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DOE OSTI2021

Interpreting Write Performance of Supercomputer I/O Systems with Regression Models

This work seeks to advance the state of the art in HPC I/O performance analysis and interpretation. In particular, we demonstrate effective techniques to: (1) model output performance in the presence of I/O interference from production loads; (2) build features from write patterns and key parameters of the system architecture and configurations; (3) employ suitable machine learning algorithms to improve model accuracy. We train models with five popular regression algorithms and conduct experiments on two distinct production HPC platforms. We find that the lasso and random forest models predict output performance with high accuracy on both of the target systems. We also explore use of the models to guide adaptation in I/O middleware systems, and show potential for improvements of at least 15% from model-guided adaptation on 70% of samples, and improvements up to 10× on some samples for both of the target systems.

Xie, Bing↗

DOE OSTI2025

High-Temperature Aquifer Thermal Energy Storage (HT-ATES) Projects in Germany and the Netherlands—Review and Lessons Learned

Aquifer thermal energy storage (ATES) is a concept that can help to address heating and cooling needs through the use of the subsurface as a seasonal thermal energy storage (STES) system. Over 2800 ATES systems have been deployed with storage temperatures typically below 25 °C and only a few with higher temperatures (>40 °C), which would increase the energy density and utility of the stored thermal fluids. Until now, only a few high-temperature aquifer thermal energy storage (HT-ATES) projects have been initiated and are still in operation. These HT-ATES projects have encountered a range of technical and non-technical challenges. This study reviews ten such projects: four in Germany and six in the Netherlands. The non-technical issues include public acceptance, a lack of regulatory framework for these systems, managing overlapping uses of the subsurface, managing changes with the providers and off-takers of thermal energy, and obtaining financing to implement these projects. Common technical issues include geological factors such as incomplete characterization of the subsurface and reservoir heterogeneity; geochemical issues such as mineral scaling, corrosion, and biofouling; lower than expected thermal recovery; and issues with system design and reliability. This review highlights benefits and challenges faced by HT-ATES projects with the goal to use the lessons learned to improve the siting, design, development, and operation of such systems. Recommendations include improved initial subsurface site characterization, use of coupled process models to optimize system design and predict system performance, cascaded uses of stored thermal energy to better utilize the stored heat, monitoring networks to provide feedback on system performance, and expanded system scale to allow for continued operation even when maintenance of some system components is required. Techno-economic modeling and risk analysis could be used to optimize such HT-ATES project design and identify key factors that will affect sustained economic viability. In addition, design flexibility is important for these systems to allow for changing conditions regarding the supply and demand of thermal energy. Adopting these findings should improve the performance and reduce the risks for future HT-ATES projects worldwide.

15 - GEOTHERMAL ENERGY↗

DOE OSTI2026

Are Deep Energy Retrofits in Commercial Buildings Including Window Upgrades?

U.S. Commercial buildings account for about 20% of total U.S. energy consumption. Because the thermal performance of windows significantly affects building energy efficiency and HVAC system performance, best practice guidance often includes window and envelope improvements in conjunction with HVAC upgrades to optimize energy use and improve occupant comfort. It is an open question, however, regarding how often these best practices are implemented in the field. This paper aims to address that gap by conducting a literature review and a series of interviews with commercial building auditing and management professionals to explore the factors that drive window retrofits in commercial buildings. The paper explores a range of case studies from deep energy retrofits across the globe, comparing projects with and without window retrofits. The primary goals of this review are to: (1) provide data from real-world case studies illustrating the role of windows in deep energy renovations and HVAC upgrades, (2) conduct retrofit cost analyses for windows and high-performance HVAC systems and (3) offer insights into how window upgrade decisions are made and when they are implemented as part of deep energy retrofits. Most of the retrofit studies focused exclusively on high performance HVAC upgrades without considering how window upgrades might further enhance the overall energy efficiency of commercial buildings. Interviews with building industry experts shed light on the key factors influencing deep energy retrofit decisions and what factors tip the scales in favor of including window measures with more comprehensive retrofit projects.

