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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Resilient Hydrogels from the Nanoscale to the Macroscale

Biological systems illustrate how a material composed of fragile molecular components can collectively be highly resilient. While the average protein, cell, or even tissue may not last more than a few weeks, many animals and plants live for more than a century. Continual component regeneration and multiple systems to resist mechanical and chemical damage together make this longetivity possible. This project sought to develop biomimetic methods to enable a specific type of material, a hydrogel, to resist damage across multiple scales using distinct, modular damage protection mechanisms. Because hydrogels share many features with biological tissues, they are an ideal substrate for exploring biomimetic strategies for designing resilience and self-repair. We specifically focused in this study on DNA-crosslinked hydrogels, which contain DNA strands that can serve as material to link it together or to control its current state or properties. We investigated how new tools from dynamic DNA nanotechnology could make it possible to actively recover from damage by continually growing and forming a materials shape, or by identifying damage and directing an adaptive response to that damage involving chemical synthesis to reconstruct a structure. We developed embedded molecular sensors able to detect and strain of the gel before damage occurred and react to counteract damage. We also developed methods to continually create shapes and patterns using chemical processes that will allow those patterns to reform when they are damaged. The ability to design resilient materials capable of self-repair has important implications for materials engineering. Instead of designing a material to withstand the worst stresses it may encounter, we could instead design a material to survive under average conditions, but self-repair or reconfigure to resist impending damage. Resilient, self-repairing hydrogels will also have diverse applications such as sensors or actuators.

36 MATERIALS SCIENCE↗
DOE OSTI2022

On the measurement of hardness at high strain rates by nanoindentation impact testing

Recent advances in electronics have enabled nanomechanical measurements with very low noise, fast time constants and high data acquisition rates. Furthermore, these capabilities open the door for a wide range of ultra-fast nanomechanical testing. Given the inherent dynamic nature of high-speed testing, a thorough understanding of the testing system's dynamics and electronics is extremely important for accurate measurements. In this work, an analytical framework that includes the mechanical and electronic contributions of the instrument and the material constitutive response is presented to provide guidelines for performing high strain rate measurements of hardness by nanoindentation testing. Simple closed-form solutions that provide insights on the choice of test methodology, test parameters and instrument design are presented along with the strain rate range over which accurate measurements can be performed with the commercially available nanoindenters.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Thermal and electric multidomain dynamic model for integration of power grid distribution with behind-the-meter devices

As renewable energy sources like solar and wind power become more integrated into the grid, coordinated control of behind-the-meter devices is crucial for enhancing grid flexibility and reliability and for meeting cost targets, with standardized models being developed to support this transition. The increasing flexibility and uncertainty of integrated renewable energy grids, along with interactions between various subsystems, make traditional steady-state modeling insufficient to capture transient and dynamic behaviors. Current models (e.g., composite load and battery equivalent models) focus on thermodynamic or electrical characteristics but overlook critical electromechanical interactions. This limits the ability to share performance information for grid services and hampers fast dynamic simulations. In addition, motor stalling is usually triggered by a fault event and attributed to the characteristics of the mechanical torque of the motor, resulting in absorption of a large amount of reactive power during the stalling period. Further, this significant withdrawal of reactive power will deteriorate the dynamic voltage stability of power grids and cause delayed voltage recovery. Therefore, an in-depth modeling of the thermodynamics or mechanical torque is essential to study the impacts of the realistic torque characteristics of those behind-the-meter devices on power system voltage stability. This study developed a dynamic multidomain model for building HVAC systems, such as air-source heat pumps, to simulate their thermal and electrical responses to grid transients. The model can accurately predict power metrics with a mean absolute percentage error of 10%, by validating against with power system computer-aided design performance data. Case studies demonstrate the model capability of capturing the transient response to sudden voltage changes, rapid load fluctuations, and system shutdowns respectively. During a sudden voltage drop (30% for 0.1s), a fully loaded heat pump’s motor speed dropped, continued declining, and shut down after 3.6s, with severe power oscillations and a torque spike. A partially loaded unit experienced temporary oscillations but stabilized. Under higher building loads, compressor speed increased from 64% to 100%, with power and torque rising before stabilizing. In safety-triggered shutdowns, power decreased after minor fluctuations, and torque briefly spiked before dropping to zero.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Laser‐Powder Melt Pool Solidification Dynamics and Microstructural Engineering of Ti‐5553 Microlattices

