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This report is part of a series describing different ResStock measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the “Heat Pump Water Heater” measure upgrade methodology and briefly discusses key results.
Since the scientific revolution in the 16th and 17th centuries, the process of scientific discovery has followed an iterative feedback process of observation, hypothesis development and testing with physical experiments, which is widely referred to as the scientific method. This process remained largely unchanged until the middle of the 20th century, when the emergence of digital computers empowered scientist to build and inspect detailed simulations of physical phenomena. Over the last century, computational tools have transformed modern approaches to scientific discovery by enabling fast and affordable hypothesis testing before physical experiments are conducted, shown in Figure 1-1. Some notable examples include: global climate forecasts to understand how the environment may change over decades [130]; modeling the behavior of plasma to design fusion reactors [59]; and understanding the behavior of molecules in biological processes [161, 223].
This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency.
While urban-scale building energy modeling is becoming increasingly common, it currently lacks standards, guidelines, or empirical validation against measured data. Empirical validation necessary to enable best practices is becoming increasingly tractable. The growing prevalence of advanced metering infrastructure has led to significant data regarding the energy consumption within individual buildings, but is something utilities and countries are still struggling to analyze and use wisely. In partnership with the Electric Power Board of Chattanooga, Tennessee, a crude OpenStudio/EnergyPlus model of over 178,000 buildings has been created and used to compare simulated energy against actual, 15-min, whole-building electrical consumption of each building. In this study, classifying building type is treated as a use case for quantifying performance associated with smart meter data. This article attempts to provide guidance for working with advanced metering infrastructure for buildings related to: quality control, pathological data classifications, statistical metrics on performance, a methodology for classifying building types, and assess accuracy. Advanced metering infrastructure was used to collect whole-building electricity consumption for 178,333 buildings, define equations for common data issues (missing values, zeros, and spiking), propose a new method for assigning building type, and empirically validate gaps between real buildings and existing prototypes using industry-standard accuracy metrics.
Data associated with the paper “Photovoltaic Windows to Offset the Intensive Energy and Carbon Footprints of Highly Glazed Buildings” by Vincent M. Wheeler, Janghyun Kim, Tom Daligault, Bryan Rosales, Chaiwat Engtrakul, Robert C. Tenent, and Lance M. Wheeler
The decision to make the Centaur Vehicle a Space Transportation System (STS) payload meant that new ground support equipment provisions at Launch Pads Pads 39A and 39B were required. These new equipment provisions were needed to service the Centaur vehicle while it was installed in the Orbiter's payload bay prior to launch. This paper describes the design of a new rolling beam umbilical syste (RBUS) being added to the pad fixed service structure (FSS) in order to provide the primary functions of liquid hydrogen (LH2) fill, drain, and vent. The carrier plate itself is a Government-furnished equipment item and of necessity became a T-O disconnect. This permits quick offloading in the event of an abort prior to lift-off. In addition to the rolling beam structure, mechanisms, and fluid lines, it was necessary to design and build a carrier plate simulator to support early development testing of the mast at the Launch Equipment Test Facility at Kennedy Space Center. The RBUS is designed to be compatible with the rotating service structure (RSS) to the extent that the umbilical may be deployed with the RSS mated with the vehicle. It is also designed to clear the RSS as the RSS rotates back out of the way. Accessibility to the Orbiter aft compartment via the 50-1 door had to be maintained.
The details of a hardware implementation of a representative small scale flight critical system is described using Advanced Information Processing System (AIPS) building block components and simulated sensor/actuator interfaces. The system was used to study application performance and reliability issues during both normal and faulted operation.
A week-long teacher workshop is described. Highlights include underwater simulation training, model rocket building and launching, map reading, and survival training.
An overview of preparations for the construction of Space Station Freedom (SSF) is presented. The video includes footage of astronauts testing materials for erectable structures in space both in the Shuttle bay while in orbit and in a neutral buoyancy tank at McDonald Douglas' Underwater Test Facility. Also shown are footage of robot systems that will assist the astronauts in building SSF, a computer simulation of an Orbiting Maneuvering Vehicle, solar dynamic mirrors that will power SSF, and mockups of the living quarters of the SSF.