Cort, Katherine↗

DOE OSTI2021

Improving the performance of first- and last-mile mobility services through transit coordination, real-time demand prediction, advanced reservations, and trip prioritization

Socio-demographic trends and recent economic development patterns have resulted in travel behavior changes that call for more flexible and accessible public transit options. Because flexible transit services vary in scope, size, and service type, new data-informed methods are useful to optimize services based on the specific needs of local communities and riders. In this study, real-world demand and vehicle trajectory data were used to evaluate and optimize system performance for an existing first-mile–last-mile (FMLM) service in Robinson Township, PA. A general FMLM model for arbitrary demand and service supply was then developed to quantify system performance—both travel time costs and day-to-day reliability—for various operational polices considering spatio-temporal demand variation and transportation network dynamics. Heuristics were used for optimal real-time vehicle routing in sizable real-world networks accommodating various service types and scopes. In this case study, total user costs were reduced by 18.6% when rides were coordinated with mainline fixed-route transit. Predictive routing strategies were shown to marginally improve system performance under sparse and variable spatio-temporal demand. The case study also highlights potentially large travel time and user reliability improvements—reductions of 51% and 53.8%, respectively—when trip requests were made in advance of their desired pickup time. Finally, we show that travel time reliability can be improved for time-inflexible trips with trip prioritization without increasing total user costs. These results were stable to changes in demand density.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2021

FY2021 Status Report on the Computing Systems for the Yucca Mountain Project TSPA-LA Models and Testing of Selected Process Models

Sandia National Laboratories continued evaluation of the total system performance assessment (TSPA) for License Application (LA) computing systems for the previously considered Yucca Mountain Project (YMP). This was done to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment) type analysis, as directed by the National Nuclear Security Administration (NNSA), DOE 2010. The FY21 task included continued operation of the cluster; maintenance of the TSPA-LA models (with GoldSim 9.60.300); continued assessment of the status of the Infiltration Model; (a process model that feeds the TSP -LA) and preliminary assessments of the Unsaturated Zone Flow Model and the Saturated Zone Flow and Transport Model Abstraction (process models that feed the TSPA-LA). The 2014 cluster and supporting software systems are currently fully operational to support TSPA-LA type analyses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Building Performance Standards and Energy Code Alignment - Technical Brief

Building energy codes focus on building design, construction and renovation and have significantly increased building efficiency since the first national energy code was published in 1975. Most jurisdictions have energy codes based on ANSI/ASHRAE/IES Standard 90.1 (hereto referred to as Standard 90.1) and the International Energy Conservation Code (IECC). Compliance options available in these model energy codes include a prescriptive path, whole building performance paths – including IECC Total Building Performance (TBP), Standard 90.1 Energy Cost Budget (ECB) method and Performance Rating Method (PRM) – and system performance paths for envelope and heating, ventilation, and air-conditioning systems. Building performance standard (BPS) policies are an emerging policy tool used by jurisdictions to reduce the operational energy use or greenhouse gas (GHG) emissions of the existing commercial building stock. BPS policies vary widely between jurisdictions and are tailored to each location’s climate and energy goals. Intuitively, projects that met a recent edition of the energy code should comply with the BPS targets. However, some new buildings may struggle with meeting the BPS for the following reasons: 1. Energy codes focus on the design of the building and its projected ability to perform efficiently, while BPS compliance is dependent on the actual ongoing performance of the building, considering variables like occupancy, operation, and maintenance. 2. There are significant differences in the methodologies used to determine BPS compliance versus code compliance, including how each handles compliance metrics, handling of building amenities, and renewable energy generation. 3. The prescriptive compliance path in the energy code is based on performance of individual building components, as opposed to the performance compliance path which accounts for holistic building design strategies and interdependent building systems. This can result in a significant variability in post-occupancy performance for buildings built using the prescriptive path. Designs on the lower end of the permitted efficiency range may struggle with meeting the BPS.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

