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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Differential Power Processing for DC Systems

This paper introduces an approach to dc power delivery that reduces power loss by minimizing redundant energy conversion. Existing power distribution techniques tend to increase the number of cascaded conversion stages, which limits overall efficiency. Differential power processing enables independent load regulation, while processing only a small portion of the total load power. Bulk power conversion occurs once. Load voltage domains are connected in series, and differential converters act as controllable current sources to regulate intermediate nodes. This enables independent, low supply voltages, which can reduce system energy consumption, especially in digital circuits and solid-state lighting. Since differential voltage regulators process a fraction of the load power, decreased size, cost, and conversion losses are attainable. Under balanced load conditions, secondary differential converters do not process any power. This paper analyzes several differential power delivery architectures that can be applied to homogenous and heterogeneous loads at various levels: chip, board, blade, etc. A variety of operating conditions for a test system with four series voltage domains are examined in simulation and verified with experimental hardware. Results in a reference application show a 7-8% decrease in input power and 6-7 percentage points increase in overall conversion efficiency as compared to a conventional cascaded approach.

Shenoy, PS↗
DOE OSTI2018

An Optical Differentiator Based on a Three-Layer Structure with a W-Shaped Refractive Index Profile

An optical differentiator based on a three-layer resonant structure with a W-shaped refractive index profile is proposed. The differentiation operation is performed in reflection and is associated with the resonant excitation of an eigenmode of the structure localized in the central layer. The presented results of numerical simulations demonstrate the possibility of spatial differentiation of the transverse profile of an incident optical beam, temporal differentiation of the envelope of an incident optical beam, and simultaneous spatial and temporal differentiation with a high quality. The proposed structure can find applications in designing analog optical computing and optical information processing systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Differentiable Quantum Programming with Unbounded Loops

The emergence of variational quantum applications has led to the development of automatic differentiation techniques in quantum computing. Existing work has formulated differentiable quantum programming with bounded loops, providing a framework for scalable gradient calculation by quantum means for training quantum variational applications. However, promising parameterized quantum applications, e.g., quantum walk and unitary implementation, cannot be trained in the existing framework due to the natural involvement of unbounded loops. To fill in the gap, we provide the first differentiable quantum programming framework with unbounded loops, including a newly designed differentiation rule, code transformation, and their correctness proof. Technically, we introduce a randomized estimator for derivatives to deal with the infinite sum in the differentiation of unbounded loops, whose applicability in classical and probabilistic programming is also discussed. We implement our framework with Python and Q# and demonstrate a reasonable sample efficiency. Through extensive case studies, we showcase an exciting application of our framework in automatically identifying close-to-optimal parameters for several parameterized quantum applications.

Computer Science↗
DOE OSTI2019

Analysis of Differential Financial Impacts of LWR Load-Following Operations. Revision 1

