Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “example”

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.

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

At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Role of Quantum Science Concepts in Enhancing Sensing and Imaging Technologies: Applications for Biology: Proceedings of a Workshop

Quantum concepts hold the potential to enable significant advances in sensing and imaging technologies that could be vital to the study of biological systems. The workshop Quantum Science Concepts in Enhancing Sensing and Imaging Technologies: Applications for Biology, held online March 8–10, 2021, was organized to examine the research and development needs to advance biological applications of quantum technology. Hosted by the National Academies of Sciences, Engineering, and Medicine, the event brought together experts working on state-of-the-art, quantum-enabled technologies and scientists who are interested in applying these technologies to biological systems. Through talks, panels, and discussions, the workshop facilitated a better understanding of the current and future biological applications of quantum-enabled technologies in fields such as microbiology, molecular biology, cell biology, plant science, mycology, and many others. The workshop was organized around three main themes. The first, quantum in biology, examined quantum concepts that are hypothesized to be important for life processes and that researchers are working to observe through biological imaging and sensing. The second, quantum for biology, addressed ways to use quantum concepts to enhance technologies for biological imaging and sensing. The third, biology for quantum, offered a wider discussion of how the frontiers of biological imaging and sensing could enable future study using quantum concepts, tools, or technologies. Throughout the workshop, participants identified a wide range of emerging approaches and opportunities at the intersection of quantum physics and biological sensing and imaging. During the workshop, there were some differences in how each speaker defined the term quantum. During one of the panels, Prem Kumar offered thoughts on what phenomena are classical versus quantum, explaining that techniques get progressively more quantum as you move from just having superposition to having superposition with measurement and entanglement. Another explanation from Clarice Aiello delineates the definition into several levels. This includes a base level of “quantum-ness,” which reflects that all matter is made of atoms, and when these particles are isolated they behave based on quantum mechanical principles. A second level is related to quantum coherence, where a single quantum object might be found in a coherent superposition state. A final level, which she described as the quantum-entangled level, involves multiple quantum systems which are entangled among themselves. Overall, the workshop touched on concepts such as superposition, entanglement, and squeezing and their potential implications for communication, computing, and simulation, in addition to the workshop’s main focal area, biological sensing and imaging. At the opening of the workshop, Thorsten Ritz of the University of California, Irvine, identified two questions at the heart of quantum biology: Is the machinery of life quantum mechanical, and can quantum mechanics be used to study the machinery of life in new ways? Participants highlighted systems in which researchers have explored these questions, from the vast array of molecular interactions involved in biological processes such as photosynthesis, to the mechanics involved in cellular functions such as differentiation and aggregation, to the role of oscillating magnetic fields in flight orientation among birds. Sensing and imaging technologies are crucial to biological research; these technologies could both enhance the study of quantum effects and be enhanced by quantum concepts. A critical challenge in biological research is to develop imaging and sensing tools that do not damage or interfere with the often fragile and fleeting systems being studied. Attendees discussed a variety of established and emerging technologies that could enhance noninvasive biological imaging, including single- and two-photon spectroscopy, single-molecule spectroscopy, quantum illumination, ghost imaging, and cryo-electron microscopy. One example came from Marlan Scully who gave a keynote address on the first day of the workshop. Scully emphasized the use of different laser technologies, which exhibit coherence and other quantum properties, in moving toward real-world biological applications, such as the detection of SARS-CoV-2. Both tools and theory will play an important role in advancing quantum biology research and applications. Several participants suggested theorists and experimentalists should work in tandem to understand and model biological processes. While physics often reduces systems to their simplest forms for fundamental insights, participants also noted the value of observing and understanding biological systems in all their “messiness,” capturing both the inner workings of biological systems and the complex interactions that occur within and between organisms. In discussions