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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Dielectric screening at TMD:hBN interfaces: Monolayer-to-bulk transition, local-field effect, and spatial dependence

The dielectric effects of a substrate have been shown to be important for modulating the electronic properties of an adsorbate, especially in van der Waals heterostructures. Here, using the first-principles dielectric embedding GW approach within the framework of many-body perturbation theory, we perform a case study on the dielectric screening effects of hexagonal boron nitride (hBN) on various transition-metal dichalcogenides (TMDs). We consider three systems, monolayer MoS 2 , bilayer MoS 2 , and a mixed WS 2 /MoS 2 bilayer adsorbed on hBN, and examine three aspects of the substrate dielectric screening: (i) the thickness dependence and the monolayer-to-bulk transition, for which we consider the effects of one-, two-, three-, and four-layer hBN; (ii) the local-field effect, for which we numerically assess a common approximation of neglecting the in-plane local-field components of the substrate polarizability; and (iii) the spatial dependence, for which we consider the mixed WS 2 /MoS 2 bilayer adsorbed on hBN with either side facing the substrate. Finally, our results provide quantitative insight into how the substrate screening effects can be leveraged for band structure engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Mitigating Algorithmic Bias in Cancer Site Classification Models

Purpose Integrating artificial intelligence in cancer diagnostics has improved tumor classification beyond rule-based systems. Despite these advancements, these models may still encode demographic biases. We conducted a large-scale, applied bias-probing study of a deep learning–based cancer site classifier to quantify race information encoded in document embeddings. We then evaluated how performance changes when race-correlated embedding dimensions are removed in a post-training sensitivity analysis. Methods The cancer site classifier was trained using 3.5 million electronic cancer pathology reports from six of the National Cancer Institute's SEER registries. We trained a hierarchical self-attention network to generate 400-dimensional document embeddings. These embeddings were used to train two downstream, gradient-boosted decision tree classifiers: one to classify the cancer sites and another to predict racial categories. We identified overlapping features by intersecting the top 50 feature-importance rankings from the site and race models and computed their cumulative feature importance in each model. As a post hoc sensitivity analysis, we progressively pruned these overlapping dimensions, retrained the site model, and compared overall macro-F1 and accuracy, race-stratified macro-F1, and group fairness metrics on the basis of demographic parity and equalized odds before and after pruning. Results The analysis revealed minimal feature overlap between the cancer site and race prediction models, and the cumulative importance scores indicated a negligible influence of racial information on clinical predictions. Post-training pruning of overlapping features did not compromise the models' diagnostic accuracy, with a 0.07% loss in accuracy. Conclusion Our findings demonstrate that HiSAN-generated embeddings from SEER data can be used effectively in cancer site classification without significant demographic bias influencing the outcomes. Post-training pruning therefore functions as a practical audit and sensitivity check.

Shivanna, Abhishek [ORNL] (ORCID:0009000665228593)↗
DOE OSTI2020

Targets and processes for fabricating same

In one embodiment, the present disclosure provides a target or mold having one or more support arms coupled to a substrate. The support arm can be used in handling or positioning a target. In another embodiment, the present disclosure provides target molds, targets produced using such molds, and a method for producing the targets and molds. In various implementations, the targets are formed in a number of disclosed shapes, including a funnel cone, a funnel cone having an extended neck, those having Gaussian-profile, a cup, a target having embedded metal slugs, metal dotted foils, wedges, metal stacks, a Winston collector having a hemispherical apex, and a Winston collector having an apex aperture. In yet another embodiment, the present disclosure provides a target mounting and alignment system.

Malekos, Steven↗
DOE OSTI2022

Hierarchical Model-Free Transactive Control of Building Loads to Support Grid Services