This document reports the application of HERON (Holistic Energy Resource Optimization Network), a newly-developed RAVEN (Risk Analysis Virtual ENvironment) plugin for grid and capacity optimization, to a case study conducted in collaboration with Arizona Public Service (APS). The study is a work in progress; this report presents a description of current models and corresponding results for a particular consideration. APS is anticipating several operational challenges. The first is rapid growth of Variable Renewable Energy (VRE) sources such as wind and solar power providing energy to the grid in the APS service region. This creates challenges for baseload operation of the Palo Verde Generating Station (PVGS), a nuclear power plant co-owned and operated by APS. The second challenge for APS is a change in the cooling water acquisition contract with the Sub Regional Operating Group (SROG). The existing contract is expiring soon, and a renewal is only available for a significantly higher price of water. These two challenges represent a difficulty in maintaining profitability for PVGS as local area demand is highly fluctuant and water prices may increase. Furthermore, excess production at PVGS is currently traded on the Palo Verde energy Hub (PVH), which occasionally offers very low or negative locational marginal prices (LMP) for electricity. This analysis considers the potential benefits of occasional curtailment of PVGS as a method to mitigate unprofitable production hours. Similar to load following, optional daily curtailment would allow APS to elect curtailment of PVGS as low as 70% of its nominal operating level between the hours of 08:00 and 16:00 provided local area demand is met and it is profitable to do so. This document reports a stochastic differential analysis of the Net Present Value (NPV) of PVGS as operated by NPS as a result of optional curtailment operation. To model this differential case, HERON has been designed from previous efforts in this study as a generic tool to accelerate the design and execution of RAVEN workflows for technoeconomic analysis of grid energy systems. HERON leverages both RAVEN and the CashFlow plugin for RAVEN to stochastically explore or optimize a response surface with energy grid system production capacities as inputs and mean NPV as a goal function. HERON performs these evaluations by stochastically sampling market, weather, and demand events using RAVEN’s ARMA reduced-order model capability to generate boundary conditions for dispatch optimization. The algorithm performs the dispatch optimization for each of the synthetic scenarios. For this case, HERON designs workflows to simulate behavior of the PVH, wind, solar, and APS local area demand, and use these synthetic scenarios to stochastically evaluate the differential benefit of optional curtailment for each scenario. While this study finds optional curtailment to be a potential source of revenue, the magnitude of the differential NPV is sufficiently small that it is unlikely a priority for increasing the profitability of PVGS. Further, the differential NPV shrinks throughout the simulated years, suggesting the long-term benefit of such operation given current assumptions is negligible.

14 SOLAR ENERGY↗
DOE OSTI2021

Double-differential Measurements of Mesonless Charged-current Muon Neutrino Interactions on Argon with Final-State Protons using the MicroBooNE Detector

This note reports the first double-differential measurements of charged-current $ν_µ$ scattering on argon leading to final states containing zero mesons and one or more protons. This event topology (hereafter abbreviated as CC0$πNp$) is the most common at the neutrino energies typically produced by the Fermilab Booster Neutrino Beam. A detailed understanding of neutrino-argon scattering in the CC0$πNp$ channel is therefore crucial for the success of the precision neutrino oscillation analyses planned for the Short-Baseline Neutrino (SBN) program. This remains true for the upcoming Deep Underground Neutrino Experiment (DUNE), but the higher mean neutrino energy used there will ensure that more inelastic reaction modes, such as single pion production, will also play a major role. The analysis described in this note builds on a previous MicroBooNE study of the CC0$πNp$ channel which obtained the first single-differential cross-section measurements on an argon target. Since that foundational work, significant improvements have been made to MicroBooNE’s simulation software, event reconstruction algorithms, and procedure for calculating systematic uncertainties. When combined with a larger dataset (corresponding to a beam exposure of 6.79 × 10 20 protons-on-target versus 1.60 × 10 20 in Ref. [3]), these enhancements allow the important CC0$πNp$ channel to be studied in more detail. This note begins with a description of the data and simulation samples used as input to the analysis. Section 3 then defines the CC0$πNp$ signal event topology, and Section 4 describes a set of selection criteria designed to identify these events in MicroBooNE data. Binning schemes are then defined in Section 5 for two double-differential measurements of event rates. The first of these considers the momentum and scattering cosine of the outgoing muon, while the second reports the same observables for the leading proton, i.e., the final-state proton with the largest momentum. After a discussion of systematic uncertainties in Section 6, the note concludes by comparing the predictions of MicroBooNE Monte Carlo (MC) simulations to the measured double-differential distributions. These results will form the basis for a future extraction of flux-averaged double-differential CC0$πNp$ cross sections that will be immediately comparable to the theoretical predictions of multiple neutrino event generators. The selection described herein may also be used to study various other observables in CC0$πNp$ events, including those which are sensitive to correlations between leptonic and hadronic kinematics in the final state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Differentially rotating white dwarfs. I. Regimes of internal rotation

