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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Behavior of the Mo, Tl, and U isotope systems during differentiation in the Kilauea Iki lava lake

Stable molybdenum (Mo), thallium (Tl), and uranium (U) isotope ratios were determined in a suite of samples from the 1959 Kilauea eruption and from Kilauea Iki lava lake with the aim of understanding the effects of igneous differentiation on these isotope systems. The samples range from olivine cumulate with MgO up to 27% to internal differentiates with MgO less than 3%, representing a tholeiitic differentiation series. Molybdenum, Tl, and U behave incompatibly during differentiation, and Mo and U isotope ratios do not systematically vary amongst the different samples. δ 98 Mo values range from -0.17 to -0.31‰ and δ 238 U values range from -0.20 to -0.38‰. Most individual analyses for both isotope systems overlap within measurement uncertainty (± ~0.7 and ~ 0.6, respectively). Mean δ 98 Mo and δ238U values are -0.22 ± 0.08‰ (2σ) and - 0.29 ± 0.09‰ (2σ), respectively, which overlap with Pacific mid ocean ridge basalt (MORB). In contrast, Tl isotopes show small but resolvable variations, with ε 205 Tl ranging from +1.20 to -1.38. The most negative ε 205 Tl values are confined to some of the lowest [Tl] samples, but the ε 205 Tl values do not otherwise vary smoothly with MgO or [Tl]. Possible mechanisms for thallium isotope fractionation are considered (e.g., degassing, water leaching, sulfide fractionation) but none are found to be satisfactory. Overall, the lack of resolvable variation in the Mo and U isotope systems and the small magnitude of heterogeneity in the Tl isotope system indicate that differentiation in tholeiitic systems is unlikely to be a major contributor to global variation in these isotope systems.

58 GEOSCIENCES↗
DOE OSTI2020

On differentiable local bounds preserving stabilization for Euler equations

This work presents the design of nonlinear stabilization techniques for the finite element discretization of Euler equations in both steady and transient form. Implicit time integration is used in the case of the transient form. A differentiable local bounds preserving method has been developed, which combines a Rusanov artificial diffusion operator and a differentiable shock detector. Nonlinear stabilization schemes are usually stiff and highly nonlinear. This issue is mitigated by the differentiability properties of the proposed method. Moreover, in order to further improve the nonlinear convergence, we also propose a continuation method for a subset of the stabilization parameters. The resulting method has been successfully applied to steady and transient problems with complex shock patterns. Numerical experiments show that it is able to provide sharp and well resolved shocks. Furthermore, the importance of the differentiability is assessed by comparing the new scheme with its non-differentiable counterpart. Numerical experiments suggest that, for up to moderate nonlinear tolerances, the method exhibits improved robustness and nonlinear convergence behavior for steady problems. Additionally, in the case of transient problem, we also observe a reduction in the computational cost.

42 ENGINEERING↗
DOE OSTI2021

N6-methyladenine demethylase ALKBH1 inhibits the differentiation of skeletal muscle

Highlights: • The DNA N6-mA modification increased during skeletal muscle development, accompanied by a strong decrease of ALKBH1. • ALKBH1 enhanced proliferation and inhibited differentiation of C2C12 cells. • ALKBH1 impaired multiple genes and signaling pathways to promote proliferation and inhibit differentiation of C2C12 cells. DNA N{sup 6}-methyladenine (N6-mA) was recently recognized as a new epigenetic modification in mammalian genome, and ALKBH1 was discovered as its demethylase. Knock-out mice studies revealed that ALKBH1 was indispensable for normal embryonic development. However, the function of ALKBH1 in myogenesis is largely unknown. In this study, we found that N6-mA showed a steady increase, going along with a strong decrease of ALKBH1 during skeletal muscle development. Our results also showed that ALKBH1 enhanced proliferation and inhibited differentiation of C2C12 cells. Genome-wide transcriptome analysis and reporter assays further revealed that ALKBH1 accomplished the differentiation inhibiting function by regulating a core set of genes and multiple signaling pathways, including increasing chemokine (C-X-C motif) ligand 14 (CXCL14) and activating ERK signaling. Taken together, our results demonstrated that ALKBH1 is critical for the myogenic differentiation of C2C12 cells, and suggested that N6-mA might be a new epigenetic mechanism for the regulation of myogenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Long noncoding RNA MEG3 expressed in human dental pulp regulates LPS-Induced inflammation and odontogenic differentiation in pulpitis