among participants, several attendees stressed the need to match emerging tools with the right scientific questions. Rather than developing quantum technologies as “a hammer looking for a nail,” participants emphasized a focus on exploring the problems these technologies are best suited to address. For example, it is important to consider the size of the phenomenon being studied, the timescales that are important in answering the scientific question, and other relevant considerations. Every tool along the spectrum from classical to quantum involves its own set of trade-offs. For example, Ted Laurence of the Lawrence Livermore National Laboratory said that quantum measurements, such as single photon counting, fluorescence transitions, and lasers, can take longer to produce the same results as classical measurements. These quantum measurements, however, do not require calibration and can enable new research questions to be answered. Prem Kumar, Northwestern University, noted that, despite their promise, quantum approaches should not be used simply for the sake of using quantum, especially in situations where classical approaches better meet the needs of the researcher. Understanding and applying quantum concepts could enable advances in a wide range of application areas including energy, synthetic biology, medicine, and sustainability. For example, Michelle O’Malley, University of California, Santa Barbara, described how improved noninvasive imaging approaches could help capture the complex interactions and functions involved in the breakdown of organic matter by microbial communities and lead to new technologies for capturing valuable products from plant waste. Several other participants discussed needs in tracking the movement of metabolites and molecules in microbial communities for insights into nutrient cycling in environments such as soil. Margaret Ahmad, Sorbonne University, discussed potential opportunities to leverage the magnetic properties of cryptochromes to advance new treatment approaches for diseases such as COVID-19 and cancer. Looking toward the future development of the field, participants discussed challenges to advancing quantum biology that arise from disciplinary disconnects between physicists and biologists. The siloing of academic research disciplines represents a significant barrier to progress. Disconnects in terminology, motivations and priorities, and structural barriers to collaborative work underscore the need for concerted efforts to bridge these divides. Attendees and speakers offered suggestions for resolving these divergences, establishing a shared language, moving the field forward, and fostering meaningful feedback between disciplines. Overall, participants stressed a need for balance, open communication, collaboration, unity, and clear dialogue on trade-offs between quantum and classical approaches. Keiko Torii, The University of Texas at Austin, said that the best collaborations happen when the project provides mutual advantages that can show off everyone’s talents, each team finds the work interesting, and partners develop a camaraderie to pursue new knowledge. To enable near- and long-term opportunities in this space, participants suggested that exploratory, high-risk funding could improve existing instrumentation to explore quantum enhancement collaboratively. They also emphasized the need for collaboration between quantum physicists and sensing/imaging scientists, which could be advanced through a dedicated quantum biology investigator program. People, even more than technology, will be crucial to the future of quantum biology. Participants explored training, education, and workforce needs to further develop this burgeoning field and cultivate the next generation of scientists. While many programs are still in their nascent stages, participants highlighted examples of approaches and programs being developed at various types of institutions to engage students and professional scientists in quantum biology research. Several participants stressed the need for an inclusive approach, spanning disciplines as well as communities to foster a diverse field fueled by the intellectual contributions of a wide range of people, including historically under-resourced schools and students. To increase awareness and excitement about quantum physics and related areas of biology, attendees suggested capitalizing on the “buzz” around quantum. Several participants emphasized the need to start early, introducing students to quantum concepts and their appealing “weirdness” in K–12 education. Engaging students early—before they become entrenched in traditional disciplinary siloes as typically happens in graduate school—could help to galvanize interest in the area and foster a generation of scientists with the interdisciplinary mindset and skills needed to advance this interdisciplinary field. While these efforts could be advanced at many levels and across multiple sectors, several participants suggested a national quantum biology center could be a valuable hub to coordinate and support quantum biology education and workforce development across academia, industry, and government.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Grassmannian diffusion maps based surrogate modeling via geometric harmonics

