SEARCH · Engineering Papers

Results for “Differentiability”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 685 records · Page 38

All sources NASA NTRS DOE OSTI

DOE OSTI2014

Harnessing the Heterogeneity of T Cell Differentiation Fate to Fine-Tune Generation of Effector and Memory T Cells

Recent studies show that naïve T cells bearing identical T cell receptors experience heterogeneous differentiation and clonal expansion processes. The factors controlling this outcome are not well characterized, and their contributions to immune cell dynamics are similarly poorly understood. In this study, we develop a computational model to elaborate mechanisms occurring within and between two important physiological compartments, lymph nodes and blood, to determine how immune cell dynamics are controlled. Our multiorgan (multi-compartment) model integrates cellular and tissue level events and allows us to examine the heterogeneous differentiation of individual precursor cognate naïveT cells to generate both effector and memory T lymphocytes. Using this model, we simulate a hypothetical immune response and reproduce both primary and recall responses to infection. Increased numbers of antigen-bearing dendritic cells (DCs) are predicted to raise production of both effector and memoryT cells, and distinct “sweet spots” of peptide-MHC levels on those DCs exist that favor CD4+ or CD8+ T cell differentiation toward either effector or memory cell phenotypes. This has important implications for vaccine development and immunotherapy.

cognate↗

DOE OSTI2014

The Effect of Exogenous Zinc Concentration on the Responsiveness of MC3T3-E1 Pre-Osteoblasts to Surface Microtopography: Part II (Differentiation)

Osseointegration of bone implants is a vital part of the recovery process. Numerous studies have shown that micropatterned geometries can promote cell-substrate associations and strengthen the bond between tissue and the implanted material. As demonstrated previously, exogenous zinc levels can influence the responsiveness of pre-osteoblasts to micropatterns and modify their migratory behavior. In this study, we sought to determine the effect of exogenous zinc on differentiation of osteoblasts cultured on micropatterned vs. planar substrates. Levels of activated metalloproteinase-2 (MMP-2) and transforming growth factor-beta 1 (TGF-β1), as well as early stage differentiation marker alkaline phosphatase, were altered with the addition of zinc. These results suggest that exogenous zinc concentration and micropatterning may interdependently modulate osteoblast differentiation.

metalloproteinase-2↗

DOE OSTI2022

Site-Differentiated Iron–Sulfur Cluster Ligation Affects Flavin-Based Electron Bifurcation Activity

Electron bifurcation is an elegant mechanism of biological energy conversion that effectively couples three different physiologically relevant substrates. As such, enzymes that perform this function often play critical roles in modulating cellular redox metabolism. One such enzyme is NADH-dependent reduced-ferredoxin: NADP+ oxidoreductase (NfnSL), which couples the thermodynamically favorable reduction of NAD+ to drive the unfavorable reduction of ferredoxin from NADPH. The interaction of NfnSL with its substrates is constrained to strict stoichiometric conditions, which ensures minimal energy losses from non-productive intramolecular electron transfer reactions. However, the determinants for this are not well understood. One curious feature of NfnSL is that both initial acceptors of bifurcated electrons are unique iron–sulfur (FeS) clusters containing one non-cysteinyl ligand each. The biochemical impact and mechanistic roles of site-differentiated FeS ligands are enigmatic, despite their incidence in many redox active enzymes. Herein, we describe the biochemical study of wild-type NfnSL and a variant in which one of the site-differentiated ligands has been replaced with a cysteine. Results of dye-based steady-state kinetics experiments, substrate-binding measurements, biochemical activity assays, and assessments of electron distribution across the enzyme indicate that this site-differentiated ligand in NfnSL plays a role in maintaining fidelity of the coordinated reactions performed by the two electron transfer pathways. Given the commonality of these cofactors, our findings have broad implications beyond electron bifurcation and mechanistic biochemistry and may inform on means of modulating the redox balance of the cell for targeted metabolic engineering approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Differential Accumulation of Misfolded Prion Strains in Natural Hosts of Prion Diseases