Residential buildings consume 4.4 quads of electricity annually, approximately 37% of the total electricity consumption in the United States. This represents a vast resource that can be used for demand management and other ancillary services. This project aims to develop a robust, scalable hierarchical transactional control mechanism incorporating elements of model-free control (MFC) and game theory to harness buildings to provide ancillary services to the grid. This approach is being taken to address the challenges of incorporating traditional transactional control schemes into existing buildings. The challenges include small individual building sizes requiring aggregation of many buildings, unpredictable energy usage that makes model identification difficult, and satisfying the sensitive occupant comfort constraints. In the proposed approach, by separating the control mechanism into two layers above and below the load aggregator, MFC can be used below the aggregator to modulate flexible building loads in response to pricing signals with guaranteed performance. This allows the burden of identifying an accurate model of the system to be shifted to the above-aggregator layer, where fluctuations in individual building usage have less impact on predicted building system behavior. Game theory concepts can then be used to determine pricing curves and control signals among regional aggregators. Managing this control in a game-theoretic approach will allow us to build in financial incentives that increase customer engagement. Additionally, the usage of MFC necessitates less burdensome computational and communication requirements, thus, it is easily deployable on small, embedded devices. In a broader sense, developing a strategy capable of effectively incorporating residential and small commercial buildings will allow greater throughput of existing and emerging grid services in addition to future transactive energy grid management methods. Using MFC within a hierarchical control architecture will allow the shifting of existing forecasting challenges to an aggregate level, where dynamics are slower and more predictable. This will enable a smooth interface between the grid services requests of utilities and the reliable control required by participating buildings. MFC, which supports distributed control architecture, permits a scalable solution that can be deployed to neighborhood-size systems as well as individual buildings. This project focuses on three objectives: (1) developing the mathematical framework, algorithm toolkit, and software toolset of the two-layer transactive control testbed; (2) developing a scalable solution for application over many residential and small-size commercial buildings with sparse distributed communication; and (3) field testing and implementation on hardware of the control strategies developed in the previous two objectives. The research and development activities are focused and designed to be impactful within the relevant 2025 targets timeframe. An open-source control framework for exploiting variability and dispatchability of building loads will be delivered as the outcome of the project. This capability enables greater participation of loads in electricity markets and ancillary services that are both useful for the utility and financially beneficial for building owners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

An encoder–decoder LSTM-based EMPC framework applied to a building HVAC system

Numerous studies have demonstrated the benefit of economic model predictive control (EMPC) applied to building heating, ventilation, and air conditioning (HVAC) systems. However, the construction and training of predictive models for building HVAC systems are widely recognized as a key technological barrier preventing large-scale adoption of EMPC for buildings. In this work, an encoder–decoder long short-term memory-based EMPC framework is developed. The key advantage of the approach is that a model may be automatically generated from a list of inputs and outputs. From the definition of inputs and outputs, the constructed model may be trained and automatically embedded into the EMPC framework for real-time estimation and control. The overall end-to-end EMPC framework from model training to on-line estimation and control are described. To this end, the encoder–decoder model provides a natural framework for state estimation (encoder), which is required to provide an initial condition for the predictive model of EMPC (decoder). Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated closed-loop system consists of a building zone from a multi-zone building, which is served by an air handling unit-variable air volume HVAC system. For the HVAC example considered, the trained encoder–decoder model can predict the indoor air temperature and HVAC sensible cooling rate of a building zone over a two-day horizon with high accuracy. Overall, we find that considering a time-of-use electric rate structure, the EMPC, which manipulates the zone temperature setpoint, can reduce the HVAC power consumption cost relative to keeping the zone temperature setpoint at its maximum value (i.e., minimum energy approach).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Reassessing the Origins and Contemporary Relevance of ck Acceptability Parameters: Evolving Perspectives on Similarity

“Sensitivity and Uncertainty Analyses Applied to Criticality Safety Validation,” introduces sensitivity and uncertainty methods to address challenges in defining and extending areas of applicability for criticality safety validation. These areas are traditionally defined by the bounds or limits on key parameters, but establishing valid ranges and managing complex parameter variations remain challenging. NUREG/CR-6655 introduces ck and other integral indices, as well as concepts such as the completeness of benchmark coverage, to better quantify system similarities. The work proposed herein seeks to evaluate these foundational concepts to ensure that the bounds remain effective in guiding the assessment of similarity and applicability in modern applications. The concept of completeness, along with other parameters envisioned within the framework, serves as an example of the foundational ideas that have been established, though their effectiveness in practice may not be fully understood. Advancements in scripting tools, coupled with the speed and efficiency of modern computing and statistical models, now allow for faster and more thorough assessments than previously possible. These advancements also enable the identification of trends within the data, which could provide additional insight into system behavior and further broaden the scope of previously performed benchmarks. By leveraging these capabilities, we will revisit and expand the scope of these foundational methods to determine whether the necessary elements for robust similarity evaluation are already embedded, partially realized, or remain untapped.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Development of polarization-sensitive optical coherence tomography imaging platform and metrics to quantify electrostimulation-induced peripheral nerve injury in vivo in a small animal model