Abstract A novel surrogate model based on the Grassmannian diffusion maps (GDMaps) and utilizing geometric harmonics (GH) is developed for predicting the response of complex physical phenomena. The method utilizes GDMaps to obtain a low‐dimensional representation of the underlying behavior of physical/mathematical systems with respect to uncertain input parameters. Using this representation, GH, an out‐of‐sample extension technique, is employed to create a global map from the input parameter space to a Grassmannian diffusion manifold. GH is further employed to locally map points on the diffusion manifold onto the tangent space of a Grassmann manifold. The exponential map is then used to project the points in the tangent space onto the Grassmann manifold, where reconstruction of the full solution is performed. The performance of the proposed surrogate model is verified with three examples. The first problem is a toy example used to illustrate the technique. In the second example, errors associated with the various mappings are assessed by studying response predictions of the electric potential of a dielectric cylinder in a homogeneous electric field. The last example applies the method for uncertainty prediction in the strain field evolution in a model amorphous material using the shear transformation zone theory of plasticity.

42 ENGINEERING↗
DOE OSTI2025

Persistent Classification: Understanding Adversarial Attacks by Studying Decision Boundary Dynamics

ABSTRACT There are a number of hypotheses underlying the existence of adversarial examples for classification problems. These include the high‐dimensionality of the data, the high codimension in the ambient space of the data manifolds of interest, and that the structure of machine learning models may encourage classifiers to develop decision boundaries close to data points. This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary. Similarly to the smoothed classifier literature, we define a (natural or adversarial) data point to be ( γ , σ)‐stable if the probability of the same classification is at least for points sampled in a Gaussian neighborhood of the point with a given standard deviation . We focus on studying the differences between persistence metrics along interpolants of natural and adversarial points. We show that adversarial examples have significantly lower persistence than natural examples for large neural networks in the context of the MNIST and ImageNet datasets. We connect this lack of persistence with decision boundary geometry by measuring angles of interpolants with respect to decision boundaries. Finally, we connect this approach with robustness by developing a manifold alignment gradient metric and demonstrating the increase in robustness that can be achieved when training with the addition of this metric.

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

Quantifying modeling uncertainty in simplified beam models for building response prediction

The use of simple models for response prediction of building structures is preferred in earthquake engineering for risk evaluations at regional scales, as they make computational studies more feasible. The primary impediment in their gainful use presently is the lack of viable methods for quantifying (and reducing upon) the modeling errors/uncertainties they bear. This study presents a Bayesian calibration method wherein the modeling error is embedded into the parameters of the model. Here, the method is specifically described for coupled shear-flexural beam models here, but it can be applied to any parametric surrogate model. The major benefit the method offers is the ability to consider the modeling uncertainty in the forward prediction of any degree-of-freedom or composite response regardless of the data used in calibration. The method is extensively verified using two synthetic examples. In the first example, the beam model is calibrated to represent a similar beam model but with enforced modeling errors. In the second example, the beam model is used to represent the detailed finite element model of a 52-story building. Both examples show the capability of the proposed solution to provide realistic uncertainty estimation around the mean prediction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network

Machine learning (ML) is emerging as a promising tool for modeling hydro-ecological processes due to the increasing availability of large environmental data. However, the use of ML requires sufficient programming knowledge due to a lack of a graphical user interface (GUI). In this study, we introduced a GUI package, named HydroEcoLSTM, with the long short-term memory network (LSTM) as the core model, that allows non-ML experts to utilize their domain knowledge to construct complex ML models. We demonstrated the functionalities of HydroEcoLSTM with two practical examples, including (1) predictions of streamflow in both gauged and ungauged catchments and (2) predictions of multiple outputs (i.e., streamflow and isotope transport from two catchments). The simulation results obtained in both case experiments are satisfactory. In the first example, the average Nash–Sutcliffe Efficiency (NSE) for streamflow simulation during the testing period is 0.79 while the application of the trained model in two assumed ungauged catchments also achieves the average NSE of 0.68. In the second example, the average NSE for streamflow and instream isotope simulation during the testing period is 0.71. Ultimately, applications of HydroEcoLSTM with real-world examples demonstrate its potential use for practical applications and research without requiring extensive coding skills.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The effect of building retrofit measures on CO2 emission reduction – A case study with U.S. Medium office buildings

Building retrofit has great potential to reduce CO2 emissions since buildings are responsible for 36% of emissions in the United States. Several existing studies have examined the effect of building retrofit measures on CO2 emission reduction. However, these studies oversimplified emission factors of electricity by adopting constant annual emission factors. This study uses hourly emission factors of electricity to analyze the effect of building retrofit measures on emission reduction using U.S. medium office buildings as an example. We analyzed CO2 emission reduction effects of eight building retrofit measures that related to envelop and mechanical system in five locations: Tampa, San Diego, Denver, Great Falls, and International Falls. The main findings are: (1) estimating CO2 emission reduction with constant emission factors overestimates the emission reduction for most measures in San Diego, while it underestimates the emission reduction for most measures in Denver and International Falls; (2) The same retrofit measure may have different effects in CO2 emission reduction depending on the climates. For instance, improving lighting efficiency and improving equipment efficiency have less impacts in emission reduction in cold climates than hot climates; and (3) The most energy efficient measure may not be the most emission efficient measure. For example, in Great Falls, the most energy efficient measure is improving equipment efficiency, but the most emission efficient measure is improving heating efficiency.