Each year one ship out of every five afloat collides with another vessel, rams a dock, or runs a ground. CAORF (Computer Aided Operations Research Facility), designed and built by Sperry Rand Corporation, incorporates technology developed in a wide variety of aerospace simulation and technical training programs. CAORF can be set up to duplicate the exact handling qualities of any vessel under various conditions of wind, tide and current. Currently a dozen different ships can be "plugged in." Bridge instrumentation is typical of modern shipboard equipment including radar, internal and external c.ommunications and new collision avoidance systems. From repetitive operation of simulated ships, MarAd is building a valuable data base for improving marine safety.
Dark energy has simultaneously been the most elusive and most important phenomenon in the shaping of the universe. A case for a proposed space-telescope called SNAP (SuperNova Acceleration Probe) is being built, a crucial component of which is image simulations. One method for this is "Shapelets," developed at Caltech. Shapelets form an orthonormal basis and are uniquely able to represent realistic space images and create new images based on real ones. Previously, simulations were created using the Hubble Deep Field (HDF) as a basis Set in one band. In this project, image simulations are created.using the 4 bands of the Hubble Ultra Deep Field (UDF) as a basis set. This provides a better basis for simulations because (1) the survey is deeper, (2) they have a higher resolution, and (3) this is a step closer to simulating the 9 bands of SNAP. Image simulations are achieved by detecting sources in the UDF, decomposing them into shapelets, tweaking their parameters in realistic ways, and recomposing them into new images. Morphological tests were also run to verify the realism of the simulations. They have a wide variety of uses, including the ability to create weak gravitational lensing simulations.
BACKGROUND The Artemis campaign is a Moon exploration program with a series of six planned missions, five of which will be crewed. These five crewed missions will contain a single mission segment (space flight), or multiple mission segments involving space flight (Orion), lunar landing (LTV) and/or space habitat (Gateway). Each crewed segment faces the risk of unique medical conditions, necessitating medical sets/kits tailored to those specificities. To support and enable a data-driven and evidence-based decision-making process through out a mission’s life cycle, a software tool called IMPACT was developed. Using probabilistic risk assessment (PRA) methodologies, IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a novel tool built for analyzing the possibility of encountering complex medical risks during space flight, and for identifying the medical resources and capabilities needed to treat those potential at-risk medical conditions. This presentation will seek to compare IMPACT’s computational results upon potential complex space medical conditions (e.g., sprain/strain back or sleep disturbance) using IMPACT’s risk metrics and the associated optimized medical sets/kits between two Artemis missions: single segment Artemis 2 and multi-segmented Artemis 5. OVERVIEW By identifying potential medical conditions in space using input criteria such as crew quantity and composition, certain crew physical characteristics, mission duration and mission activities, IMPACT can produce analyses on the type of medical resources and capabilities needed to produce an optimized medical set/kit to address those medical conditions. IMPACT achieves this by performing hundreds of thousands of Monte Carlo simulations of missions to build aggregate pictures of medical risk. IMPACT’s risk metrics include loss of crew life (LOCL) – a measure of crew mortality due to medical conditions in space, return to definitive care (RTDC) – the need to perform crew evacuation, and task time lost (TTL) – a measure of the inability to perform activities due to crew disability. These risk metrics are applied to every medical condition identified by IMPACT’s computation analyses for every segment of the mission. Medical sets/kits are optimized to address these medical conditions but must fit within the stated Artemis Design Reference Mission (DRM) request for mass and volume physical size constraints. ANTICIPATED ANALYSIS AND CONCLUSION Using two Artemis missions, Artemis 2 and Artemis 5, IMPACT will provide the analyses for comparison of medical set/kit contents based upon mass and/or volume requirements and identify the at-risk medical conditions within both missions. This paper serves as an initial exploration of probabilistic risk assessment (PRA) medical risk calculations between two crewed Artemis missions and is not intended to be deemed the official medical response for the Artemis campaign.
Demand response is a broad suite of technologies that enables changes in electrical load operations in support of power system reliability and efficiency. Although demand response is not a new concept, there is new appetite for comprehensively evaluating its technical potential in the context of renewable energy integration. The complexity of demand response makes this task difficult -- we present new methods for capturing the heterogeneity of potential responses from buildings, their time-varying nature, and metrics such as thermal comfort that help quantify likely acceptability of specific demand response actions. Computed with an automated software framework, the methods are scalable.