The fine geometric and topological control afforded by additive manufacturing technologies has enabled the manufacture of architected materials across length scales, and enabling tunable mechanical performance as a function of local and global design. Progress has been made to tune the mechanical response of architected materials through geometry, but understanding how the geometry and processing conditions will inform the microstructure remains a challenge due to the rapid solidification in laser powder bed fusion. This study uses in situ X-ray imaging and electron backscatter diffraction microscopy to demonstrate that the melt pool size, microstructure morphology, and elastic strain distribution is influenced by a combination of lattice geometry and laser processing conditions. These results indicate that within larger melt pools the local thermal gradients are sufficient to enable a columnar-to-equiaxed transition across the melt pool. Furthermore, the solidification mechanisms producing these microstructures are examined across the first 5 ms of melting and solidification, described via in situ high-speed X-ray imaging and mirrored via multiphysics simulation.

additive manufacturing↗
DOE OSTI2025

Sequence Programmable Order–Disorder Transitions in Supramolecular Assembly of Peptide Nanofibers

Protein–protein interactions determine the assembly of complexes that are responsible for numerous key biological processes. The assembly of many natural protein complexes is mediated by post-translational structural changes and environmental stimuli. In this study, we show that incorporation of adjacent lysine residues results in the pH-tunable stability of peptide secondary structure and assembly, allowing for the incorporation of complementary order-inducing motifs. The strategic placement of cysteine pairs in the same peptide sequence results in redox-dependent disulfide staple formation, inducing a transition from random coil to β-sheet conformation and subsequent supramolecular nanofiber assembly from otherwise disordered peptide monomers. Spectroscopic, imaging, molecular dynamics, and kinetic studies highlight the critical role of sequence motif location, oligomerization, and the competitive interplay between intra- and interpeptide disulfide bonding in determining assembly outcomes. We extend this approach to demonstrate phosphorylation-dependent assembly from the design of the same parent peptide sequence, suggesting a general approach to the design of diverse stimulus-responsive peptide sequences for supramolecular assembly. Furthermore, these findings also provide a framework for investigating sequence-dependent pathways in amyloid fiber formation with potential implications for neurodegenerative disease research.

Disulfides↗
DOE OSTI2023

Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties

Here we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generative models that use diffusion-based dynamics to gradually denoise images and generate realistic synthetic samples. By learning the reverse of a Markov diffusion process, we design an artificial intelligence to efficiently manipulate the topology of microstructures to generate a massive number of prototypes that exhibit constitutive responses sufficiently close to designated nonlinear constitutive behaviors. To identify the subset of microcstructures with sufficiently precise fine-tuned properties, a convolutional neural network surrogate is trained to replace high-fidelity finite element simulations to filter out prototypes outside the admissible range. Results of this study indicate that the denoising diffusion process is capable of creating microstructures of fine-tuned nonlinear material properties within the latent space of the training data. More importantly, this denoising diffusion algorithm can be easily extended to incorporate additional topological and geometric modifications by introducing high-dimensional structures embedded in the latent space. Numerical experiments are conducted on the open-source mechanical MNIST data set (Lejeune, 2020). Consequently, this algorithm is not only capable of performing inverse design of nonlinear effective media, but also learns the nonlinear structure–property map to quantitatively understand the multiscale interplay among the geometry, topology, and their effective macroscopic properties.

