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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Chapter 6: Surrogate Model Guided Optimization Algorithms and Their Potential Use in Autonomous Experimentation

This chapter reviews the basics of derivative-free optimization methods based on surrogate models and outlines how these methods can straightforwardly be applied to autonomously steering experimentation. It summarizes general solution approaches that use surrogate models and active learning. Surrogate modeling is often combined with active learning strategies, where in each iteration of the optimization algorithm, the surrogate model is used to identify which new inputs should be evaluated next and given the new input-output pair, the surrogate model is updated. Regardless of feasibility, the surrogate models for the constraints are updated in each iteration of the optimization algorithm, while the surrogate model for the objective function is only updated when a feasible point has been found. Similarly to the case of computationally cheap constraints, the surrogate models of the constraints should be incorporated into the definition of the auxiliary optimization problem that is solved to select new sample points.

active learning↗
DOE OSTI2021

Data from Response Surface Methodology Guided Adsorption and Recovery of Free Fatty Acids from Oil Using Resin

The presence of free fatty acids interferes with the conversion of plant oils to biodiesel. Four strong and weak base resins were evaluated for the removal of free fatty acids (FFA) from oil. Amberlite FPA 51 showed the highest adsorption capacity of FFA. A resin concentration above 3% could enable a higher percentage FFA adsorption. The adsorption process fitted a pseudo-first-order kinetic model and achieved equilibrium in approximately 8 h. A full factorial design was used to optimize the resin and FFA concentrations at a fixed temperature (40° C). A ratio of resin to fatty acid concentrations above 1.875 was sufficient for 70% adsorption and the amount adsorbed continued to increase with further added resin. A two-step washing of resin using hexane and ethanol recovered approximately 67.55% ± 4.05% of the initially added fatty acid. The resin that was used was regenerated with 5% NaOH and re-used for a minimum of three consecutive cycles. However, the adsorption capacity diminished to 75% of the initial cycle in cycles 2 and 3. Thus, the work presents a resin-based process for deacidification of oil to reduce fatty acid content of oil for biodiesel production.

Conversion↗
DOE OSTI2021

A field guide to cultivating computational biology

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Demonstrating the importance of porcine reproductive and respiratory syndrome virus papain-like protease 2 deubiquitinating activity in viral replication by structure-guided mutagenesis

Deubiquitination of cellular substrates by viral proteases is a mechanism used to interfere with host cellular signaling processes, shared between members of the coronavirus- and arterivirus families. In the case of Arteriviruses, deubiquitinating and polyprotein processing activities are accomplished by the virus-encoded papain-like protease 2 (PLP2). Several studies have implicated the deubiquitinating activity of the porcine reproductive and respiratory syndrome virus (PRRSV) PLP2 in the downregulation of cellular interferon production, however to date, the only arterivirus PLP2 structure described is that of equine arteritis virus (EAV), a distantly related virus. Here we describe the first crystal structure of the PRRSV PLP2 domain both in the presence and absence of its ubiquitin substrate, which reveals unique structural differences in this viral domain compared to PLP2 from EAV. To probe the role of PRRSV PLP2 deubiquitinating activity in host immune evasion, we selectively removed this activity from the domain by mutagenesis and found that the viral domain could no longer downregulate cellular interferon production. Interestingly, unlike EAV, and also unlike the situation for MERS-CoV, we found that recombinant PRRSV carrying PLP2 DUB-specific mutations faces significant selective pressure to revert to wild-type virus in MARC-145 cells, suggesting that the PLP2 DUB activity, which in PRRSV is present as three different versions of viral protein nsp2 expressed during infection, is critically important for PRRSV replication.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2. In this study, we used a combination of high throughput screening and machine learning (ML) models to identify HCAbs with potent efficacy against SARS-CoV-2 viral variants of interest (VOIs) and concern (VOCs). To start, we screened our highly diverse Nb phage display library against several pre-Omicron VOI and VOC receptor binding domains (RBDs) to identify panels of cross-reactive HCAbs. Using HCAb affinity for SARS-CoV-2 VOI and VOCs (pre-Omicron variants) and model features from other published data, we were able to develop a ML model that successfully identified HCAbs with efficacy against Omicron variants, independent of our experimental biopanning workflow. This biopanning informed ML approach reduced the experimental screening burden by 78% to 90% for the Omicron BA.5 and Omicron BA.1 variants, respectively. The combined approach can be applied to other emerging viruses with pandemic potential to rapidly identify effective therapeutic antibodies against emerging variants.

Antibodies↗
DOE OSTI2021

Data from: "A guide to using GitHub for developing and versioning data standards and reporting formats"

These data are the results of a systematic review that investigated how data standards and reporting formats are documented on the version control platform GitHub. Our systematic review identified 32 data standards in earth science, environmental science, and ecology that use GitHub for version control of data standard documents. In our analysis, we characterized the documents and content within each of the 32 GitHub repositories to identify common practices for groups that version control their documents on GitHub.In this data package, there are 8 CSV files that contain data that we characterized from each repository, according to the location within the repository. For example, in 'readme_pages.csv' we characterize the content that appears across the 32 GitHub repositories included in our systematic review. Each of the 8 CSV files has an associated data dictionary file (names appended with '_dd.csv' and here we describe each content category within CSV files.There is one file-level metadata file (flmd.csv) that provides a description of each file within the data package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Manipulation and statistical analysis of the fluid flow of polymer semiconductor solutions during meniscus-guided coating

