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DOE OSTI2015

Increasing Flexibility in Energy Code Compliance: Performance Packages

Energy codes and standards have provided significant increases in building efficiency over the last 38 years, since the first national energy code was published in late 1975. The most commonly used path in energy codes, the prescriptive path, appears to be reaching a point of diminishing returns. As the code matures, the prescriptive path becomes more complicated, and also more restrictive. It is likely that an approach that considers the building as an integrated system will be necessary to achieve the next real gains in building efficiency. Performance code paths are increasing in popularity; however, there remains a significant design team overhead in following the performance path, especially for smaller buildings. This paper focuses on development of one alternative format, prescriptive packages. A method to develop building-specific prescriptive packages is reviewed based on a multiple runs of prototypical building models that are used to develop parametric decision analysis to determines a set of packages with equivalent energy performance. The approach is designed to be cost-effective and flexible for the design team while achieving a desired level of energy efficiency performance. A demonstration of the approach based on mid-sized office buildings with two HVAC system types is shown along with a discussion of potential applicability in the energy code process.

Hart, Philip R.↗

DOE OSTI2015

Increasing Flexibility in Energy Code Compliance: Performance Packages

Hart, Reid↗

DOE OSTI2025

Mechanical Characteristics of Additively Manufactured ODS 316L and 316H Alloys with and Without Post-build Processing

This research aims to explore an accelerated development path for oxide dispersion-strengthened (ODS) alloys by integrating additive manufacturing (AM) technologies with recent advances in ODS materials and traditional manufacturing methods. Novel AM and post-build processing routes have been developed for ODS austenitic alloys, specifically Fe-Cr-Ni alloys like 316L and 316H. Electron microscopy and mechanical characterizations were conducted to evaluate the effects of process variables on microstructure and properties, aiming for an economically feasible route property optimization. Traditionally, ODS alloy production involves multi-day high-energy mechanical milling of alloy powder with yttria (Y 2 O 3 ) followed by powder consolidation via extrusion or other methods and additional thermomechanical processing (TMP) for property control. Here, to address these challenges associated with this complex and costly approach, we propose exploring alternative, cost-effective processing routes focusing on AM and traditional TMP methods. The new ODS alloy processing routes have achieved up to a 400% increase in yield strength and a 60% increase in ultimate tensile strength compared to wrought stainless steels while still maintaining significant ductility and fracture toughness. This paper details the novel and economical AM-based processing routes for ODS austenitic alloys, combined with post-build TMPs, and discusses the mechanical and microstructural characteristics of the developed materials.

Byun, Thak Sang [Oak Ridge National Laboratory (OR↗

DOE OSTI2018

Modeling ocean, rail, and truck transportation flows to support policy analysis

Freight transportation represents about 9.5% of GDP in the U.S., it is responsible for about 8% of greenhouse gas emissions, and supports the import and export of about 3.6 trillion in international trade. It is therefore important that the national freight transportation system is designed and operated efficiently. Hence, this paper develops a mathematical model to estimate international and domestic freight flows across ocean, rail, and truck modes, which can be used to study the impacts of changes in our infrastructure, as well as the imposition of new user fees and changes in operating policies. The model integrates a user equilibrium-based logit argument for path selection with a system optimal argument for rail network operations. This leads to the development of a unique solution procedure that is demonstrated in a large-scale analysis focused on all intercity freight and U.S export/import containerized freight. The model results are compared with the reported flow volumes. Here, the model is applied to two case studies: a disruption of the seaports of Los Angeles and Long Beach (LA and LB) similar to the impacts that would be felt in an earthquake; and implementation of new user fees at the California ports.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Automated Generation of Integrated Digital and Spiking Neuromorphic Machine Learning Accelerators