Lou, Yingli↗
DOE OSTI2025

From TPL assessment to design optimization: Wave energy converter control co-design applied to the RM3

The Reference Model (RM) project developed six marine energy converter concepts using a sequential design methodology, which, while widely adopted in the industry, often overlooks interactions between system components, resulting in suboptimal designs. One such example is the Reference Model 3 (RM3), a two-body point absorber wave energy converter (WEC). An assessment using the Technology Performance Level (TPL) revealed that RM3’s low power-to-cost ratio, partly due to expensive steel construction, limits its techno-economic performance. Here, this study aims to redesign RM3 by reducing its scale and employing control co-design to integrate WEC and Power Take-Off (PTO) dynamics, constraints, and cost considerations within an optimization framework. We demonstrate the limitations of RM3’s current PTO design and explore the benefits of scaling down to enhance techno-economic viability by lowering material costs. Using WecOptTool, we conduct a parameter sweep over gear ratios and spring stiffnesses for various Commercial Off-The-Shelf generators in irregular wave conditions. Our findings emphasize the importance of aligning PTO components with WEC dynamics, showing that control co-design and strategic scaling can improve RM3’s power-to-cost ratio. This study presents a transferable example of applied control co-design for other WECs, supporting early-stage developers in their design decisions.

Off the shelf components↗
DOE OSTI2023

Beyond the Stokes approximation: shallow visco-elastic ice-sheet models

Abstract The hypothesis that ice-sheet evolution is only controlled by the long-term non-Newtonian viscous behavior of ice has been challenged by observations indicating that effects like brittle failure, stick-slip sliding, tides and wave action may affect ice-sheet evolution on sub-daily timescales. Over these timescales, the quasi-static-creep approximation is no longer appropriate and elastic effects become important. Simulating elastic effects in ice-sheet models over relevant timescales, however, remains challenging. Here, we show that by including a visco-elastic rheology and reintroducing the oft neglected acceleration term back into the ice-sheet stress balance, we can create a visco-elastic system where the velocity is locally determined and information propagates at the elastic wave speed. Crucially, the elastic wave speed can be treated like an adjustable parameter and set to any value to reproduce a range of phenomena, provided the wave speed is large compared to the viscous velocity. We illustrate the system using three examples. The first two examples demonstrate that the system converges to the steady-state viscous and elastic limits. The third example examines ice-shelf rifting and iceberg calving. This final example hints at the utility of the visco-elastic formulation in treating both long-term evolution and short-term environmental effects.

58 GEOSCIENCES↗
DOE OSTI2025

Controlled gate networks: theory and application to eigenvalue estimation

We introduce a new scheme for quantum circuit design called controlled gate networks. Rather than trying to reduce the complexity of individual unitary operations, the new strategy is to toggle between all of the unitary operations needed with the fewest number of gates. We present the general theory of controlled gate networks and show that, under quite general conditions, it can significantly reduce the number of two-qubit gates needed to produce linear combinations of unitary operators. The first example we consider is a variational subspace calculation for a two-qubit system. The second example is estimating the eigenvalues of a two-qubit Hamiltonian via the rodeo algorithm (Choi et al. in Phys Rev Lett 127(4):040505, 2021. https://doi.org/10.1103/PhysRevLett.127.040505) using operators that we call controlled reversal gates. We use the Quantinuum H1-2 and IBM Perth devices to realize the quantum circuits. The third example is the application of controlled gate networks to the controlled time evolution of a free nucleon on a three-dimensional lattice. For all of the examples, we show very substantial reductions in the number of two-qubit gates required. Our work demonstrates that controlled gate networks are a useful tool for reducing gate complexity in quantum algorithms for quantum many-body problems such as those relevant to nuclear physics.

Bee-Lindgren, Max [Georgia Institute of Technology↗
DOE OSTI2021

Layered Double Perovskites

Successful strategies for the design of crystalline materials with useful function are frequently based on the systematic tuning of chemical composition within a given structural family. Perovskites with the formula ABX 3 , perhaps the best-known example of such a family, have a vast range of elements on A, B, and X sites, which are associated with a similarly vast range of functionality. Layered double perovskites (LDPs), a subset of this family, are obtained by suitable slicing and restacking of the perovskite structure, with the additional design feature of ordered cations and/or anions. In addition to inorganic LDPs, we also discuss hybrid (organic-inorganic) LDPs here, where the A-site cation is a protonated organic amine. Several examples of inorganic LDPs are presented with a discussion of their ferroic, magnetic, and optical properties. The emerging area of hybrid LDPs is particularly rich and is leading to exciting discoveries of new compounds with unique structures and fascinating optoelectronic properties. We provide context for what is important to consider when designing new materials and conclude with a discussion of future opportunities in the broad LDP area.