Prion diseases, also known as transmissible spongiform encephalopathies (TSEs), are a group of neurodegenerative protein misfolding diseases that invariably cause death. TSEs occur when the endogenous cellular prion protein (PrP C ) misfolds to form the pathological prion protein (PrP Sc ), which templates further conversion of PrP C to PrP Sc , accumulates, and initiates a cascade of pathologic processes in cells and tissues. Different strains of prion disease within a species are thought to arise from the differential misfolding of the prion protein and have different clinical phenotypes. Different strains of prion disease may also result in differential accumulation of PrP Sc in brain regions and tissues of natural hosts. Here, we review differential accumulation that occurs in the retinal ganglion cells, cerebellar cortex and white matter, and plexuses of the enteric nervous system in cattle with bovine spongiform encephalopathy, sheep and goats with scrapie, cervids with chronic wasting disease, and humans with prion diseases. By characterizing TSEs in their natural host, we can better understand the pathogenesis of different prion strains. This information is valuable in the pursuit of evaluating and discovering potential biomarkers and therapeutics for prion diseases.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

A guide to the design of the virtual element methods for second- and fourth-order partial differential equations

Here we discuss the design and implementation details of two conforming virtual element methods for the numerical approximation of two partial differential equations that emerge in phase-field modeling of fracture propagation in elastic material. The two partial differential equations are: (i) a linear hyperbolic equation describing the momentum balance and (ii) a fourth-order elliptic equation modeling the damage of the material. Inspired by, we develop a new conforming VEM for the discretization of the two equations, which is implementation-friendly, i.e., different terms can be implemented by exploiting a single projection operator. We use C 0 and C 1 virtual elements for the second-and fourth-order partial differential equation, respectively. For both equations, we review the formulation of the virtual element approximation and discuss the details pertaining the implementation.

42 ENGINEERING↗

DOE OSTI2015

Investigation of differential surface removal due to electropolishing at JLab

Surface chemistry carried out for Superconducting Radio Frequency (SRF) cavities such as Buffered Chemical Polishing (BCP) and Electropolishing (EP) aims to uniformly remove the internal surface of a cavity along the entire structure and within each cell from equator to iris in order to obtain an equally etched surface. A uniform removal, however, is not readily achievable due to the complex fluid flow and varying temperatures of the acid mixture, which can lead to differential etching. This needs to be considered when envisaging a certain surface damage removal throughout the interior. The process-specific differential etching influences the target frequency set at the manufacturing stage as well as the field flatness and length of the as-built cavity. We report on analyses of JLab's present EP system using experimental data for six nine-cell cavities that have been processed recently in the frame of the LCLS-II high-Q development plan. In conjunction with numerical simulations, the differential etching and the impact on field flatness is assessed.

43 PARTICLE ACCELERATORS↗

DOE OSTI2017

Modular Expression Language for Ordinary Differential Equation Editing

MELODEEis a system for describing systems of initial value problem ordinary differential equations, and a compiler for the language that produces optimized code to integrate the differential equations. Features include rational polynomial approximation for expensive functions and automatic differentiation for symbolic jacobians

Blake, Robert C.↗

DOE OSTI2020

Battery models, systems, and methods using robust fail-safe iteration free approach for solving differential algebraic equations

Battery models using robust fail-safe iteration free approach for solving Differential Algebraic Equations, and associated systems and methods are disclosed. In one embodiment, a method includes generating a model of the rechargeable battery; determining one or more initial conditions for one or more algebraic variables of the model using a solver; holding differential variables of the model static by a switch function while determining the one or more initial conditions; applying the initial conditions to the model by the switch function; and determining one or more parameters for the rechargeable battery by solving the algebraic and differential equations.