The growing numbers of application areas for artificial intelligence (AI) methods have led to an explosion of domain-specific accelerators that could support every new machine learning (ML) algorithm advancement, clearly highlighting the need for a capability to quickly and automatically transition from algorithm definition to hardware implementation and explore design space along a variety of SWaP (size, weight and Power). The software defined architectures (SODA) synthesizer implements a compiler-based modular infrastructure for the end-to-end generation of machine learning accelerators from high-level frameworks to hardware description language. At the same time, neuromorphic computing, by mimicking how the brain operates, promises to perform artificial intelligence tasks at efficiencies orders of magnitude higher than the current conventional tensor-processing based accelerators, as demonstrated by a variety of specialized designs leveraging Spiking Neural Networks (SNNs). Nevertheless, the mapping of an artificial neural network (ANN) to solutions supporting SNNs is still a non-trivial and very device-specific task, and completely lack the possibility to design hybrid systems that integrate conventional and spiking neural models. In this paper we discuss the support for such an integrated generation leveraging the SODA Synthesizer framework and its modular structure. In particular, we present a new MLIR dialect (part of the SODA frontend) that allows expressing spiking neural network features (e.g., available resources, spiking sequences, analog signal reading, etc.) and illustrate how it enables mapping to Spiking Neurons and deployment to the related specialized hardware (which, in the digital domain, could be generated through the other existing layers of the SODA Synthesizer). We then discuss the opportunities for even deeper integration afforded by the hardware compilation infrastructure, providing a path towards the generation of complex heterogeneous artificial intelligence systems.

Curzel, Serena↗

DOE OSTI2022

Adversarial Sampling-Based Motion Planning

In this report there are many scenarios in which a mobile agent may not want its path to be predictable. Examples include preserving privacy or confusing an adversary. However, this desire for deception can conflict with the need for a low path cost. Optimal plans such as those produced by RRT* may have low path cost, but their optimality makes them predictable. Similarly, a deceptive path that features numerous zig-zags may take too long to reach the goal. We address this trade-off by drawing inspiration from adversarial machine learning. We propose a new planning algorithm, which we title Adversarial RRT*. Adversarial RRT* attempts to deceive machine learning classifiers by incorporating a predicted measure of deception into the planner cost function. Adversarial RRT* considers both path cost and a measure of predicted deceptiveness in order to produce a trajectory with low path cost that still has deceptive properties. We demonstrate the performance of Adversarial RRT*, with two measures of deception, using a simulated Dubins vehicle. We show how Adversarial RRT* can decrease cumulative RNN accuracy across paths to 10%, compared to 46% cumulative accuracy on near-optimal RRT* paths, while keeping path length within 16% of optimal. We also present an example demonstration where the Adversarial RRT* planner attempts to safely deliver a high value package while an adversary observes the path and tries to intercept the package.

42 ENGINEERING↗

DOE OSTI2016

Superconductivity in epitaxially grown self-assembled indium islands: progress towards hybrid superconductor/semiconductor optical sources

Currently, superconducting qubits lead the way in potential candidates for quantum computing. This is a result of the robust nature of superconductivity and the non-linear Josephson effect which make possible many types of qubits. At the same time, transferring quantum information over long distances typically relies on the use of photons as the elementary qubit. Converting between stationary electronic qubits in superconducting systems and traveling photonic qubits is a challenging yet necessary goal for the interface of quantum computing and communication. The most promising path to achieving this goal appears to be the integration of superconductivity with optically active semiconductors, with quantum information being transferred between the two by means of the superconducting proximity effect. Obtaining good interfaces between superconductor and semiconductor is the next obvious step for improving these hybrid systems. As a result, we report on our observation of superconductivity in self-assembled indium structures grown epitaxially on the surface of semiconductor material.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Multistage Stabilized Continuation for Indirect Optimal Control of Three-Dimensional Hypersonic Trajectories

