limitations. Currently existing medium access queueing models are extended by adding modeling techniques which will handle exhaustive limited service both with and without priority traffic, and modeling capabilities are extended into the upper layers of the OSI model. Some of the model are parameterized solution methods, since it is shown that certain models do not exist as parameterized solutions, but rather as solution methods.

Peden, Jeffery H.↗
NASA NTRS2017

Application of time-variable process noise in terrestrial reference frames determined from VLBI data

In recent years, Kalman filtering has emerged as a suitable technique to determine terrestrial reference frames (TRFs), a prime example being JTRF2014. The time series approach allows variations of station coordinates that are neither reduced by observational corrections nor considered in the functional model to be taken into account. These variations are primarily due to non-tidal geophysical loading effects that are not reduced according to the current IERS Conventions (2010). It is standard practice that the process noise models applied in Kalman filter TRF solutions are derived from time series of loading displacements and account for station dependent differences. So far, it has been assumed that the parameters of these process noise models are constant over time. However, due to the presence of seasonal and irregular variations, this assumption does not truly reflect reality. In this study, we derive a station coordinate process noise model allowing for such temporal variations. This process noise model and one that is a parameterized version of the former are applied in the computation of TRF solutions based on very long baseline interferometry data. In comparison with a solution based on a constant process noise model, we find that the station coordinates are affected at the millimeter level.

Schuh, Harald↗
DOE OSTI2024

Optimizing high energy density sulfur cathodes: A multivariate approach to electrode formulation and processing

Lithium-sulfur (Li-S) batteries involve complex solid-liquid-solid phase transformations during both discharging and charging processes, where cathode materials, formulation, and structure play a crucial role. Here, a design of experiments (DoE) methodology and an empirical model are developed to systematically explore the interactions and trade-offs among cathode factors and process variables, and to obtain generalizable effects estimates for the multivariate system. Compared to the conventional one-factor-at-a-time (OFAT) approach, this work demonstrates advantages in both efficiency and accuracy by allowing the data to guide future research and decisions. Further, an optimized cathode formulation and processing parameters are predicted and validated experimentally, achieving over 1000 mAh g -1 in discharge capacity and improved cycling under practical lean electrolyte (4 µL mg -1 S) and high S-loading cathodes (>4 mg cm -2 ) conditions. The optimized cathode was scaled up and assembled into Li-S pouch cells, achieving 316 Wh kg -1 in cell-level energy, proving that the comprehensive and rigorous framework for optimizing complex systems with DoE leads to improved performance in a practical pouch cell system.

25 ENERGY STORAGE↗
DOE OSTI2026

Coupled heat and mass transfer in internally cooled silica gel-coated aluminum foam heat exchangers for dehumidification

Solid desiccant dehumidifiers offer significant potential to improve humidity control in buildings due to their ability to independently regulate latent (humidity) and sensible (temperature) loads. However, adsorption of water vapor by desiccants is an exothermic process; the resulting heat of adsorption increases both the desiccant bed temperature and the temperature of the air flowing over the bed, thereby limiting the performance of desiccant dehumidifiers. To address this problem, desiccant-coated heat exchanger prototypes were developed by coating silica gel onto aluminum foam substrates and integrated with chilled water circulation for effective removal of the heat of adsorption. Prototype heat exchangers with two different pore densities (10 and 20 pores per inch (PPI)) and two different desiccant loadings (∼10.36% and ∼17.80%) were fabricated. Experiments were conducted by varying the inlet air face velocity, chilled water inlet temperature, and chilled water flow rate to investigate the coupled heat and mass transfer characteristics of the prototype heat exchangers over a wide range of operating conditions. The underlying transport mechanisms were analyzed using transient desiccant bed temperature, outlet air temperature, and outlet humidity profiles. The results demonstrate that internal cooling significantly enhances dehumidification performance by maintaining more favorable thermodynamic conditions for water vapor adsorption. Under identical operating conditions, a 20 PPI heat exchanger coated with 25.05 g of silica gel achieved 98.98% higher cumulative water uptake and 97.75% higher moisture removal capacity than the corresponding non-cooled case after 5 min of adsorption. This substantial enhancement was further supported by a validated one-dimensional coupled heat and mass transfer model developed in the present study. To date, this is one of the first experimental studies to systematically investigate the combined effects of internal cooling, foam pore density, and desiccant loading on the performance of silica-gel-coated metal foam heat exchangers under HVAC-relevant operating conditions.