Materials Science↗
DOE OSTI2020

Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

ESS-DIVE Reporting Format for Sample-based Water and Soil Chemistry Measurements

The ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) reporting format for sample-based water and soil chemistry measurements are written guidelines and spreadsheet templates, that facilitate archiving water/soil/sediment chemistry data. The templates and terminology lists provided as part of the reporting format can help organize data and potentially enable data reuse.The reporting format consists of a general instructions file (instructions.md) as well as more detailed instructions files for each template (files beginning “Detailed_Instructions_*.md). The “examples” folder includes an example of each data template with just the limited set of required fields filled out as well as other examples with both required and optional fields complete.The 'templates' folder contains CSV templates for data, methods, and terminology file templates. Similar to the examples folder, the blank templates are provided with versions for required fields only as well as required and optional fields. Lastly, the term list folder provides common terminology used in each of the templates as well as a definition and any constraints for the fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

KENO V.a Primer: Performing Calculations using SCALE’s Criticality Safety Analysis Sequence (CSAS5) with Fulcrum

The SCALE code system developed at Oak Ridge National Laboratory is widely used and accepted around the world for criticality safety analyses. The well-known KENO V.a three-dimensional Monte Carlo criticality computer code is one of the primary criticality safety analysis tools in SCALE. The KENO V.a primer is designed to help a new user understand and use the SCALE/KENO V.a Monte Carlo code for nuclear criticality safety analyses. It assumes that the user has a college education in a technical field. There is no assumption of familiarity with Monte Carlo codes in general or with SCALE/KENO V.a in particular. The primer is designed to teach by example, with each example illustrating two or three features of SCALE/KENO V.a that are useful in criticality analyses. The primer is based on SCALE 6.2 and 6.3, which includes the Fulcrum graphical user interface (GUI). Each example uses Fulcrum to provide the framework for preparing input data and viewing output results. Starting with a Quickstart section, the primer gives an overview of the basic requirements for SCALE/KENO V.a input and allows the user to quickly run a simple criticality problem with SCALE/KENO V.a. The sections that follow Quickstart include a list of basic objectives at the beginning that identifies the goal of the section and the individual SCALE/KENO V.a features that are covered in detail in the sample problems in that section. Upon completion of the primer, a new user should be comfortable using Fulcrum to set up criticality problems in SCALE/KENO V.a. The primer provides a starting point for the criticality safety analyst who uses SCALE/KENO V.a. Complete descriptions are provided in the SCALE/KENO V.a manual. Although the primer is self-contained, it is intended as a companion volume to the SCALE/KENO V.a training and documentation. The SCALE manual and training schedule are available at https://scale.ornl.gov. The primer provides specific examples of using SCALE/KENO V.a for criticality analyses; the SCALE/KENO V.a manual provides information on the use of SCALE/KENO V.a and all its modules. The primer also contains an appendix with sample input files. In addition, this primer, its errata and sample inputs are also available at https://code.ornl.gov/scale/primers/kenova.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

KENO-VI Primer: Performing Calculations using SCALE’s Criticality Safety Analysis Sequence (CSAS6) with Fulcrum

The SCALE code system developed at Oak Ridge National Laboratory is widely used and accepted around the world for criticality safety analysis. The well-known KENO-VI three-dimensional Monte Carlo criticality computer code is one of the primary criticality safety analysis tools in SCALE. The KENO-VI primer is designed to help a new user understand and use the SCALE/KENO-VI Monte Carlo code for nuclear criticality safety analysis. It assumes that the user has a college education in a technical field. There is no assumption of familiarity with Monte Carlo codes in general or with SCALE/KENO-VI in particular. The primer is designed to teach by example, with each example illustrating two or three features of SCALE/KENO-VI that are useful in criticality analysis. The primer is based on SCALE 6.2 and 6.3, which includes the Fulcrum graphical user interface. Each example uses Fulcrum to provide the framework for preparing input data and viewing output results. Starting with a Quickstart section, the primer gives an overview of the basic requirements for SCALE/KENO-VI input and allows the user to quickly run a simple criticality problem with SCALE/KENO-VI. Each following section begins with a list of basic objectives identifying the goal of the section and the individual SCALE/KENO-VI features covered in detail in the section’s sample problems. Upon completion of the primer, a new user should be comfortable using Fulcrum to set up criticality problems in SCALE/KENO-VI. The primer provides a starting point for the criticality safety analyst who uses SCALE/KENO-VI. Complete descriptions are provided in the SCALE/KENO-VI manual. Although the primer is self-contained, it is intended as a companion volume to the SCALE/KENO-VI training and documentation. The SCALE manual and training schedule are available at https://scale.ornl.gov. The primer provides specific examples of using SCALE/KENO-VI for criticality analysis; the SCALE/KENO-VI manual provides information on the use of SCALE/KENO-VI and all its modules. The primer also contains an appendix with sample input files. In addition, this primer, its errata, and sample inputs are also available at https://code.ornl.gov/scale/primers/kenovi/.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Advanced Energy Approaches for Mixed-Use Developments in the Front Range Urban Corridor