PSTN-054: Updated estimates of the Rubin system throughput and expected LSST image depth

This document presents updated estimates, as of May 2022, of the Rubin system throughput and compares them to throughput requirements from the LSST Science Requirements Document. In addition, it uses these estimates to forecast LSST's median single-visit and co-added image depths for the current (OpSim v2.0) baseline LSST cadence simulation: (23.8, 24.5, 24.0, 23.4, 22.7, 22.0) and (25.6, 26.9, 26.9, 26.4, 25.6, 24.8) in ugrizy, respectively. In addition, it uses these estimates to forecast LSST’s median single-visit and co-added image depths for the current baseline LSST cadence simulation. Estimated system performance relies on actual measurements of the performance of various system hardware components, and on simulations where measurements are still unavailable. The updated performance estimates meet all the relevant requirements from the LSST Science Requirements Document. As our knowledge of the as-buit system continues to improve, updates to these estimates will continue to be provided.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Optimization of a Residential Air Source Heat Pump using Refrigerants with GWP <150 for Improved Performance and Reduced Emission

Using low-GWP refrigerants can reduce the Green House Gas (GHG) emission of heat pump systems. Heat exchangers and compressors are the key components and have a prominent impact on system performance, significant research is devoted to reducing the cost of the heat exchangers while achieving the same or better system performance with refrigerant charge reduction.To better understand the environmental impacts of optimized systems with low-GWP refrigerants, Life Cycle Climate Performance (LCCP) evaluation method was used to evaluate the direct and indirect emissions of the system over the course of its lifetime from manufacturing to disposal. The DOE/ORNL Heat Pump Design Model (HPDM) is used to evaluate the performance of heat pumps. Multi-objective optimizations using Particle Swarm Optimization (PSO) algorithm are performed on a 3-ton R410A residential air source heat pump on market. Seven R410A alternatives, i.e., R32, R454B, R454C, R455A, R457A, R1234yf and R1234ze(E) are investigated. The last five fluids have GWP lower than 150.As a result, 5.5%-12.8% seasonal energy efficiency ratio 2 (SEER2) improvement is achieved, and the optimized systems reduce life cycle CO2 emission by 8.5%-28.6% with GWP lower than 150 refrigerants. The optimal heat exchangers can fit into the original R410A fan-coil units; therefore, the proposed design method establishes a production and installation path to produce cost-effective low-GWP heat pumps easily accepted by end users.

Li, Zhenning↗

DOE OSTI2021

Integrated Water-Power System Resilience Analysis in a Southeastern Idaho Irrigation District: Minidoka Case Study

This study investigates the joint water–power system resilience of an irrigation district in southeastern Idaho. Irrigation districts face difficulties in the delivery of water to farmers under drought conditions, during equipment failures, or unplanned infrastructure disruptions. The resilience of interconnected water and power systems can be better analyzed and understood through an integrated approach, using a model that connects the dependencies between the two halves of the system. Using a multi-agent system model capturing both water and power system components, as well as their linkages, we capture the interdependencies of these systems and highlight opportunities for improvement when faced with disruptions. Through simulation scenarios, we examine the system resilience using system performance, quantified as the percentage of met demand of the power and water system, when subjected to drought water year, an unforeseen water demand increase, power outage and dam failure. Scenario results indicate that the effects of low flow years are mostly felt in the power system; unexpected increases in water demand marginally impact irrigation system performance; dams and pumps present vulnerabilities of the system, causing substantial unmet demand during disruptions. Noting the interdependencies between the water–power system halves while leveraging an integrated simulation allows for an insightful analysis of the system impacts during disruptions.