This work presents the development of a multistaged stabilized continuation for the three-dimensional unpowered hypersonic trajectory planning problem using indirect optimal control methods. The stabilized continuation method is noniterative and guaranteed to terminate within a finite number of floating point operations, thereby making it well suited for onboard autonomous implementations. Here, we present a multistage formulation of the stabilized continuation scheme that involves starting with a “loose” integration tolerance during the first stage and ramping up toward a “strict” integration tolerance through subsequent stages. An important benefit of this approach is that even when the solution to the underlying optimal control problem is numerically unstable, such as with the hypersonic vehicle footprint generation problem, the stabilized continuation algorithm is shown to be successful in finding a solution while providing some additional insights into the underlying cause of the numerical instability.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2016

Proactive routing mutation against stealthy Distributed Denial of Service attacks: metrics, modeling, and analysis

The Infrastructure Distributed Denial of Service (IDDoS) attacks continue to be one of the most devastating challenges facing cyber systems. The new generation of IDDoS attacks exploit the inherent weakness of cyber infrastructure including deterministic nature of routes, skew distribution of flows, and Internet ossification to discover the network critical links and launch highly stealthy flooding attacks that are not observable at the victim end. In this paper, first, we propose a new metric to quantitatively measure the potential susceptibility of any arbitrary target server or domain to stealthy IDDoS attacks, and es- timate the impact of such susceptibility on enterprises. Second, we develop a proactive route mutation technique to minimize the susceptibility to these attacks by dynamically changing the flow paths periodically to invalidate the adversary knowledge about the network and avoid targeted critical links. Our proposed approach actively changes these network paths while satisfying security and qualify of service requirements. We present an integrated approach of proactive route mutation that combines both infrastructure-based mutation that is based on reconfiguration of switches and routers, and middle-box approach that uses an overlay of end-point proxies to construct a virtual network path free of critical links to reach a destination. We implemented the proactive path mutation technique on a Software Defined Network using the OpendDaylight controller to demonstrate a feasible deployment of this approach. Our evaluation validates the correctness, effectiveness, and scalability of the proposed approaches.

ARC↗

DOE OSTI2023

A feasible path for the use of ferromagnetic josephson junctions in quantum circuits: The ferro-transmon

We discuss the capabilities of ferromagnetic (F) Josephson junctions (JJs) in a variety of layouts and configurations. The main goal is to demonstrate the potential of these hybrid JJs to disclose new physics and the possibility to integrate them in superconducting classical and quantum electronics for various applications. The feasible path towards the use of ferromagnetic Josephson junctions in quantum circuits starts from experiments demonstrating macroscopic quantum tunneling in NbN/GdN/NbN junctions with ferro-insulator barriers and with triplet components of the supercurrent, supported by a self-consistent electrodynamic characterization as a function of the barrier thickness. This has inspired further studies on tunnel ferromagnetic junctions with a different layout and promoted the first generation of ferromagnetic Al-based JJs, specifically Al/AlO x /Al/Py/Al. This layout takes advantage of the capability to integrate the ferromagnetic layer in the junction without affecting the quality of the superconducting electrodes and of the tunnel barrier. The high quality of the devices paves the way for the possible implementation of Al tunnel-ferromagnetic JJs in superconducting quantum circuits. These achievements have promoted the notion of a novel type of qubit incorporating ferromagnetic JJs. This qubit is based on a transmon design featuring a tunnel JJ in parallel with a ferromagnetic JJ inside a SQUID loop capacitively coupled to a superconducting readout resonator. In conclusion, the effect of an external RF field on the magnetic switching processes of ferromagnetic JJs has been also investigated.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Stabilized Oxygen Vacancy Chemistry toward High-Performance Layered Oxide Cathodes for Sodium-Ion Batteries