25 ENERGY STORAGE↗

DOE OSTI2015

Monte-Carlo Simulations of the Nuclear Energy Deposition Inside the CARMEN-1P Differential Calorimeter Irradiated into OSIRIS Reactor

The nuclear heating measurements in Material Testing Reactors (MTRs) are crucial for the study of nuclear materials and fuels under irradiation. The reference measurements of this nuclear heating are especially performed by a differential calorimeter including a graphite sample material. These measurements are then used for other experimental conditions in order to predict the nuclear heating and thermal conditions induced in the irradiation devices. Nuclear heating is a great deal of interest at the moment as the measurement of such heating is an important issue for MTRs reactors. This need is especially generated by the new Jules Horowitz Reactor (JHR), under construction at CEA/Cadarache 'French Alternative Energies and Atomic Energy Commission'. This new reactor, that will be operational in late 2019, is a new facility for the nuclear research on materials and fuels. Indeed the expected nuclear heating rate is about 20 W/g for nominal capacity of 100 MW. The present Monte Carlo calculation works belong to the IN-CORE (Instrumentation for Nuclear radiation and Calorimetry On line in Reactor): a joint research program between the CEA and Aix- Marseille University in 2009. One scientific aim of this program is to design and develop a multi-sensors device, called CARMEN, dedicated to the measurements of main physical parameters simultaneously encountered inside JHR's experimental channels (core and reflector) such as neutron fluxes, photon fluxes, temperature, and nuclear heating. A first prototype was already developed. This prototype includes two mock-ups dedicated respectively to neutronic measurements (CARMEN-1N) and to photonic measurements (CARMEN-1P) with in particular a specific differential calorimeter. Two irradiation campaigns were performed successfully in the periphery of OSIRIS reactor (a MTR located at Saclay, France) in 2012 for nuclear heating levels up to 2 W/g. First Monte Carlo calculations reduced to the graphite sample of the calorimeter were carried out. A preliminary analysis shows that the numerical results overestimate the measurements by about 20 %. A new approach has been developed in order to estimate the nuclear heating by two methods (energy deposition or KERMA) by considering the whole complete geometry of the sensor. This new approach will contribute to the interpretation of the irradiation campaign and will be useful to improve the out-of-pile calibration procedure of the sensor and its thermal response during irradiations. The aim of this paper is to present simulations made by using MCNP5 Monte-Carlo transport code (using ENDF/B-VI nuclear data library) for the nuclear heating inside the different parts of the calorimeter (head, rod and base). Calculations into two steps will be realized. We will use as an input source in the model new spectra (neutrons, prompt-photons and delayed-photons) calculated with the Monte Carlo code TRIPOLI-4{sup R} inside different experimental channels (water) located into the OSIRIS periphery and used during the CARMEN-1P irradiation campaign. We will consider Neutrons- Photons-Electrons and Photons-Electrons modes. We will begin by a brief description of the differential-calorimeter device geometry. Then the MCNP5 model used for the calculations of nuclear heating inside the calorimeter elements will be introduced. The energy deposition due to the prompt-gamma, delayed-gamma and neutrons, the neutron-activation of the device will be considered. The different components of the nuclear heating inside the different parts of the calorimeter will be detailed. Moreover, a comparison between KERMA and nuclear energy deposition estimations will be given. Finally, a comparison between this total nuclear heating Calculation and Experiment in graphite sample will be determined. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Rare Lepton Decays and Differentiable Hadronization Models - From Signatures of New Physics to Data-driven Event Generation

This dissertation is partitioned into two parts: phenomenological studies focused on rare lepton decays as probes of heavy and light new physics, and the development of differentiable, data-driven hadronization models. Part I develops the phenomenology of new physics signatures stemming from rare charged lepton flavor violating decays probed by experiments at the intensity frontier. These include interactions mediated by both high-scale effective operators and light new physics, manifesting in multi-lepton final states ($\mu \to 5e$), elastic nuclear transitions ($\mu \to e$ conversion), baryon-number-violating muon capture, and time-dependent signals from ultralight dark matter ($\mu \to e \phi, \tau \to \ell \phi$). Part II develops two distinct strategies for advancing differentiable and data-driven hadronization models. One involves comprehensive reweighting frameworks for hadronization that enable efficient uncertainty estimation, facilitate parameter tuning, and interface naturally with differentiable programming paradigms. The other introduces machine-learning-based methods for extracting microscopic fragmentation dynamics directly from macroscopic observables through the deformation of existing models -- effectively providing solutions to the inverse problem of hadronization. Altogether, these studies advance the interpretability, flexibility, and precision of theoretical predictions for both high-intensity and high-energy experiments.