13 HYDRO ENERGY↗

DOE OSTI2026

ORCHA: A performance portability system for extreme heterogeneity

Heterogeneity is the prevalent trend in the rapidly evolving high-performance computing (HPC) landscape in both hardware and application software. The diversity in hardware platforms, currently comprising various accelerators and a future possibility of specializable chiplets, poses a significant challenge for scientific software developers aiming to harness optimal performance across different computing platforms while maintaining the quality of solutions when their applications are simultaneously growing more complex. Code synthesis and code generation can provide mechanisms to mitigate this challenge. We have developed a divide and conquer approach where different aspects of performance are handled by different stand-alone tools that are interfaced with the application through generated code. This portability system, ORCHA, enables users to configure and orchestrate their computations among available resources on a platform by specifying a high-level recipe, thereby permitting a many-to-many paradigm where each recipe results in a different variant of the application. The core design goal is to let users decide the application’s hardware mapping and orchestration by editing only the high-level recipe—without modifying the maintained source code or binding the application to a particular runtime system. Tools in ORCHA distribution are: CG-Kit for translating the recipe into an execution graph; Milhoja to execute the graph by orchestrating data and task movement among hardware resources; and Macroprocessor that enables users to define their own code-shorthand for higher composability and easier management of code variants. Additionally, the design of ORCHA permits tools to work in a plug-and-play mode where the application can build and run without CG-Kit and Milhoja, and either tool can be swapped out for other tools with similar capabilities by modifying the code generation portion of ORCHA. In this paper, we describe the design of ORCHA and the role that code-generation plays in isolating applications from tools. We demonstrate the breadth of configurations ORCHA enables with a case study in which an application configuration is realized on three distinct hardware mappings—a GPU-centric, a CPU/GPU balanced, and a CPU/GPU concurrent layouts by using different recipes.

Lee, Youngjun↗

DOE OSTI2020

Resiliency-based restoration optimization for dependent network systems against cascading failures

Due to the increasing importance of large-scale and complex network systems and the potential for massive cascading failures in these real-world systems, modeling of system resiliency and optimization of restoration strategies to mitigate system performance loss caused by diverse disruptions is of significant interest among researchers and practitioners. Although society has experienced many incidents that demonstrate the influence of cascading failures aggravated by dependencies inside network systems, existing resiliency-based restoration optimization research rarely if ever jointly considers the impact of system dependencies on cascading failures. In this paper, different restoration prioritization strategies are applied to network systems subject to cascading failures that take into account system dependencies. By conducting case studies on synthetic networks and the U.S. airport network system, the effects of restoration strategies are evaluated using a system resiliency metric and two system performance measurements. Furthermore, the influence of system dependency characteristics and the interplay between them and restoration strategies on system resiliency regarding cascading failures are also investigated. This work demonstrates the distinct effects of restoration prioritization actions against cascading failures to mitigate the performance loss in network systems with different properties. It also provides insights about restoration improvement by considering dependency impacts to effectively reduce the intensity and extent of cascading failures.

42 ENGINEERING↗

DOE OSTI2021

Numerical Investigation of Two-Stage Vapor Compression System with Simultaneous Vapor and Liquid Injection

Two-stage vapor compression systems can be advantageous over single-stage systems by providing improved system performance, lower discharge temperature and reduced throttling losses. These systems employ various intermediate configurations, such as liquid injection and vapor injection. This paper presents a configuration for a R1234ze(E) two-stage air-conditioning system using a turbo compressor with two injection ports, one for vapor injection and the second for liquid injection. The liquid injection is used to cool the motor and electronics. A component-based representation and solution approach was used to simulate the two-stage compression system with simultaneous vapor and liquid injection at steady state. The turbo compressor was represented using a customized performance map. The condenser and the evaporator were modeled using finite-volume approach. A parametric study was conducted to assess the impact of the following three variables on the system performance: vapor injection ratio, condenser air flow rate, and discharge pipe pressure drop. The simulation results show that as the vapor injection ratio increased, the system performance undergoes a tradeoff between an enhanced subcooling effect (and thus enhanced unit refrigeration capacity) and a decreased suction mass flow rate. Maximum COP occurs when the vapor injection ratio was 0.1. The results also show that as the condenser air flow rate increased, both the capacity and power consumption (including fan power) increased monotonically, and COP increased first and then decreased. At 75% load, the COP improvement at the optimum flow rate was marginally less than 0.5%. Lastly, higher discharge pipe pressure drop increased the discharge pressure. It showed very small effect on the overall system performance at the condition selected for the current study.