42 ENGINEERING↗
DOE OSTI2020

Optimizing Cornell’s future geothermal district heating performance through systems engineering and simulation

Cornell University’s intention to lower its carbon footprint has motivated this engineering evaluation of using low-temperature geothermal energy to supply heat to the campus district energy system. Optimal selection and operation of heat pumps can significantly improve system performance. To support this analysis, we model the quantitative relationships between heat pump configurations and source flow, supply and distribution temperatures, facility heating design options, output heat generation, and carbon abatement outcomes. A systems approach is used for analysis, troubleshooting, and improvement of the model. The result of this effort is a dynamic tool that appropriately responds to hourly thermal demand and communicates energy response for techno-economic analysis. Simulations indicate that a single well-pair in the local Basement Contact Zone reservoir can satisfy almost 68% of annual thermal demand at nearly 20 MWth average capacity while remaining financially competitive compared to conventional heating with levelized cost of heating (LCOH) as low as $4.55/MMBTU ($15.53/MWh th ). A direct-use scheme with heat pump augmentation provided more carbon abatement and 50 to 150% more annual heat supply than one without augmentation. Exploration of three and four well-pair scenarios prove the capability to achieve 50 MW th baseload heating capacity with competitive LCOH and significant carbon reductions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Development of real-time density feedback control on MAST-U in L-mode

In this paper we report on the development and demonstration of density feedback control for MAST-U. Sinusoidal perturbations are used to measure the frequency response from a deuterium gas valve (actuator) to line-integrated core electron density measured by the interferometer (sensor). In the frequency range relevant for control design, only two system-identification experiments were needed to regress a first-order dynamic model. This control-oriented model informs the offline design of a proportional integral controller with the established loop-shaping controller design method. After offline verification of the controller implementation, control is demonstrated by experimentally tracking a staircase reference for the line-integrated electron density. This paper demonstrates the efficiency of controller design using system-identification and loop-shaping, providing reliable density control for MAST-U.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

TRUST Contact Thermal Conductance (TRUST-CTC) Report: FY24

The objective of the Delivery Environments (DE) Testbeds to Reduce Uncertainties in Simulations and Tests (TRUST) project is to quantify and help increase confidence in specific areas of computational and experimental capabilities that are applicable to current and future delivery environments. More complete quantification of confidence in experimental and computational capabilities and the sufficient increase of confidence in those capabilities is critical to improving weapons engineering design, qualification, and assessment efforts that are critical to the current and future stockpile. Staff development will include cross-discipline collaboration to provide engineers with experience in both numerical simulations and experimental methods. This work uses and provides feedback on analysis tools and experimental results databases for efficient and responsive engineering which are currently under development. TRUST currently includes five testbeds and their associated engineering analysis baseline models (EABMs): 1. contact thermal conductivity, (CTC) 2. nonlinear dynamics, (ND) 3. sensors in environments for accelerometers, (SEA) 4. sensors in environments for fiber optic displacement gages, and (SEFOD) 5. sensors in environments for thermocouples (SETC). The TRUST project uses single-feature testbeds to quantify uncertainties in specific models and experiments and to identify capability development needs that can help to reduce these uncertainties. Each testbed is designed, configured, and tested in collaboration with groups with design and experimental capability: E-14 and MPA-CINT. The complementary simulations are conducted using W-13 analysis tools and stored in model repositories with plans for incremental progress toward EABM requirements. W-13 extends and exercises the testbed simulations in collaboration with experimentalists for uncertainty quantification of current and future materials, geometries, and environments. Additionally, TRUST is intended to provide engineers in W-13 and E-14 with experience in both numerical simulations and experimental methods through cross-discipline collaborations. Following the introduction to the TRUST project, the remainder of this report focuses on experimental and analytical efforts conducted in Fiscal Year (FY) 2024 relevant to the TRUST Contact Thermal Conductance (CTC) testbed.