The primary objective of the document is to present example technical approaches and strategies that planners and developers can reference for all-electric mixed-use developments in the Rocky Mountain Front Range urban corridor that are considering advanced energy performance goals such as high levels of energy efficiency and renewable integration. The strategies are tailored toward the vision for a zero energy, all-electric, district thermal system-connected starter district. In this example, on-site solar generation is emphasized as a strategy that can be combined with electrical and thermal energy storage for enhanced community resilience and demand flexibility. Opportunities for utilizing or adapting these strategies include: sharing with vertical developers for potential integration of strategies into vertical design guidelines; incorporation into Owner Project Requirements (OPRs); providing example strategies to design teams as a starting point for design; informing structure of agenda for a building design charrette; comparing building designs to example strategies at various points in the design process to evaluate design progression. While the technical strategies outlined in this document can help inform the development of guidelines for projects in the Front Range urban corridor, this document does not set requirements for any rating, certification, program, or policy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Integrating static PRA information with risk informed safety margin characterization (RISMC) simulation methods

The overall objective of the project was to develop a computationally feasible and user-friendly mechanized process to integrate traditional probabilistic risk assessment (PRA) and dynamic PRA (DPRA) results. Starting with the systematic identification of items in an existing PRA that need dynamic augmentation, the project used a generic 4-loop pressurized reactor (PWR) and 3-loop PWR as example plants. Station blackout (SBO) and large break loss of coolant accident SBLOCA) were selected as the example initiating events. Using the traditional event-tree (ET)/fault-tree (FT) methodology augmented by dynamic evet tree approach, the potential consequences of the initiating events were simulated with RELAP-3D and MELCOR/RASCAL codes to cover Level 1 through Level 3 of PRA. RAVEN and ADAPT software were used to generate Level 1 simulations with RELAP-3D and Level 2/3 simulations with MELCOR (Level 2)/RASCAL (Level 3), respectively. Example branching conditions (BCs) for SBO included AC power recovery time, valve repair failure time, reactor coolant pump leak time/break size and emergency power supply duration to a total of 9. Example BCs for LOCA included off-site power recovery time, diesel generator power recovery time, auxiliary feed water system operation time, safety relief valve failure to open upon demand, reactor coolant pump seal break time and size to a total of 21. Each RELAP-3D simulation (9,587 scenarios) was labelled OK or Core Damage based on the maximum allowed peak clad temperature (2,100oF). Each MELCOR simulation (4610 scenarios) was labeled as Bin over 10rem or Bin 0-10rem based on the dose at the site boundary. The scenarios were clustered based on the criteria above using the mean shift methodology. Classical PRA (CPRA) and DPRA results were compared to identify the ET sequences that need DPRA augmentation. Several approaches were proposed for the incorporation of these sequences into CPRA using clustering with the mean shift methodology, restructuring the CPRA ETs by adding new BCs/sequences, and using the concept of a limit surface. Procedures for decision making regarding the possible consequences of an initiating event (e.g. core damage or not, site evacuation or not) were developed using a convolutional neural network (CNN), a recurrent neural network (RNN) and a transformer neural network (TNN). The project has led to two PhD degrees, three archival journal papers and five refereed conference proceedings.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