Highlights: • LncRNA MEG3, upregulated in inflamed dental pulp, is involved in the inflammation and regeneration in pulpitis. • The expression of lncRNA MEG3 was semiquantitatively detected in the pulp tissue by the innovative RNAscope® technique. • MEG3 downregulation can inhibit inflammatory cytokines secretion and also promote the odontogenic differentiation of hDPCs. • LncRNA MEG3 can be a potential therapeutic target in the inflammation and regeneration of dentin-pulp complex in pulpitis. Pulpitis refers to inflammation of the inner pulp by invading microbes, and tissue repair occurs due to odontogenic differentiation of human dental pulp cells (hDPCs) with multidifferentiation potential. Long noncoding RNAs (lncRNAs) can modulate numerous pathological and biological processes; however, the role of lncRNAs in the inflammation and regeneration of the dentin-pulp complex in pulpitis is unclear. Here, we performed high-throughput sequencing to identify differentially expressed lncRNAs between human normal and inflamed pulp and concluded that lncMEG3 (lncRNA maternally expressed gene 3, MEG3) was signicantly upregulated in both inflamed pulp and LPS-treated hDPCs. MEG3 expression in the pulp tissue was detected using the RNAscope® technique. RNA pulldown assays identified the MEG3-interacting proteins and the potential mechanisms. With MEG3 knockdown, we investigated the role of MEG3 in the secretion of inflammatory cytokines in LPS-treated hDPCs and odontogenic differentiation of hDPCs. MEG3 downregulation inhibited the secretion of TNF-α, IL-1β and IL-6 in LPS-treated hDPCs, and the p38/MAPK signaling pathway may be related to this effect. MEG3 knockdown promoted odontogenic differentiation of hDPCs by regulating the Wnt/β-catenin signaling pathway. Our study suggested that MEG3 has a negative effect on inflammation and regeneration of the dentin-pulp complex in pulpitis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Necrosulfonamide reverses pyroptosis-induced inhibition of proliferation and differentiation of osteoblasts through the NLRP3/caspase-1/GSDMD pathway

The acute inflammatory stimulation occurring after a bone fracture regulates the repair and healing of local bone injury; however, under certain conditions, pyroptosis may occur in osteoblasts, which affects osteoblast proliferation and differentiation, thereby affecting the growth, development and morphological changes of bone tissue. The aim of the present study was to examine the effect of the pyroptosis inhibitor necrosulfonamide (NSA) on the proliferation and differentiation of osteoblasts and elucidate the underlying mechanism. The results revealed that NSA reversed the effects of ATP/lipopolysaccharide (LPS) on cell viability and pyroptosis, and on the mRNA and protein expression of pyroptosis-related genes. It also suppressed the secretion of IL-6, TNF-α and IL-1β and reversed the effects of ATP/LPS on the activity of ALP and the mRNA expression of differentiation-related genes in osteoblasts. The fact that overexpression of caspase-1, gasdermin D (GSDMD) and NLRP3 abolished the effects of NSA on the viability and pyroptosis of osteoblasts, as well as the mRNA expression of differentiation-related genes and the activity of ALP in osteoblasts, indicated that NSA promoted the proliferation and differentiation of osteoblasts by inhibiting the NLRP3/caspase-1/GSDMD pyroptosis pathway. The present study provides proof supporting the potential application of NSA for improving the function of osteoblasts in fracture repair and indicates the value of the NLRP3/caspase-1/GSDMD pyroptosis pathway as a pharmaceutical target.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Quantum machine learning with differential privacy

Abstract Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. There exists the potential for a quantum advantage due to the intractability of quantum operations on a classical computer. Many datasets used in machine learning are crowd sourced or contain some private information, but to the best of our knowledge, no current QML models are equipped with privacy-preserving features. This raises concerns as it is paramount that models do not expose sensitive information. Thus, privacy-preserving algorithms need to be implemented with QML. One solution is to make the machine learning algorithm differentially private, meaning the effect of a single data point on the training dataset is minimized. Differentially private machine learning models have been investigated, but differential privacy has not been thoroughly studied in the context of QML. In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm. This marks the first proof-of-principle demonstration of privacy-preserving QML. The experiments demonstrate that differentially private QML can protect user-sensitive information without signficiantly diminishing model accuracy. Although the quantum model is simulated and tested on a classical computer, it demonstrates potential to be efficiently implemented on near-term quantum devices [noisy intermediate-scale quantum (NISQ)]. The approach’s success is illustrated via the classification of spatially classed two-dimensional datasets and a binary MNIST classification. This implementation of privacy-preserving QML will ensure confidentiality and accurate learning on NISQ technology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Nucleoporin downregulation modulates progenitor differentiation independent of nuclear pore numbers

Nucleoporins (NUPs) comprise nuclear pore complexes, gateways for nucleocytoplasmic transport. As primary human keratinocytes switch from the progenitor state towards differentiation, most NUPs are strongly downregulated, with NUP93 being the most downregulated NUP in this process. To determine if this NUP downregulation is accompanied by a reduction in nuclear pore numbers, we leveraged Stochastic Optical Reconstruction Microscopy. No significant changes in nuclear pore numbers were detected using three independent NUP antibodies; however, NUP reduction in other subcellular compartments such as the cytoplasm was identified. To investigate how NUP reduction influences keratinocyte differentiation, we knocked down NUP93 in keratinocytes in the progenitor-state culture condition. NUP93 knockdown diminished keratinocytes’ clonogenicity and epidermal regenerative capacity, without drastically affecting nuclear pore numbers or permeability. Using transcriptome profiling, we identified that NUP93 knockdown induces differentiation genes related to both mechanical and immune barrier functions, including the activation of known NF-κB target genes. Consistently, keratinocytes with NUP93 knockdown exhibited increased nuclear localization of the NF-κB p65/p50 transcription factors, and increased NF-κB reporter activity. Taken together, these findings highlight the gene regulatory roles contributed by differential NUP expression levels in keratinocyte differentiation, independent of nuclear pore numbers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Can we detect the stellar differential rotation of WASP-7 through the Rossiter–McLaughlin observations?