42 ENGINEERING↗
DOE OSTI2021

Nonlinear Interface Reduction for Time-Domain Analysis of Hurty/Craig-Bampton Superelements with Frictional Contact

Virtual prototyping in engineering design relies today on modern numerical models of contacting structures with accurate resolution of interface mechanics, which strongly affect the system-level stiffness and energy dissipation due to frictional losses. High-fidelity modeling within the localized interfaces is required to resolve local quantities of interest that may drive design decisions. The high-resolution finite element meshes necessary to resolve inter-component stresses tend to be computationally expensive, particularly when the analyst is interested in response time histories. The Hurty/Craig-Bampton (HCB) transformation is a widely used method in structural dynamics for reducing the interior portion of a finite element model while having the ability to retain all nonlinear contact degrees of freedom (DOF) in physical coordinates. These models may still require many DOF to adequately resolve the kinematics of the interface, leading to inadequate reduction and computational savings. This study proposes a novel interface reduction method to overcome these challenges by means of system-level characteristic constraint (SCC) modes and properly orthogonal interface modal derivatives (POIMDs) for transient dynamic analyses. Both SCC modes and POIMDs are computed using the reduced HCB mass and stiffness matrices, which can be directly computed from many commercial finite element analysis software. Comparison of time history responses to an impulse-type load in a mechanical beam assembly indicate that the interface-reduced model correlates well with the HCB truth model. Localized features like slip and contact area are well-represented in the time domain when the beam assembly is loaded with a broadband excitation. The proposed method also yields reduced-order models with greater critical timestep lengths for explicit integration schemes.

42 ENGINEERING↗
DOE OSTI2021

A compact neutron spectrometer system

A portable neutron spectrometer, nSpec, has been developed for passive interrogation of fast neutrons ( 1 MeV) from neutron sources. The design of the system includes four EJ-301 liquid scintillation cells, a custom stand, and tungsten caps for gamma-ray shielding. Data acquisition settings were optimized to provide a dynamic range of up to > 15 MeV neutron energy. Additionally, the detector response matrix was characterized using a time-of-flight method based on gamma-ray tagged neutron emission from 252 Cf. The spectrum unfolding was characterized using measured and simulated spectra of AmBe and 252 Cf with the GRAVEL and MLEM methods. Monte Carlo methods were used to examine the uncertainty propagation and determine sufficient counts for a measurement.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Achieving tissue-level softness on stretchable electronics through a generalizable soft interlayer design

Soft and stretchable electronics have emerged as highly promising tools for biomedical diagnosis and biological studies, as they interface intimately with the human body and other biological systems. Most stretchable electronic materials and devices, however, still have Young’s moduli orders of magnitude higher than soft bio-tissues, which limit their conformability and long-term biocompatibility. Here, we present a design strategy of soft interlayer for allowing the use of existing stretchable materials of relatively high moduli to versatilely realize stretchable devices with ultralow tissue-level moduli. We have demonstrated stretchable transistor arrays and active-matrix circuits with moduli below 10 kPa—over two orders of magnitude lower than the current state of the art. Benefiting from the increased conformability to irregular and dynamic surfaces, the ultrasoft device created with the soft interlayer design realizes electrophysiological recording on an isolated heart with high adaptability, spatial stability, and minimal influence on ventricle pressure. In vivo biocompatibility tests also demonstrate the benefit of suppressing foreign-body responses for long-term implantation. With its general applicability to diverse materials and devices, this soft-interlayer design overcomes the material-level limitation for imparting tissue-level softness to a variety of bioelectronic devices.

42 ENGINEERING↗
DOE OSTI2020

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. Additionally, this methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A Forward Feedback Control Scheme for a Solar Thermochemical Moving Bed Counter-Current Flow Reactor