Neuromodulation devices are rapidly evolving for the treatment of neurological diseases and conditions. Injury from implantation or long-term use without obvious functional losses is often only detectable through terminal histology. New technologies are needed that assess the peripheral nervous system (PNS) under normal and diseased or injured conditions. We aim to demonstrate an imaging and stimulation platform that can elucidate the biological mechanisms and impacts of neurostimulation in the PNS and apply it to the sciatic nerve to extract imaging metrics indicating electrical overstimulation. A sciatic nerve injury model in a 15-rat cohort was observed using a newly developed imaging and stimulation platform that can detect electrical overstimulation effects with polarization-sensitive optical coherence tomography. The sciatic nerve was electrically stimulated using a custom-developed nerve holder with embedded electrodes for 1 h, followed by a 1-h recovery period, delivered at above-threshold Shannon model k-values in experimental groups: sham control (SC, n = 5, 0.0 mA / 0 Hz), stimulation level 1 (SL1, n = 5, 3.4 mA / 50 Hz, and k = 2.57), and stimulation level 2 (SL2, n = 5, 6.8 mA / 100 Hz, and k = 3.17). The stimulation and imaging system successfully captured study data across the cohort. When compared to a SC after a 1-week recovery, the fascicle closest to the stimulation lead showed an average change of +4 % / – 309 % (SL1/SL2) in phase retardation and –79 % / – 148 % in optical attenuation relative to SC. Analysis of immunohistochemistry (IHC) shows a +1 % / – 36 % difference in myelin pixel counts and –13 % / + 29 % difference in axon pixel counts, and an overall increase in cell nuclei pixel count of +20 % / + 35 % . These metrics were consistent with IHC and hematoxylin/eosin tissue section analysis. The poststimulation changes observed in our study are manifestations of nerve injury and repair, specifically degeneration and angiogenesis. Optical imaging metrics quantify these processes and may help evaluate the safety and efficacy of neuromodulation devices.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

High-Fidelity Large-Signal Order Reduction Approach for Composite Load Model

With the increasing penetration of electronic loads and distributed energy resources, conventional load models cannot capture their dynamics. Therefore, a new comprehensive composite load model is developed by Western Electricity Coordinating Council (WECC). However, this model is a complex high-order non-linear system with multi-time-scale property, which poses challenges on power system studies with heavy computational burden. In order to reduce the model complexity, the authors firstly develop a large-signal order reduction (LSOR) method using singular perturbation theory. In this method, the fast dynamics are integrated into the slow ones to preserve transient characteristics of the former. Then, accuracy assessment conditions are proposed and embedded into the LSOR to improve and guarantee the accuracy of reduced-order model. Finally, the reduced-order WECC composite load model is derived by using the proposed algorithm. Overall, simulation results show that the reduced-order large-signal model significantly alleviates the computational burden while maintaining similar dynamic responses as the original composite load model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Group-Additivity–Embedded Multiscale Modeling for Electric Field-Enhanced Nanocatalysis

Elucidating structure-performance relationships remains a central challenge in field-enhanced catalysis, where nanoparticles exhibit nonuniform surface sites with site-dependent responses to electric fields. Low-coordination sites (edges, corners, and tips) are particularly electric field-sensitive (EF), leading to nonuniform charge distribution, adsorption energies, and catalytic activity. Here, using ammonia decomposition on a ruthenium cluster as a model system, we develop a transferable multiscale framework integrating density functional theory, group additivity (GA), Brønsted-Evans-Polanyi scaling, and microkinetic modeling to predict EF-dependent activity across nonuniform cluster sites. Across sites and fields, the nitrogen adsorption energy (E N ) emerges as the governing descriptor, yielding robust volcano relationships whose optimum shifts systematically with field: negative fields strengthen N binding via electron accumulation, while positive fields weaken N binding via charge depletion, moving the optimal E N toward weaker binding. Microkinetic analysis shows that N≡N bond formation remains the key kinetic bottleneck over most conditions; positive fields lower the effective barrier and, critically, increase the fraction of near-optimal active sites, leading to a net enhancement in overall activity relative to zero-field and negative-field cases. By capturing EF- and site-dependent energetics with high accuracy and low computational cost, this GA-embedded multi-scale simulation workflow provides a physically interpretable route to predict and design field-enhanced nanocatalysis.

ammonia decomposition↗
DOE OSTI2022

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as microgrid (MG) and active distribution networks (ADN) have been recently playing an important role in power system operation. The design and implementation of appropriate protection systems for such networks must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. Artificial NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small Graphic Processing Unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE-13 Node Test Feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications.