2022 American Conference on Neutron Scattering (ACNS 2022)

The 11th American Conference on Neutron Scattering (ACNS 2022) will be held on June 5-9, 2022, in Boulder, CO. The Conference will provide essential information on the breadth and depth of current neutron-related research worldwide. Hosted by the Neutron Scattering Society of America, this year’s Conference will feature a combination of invited and contributed talks, poster sessions, and tutorials. Topics of the conference are: Advances in Neutron Facilities, Instrumentation and Software: Developments in sources, instrumentation, sample environments and control software. Hard Condensed Matter: Magnetism, correlated metals, quantum/topological materials, superconductors, ferroelectrics, multiferroics, glasses, and disorder phenomena. Submissions outlining examples of neutron scattering in industrial and engineering applications involving hard condensed matter systems are also encouraged. Soft Matter: Neutron studies of soft materials and related fields including in situ and in operando studies. Polymers, surfactants, emulsions, gels, nanoparticles, colloidal suspensions and more. Submissions of computational studies or applications of machine learning beneficial to neutron scattering experiments, as well as examples of neutron scattering in industrial and engineering applications are strongly encouraged. Biology, Biophysics and Biotechnology: Neutron studies of biological and biologically relevant systems. Proteins, bio membranes, biological assemblies, natural materials, nucleic acids, drug-delivery platforms and biomedical systems. Submissions of computational studies or applications of machine learning beneficial to biological neutron scattering experiments, as well as examples of neutron scattering in applied research involving biological systems, are strongly encouraged. Materials Chemistry and Energy: Neutron-based studies of functional materials and materials for energy applications. Examples include porous materials such as metal organic frameworks (MOFs), zeolites; phosphors; novel pigments; electrolytes; catalysts; ionic conductors/cathode materials; photovoltaic materials (hybrid perovskites); thermoelectrics; magnetocalorics/electrocalorics. Structural Materials and Engineering: Neutron scattering studies of materials and engineering processes including structural materials, concrete and metals, as well as engineering processes including combustion, corrosion, additive manufacturing, and others. Neutron Physics: Fundamental physical studies of the neutron and related areas. Emerging Applications in Neutron Scattering: Machine Learning and Data Science: Advances in computing power have contributed to rapidly evolving machine learning and data science fields that can be leveraged to the benefit of the neutron scattering community. The purpose of this session is to highlight recent advances in machine learning and data science and to serve as the foundation of a parallel data and computation track highlighting computation advances and applications in neutron scattering throughout the conference.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Detonation Waves in High Explosives

A material at high temperature can react or decompose. For an energetic material, the reaction is exothermic and releases chemical energy that would further increase the temperature. Under some circumstances, when a reaction is triggered, such a reaction can propagate and the material rapidly releases a large amount of energy giving rise to an explosion. Examples of such materials are aerosols, suspensions of solid particles or liquid droplets in a gas; such as coal dust, grain dust and fuel-air explosions. Frequently, explosions are due to accidents. A spectacularly destructive example is the recent explosion of a large quantity of ammonium nitrate (thousands of tons) in Beirut, Lebanon (August 2020); see for example Beirut explosion. Ammonium nitrate is used as a fertilizer. It and the aerosols are not considered to be explosives due to the limited conditions for which an explosion can occur. An aerosol gets the oxidizer from the surrounding air. Burning requires diffusion of the oxidizer to the particle surface where the reaction occurs. A large density of small particles is required for a fast enough reaction to support an explosion. In contrast, an explosive is an energetic material with both fuel and oxidizer mixed on a molecular scale (either premixed gases or within molecules of a solid). This allows fast enough reactions over a wide range of conditions to support a self-propagating reactive wave known as a detonation wave. A detonation wave can be controlled and an explosive used for useful purposes such as in mining, construction, demolition, explosive welding, argon flash lamp, pulsed power using a magnetic flux generator [see also Goforth et al., 2015], jet cutter with shaped charge, explosive art, and generating conditions to study the response of materials at high strain rates and high pressures [see for example, Marsh, 1980]. Explosives are also used in conventional munitions and nuclear weapons. The focus of this book is on the theory and phenomenology of solid high explosives (HEs); in particular, plastic-bonded explosives (PBXs). Some aspects of detonation wave theory are needed to interpret explosive data. Hence, the theory is presented before the detonation wave phenomenology. A familiarity with fluid flow, specifically the notion of shock waves and the shock loci are assumed. In the remainder of this chapter we give a brief overview on the basic properties of detonation waves and PBXs.

36 MATERIALS SCIENCE↗