component-based steady-state simulation↗

DOE OSTI2024

Building Performance Standards and Energy Code Alignment: Technical Brief

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Anode Catalysts in Anion-Exchange-Membrane Electrolysis without Supporting Electrolyte: Conductivity, Dynamics, and Ionomer Degradation

Not provided.

Chemistry↗

DOE OSTI2023

Analysis of a Runtime Data Sharing Architecture over LTE for a Heterogeneous CAV Fleet

This paper describes a lightweight runtime architecture for telemetry, communication, and control of cars deployed with advanced driver assistance systems where a human is in the loop with the car, via an LTE connection. The system architecture supports both local control decisions based on car sensors and safety algorithms as well as high-level input from external systems that may provide insight into traffic state ahead of sensor data. Implementation of the architecture is done in ROS and depends on open-source software packages for runtime decoding of information from the vehicle’s controller area network (CAN) and integration of GPS data from accompanying sensors. The contribution of the paper is to describe the overall architecture, the data it can communicate to other systems, performance of the system at runtime, and challenges faced when deploying the architecture across a heterogeneous fleet. Preliminary results from analysis of test data will provide insights into whether the use of high-latency communication can be effective for societal-scale intelligent transportation systems when applied in future scenarios

Richardson, Alex↗

DOE OSTI2024

Preliminary Design of Nuclear Reactor Heat Delivery Systems: Integration with Reference Oil Refinery, Methanol Synthesis, and Hydrogen Production

The Department of Energy’s (DOE) Integrated Energy Systems (IES) program is generating comprehensive analyses validating the opportunity for using nuclear energy in a variety of applications including future clean grids, providing heat for direct use, and providing heat to help reduce emissions in chemical commodities. This work focuses on the preliminary designs of thermal delivery systems that can integrate heat produced from a nuclear core to industrial processes. The key research question that needs to be answered is: what is the prospective method for integrating nuclear generated heat energy into non-electric applications that can facilitate combined heat and power operations by advanced nuclear reactor systems? This research is a composition of case studies showing preliminary conceptual designs for thermal delivery systems integrating advanced nuclear systems with a few industrial systems including high temperature steam electrolysis, a reference oil refinery, and potential future methanol systems that supplant some natural gas use with nuclear energy. Piping and instrumentation diagrams have been developed to show the conceptual integration of nuclear systems with representative industrial systems. Different features of the configurations are dependent on the specific integration requirements including energy source conditions, demand quantity, and require energy application conditions. Design concepts are validated using thermodynamic balance calculations to verify system performance including calculating system losses during transport. Key components: pumps and compressors, heat exchangers, and network piping are reported with key design information and sizing.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Substation-Level Grid Topology Optimization Using Bus Splitting: Preprint

Operations of substation circuit breakers are of high significance for performing system maintenance and topology optimization. Bus splitting is one type of topology changes where the two bus-bars at a substation become electrically disconnected after certain actions of circuit breakers. As these events involve detailed substation modeling, they are not typically considered in power system routine operation and control. In this paper, an improved substation-level topology optimization is developed by expanding traditional line switching with breaker-level bus splitting, which can further reduce grid congestion and generation costs. A tight McCormick relaxation is proposed to reformulate the bi-linear terms in the resultant topology optimization model. Thus, a tractable mixed-integer linear program formulation is presented which can be efficiently solved for real-time control. Numerical studies on the IEEE 14-bus and 118-bus systems demonstrate the performance and economic benefits of the proposed topology optimization approach.

bus split↗