42 ENGINEERING↗
DOE OSTI2023

Fast and accurate reduced-order modeling of a MOOSE-based additive manufacturing model with operator learning

One predominant challenge in additive manufacturing (AM) is to achieve specific material properties by manipulating manufacturing process parameters during the runtime. Such manipulation tends to increase the computational load imposed on existing simulation tools employed in AM. The goal of the present work is to construct a fast and accurate reduced-order model (ROM) for an AM model developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, ultimately reducing the time/cost of AM control and optimization processes. Our adoption of the operator learning (OL) approach enabled us to learn a family of differential equations produced by altering process variables in the laser’s Gaussian point heat source. More specifically, we used the Fourier neural operator (FNO) and deep operator network (DeepONet) to develop ROMs for time-dependent responses. Furthermore, we benchmarked the performance of these OL methods against a conventional deep neural network (DNN)-based ROM. Ultimately, we found that OL methods offer comparable performance and, in terms of accuracy and generalizability, even outperform DNN at predicting scalar model responses. The DNN-based ROM afforded the fastest training time. Furthermore, all the ROMs were faster than the original MOOSE model yet still provided accurate predictions. FNO had a smaller mean prediction error than DeepONet, with a larger variance for time-dependent responses. Unlike DNN, both FNO and DeepONet were able to simulate time series data without the need for dimensionality reduction techniques. Finally, the present work can help facilitate the AM optimization process by enabling faster execution of simulation tools while still preserving evaluation accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Final Report DE-FE0031785 Mohsen Ahmadian, Ph.D. Demonstration of Proof of Concept of a Multiphysics Approach for Real-Time Remote Monitoring of Dynamic Changes in Pressure and Salinity in Hydraulically Fractured Networks

Hydraulic fracturing has evolved into a multistep process with varying flow rates, carrier fluids (e.g., gel or slickwater), proppant loadings, and proppant grain sizes. As a result, primary recovery from a hydraulically fractured tight-oil reservoir is often a tiny fraction of the original oil in place, ranging between 5 and 10%. As stated in the FOA1990, “part of this problem is due to the inability of current well completion processes to effectively stimulate the entire reservoir volume in contact with the wellbore. Innovative technologies are needed that can help improve the effectiveness of reservoir completion methods, maximize stimulated reservoir volumes, and optimize recovery over the entire producing life span of a well”. We first need to enhance the current fracture diagnostic techniques to improve a well-completion design. However, detecting and delineating a subsurface hydraulic fracture is extremely difficult because the induced fracture network is only fractionally propped, and these propped fractures are generally very thin. Microseismic and tiltmeter monitoring techniques can provide information on the fracture extent but provide little or no information on the movement and final distribution of proppant or production fluids. On the other hand, electromagnetic (EM) imaging has shown the capability to monitor proppant distribution throughout the fracture area, especially in the presence of Electrically Active Proppants (EAPs). A previous EM survey of hydraulic fracturing at the Devine Fracture Pilot Site (DFPS) and subsequent EM code developments demonstrated this survey as a robust technique to remotely interrogate the extent of the EAP-filled hydraulic fracture during its propagation. The objectives of the project were threefold: (1) to capitalize on the material properties of an EAP to demonstrate remote monitoring of relative changes in pressure, pressure, and flow that are commonly encountered during production from a hydraulically fractured reservoir; (2) to evaluate EM imaging tools, to achieve Objective 1 in near real-time; and (3) to develop a multi-physics joint inversion approach to precisely predict flow patterns and physiochemical changes within an EAP-filled fracture network. This research project was built upon our previous work at the Devine Test Site managed by the Bureau of Economic Geology (BEG) at The University of Texas at Austin (UT-Austin). It also leveraged a significant investment from the Advanced Energy Consortium (AEC) to address the DOE's interest in subsurface flow, containment, and characterization by multiple signals. This three-year and three-month project succeeded in demonstrating the feasibility of a real-time dynamic fluid flow mapping technique at Technology Readiness Level 5 (TRL5) by utilizing a commercially available surface-based Controlled-Source Electromagnetic (CSEM) method (Objectives 1, 2). We demonstrated that injections into an EAP-filled fracture could be successfully coupled with real-time electric field measurements on the surface, leading to remote monitoring of dynamic changes within the EAP-filled fracture. Furthermore, the observed electric field in our study is influenced by bottomhole pressure, flow rate, and salinity, which is demonstrated by comparing these parameters with the electrical field potentials. EM simulations solely based on assumptions of fracture conductivity changes during injection did not reproduce the whole measured electric field magnitudes. Preliminary estimates showed that including Streaming Potential (SP) in our geophysical model is likely needed to reduce the simulation misfit.