Menzo, Tony [Cincinnati U.] (ORCID:000000022013457↗

DOE OSTI2018

Boundary Value Problems for Nonclassical Systems of Second Order Differential Equations

We study boundary value problems for systems of second order differential equations with principal part in the form of the squared first order differential operator. We establish the unique solvability of the boundary value problem for a nonclassical system in the plane, which can be regarded as the Riemann–Hilbert problem with discontinuous boundary conditions. Similar problems are considered in the space.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

A semi-discrete scheme for solving fourth-order partial integro-differential equation with a weakly singular kernel using Legendre wavelets method

In this paper, a semi-discrete scheme is presented for solving fourth-order partial integro-differential equations with a weakly singular kernel. The second-order backward difference formula is used to discretize the temporal derivatives. After discretizing the temporal derivatives, the considered problems are converted into a set of ordinary differential equations which are solved by using Legendre wavelets collocation method. The stability and convergence properties related to the time discretization are discussed and theoretically proven. Several numerical examples are included to demonstrate the accuracy and efficiency of the proposed method.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Solving high-dimensional partial differential equations using deep learning

Developing algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notoriously difficult problem known as the “curse of dimensionality.” This paper introduces a deep learning-based approach that can handle general high-dimensional parabolic PDEs. To this end, the PDEs are reformulated using backward stochastic differential equations and the gradient of the unknown solution is approximated by neural networks, very much in the spirit of deep reinforcement learning with the gradient acting as the policy function. Numerical results on examples including the nonlinear Black–Scholes equation, the Hamilton–Jacobi–Bellman equation, and the Allen–Cahn equation suggest that the proposed algorithm is quite effective in high dimensions, in terms of both accuracy and cost. Furthermore, this opens up possibilities in economics, finance, operational research, and physics, by considering all participating agents, assets, resources, or particles together at the same time, instead of making ad hoc assumptions on their interrelationships.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Corexit-EC9527A Disrupts Retinol Signaling and Neuronal Differentiation in P19 Embryonal Pluripotent Cells

Corexit-EC9500A and Corexit-EC9527A are two chemical dispersants that have been used to remediate the impact of the 2010 Deepwater Horizon oil spill. Both dispersants are composed primarily of organic solvents and surfactants and act by emulsifying the crude oil to facilitate biodegradation. The potential adverse effect of the Corexit chemicals on mammalian embryonic development remains largely unknown. Retinol (vitamin A) signaling, mediated by all-trans retinoic acid (RA), is essential for neural tube formation and the development of many organs in the embryo. The physiological levels of RA in cells and tissues are maintained by the retinol signaling pathway (RSP), which controls the biosynthesis of RA from dietary retinol and the catabolism of RA to polar metabolites for removal. RA is a potent activating ligand for the RAR/RXR nuclear receptors. Through RA and the receptors, the RSP modulates the expression of many developmental genes; interference with the RSP is potentially teratogenic. In this study the mouse P19 embryonal pluripotent cell, which contains a functional RSP, was used to evaluate the effects of the Corexit dispersants on retinol signaling and associated neuronal differentiation. The results showed that Corexit-EC9500A was more cytotoxic than Corexit-EC9527A to P19 cells. At non-cytotoxic doses, Corexit-EC9527A inhibited retinol-induced expression of the Hoxa1 gene, which encodes a transcription factor for the regulation of body patterning in the embryo. Such inhibition was seen in the retinol- and retinal- induced, but not RA-induced, Hoxa1 up-regulation, indicating that the Corexit chemicals primarily inhibit RA biosynthesis from retinal. In addition, Corexit-EC9527A suppressed retinol-induced P19 cell differentiation into neuronal cells, indicating potential neurotoxic effect of the chemicals under the tested conditions. In conclusion, the surfactant ingredient, dioctyl sodium sulfosuccinate (DOSS), may be a major contributor to the observed effect of Corexit-EC9527A in the cell.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Handling Iterative Solvers in an Algorithmic Differentiation Framework Using Implicit Methods