Most viable models of Type Ia supernovae (SNe Ia) require the thermonuclear explosion of a carbon/oxygen white dwarf that has evolved in a binary system. Rotation could be an important aspect of any model for SNe Ia, whether single or double degenerate, with the white dwarf mass at, below, or above the Chandrasekhar limit. Differential rotation is specifically invoked in attempts to account for the apparent excess mass in the super-Chandrasekhar events. Some earlier work has suggested that only uniform rotation is consistent with the expected mechanisms of angular momentum transport in white dwarfs, while others have found pronounced differential rotation. We show that if the baroclinic instability is active in degenerate matter and the effects of magnetic fields are neglected, both nearly uniform rotation and strongly differential rotation are possible. We classify rotation regimes in terms of the Richardson number, Ri. At small values of Ri ⩽ 0.1, we find both the low-viscosity Zahn regime with a nonmonotonic angular velocity profile and a new differential rotation regime for which the viscosity is high and scales linearly with the shear, σ. Employment of Kelvin–Helmholtz viscosity alone yields differential rotation. Large values of Ri ≫ 1 produce a regime of nearly uniform rotation for which the baroclinic viscosity is of intermediate value and scales as σ{sup 3}. We discuss the gap in understanding of the behavior at intermediate values of Ri and how observations may constrain the rotation regimes attained by nature.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

A New View on the Maximum Mass of Differentially Rotating Neutron Stars

We study the main astrophysical properties of differentially rotating neutron stars described as stationary and axisymmetric configurations of a moderately stiff Γ=2 polytropic fluid. The high level of accuracy and of stability of our relativistic multidomain pseudo-spectral code enables us to explore the whole solution space for broad ranges of the degree of differential rotation, but also of the stellar density and oblateness. Staying within an astrophysically motivated range of rotation profiles, we investigate the characteristics of neutron stars with maximal mass for all types of families of differentially rotating relativistic objects identified in a previous article. We find that the maximum mass depends on both the degree of differential rotation and the type of solution. It turns out that the maximum allowed mass can be up to 4 times higher than what it is for nonrotating stars with the same equation of state. Such values are obtained for a modest degree of differential rotation but for one of the newly discovered types of solutions. Since such configurations of stars are not that extreme, this result may have important consequences for the gravitational wave signal expected from coalescing neutron star binaries or from some supernova events.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling

Recent advances in differentiable modeling, a genre of physics-informed machine learning that trains neural networks (NNs) together with process-based equations, have shown promise in enhancing hydrological models' accuracy, interpretability, and knowledge-discovery potential. Current differentiable models are efficient for NN-based parameter regionalization, but the simple explicit numerical schemes paired with sequential calculations (operator splitting) can incur numerical errors whose impacts on models' representation power and learned parameters are not clear. Implicit schemes, however, cannot rely on automatic differentiation to calculate gradients due to potential issues of gradient vanishing and memory demand. Here we propose a “discretize-then-optimize” adjoint method to enable differentiable implicit numerical schemes for the first time for large-scale hydrological modeling. The adjoint model demonstrates comprehensively improved performance, with Kling–Gupta efficiency coefficients, peak-flow and low-flow metrics, and evapotranspiration that moderately surpass the already-competitive explicit model. Therefore, the previous sequential-calculation approach had a detrimental impact on the model's ability to represent hydrological dynamics. Furthermore, with a structural update that describes capillary rise, the adjoint model can better describe baseflow in arid regions and also produce low flows that outperform even pure machine learning methods such as long short-term memory networks. The adjoint model rectified some parameter distortions but did not alter spatial parameter distributions, demonstrating the robustness of regionalized parameterization. Despite higher computational expenses and modest improvements, the adjoint model's success removes the barrier for complex implicit schemes to enrich differentiable modeling in hydrology.