02 PETROLEUM↗
DOE OSTI2026

High-Capacity Enzymatic Degradation of Postconsumer Poly(ethylene terephthalate): Modeling and Experimental Investigations

Enzymatic degradation is considered as one of the key steps for biorecycling of polyethylene terephthalate (PET), a widely used plastic. Recently, we have developed an optimized leaf-branch compost cutinase PelB-LCC ICCG and demonstrated ∼80% degradation of 200 g L −1 recycled PET (RPET) within two days in bioreactors. In this study, continued research efforts were made to achieve a complete degradation of up to 300 g L −1 RPET in bioreactors. First, low-capacity reaction conditions with ≤5 g L −1 PET were examined to identify the key variables for PET degradation with PelB-LCC ICCG , including temperature, enzyme loading, and product inhibition. To better understand and further optimize the enzymatic degradation process, a mechanism-based model was established to describe the kinetics of PET degradation and formation of the main product terephthalic acid (TPA) and byproducts MHET and BHET in high-capacity bioreactors. Model simulation suggested that a minimal enzyme loading of ∼1.4 mg PelB-LCC ICCG g −1 RPET is required to achieve a nearly complete degradation of RPET within 48 h, which was used to guide more high-capacity experiments with 100−300 g L −1 RPET in fully controlled 1 L bioreactors. A higher temperature (≥65 °C) was found not only to enable rapid degradation in the beginning but also to induce a gradual increase in RPET’s crystallinity and significantly slow down the degradation after 48 h. A high loading of RPET solids and the accumulation of the produced insoluble TPA pose a big challenge on mixing and mass transfer in the stirred bioreactor, which can be addressed by increasing the stirring speed. The results pave the way toward biorecycling of PET at a large scale.

enzymatic degradation↗
NASA NTRS2018

Adaptive Load Control of Flexible Aircraft Wings Using Fiber Optic Sensing

Over the past century aircraft wing design has transformed from the morphing wing used on the Wright Flyer to rigid wings with little to no shape-tailoring abilities. Modern day wings are designed to fly at a single trim condition and optimized to have a maximum aerodynamic efficiency at only this condition. Shape morphing wings on the other hand have the potential to undergo geometric changes allowing them to adapt to their mission profiles. Several flight demonstrations have been conducted over the decades using morphing-wing technologies. Active wing-twist control was demonstrated on the X-53 Active Aeroelastic Wing (AAW) research project by utilizing multiple leading- and trailing-edge control surfaces. Passive morphing technology has been demonstrated on the Rockwell RPRV-870 Highly Maneuverable Aircraft Technology (HiMAT) aircraft. In the current study, the wings of a small unmanned aerial system (sUAS) were modified to have segmented control surfaces (SCS). The modifications include segmenting the original wing control surfaces (one flap and one aileron per wing) into 44 individual sections, each section having its own independent servo control motor. The wings were also instrumented with a network of over 1800 fiber-optic strain sensors (on four sensing fibers distributed over the top and bottom surfaces of the wing) monitoring the strain response of the wing to aerodynamic loading. The SCS positions were manipulated in real time to modify the spanwise lift distribution of the wings on the sUAS. The change in the structural response of the wings caused by load redistribution was quantified by measuring the bending strains on the upper and lower wing surfaces using an on-board compact fiber-optic strain sensing (cFOSS) system. A feedback controller was developed to control the SCS positions using strain-based shape estimations from the Displacement Transfer Function (DTF). Post-processing of the strain data allowed for the transverse displacement distributions and load distributions to be compared for the conventional and segmented control surface cases using displacements and loads algorithms developed by Richards and Ko at AFRC (refs. 5-10). While the current study focused on the shifting of the spanwise aerodynamic loads as quantified by displacements, future applications for loads or displacement control might include active gust alleviation and flutter suppression.