Differentiable programming is a powerful concept as it enables the seemly propagation of gradients through functions, algorithms, and/or whole physics simulations. These gradients are useful for a wide variety of applications, including sensitivity studies and machine learning, but one of particular interest is optimization. Gradient-based optimization, enabled through automatic/algorithmic differentiation (AD), can be used on predictive physical models to efficiently optimize a set of design variables. AD methods are a particularly promising approach to complex physics simulations because they can be shown to scale well with an increasing number of design variables; however, care must be taken when coupling between different models or different states of a single model.

algorithmic differentiation↗

DOE OSTI2021

Calibration of elastoplastic constitutive model parameters from full-field data with automatic differentiation-based sensitivities

Here, we present a framework for calibration of parameters in elastoplastic constitutive models that is based on the use of automatic differentiation (AD). The model calibration problem is posed as a partial differential equation-constrained optimization problem where a finite element (FE) model of the coupled equilibrium equation and constitutive model evolution equations serves as the constraint. The objective function quantifies the mismatch between the displacement predicted by the FE model and full-field digital image correlation data, and the optimization problem is solved using gradient-based optimization algorithms. Forward and adjoint sensitivities are used to compute the gradient at considerably less cost than its calculation from finite difference approximations. Through the use of AD, we need only to write the constraints in terms of AD objects, where all of the derivatives required for the forward and inverse problems are obtained by appropriately seeding and evaluating these quantities. We present three numerical examples that verify the correctness of the gradient, demonstrate the AD approach's parallel computation capabilities via application to a large-scale FE model, and highlight the formulation's ease of extensibility to other classes of constitutive models.

42 ENGINEERING↗

DOE OSTI2025

Uncertainty Quantification Enabled by Automatic Differentiation for Hydrodynamic Simulation of Shock‐to‐Detonation Transition in High Explosives

Quantifying the effects of uncertainty in a reactive burn model on the run-to-detonation time in high explosives (HEs) provides a robust methodology for assessing the probability of an HE failing the IHE qualification standard. Moreover, uncertainty quantification helps evaluate whether the model calibration accurately represents data outside the calibration set. This study uses a specialized hydrodynamic simulation code for modeling detonation to determine the run-to-detonation time of the HE PBX 9502 for various impact velocities. To quickly approximate uncertainties in the model, a surrogate was constructed using a Taylor series expansion centered at the mean of the input parameters. To obtain the sensitivities required for constructing the Taylor series, HYP-percomplex Automatic Differentiation (HYPAD) was implemented. HYPAD is a methodology for infusing existing codes with automatic differentiation capabilities by augmenting variables with one or more imaginary units to compute step-size independent partial derivatives. These derivatives are accurate to machine precision with respect to the implemented numerical algorithm, meaning their accuracy reflects that of the underlying method (e.g., integration or discretization schemes). Using reduced order modeling techniques, the mean and standard deviation of the run-to-detonation time of a shock within PBX 9502 were computed for a number of initial impact velocities. A weighted least squares regression was then performed to obtain a best fit curve and prediction interval for the computed statistics. Historical data points from explosively driven wedge tests were utilized to validate the prediction interval, ensuring its reliability in predicting future outcomes. With this prediction interval and a known safety constraint curve, the most probable point of failure and the probability of failure for the HE PBX 9502 were determined.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

A Framework for Distributed Approximation of Moments with Higher-Order Derivatives through Automatic Differentiation

We present a framework for the distributed approximation of moments, enabling the evaluation of the uncertainty in a dynamical system. The first and second moment, mean, and variance are computed with up to third-order Taylor series expansion. The required derivatives for the expansion are generated automatically by automatic differentiation and propagated through an implicit time stepper. The computational kernels are the accumulation of the derivatives (Jacobian, Hessian, tensor) and the covariance matrix. We apply distributed parallelism to the Hessian or third-order tensor, and the user merely has to provide a function for the differential equation, thus achieving similar ease of use as Monte Carlo-based methods. We demonstrate our approach using with benchmarks on Theta, a KNL-based system at the Argonne Leadership Computing Facility.

Automatic Differentiation↗