58 GEOSCIENCES↗
DOE OSTI2020

Logarithmic forms and differential equations for Feynman integrals

We describe in detail how a d log representation of Feynman integrals leads to simple differential equations. We derive these differential equations directly in loop momentum or embedding space making use of a localization trick and generalized unitarity. For the examples we study, the alphabet of the differential equation is related to special points in kinematic space, described by certain cut equations which encode the geometry of the Feynman integral. At one loop, we reproduce the motivic formulae described by Goncharov [1] that reappeared in the context of Feynman integrals in [2–4]. The d log representation allows us to generalize the differential equations to higher loops and motivates the study of certain mixed-dimension integrals.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Prym Differentials on Variable Tori

We consider multiplicative functions and Prym differentials on variable tori. We prove counterparts of the theorem on the total sum of residues for Prym differentials of any integer order on tori. As a consequence, reciprocity laws are proved. We construct elementary Prym differentials of all kinds with any integer order which holomorphically depend on the moduli of tori and characters. We derive an analogue of the Appell decomposition formula for functions with characters. We also study vector bundles of Prym differentials of any integer order over the product of Teichmueller spaces for a torus and a group of characters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Forced expression of NR4A3 induced the differentiation of human neuroblastoma-derived NB1 cells

Nuclear receptor subfamily 4, group A, member 3 (NR4A3) is a member of the NR4A subgroup of orphan nuclear receptors, implicated in the regulation of diverse biological functions, including metabolism, angiogenesis, inflammation, cell proliferation, and apoptosis. Although many reports have suggested the involvement of NR4A3 in the development and/or progression of tumors, its role varies among tumor types. Previously, we reported that DNA hypomethylation at NR4A3 exon 3 is associated with lower survival rate of neuroblastoma (NB) patients. As hypomethylation of this region results in reduced expression of NR4A3, our observations suggested that NR4A3 functions as a tumor suppressor in NB. However, the exact mechanisms underlying its functions have not been clarified. In the present study, we analyzed public databases and showed that reduced NR4A3 expression was associated with shorter survival period of NB in two out of three datasets. An in vitro study revealed that forced expression of NR4A3 in human NB-derived cell line NB1 resulted in elongation of neurites along with overexpression of GAP43, one of the differentiation markers of NB. On the other hand, siRNA-mediated knockdown of NR4A3 suppressed the expression level of GAP43. Interestingly, the forced expression of NR4A3 induced only the GAP43 but not the other molecules involved in NB cell differentiation, such as MYCN, TRKA, and PHOX2B. These results indicated that NR4A3 directly activates the expression of GAP43 and induces differentiated phenotypes of NB cells, without affecting the upstream signals regulating GAP43 expression and NB differentiation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Differentiable hybrid neural network approach for enhancing reactor dynamics simulations

Reactor dynamics simulations provide essential insights into the time-dependent behavior of nuclear reactors under various operating conditions. However, high-fidelity simulations can be computationally intensive, requiring significant computational resources. Here, to address this challenge, this study employs a differentiable hybrid model that utilizes neural networks as a corrector to enhance the performance of a low-fidelity simulation, aligning its predictions with those of a high-fidelity simulation. Low-fidelity and high-fidelity simulations were obtained by adjusting the mesh size in the System Dynamics Analysis Tool. The differentiable hybrid model was trained in two approaches: time-step-wise and sequence-wise. It was then applied to simulate various transients in a molten salt reactor. Its performance was evaluated by comparing its responses to transients against those of the high-fidelity simulation. An additional approach was performed using a data-driven model to correct the low-fidelity simulation. In comparison, the differentiable hybrid model showed significant improvements in transient prediction, effectively addressing the limitations of the low-fidelity simulations. The results highlighted the robustness of the differentiable hybrid model in both training approaches. It delivered simulations that were at least 3.8 times faster than high-fidelity models. In the time-step-wise approach, it achieved at least a 39% improvement in accuracy. In the sequence-wise approach, it showed at least an 81% accuracy improvement over the full transient. This approach offers a promising path for improving computational efficiency without compromising accuracy in nuclear reactor simulations, making it suitable for real-time digital twin applications.