Recent work in structure–processing relationships of polymer semiconductors have demonstrated the versatility and control of thin-film microstructure offered by meniscusguided coating (MGC) techniques. Here, we analyze the qualitative and quantitative aspects of solution shearing, a model MGC method, using coating blades augmented with arrays of pillars. The pillars induce local regions of high strain rates—both shear and extensional—not otherwise possible with unmodified blades, and we use fluid mechanical simulations to model and study a variety of pillar spacings and densities. We then perform a statistical analysis of 130 simulation variables to find correlations with three dependent variables of interest: thin-film degree of crystallinity and transistor field-effect mobilities for charge-transport parallel (Μ para ) and perpendicular (Μ perp ) to the coating direction. Our study suggests that simple fluid mechanical models can reproduce substantive correlations between the induced fluid flow and important performance metrics, providing a methodology for optimizing blade design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Attribute-Guided Adversarial Training for Robustness to Natural Perturbations

We report while existing work in robust deep learning has focused on small pixel-level norm-based perturbations, this may not account for perturbations encountered in several real world settings. In many such cases although test data might not be available, broad specifications about the types of perturbations (such as an unknown degree of rotation) may be known. We consider a setup where robustness is expected over an unseen test domain that is not i.i.d. but deviates from the training domain. While this deviation may not be exactly known, its broad characterization is specified a priori, in terms of attributes. We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space, without having access to the data from the test domain. Our adversarial training solves a min-max optimization problem, with the inner maximization generating adversarial perturbations, and the outer minimization finding model parameters by optimizing the loss on adversarial perturbations generated from the inner maximization. We demonstrate the applicability of our approach on three types of naturally occurring perturbations --- object-related shifts, geometric transformations, and common image corruptions. Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations. We demonstrate the usefulness of the proposed approach by showing the robustness gains of deep neural networks trained using our adversarial training on MNIST, CIFAR-10, and a new variant of the CLEVR dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A Central Plant Retrofit Assessment Guide for Owners

This document offers support to owners considering optimization upgrades, retrofits, or complete replacement of a central plant, with a particular focus on improving energy efficiency and reliability of the central plant, ultimately leading to reduced energy costs. It outlines recommended steps to prepare for and facilitate a thorough central plant assessment, empowering owners to move forward with the appropriate next steps. While the primary focus is on central plants serving commercial and institutional buildings, the principles and recommendations presented can also be adapted for use in other building types, such as industrial or manufacturing facilities.

42 ENGINEERING↗
DOE OSTI2020

A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty

Deep learning tools can incorporate all of the available information into a search for new particles, thus making the best use of the available data. This paper reviews how to optimally integrate information with deep learning and explicitly describes the corresponding sources of uncertainty. Simple illustrative examples show how these concepts can be applied in practice.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

A beginner's guide to non-abelian iPEPS for correlated fermions

Infinite projected entangled pair states (iPEPS) have emerged as a powerful tool for studying interacting two-dimensional fermionic systems. In this review, we discuss the iPEPS construction and some basic properties of this tensor network (TN) ansatz. Special focus is put on (i) a gentle introduction of the diagrammatic TN representations forming the basis for deriving the complex numerical algorithm, and (ii) the technical advance of fully exploiting non-abelian symmetries for fermionic iPEPS treatments of multi-band lattice models. The exploitation of non-abelian symmetries substantially increases the performance of the algorithm, enabling the treatment of fermionic systems up to a bond dimension D=24 D = 24 on a square lattice. A variety of complex two-dimensional (2D) models thus become numerically accessible. Here, we present first promising results for two types of multi-band Hubbard models, one with 2 2 bands of spinful fermions of \mathrm{SU}(2)_\mathrm{spin} \otimes \mathrm{SU}(2)_\mathrm{orb} S U ( 2 ) s p i n ⊗ S U ( 2 ) o r b symmetry, the other with 3 3 flavors of spinless fermions of \mathrm{SU}(3)_\mathrm{flavor} S U ( 3 ) f l a v o r symmetry.

Bruognolo, Benedikt↗
DOE OSTI2022

The hitchhiker's guide to 4d $\mathcal{N}=2$ superconformal field theories

Superconformal field theory with \mathcal{N}=2 𝒩 = 2 supersymmetry in four dimensional spacetime provides a prime playground to study strongly coupled phenomena in quantum field theory. Its rigid structure ensures valuable analytic control over non-perturbative effects, yet the theory is still flexible enough to incorporate a large landscape of quantum systems. Here we aim to offer a guidebook to fundamental features of the 4d \mathcal{N}=2 𝒩 = 2 superconformal field theories and basic tools to construct them in string/M-/F-theory. The content is based on a series of lectures at the Quantum Field Theories and Geometry School in July 2020.

Akhond, Mohammad↗
DOE OSTI2019

Gap Analysis to Guide DOE R&D in Supporting Extended Storage and Transportation of Spent Nuclear Fuel: An FY2019 Assessment (Final Report)

This report is a condensed version of previous reports identifying technical gaps that, if addressed, could be used to ensure the continued safe storage of SNF for extended periods and support licensing activities. This report includes updated gap priority assessments because the previous gap priorities were based on R&D performed through 2017. Much important work has been done since 2017 that requires a change in a few of the priority rankings to better focus the near-term R&D program. Background material, regulatory positions, operational and inventory status, and prioritization schemes are discussed in detail in Hanson et al. (2012) and Hanson and Alsaed (2019) and are not repeated in this report. One exception is an overview of the prioritization criteria for reference. This is meant to give the reader an appreciation of the framework for prioritization of the identified gaps. A complete discussion of the prioritization scheme is provided in Hanson and Alsaed (2019).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