42 ENGINEERING↗
DOE OSTI2022

A 100-Mrad (Si) JFET-based Sensing and Communications System for Extreme Nuclear Instrumentation Environments

Dry cask storage is one of two storage methods approved by the U.S. Nuclear Regulatory Commission for spent fuel after removal from reactor cores. Dry casks consist of a stainless steel canister enclosed in a concrete overpack to contain the hazardous radioactive spent fuel rods and provide radiation shielding. Monitoring spent fuel storage casks is desired to ensure the safe containment of the enclosed spent fuel, but is very difficult due to the related harsh temperature and radiation environment. The sensors and associated electronics to monitor temperature, pressure, and/or radiation need to survive high temperatures and radiation doses for extended time periods. For this reason, there is a severe need for radiation-hardened electrical systems that survive well beyond the existing capabilities of commercially available radiation-rated electronic components, which have primarily been developed for space applications. Junction-gate field-effect transistor (JFET) devices are inherently radiation hardened [exceeding 100 Mrad (Si)]. Additionally, when JFETs are used as building blocks for sensing and communication electronics (i.e., oscillators, amplifiers, filters, and mixers), inherently radiation-hardened circuits can be achieved. To this end, JFET-based radiation-hardened electronics interfacing with cask-embedded sensors capable of driving modulated sensor signals through a stainless steel barrier were designed and tested at a dose rate of approximately 500 krad/h (Si) to beyond a 200-Mrad (Si) total ionizing dose. After 200 Mrad (Si), the sensor and communication circuit signals were correctly decoded at the receiver despite oscillator drift. The results from this experiment demonstrate the potential for creating more complex radiation-hardened JFET-based electrical systems for nuclear environments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Effect of Many Modes on Self-Polarization and Photochemical Suppression in Cavities

The standard description of cavity-modified molecular reactions typically involves a single (resonant) mode, while in reality the quantum cavity supports a range of photon modes. In this work we demonstrate that as more photon modes are accounted for, physico-chemical phenomena can dramatically change, as illustrated by the cavity-induced suppression of the important and ubiquitous process of proton-coupled electron-transfer. Using a multi-trajectory Ehrenfest treatment for the photonmodes, we find that self-polarization effects become essential, and we introduce the concept of selfpolarization- modified Born-Oppenheimer surfaces as a new construct to analyze dynamics. As the number of cavity photon modes increases, the increasing deviation of these surfaces from the cavityfree Born-Oppenheimer surfaces, together with the interplay between photon emission and absorption inside the widening bands of these surfaces, leads to enhanced suppression. The present findings are general and will have implications for the description and control of cavity-driven physical processes of molecules, nanostructures and solids embedded in cavities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Nonequilibrium spintronic transport through Kondo impurities

In this work we analyze the nonequilibrium transport through a quantum impurity (quantum dot or molecule) attached to ferromagnetic leads by using a hybrid numerical renormalization group–time-dependent density matrix renormalization group thermofield quench approach. For this, we study the bias dependence of the differential conductance through the system, which shows a finite zero-bias peak, characteristic of the Kondo resonance and reminiscent of the equilibrium local density of states. In the nonequilibrium settings, the resonance in the differential conductance is also found to decrease with increasing the lead spin polarization. The latter induces an effective exchange field that lifts the spin degeneracy of the dot level. Therefore, as we demonstrate, the Kondo resonance can be restored by counteracting the exchange field with a finite external magnetic field applied to the system. Lastly, we investigate the influence of temperature on the nonequilibrium conductance, focusing on the split Kondo resonance. Our work thus provides an accurate quantitative description of the spin-resolved transport properties relevant for quantum dots and molecules embedded in magnetic tunnel junctions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

MULTI-LEADER: MULTI-source LEarning-Accelerated Design of high-Efficiency multi-stage compRessor (Final Technical Report)