Pena, Francisco↗
DOE OSTI2020

Ammonia-salt solvent promotes cellulosic biomass deconstruction under ambient pretreatment conditions to enable rapid soluble sugar production at ultra-low enzyme loadings

In this paper, we report a novel ammonia : ammonium salt solvent based pretreatment process that can rapidly dissolve crystalline cellulose into solution and eventually produce highly amorphous cellulose under near-ambient conditions. Pre-activating the cellulose I allomorph to its ammonia–cellulose swollen complex (or cellulose III allomorph) at ambient temperatures facilitated rapid dissolution of the pre-activated cellulose in the ammonia-salt solvent ( i.e. , ammonium thiocyanate salt dissolved in liquid ammonia) at ambient pressures. For the first time in reported literature, we used time-resolved in situ neutron scattering methods to characterize the cellulose polymorphs structural modification and understand the mechanism of crystalline cellulose dissolution into a ‘molecular’ solution in real-time using ammonia-salt solvents. We also used molecular dynamics simulations to provide insight into solvent interactions that non-covalently disrupted the cellulose hydrogen-bonding network and understand how such solvents are able to rapidly and fully dissolve pre-activated cellulose III. Importantly, the regenerated amorphous cellulose recovered after pretreatment was shown to require nearly ~50-fold lesser cellulolytic enzyme usage compared to native crystalline cellulose I allomorph for achieving near-complete hydrolytic conversion into soluble sugars. Lastly, we provide proof-of-concept results to further showcase how such ammonia-salt solvents can pretreat and fractionate lignocellulosic biomass like corn stover under ambient processing conditions, while selectively co-extracting ~80–85% of total lignin, to produce a highly digestible polysaccharide-enriched feedstock for biorefinery applications. Unlike conventional ammonia-based pretreatment processes ( e.g. , Ammonia Fiber Expansion or Extractive Ammonia pretreatments), the proposed ammonia-salt process can operate at near-ambient conditions to greatly reduce the pressure/temperature severity necessary for conducting effective ammonia-based pretreatments on lignocellulose.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

RACE pulls for shared control

Maintaining and supporting an aircraft fleet, in a climate of reduced manpower and financial resources, dictates effective utilization of robotics and automation technologies. To help develop a winning robotics and automation program the Air Force Logistics Command created the Robotics and Automation Center of Excellence (RACE). RACE is a command wide focal point. Race is an organic source of expertise to assist the Air Logistic Center (ALC) product directorates in improving process productivity through the judicious insertion of robotics and automation technologies. RACE is a champion for pulling emerging technologies into the aircraft logistic centers. One of those technology pulls is shared control. Small batch sizes, feature uncertainty, and varying work load conspire to make classic industrial robotic solutions impractical. One can view ALC process problems in the context of space robotics without the time delay. The ALC's will benefit greatly from the implementation of a common architecture that supports a range of control actions from fully autonomous to teleoperated. Working with national laboratories and private industry, we hope to transition shared control technology to the depot floor. This paper provides an overview of the RACE internal initiatives and customer support, with particular emphasis on production processes that will benefit from shared control technology.

Leahy, M. B., Jr.↗
DOE OSTI2024

Distributed Multi-GPU Community Detection on Exascale Computing Platforms

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (distributed parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

graph algorithms, high performance comptuing↗
DOE OSTI2024

Distributed Multi-GPU Community Detection on Exascale Computing Platforms

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (Distributed Parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

Sattar, Naw Safrin↗