Abstract Pelletized thermochemical energy storage media has a potential for long-duration energy storage. Production of solid-state energy storage media can be done within a cavity chemical reactor that captures concentrated solar radiation from a solar thermal field. The temperature stability of a solar reactor is directly influenced by the solar flux intercepted. This paper presents a low-order physical model to simulate the dynamic response of temperature inside a tubular plug-flow reactor prototype. Solid granular particles are fed to the reactor from the top whereas a counter-current flowing gas enters the reactor from the bottom. An in-house code was developed to model transient heat transfer of the reactor wall, gas, and moving particles. The model was preliminarily validated with packed beds for different temperature ranges and two gas flowrates. Dynamic response of the reactor temperature is simulated for different input power and gas/particle flowrates. The results show that the system response can be controlled efficiently by utilizing input power (solar flux) as a control parameter. A conventional proportional integral (PI) controller is designed to control the temperature inside the reactor and to maintain it during the solar flux intermittency. The controller parameters are tuned using the Ziegler–Nichols method to ensure optimal system response. The results show that the feedback control model is successful in tracking different reference reactor temperatures within a reasonable settling time of 30 min and eliminated overshoot. This study can be extended to include a hybrid reactor with a multi-input, multi-output variable system.

Energy & Fuels↗
DOE OSTI2020

A novel active optimization approach for rapid and efficient design space exploration using ensemble machine learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using expensive function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface. The performance of the new active learning approach is compared with traditional global optimization methods, namely micro-genetic algorithm (pGA) and particle swarm optimization (PSO). It is demonstrated that the new optimizer is able to reach the global optimum much faster with a significantly fewer number of function evaluations. Subsequently, the new optimizer is also applied to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is again found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to particle swarm and genetic algorithm-based optimization techniques.

Machine learning, active learning, design optimiza↗
DOE OSTI2022

Prevalence and scalable control of localized networks

The ability to control network dynamics is essential for ensuring desirable functionality of many technological, biological, and social systems. Such systems often consist of a large number of network elements, and controlling large-scale networks remains challenging because the computation and communication requirements increase prohibitively fast with network size. Here, we introduce a notion of network locality that can be exploited to make the control of networks scalable, even when the dynamics are nonlinear. We show that network locality is captured by an information metric and is almost universally observed across real and model networks. In localized networks, the optimal control actions and system responses are both shown to be necessarily concentrated in small neighborhoods induced by the information metric. This allows us to develop localized algorithms for determining network controllability and optimizing the placement of driver nodes. This also allows us to develop a localized algorithm for designing local feedback controllers that approach the performance of the corresponding best global controllers, while incurring a computational cost orders-of-magnitude lower. Here, we validate the locality, performance, and efficiency of the algorithms in Kuramoto oscillator networks, as well as three large empirical networks: synchronization dynamics in the Eastern US power grid, epidemic spreading mediated by the global air-transportation network, and Alzheimer’s disease dynamics in a human brain network. Taken together, our results establish that large networks can be controlled with computation and communication costs comparable to those for small networks.

42 ENGINEERING↗
DOE OSTI2020

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

Here, we present a novel online system identification methodology for monitoring the performance of power systems. This methodology was demonstrated in a gas turbine recuperated power plant designed for a hybrid configuration. A 120-kW Garrett microturbine modified to test dynamic control strategies for hybrid power systems designed at the National Energy Technology Laboratory (NETL) was used to implement and validate this online system identification methodology. The main component of this methodology consists of an empirical transfer function model implemented in parallel to the turbine speed operation and the fuel control valve, which can monitor the process response of the gas turbine system while it is operating. During fully closed-loop operations or automated control, the output of the controller, fuel valve position, and the turbine speed measurements were fed for a given period of time to a recursive algorithm that determined the transfer function parameters during the nominal condition. After the new parameters were calculated, they were fed into the transfer function model for online prediction. The turbine speed measurement was compared against the transfer function prediction, and a control logic was implemented to capture when the system operated at nominal or abnormal conditions. To validate the ability to detect abnormal conditions during dynamic operations, drifting in the performance of the gas turbine system was evaluated. A leak in the turbomachinery working fluid was emulated by bleeding 10% of the airflow from the compressor discharge to the atmosphere, and electrical load steps were performed before and after the leak. This tool could detect the leak 7 s after it had occurred, which accounted for a fuel flow increase of approximately 15.8% to maintain the same load and constant turbine speed operations.

algorithms↗