Anionic redox has emerged as a transformative paradigm for high-energy layered transition-metal (TM) oxide cathodes, but it is usually accompanied by the formation of anionic redox-mediated oxygen vacancies (OVs) due to irreversible oxygen release. Additionally, external factor-induced OVs (defined as intrinsic OVs) also play a pivotal role in the physicochemical properties of layered TM oxides. However, an in-depth understanding of the interplay between intrinsic and anionic redox-mediated OVs and the corresponding regulation mechanism of the dynamic evolution of OVs is still missing. Herein, we disclose the strong interrelationship between these OVs and demonstrate that the presence of intrinsic OVs in the TMO2 layers could induce weak integrity of the TM-O frameworks and unlock additional diffusion paths to trigger the generation and migration of anionic redox-mediated OVs. Accordingly, an OV stabilization strategy is proposed by deliberately introducing high-valence Nb5+, which could serve as an important building block in anchoring the oxygen sublattice and preventing the formation of a percolating OV migration network, thereby suppressing the formation/diffusion of anionic redox-mediated OVs. Consequently, superb structural integrity and improved electrochemical performance with reversible anionic redox chemistry are achieved. This work advances our understanding of the role of OVs for developing high-performance energy storage systems utilizing anionic redox.

anionic redox↗

DOE OSTI2017

15.3%-Efficient GaAsP Solar Cells on GaP/Si Templates

As single-junction Si solar cells approach their practical efficiency limits, a new pathway is necessary to increase efficiency in order to realize more cost-effective photovoltaics. Integrating III-V cells onto Si in a multijunction architecture is a promising approach that can achieve high efficiency while leveraging the infrastructure already in place for Si and III-V technology. In this Letter, we demonstrate a record 15.3%-efficient 1.7 eV GaAsP top cell on GaP/Si, enabled by recent advances in material quality in conjunction with an improved device design and a high-performance antireflection coating. Furthermore, we present a separate Si bottom cell with a 1.7 eV GaAsP optical filter to absorb most of the visible light with an efficiency of 6.3%, showing the feasibility of monolithic III-V/Si tandems with >20% efficiency. Through spectral efficiency analysis, we also compare our results to previously published GaAsP and Si devices, projecting tandem GaAsP/Si efficiencies of up to 25.6% based on current state-of-the-art individual subcells. With the aid of modeling, we further illustrate a realistic path toward 30% GaAsP/Si tandems for high-efficiency, monolithically integrated photovoltaics.

14 SOLAR ENERGY↗

DOE OSTI2023

Elevating zero dimensional global scaling predictions to self-consistent theory-based simulations

In this work, we have developed an innovative workflow, Stability, Transport, Equilibrium, and Pedestal (STEP)-zero-dimensional (0D), within the OMFIT integrated modeling framework. Through systematic validation against the International Tokamak Physics Activity global H-mode confinement database, we demonstrated that STEP-0D, on average, predicts the energy confinement time with a mean relative error of less than 19%. Moreover, this workflow showed promising potential in predicting plasmas for proposed fusion reactors such as the affordable, robust, compact (ARC) reactor, the European demonstration power plant (EU-DEMO), and the China fusion engineering test reactor (CFETR) indicating moderate H-factors between 0.9 and 1.2. STEP-0D allows theory-based prediction of tokamak scenarios, beginning with 0D quantities. The workflow initiates with the PRO-create module, generating physically consistent plasma profiles and equilibrium using the same 0D quantities as the IPB98(y,2) confinement scaling. This sets the starting point for the STEP module, which further iterates between theory-based physics models of equilibrium, core transport, and pedestal to yield a self-consistent solution. Given these attributes, STEP-0D not only improves the accuracy of predicting plasma performance but also provides a path toward a novel fusion power plant design workflow. When integrated with engineering and costing models within an optimization, this new approach could eliminate the iterative reconciliation between plasma models of varying fidelity. This potential for a more efficient design process underpins STEP-0D's significant contribution to future fusion power plant development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