42 - ENGINEERING↗
DOE OSTI2015

MicroRNA-196b promotes cell proliferation and suppress cell differentiation in vitro

Highlights: • miRNA-196b increases proliferation and blocks differentiation of progenitor cell. • miRNA-196b inhibits apoptosis and increases viability of cells lines. • Forced expression of miR-196b blocks the differentiation of THP1 induced by PMA. - Abstract: MicroRNA-196b (miR-196b) is frequently amplified and aberrantly overexpressed in acute leukemias. To investigate the role of miR-196b in acute leukemias, it has been observed that forced expression of this miRNA increases proliferation and inhibits apoptosis in human cell lines. More importantly, we show that this miRNA can significantly increase the colony-forming capacity of mouse normal bone marrow progenitor cells alone, as well as partially blocking the cells from differentiation. Taken together, our studies suggest that miRNA-196b may play an essential role in the development of MLL-associated leukemias through inhibiting cell differentiation and apoptosis, while promoting cell proliferation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Inhibition of FGF signaling accelerates neural crest cell differentiation of human pluripotent stem cells

Neural crest (NC) is a transient population, arising during embryonic development and capable of differentiating into various somatic cells. The defects of neural crest development leads to neurocristopathy. Several signaling pathways were revealed their significance in NC cell specification. Fibroblast growth factor (FGF) is recognized as an important signaling during NC development, for instance Xenopus and avian; however, its contributions in human species are remained elusive. Here we used human pluripotent stem cells (hPSCs) to investigate the consequences of FGF inhibition during NC cell differentiation. The specific-FGF receptor inhibitor, SU5402, was used in this investigation. The inhibition of FGF did not found to affect the proliferation or death of hPSC-derived NC cells, but promoted hPSCs to commit NC cell fate. NC-specific genes, including PAX3, SLUG, and TWIST1, were highly upregulated, while hPSC genes, such as OCT4, and E-CAD, rapidly reduced upon FGF signaling blockage. Noteworthy, TFAP-2α, a marker of migratory NC cells, abundantly presented in SU5402-induced cells. This accelerated NC cell differentiation could be due to the activation of Notch signaling upon the blockage of ERK1/2 phosphorylation, since NICD was increased by SU5402. Altogether, this study proposed the contributions of FGF signaling in controlling human NC cell differentiation from hPSCs, the crosstalk between FGF and Notch, and might imply to the influences of FGF signaling in neurocristophatic diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Effect of essential amino acids on enteroids: Methionine deprivation suppresses proliferation and affects differentiation in enteroid stem cells

We investigated the effects of essential amino acids on intestinal stem cell proliferation and differentiation using murine small intestinal organoids (enteroids) from the jejunum. By selectively removing individual essential amino acids from culture medium, we found that 24 h of methionine (Met) deprivation markedly suppressed cell proliferation in enteroids. This effect was rescued when enteroids cultured in Met deprivation media for 12 h were transferred to complete medium, suggesting that Met plays an important role in enteroid cell proliferation. In addition, mRNA levels of the stem cell marker leucine-rich repeat-containing G protein-coupled receptor 5 (Lgr5) decreased in enteroids grown in Met deprivation conditions. Consistent with this observation, Met deprivation also attenuated Lgr5-EGFP fluorescence intensity in enteroids. In contrast, Met deprivation enhanced mRNA levels of the enteroendocrine cell marker chromogranin A (ChgA) and markers of K cells, enterochromaffin cells, goblet cells, and Paneth cells. Immunofluorescence experiments demonstrated that Met deprivation led to an increase in the number of ChgA-positive cells. These results suggest that Met deprivation suppresses stem cell proliferation, thereby promoting differentiation. In conclusion, Met is an important nutrient in the maintenance of intestinal stem cells and Met deprivation potentially affects cell differentiation. - Highlights: • Met influences the proliferation of enteroids. • Met plays a crucial role in the maintenance of stem cells. • Met deprivation potentially promotes differentiation into secretory cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

ERK1/2 signaling mediated naringin-induced osteogenic differentiation of immortalized human periodontal ligament stem cells