The objective of MULTI-LEADER is to cut design costs by 80% while generating more energy-efficient designs of multi-stage compressors by developing and implementing novel machine learning (ML) techniques, which enable faster and fewer design iterations, improved solver performance, and concurrent multi-disciplinary design. Current industrial practices for the design of multi-stage compressors involve simulation-based design optimization with successive levels of model fidelity, iteratively evaluated between distinct disciplines, one stage at a time to tackle the high dimensional design variations. This project addresses these key design challenges: (1) concurrent optimization of multiple stages under many non-linear constraints; (2) multitude of evaluation of high-fidelity and expensive solvers and their gradients during optimization convergence in high-dimensional design; (3) multi-disciplinary design to maximize aerodynamic performance while guaranteeing structural integrity and additive manufacturability; (4) utilization of multiple fidelity of solvers with disparate parameterization and modeling assumptions. MULTI-LEADER achieved more than 5x speed up in detailed design of more energy-efficient compressors via these machine learning (ML) innovations: (i) rapid design surrogates by multi-source learning from diverse fidelities across multiple disciplines, (ii) physics-constrained data-augmented modeling for improved empiricism, (iii) generative manifold embedding for high dimensional concurrent design without gradient information; (iv) budget-constrained fidelity-adaptive sampling towards fewer design iterations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Formal Methods for Provably Secure Software and Firmware

This project addresses a gap observed in verifying the programming in embedded devices used in international arms control: namely verifying that embedded programming in an arms control device does exactly what it is supposed to do, no more and no less, every time without fail, and without disclosing unauthorized information accidentally or intentionally. In critical military, aerospace, and industrial safety systems this problem is sometimes addressed using formal methods (FM). This multi-year project seeks to identify formal methods toolsets useable in arms control regimes, with emphasis on applicability, ease of use, long term availability, and support.

formal methods, Arms Control Verification↗
DOE OSTI2022

The Third Dimension of Ferroelectric Domain Walls

Abstract Ferroelectric domain walls are quasi‐2D systems that show great promise for the development of nonvolatile memory, memristor technology, and electronic components with ultrasmall feature size. Electric fields, for example, can change the domain wall orientation relative to the spontaneous polarization and switch between resistive and conductive states, controlling the electrical current. Being embedded in a 3D material, however, the domain walls are not perfectly flat and can form networks, which leads to complex physical structures. In this work, the importance of the nanoscale structure for the emergent transport properties is demonstrated, studying electronic conduction in the 3D network of neutral and charged domain walls in ErMnO 3 . By combining tomographic microscopy techniques and finite element modeling, the contribution of domain walls within the bulk is clarified and the significance of curvature effects for the local conduction is shown down to the nanoscale. The findings provide insights into the propagation of electrical currents in domain wall networks, reveal additional degrees of freedom for their control, and provide quantitative guidelines for the design of domain‐wall‐based technology.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modeling Co2 Flow Through Faulted/Fractured Reservoirs Using Tedfm in Corner-Point Grids

Interest in underground CO2 storage has increased significantly over the last decade, driven by growing concern about global warming and rising levels of greenhouse gases in the atmosphere. Given that CO2 accounts for 80% of these greenhouse gases, carbon capture, utilization, and storage (CCUS) is considered one of the most direct approaches to achieving the net-zero carbon target. Although CO2 storage in deep saline aquifers and depleted gas reservoirs has been studied extensively, most studies use commercial simulators that model faults/fractures by simply modifying the transmissibility in the direction perpendicular to the fault surfaces. This work shows that this simplistic approach ignores the accelerated flow in the directions parallel to the fault plane, leading to significantly higher leakage along the fault surface. To accurately model CO2 flow in faulted reservoirs, we present the first transient embedded discrete-fracture model for corner-point grids (tEDFM-CPG). By comparing the tEDFM-CPG results with high-resolution reference solutions, we show that this approach is accurate and efficient at predicting CO2 flow in faulted/fractured reservoirs. In conclusion, this work presents the use of mixed reality (MR) to efficiently observe CO2 gas migration in the interior of these corner-point grid systems.

02 PETROLEUM↗
DOE OSTI2020

Tin asymmetric membranes for high capacity sodium ion battery anodes

Sodium-ion batteries have attracted tremendous research attention as promising candidates for next generation energy storage systems due to low-cost and high abundancy. Furthermore, comparing to current Li-ion battery technology, limitations on the anode side seriously hinder the cell performance since sodium does not easily intercalate into graphite materials. Here, tin nanoparticles embedded in various asymmetric membrane structures were developed to leverage the high capacity of tin (847 mA h g -1 based on Na 15 Sn 4 ) and accommodate the significant volume expansion for long cycle life. It was demonstrated 762 mA h g -1 reversible capacity at 50 mA g -1 and stable cycling performance over 100 cycles with 92.1% capacity retention at 100 mA g -1 can be achieved for the tin asymmetric membrane electrode synthesized using polysulfone and tin tert-butoxide precursor.

25 ENERGY STORAGE↗