Metrics and Benchmarks for Quantum Processors: State of Play

A compelling narrative has taken hold as quantum computing explodes into the commercial sector: Quantum computing in 2018 is like classical computing in 1965. In 1965 Gordon Moore wrote his famous paper about integrated circuits, saying: "At present, [minimum cost] is reached when 50 components are used per circuit. But... the complexity for minimum component costs has increased at a rate of roughly a factor of two per year... by 1975, the number of components per integrated circuit for minimum cost will be 65,000." This narrative is both appealing (we want to believe that quantum computing will follow the incredibly successful path of classical computing!) and plausible (2018 saw IBM, Intel, and Google announce 50-qubit integrated chips). But it is also deeply misleading. Here is an alternative: Quantum computing in 2018 is like classical computing in 1938. In 1938, John Atanasoff and Clifford Berry built the very first electronic digital computer. It had no program, and was not Turing-complete. Vacuum tubes — the standard "bit" for 20 years — were still 5 years in the future. ENIAC and the achievement of "computational supremacy" (over hand calculation) wouldn't arrive for 8 years, despite the accelerative effect of WWII. Integrated circuits and the information age were more than 20 years away. Neither of these analogies is perfect. Quantum computing technology is more like 1938, while the level of funding and excitement suggest 1965 (or later!). But the point of the cautionary analogy to 1938 is simple: Quantum computing in 2018 is a research field. It is far too early to establish metrics or benchmarks for performance. The best role for neutral organizations like IEEE is to encourage and shape research into metrics and benchmarks, so as to be ready when they become necessary. This white paper presents the evidence and reasoning for this claim. We explain what it means to say that quantum computing is a "research field", and why metrics and benchmarks for quantum processors also constitute a research field. We discuss the potential for harmful consequences of prematurely establishing standards or frameworks. We conclude by suggesting specific actions that IEEE or similar organizations can take to accelerate the development of good metrics and benchmarks for quantum computing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

The BayREN Integrated Commercial Retrofits (BRICR) Project: An Introduction and Preliminary Results

BayREN Integrated Commercial Retrofits (BRICR) is a DOE-funded project which aims to enhance the capacity of energy efficiency programs to recruit participants, develop retrofits, and measure outcomes in small and medium-sized commercial buildings, a sector notoriously hard to reach and expensive to serve, that accounts for 2/3 of US commercial floor space. To address these barriers, BRICR leverages existing incentives, financing, data, and open source software to facilitate two paths for comprehensive improvements: a deep energy retrofit, or serial upgrades integrated into capital improvement and maintenance cycles. BRICR is developing an integrated workflow for iterative energy modeling of commercial buildings for city energy program managers and auditors - starting with mass building-scale simulation based on public records and proceeding through audit, retrofit, and measurement and verification stages. BRICR builds on existing tools including LBNL’s CBES, NREL’s OpenStudio, PNNL’s Audit Template, and DOE’s BuildingSync and SEED PlatformTM. At each stage, BRICR uses available information to inform simulations (starting with public records but iteratively augmented with observations from energy program staff) to improve the quality of the models that inform decision making. This paper presents initial results from energy models of 1699 office and retail buildings in San Francisco. Building stock data from public records were translated to create a BuildingSync file for each building which was stored in SEED. Each BuildingSync file was then translated to multiple OpenStudio Workflow files for EnergyPlus simulation to estimate energy savings of energy conservation measures (ECM). Energy savings predictions for each ECM were written back to an updated BuildingSync file for each building and re-uploaded to SEED. The distribution of baseline energy simulations was calibrated against the publicly disclosed distribution of energy benchmarking data to increase confidence in results.

Piette, M↗

DOE OSTI2022

Assessment of Crustal Leg Uncertainties for RSTT Path-dependent Uncertainty

The Regional Seismic Travel Time (RSTT) model and software were recently updated to allow for path-dependent uncertainty (PDU) estimates for travel-time (Begnaud et al. 2021; Begnaud et al. 2020). Begnaud et al. (2021) describes the process of improving the current distance-dependent uncertainty parameterization for RSTT using a random effects model (REM) to estimate slowness (inverse velocity) uncertainty as a mean squared error for each model parameter. The REM separates the error between observed slowness and model predicted slowness into bias and random components. The PDU is calculated by integrating these mean squared errors along a seismic-phase ray path.