Periodontal ligament stem cells (PDLSCs) are promising tools for the investigations of cell differentiation and bone regeneration. However, the limited life span significantly restricts their usefulness. In this study, we established an immortalized PDLSC cell line by the introduction of Bmi1 (PDLSC-Bmi1). Several genes related to cell cycle, cell replication and stemness were found to be changed with the overexpression of Bmi1. Compared with primary PDLSCs, the immortalized cells had a slower aging rate, maintained in a proliferative state without crisis for more than 30 passages, and retained the molecular markers and biological functions of primary ones. Using the PDLSC-Bmi1, we confirmed the promotive effect of naringin on osteogenesis. Naringin promoted the osteogenic differentiation of PDLSC-Bmi1 manifested as the increased activity of alkaline phosphatase (ALP), expression of the runt-related transcription factor 2 (Runx2) and osteocalcin (OCN), and formation of mineralized nodules. In addition, the extracellular regulated protein kinases (ERK) 1/2 was found to be activated by naringin, and the ERK1/2 specific inhibitor significantly inhibited naringin-induced osteogenic differentiation in PDLSC-Bmi1. Our results indicated that the overexpression of Bmi1 extended the life span of PDLSCs without perturbing their biological functions, and that naringin promoted the osteogenesis of PDLSC-Bmi1 at least partially through the ERK1/2 signaling pathway. - Highlights: • The PDLSCs were immortalized by introduction of Bmi1. • The immortalized PDLSCs maintained the characteristics of primary cells. • Naringin promoted the osteogenic differentiation of immortalized PDLSCs. • ERK1/2 signaling mediated naringin-induced the osteogenesis of PDLSCs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

The ferroptosis inducer erastin promotes proliferation and differentiation in human peripheral blood mononuclear cells

Peripheral blood mononuclear cells (PBMCs) contain multipotent progenitor cell populations and possess the potential to differentiate into various types of immune cells under both physiological and pathological conditions. Ferroptosis is a type of oxidative stress-associated cell death that is mainly mediated by lipid peroxidation. However, the function of ferroptosis in cell differentiation remains unknown. Here, we showed that the ferroptosis inducer erastin did not cause cell death in human PBMCs. In contrast, erastin-induced lipid peroxidation promoted human PBMC proliferation and differentiation into B cells and natural killer cells through inhibition of bone morphogenetic protein family expression. These findings uncover a new immune modulation function of erastin in promoting PBMC proliferation and differentiation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

JAX-CanVeg: A Differentiable Land Surface Model

Land surface models consider the exchange of water, energy, and carbon along the soil-canopy-atmosphere continuum, which is challenging to model due to their complex interdependency and associated challenges in representing and parameterizing them. Differentiable modeling provides a new opportunity to capture these complex interactions by seamlessly hybridizing process-based models with deep neural networks (DNNs), benefiting both worlds, that is, the physical interpretation of process-based models and the learning power of DNNs. Here, we developed a differentiable land model, JAX-CanVeg. The new model builds on the legacy CanVeg by incorporating advanced functionalities through JAX in the graphic processing unit support, automatic differentiation, and integration with DNNs. We demonstrated JAX-CanVeg's hybrid modeling capability by applying the model at four flux tower sites with varying aridity. To this end, we developed a hybrid version of the Ball-Berry equation that emulates the water stress impact on stomatal closure to explore the capability of the hybrid model in (a) improving the simulations of latent heat fluxes (LE) and net ecosystem exchange (NEE), (b) improving the optimization trade-off when learning observations of both LE and NEE, and (c) benefiting a multi-layer canopy model setup. Our results show that the proposed hybrid model improved the simulations of LE and NEE at all sites, with an improved optimization trade-off over the process-based model. Additionally, the multi-layer canopy set benefited hybrid modeling at some sites. Anchored in differentiable modeling, our study provides a new avenue for modeling land-atmosphere interactions by leveraging the benefits of both data-driven learning and process-based modeling.

54 ENVIRONMENTAL SCIENCES↗