ABSTRACT The Rossiter–McLaughlin (RM) effect is the radial velocity signal generated when an object transits a rotating star. Stars rotate differentially and this affects the shape and amplitude of this signal, on a level that can no longer be ignored with precise spectrographs. Highly misaligned planets provide a unique opportunity to probe stellar differential rotation via the RM effect, as they cross several stellar latitudes. In this sense, WASP-7, and its hot Jupiter with a projected misalignment of ∼90°, is one of the most promising targets. The aim of this work is to understand if the stellar differential rotation is measurable through the RM signal for systems with a geometry similar to WASP-7. In this sense, we use a modified version of soap3.0 to explore the main hurdles that prevented the precise determination of the differential rotation of WASP-7. We also investigate whether the adoption of the next generation spectrographs, like ESPRESSO, would solve these issues. Additionally, we assess how instrumental and stellar noise influence this effect and the derived geometry of the system. We found that, for WASP-7, the white noise represents an important hurdle in the detection of the stellar differential rotation, and that a precision of at least 2 m s−1 or better is essential.

Serrano, L. M.↗
DOE OSTI2020

Decoupled Active and Reactive Power Control without PLL Requirement for Differential Buck Converter

This paper presents a decoupled active and reactive power control for the differential buck converter. This control does not require phase locked loop (PLL) for synchronization. Also, it achieves active and reactive power control in a simple single control loop structure. The differential buck converter has the capability to compensate the double line frequency power ripple without relying on additional hardware. Particularly, the Common Mode (CM) operation is used for active double line frequency power ripple absorption. Whereas, the Differential Mode (DM) operation is used as an active and reactive power exchanging port between the converter and the grid at the point of common coupling (PCC). The motivation behind this paper proposal are: (i) to avoid PLL induced instabilities, especially, when connecting the differential buck converter with weak grid, and (ii) direct active and reactive power control for the differential buck in a single loop control. The theoretical analyses are verified with a 5 kW converter with only 50 μF decoupling capacitor connected to a weak grid.

Khan, Ahmad↗
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 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 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 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 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↗
DOE OSTI2021

Efficient quantum algorithm for dissipative nonlinear differential equations

Significance Nonlinear differential equations appear in many domains and are notoriously difficult to solve. Whereas previous quantum algorithms for general nonlinear differential equations have complexity exponential in the evolution time, we give the first quantum algorithm for dissipative nonlinear differential equations that is efficient provided the dissipation is sufficiently strong relative to nonlinear and forcing terms and the solution does not decay too rapidly. We also establish a lower bound showing that differential equations with sufficiently weak dissipation have worst-case complexity exponential in time, giving an almost tight classification of the quantum complexity of simulating nonlinear dynamics. Furthermore, numerical results for the Burgers equation suggest that our algorithm may potentially address complex nonlinear phenomena even in regimes with weaker dissipation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme

Lagrangian methods to solve the inviscid Euler equations produce numerical oscillations near shock waves. A common approach to reducing these oscillations is to add artificial viscosity (AV) to the discrete equations. The AV term acts as a dissipative mechanism that attenuates oscillations by smearing the shock across a finite number of computational cells. However, AV introduces several control parameters that are not determined by the underlying physical model, and hence, in practice are tuned to the characteristics of a given problem. We seek to improve the standard quadratic-linear AV form by replacing it with a learned neural function that reduces oscillations relative to exact solutions of the Euler equations, resulting in a hybrid numerical-neural hydrodynamic solver. Because AV is an artificial construct that exists solely to improve the numerical properties of a hydrodynamic code, there is no offline ‘viscosity data’ against which a neural network can be trained before inserting into a numerical simulation, thus requiring online training. We achieve this via differentiable programming, i.e. end-to-end backpropagation or adjoint solution through both the neural and differential equation code, using automatic differentiation of the hybrid code in the Julia programming language to calculate the necessary loss function gradients. A novel offline pre-training step accelerates training by initializing the neural network to the default numerical AV scheme, which can be learned rapidly by space-filling sampling over the AV input space. We find that online training over early time steps of simulation is sufficient to learn a neural AV function that reduces numerical oscillations in long-term hydrodynamic shock simulations. These results offer an early proof-of-principle that online differentiable training of hybrid numerical schemes with novel neural network components can improve certain performance aspects existing in purely numerical schemes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Validation and Evaluation Uses of Quasi-Differential High-Energy Scattering Data [Slides]

This presentation covers the validation and evaluation uses of quasi-differential high-energy scattering data. This lecture covers motivation behind this work. The lecture then touches on quasi-differential for evaluation, quasi-differential for covariance, and quasi-differential for validation. The presentation concludes with a summary.

97 MATHEMATICS AND COMPUTING↗