58 GEOSCIENCES↗

DOE OSTI2020

User Access to Scientific Facilities via 5G: A Cyber Security Thought Experiment

5G is more than an over-the-air radio technology upgrade. It is a strategy to extend Mobile Network Operator service offerings beyond traditional voice, instant messaging and Internet access. 5G Mobile Network Operators will offer new telecommunication services that include enhanced guarantees of confidentiality, integrity and availability. How could such services change the way Science collaborations connect scientists to supercomputers and other scientific facilities? Current scientific collaborations implicitly trust cloud service providers to securely store and process data. The perceived risks of outsourcing Science data security are counterbalanced by assurances that cloud providers operate at a scale that allows them to implement security measures impractical for Science collaborations (e.g. continuous system administrator behavioral monitoring and strict individual separation of duties). If that is true for a cloud service provider like Amazon Web Services (2018 revenue: $25.7 billion), could it also be true for Mobile Network Operators like Verizon Wireless (2018 revenue: $91.7 billion) or AT&T Mobility (2018 revenue: $71.3 billion)? DOE Leadership Class supercomputer facility users currently access them from the public Internet via Secure Shell. The sponsors and operators of the supercomputer facilities have determined that the public Internet path between the Scientist’s Device and the Login Node does not natively provide enough confidentiality or integrity to protect those communications. Therefore, the facilities achieve additional confidentiality and integrity by requiring Secure Shell encryption across those untrusted network paths. Using 5G Network Slice technology, a Mobile Network Operator may offer communication services between supercomputer users and facilities that natively provide confidentiality and integrity guarantees. Sponsors and operators of supercomputer facilities may determine that these guarantees provide enough confidentiality and integrity to protect those communications. If so, a 5G Network Slice could replace an SSH session running over the public Internet. Finally, this use case could be extended to other Office of Science user facility access requirements. Consider microscopy instruments at (e.g.) the Center for Nanoscale Materials or the Environmental Molecular Sciences Laboratory. The embedded systems controlling such instruments may not always support encrypted network access technologies like SSH. 5G Network Slices may offer an alternative to current VPN or SSH tunneling techniques, with additional benefits like guaranteed minimum bandwidth.

5G↗

DOE OSTI2016

Evaluating marginal likelihood with thermodynamic integration method and comparison with several other numerical methods

Evaluating marginal likelihood is the most critical and computationally expensive task, when conducting Bayesian model averaging to quantify parametric and model uncertainties. The evaluation is commonly done by using Laplace approximations to evaluate semianalytical expressions of the marginal likelihood or by using Monte Carlo (MC) methods to evaluate arithmetic or harmonic mean of a joint likelihood function. This study introduces a new MC method, i.e., thermodynamic integration, which has not been attempted in environmental modeling. Instead of using samples only from prior parameter space (as in arithmetic mean evaluation) or posterior parameter space (as in harmonic mean evaluation), the thermodynamic integration method uses samples generated gradually from the prior to posterior parameter space. This is done through a path sampling that conducts Markov chain Monte Carlo simulation with different power coefficient values applied to the joint likelihood function. The thermodynamic integration method is evaluated using three analytical functions by comparing the method with two variants of the Laplace approximation method and three MC methods, including the nested sampling method that is recently introduced into environmental modeling. The thermodynamic integration method outperforms the other methods in terms of their accuracy, convergence, and consistency. The thermodynamic integration method is also applied to a synthetic case of groundwater modeling with four alternative models. The application shows that model probabilities obtained using the thermodynamic integration method improves predictive performance of Bayesian model averaging. As a result, the thermodynamic integration method is mathematically rigorous, and its MC implementation is computationally general for a wide range of environmental problems.

97 MATHEMATICS AND COMPUTING↗