This paper studies the propagation and effects of faults of critical components that pertain to the secondary loop of a nuclear power plant found in Nuclear Hybrid Energy Systems (NHES). This information is used to design an on-line monitoring (OLM) system which is capable of detecting and forecasting faults that are likely to occur during NHES operation. In this research, the causes, features, and effects of possible faults are investigated by simulating the propagation of faults in the secondary loop. The simulation is accomplished by using the Integrated System Failure Analysis (ISFA). ISFA is used for analyzing hardware and software faults during the conceptual design phase. In this paper, the models of system components required by ISFA are initially constructed. Then, the fault propagation analysis is implemented, which is conducted under the bounds set by acceptance criteria derived from the design of an OLM system. The result of the fault simulation is utilized to build a database for fault detection and diagnosis, provide preventive measures, and propose an optimization plan for the OLM system.
A method for assessing spectroscopic sensor accuracy, includes building an a priori simulation of generalized etalon drift. A spectroscopic sensor is tested to determine use parameters. A specific drift model is generated by applying the determined use parameters to the built a priori simulation of generalized etalon drift. The specific drift model is analyzed to determine whether the spectroscopic sensor is satisfactory.
This report assesses energy savings and peak power reduction of three heat pumps in multiple US cold climate cities and building types. The three heat pumps comprise a baseline heat pump and two high efficiency cold climate heat pumps (CCHP). The DOE/ORNL Heat Pump Design Model was used to simulate the heat pumps and generate performance curves for EnergyPlus building energy simulations. The annual energy simulations were performed over seven cold climates: 1) Minneapolis, MN; 2) Duluth, MN; 3) Fairbanks, AK; 4) Helena, MT; 5) Indianapolis, IN; 6) Peoria, IL and 7) Eagle County, CO. In order to assess impact of residential building types matching different energy codes, we simulated six single-family houses in Indianapolis, IN, including : 1) single-family house having a crawlspace; 2) having a slab foundation; 3) having a heated basement; 4) having an unheated basement; 5) one single-family house having a heated basement matching IECC code of 2009, 6) and one single-family house having a heated basement matching IECC code of 2012. The former 4 houses match the IECC 2006 code.
Modern cosmological surveys are delivering data sets characterized by unprecedented quality and statistical completeness; this trend is expected to continue in the future as new ground- and space-based surveys come online. In order to maximally extract cosmological information from these observations, matching theoretical predictions are needed. At low redshifts, the surveys probe the non-linear regime of structure formation where cosmological simulations are the primary means of obtaining the required information. The computational cost of sufficiently resolved large-volume simulations makes it prohibitive to run very large ensembles. Nevertheless, precision emulators built on a tractable number of high-quality simulations can be used to build very fast prediction schemes to enable a variety of cosmological inference studies. We have recently introduced the Mira–Titan Universe simulation suite designed to construct emulators for a range of cosmological probes. This gravity-only set of simulations covers the standard six cosmological parameters {ω m , ω b , σ 8 , $h, n_s, w_0$} and, in addition, includes massive neutrinos and a dynamical dark energy equation of state {ω ν , $w_a$}. In this paper, we present the final emulator for the matter power spectrum based on 111 cosmological simulations, each covering a (2.1 Gpc) 3 volume and evolving 3200 3 particles. In this work, an additional set of 1776 lower resolution simulations and TimeRG perturbation theory results for the power spectrum are used to cover scales straddling the linear to mildly non-linear regimes (maximum wavenumber $\textit{k}$ = 5 Mpc –1 ). The emulator provides predictions at the 2–3 percent level of accuracy over a wide range of cosmological parameters and is publicly released as part of this paper.
Electrification of buildings and transportation coupled with increased deployment of distributed energy resources (DERs) has been identified as a key step toward meeting emissions reduction goals across the U.S. Examples of pilot projects that feature innovative, 'smart' electric neighborhoods are largely focused on new construction projects, but there is a need to address the millions of existing homes so that they too may accommodate cleaner yet variable energy production in ways that benefit both the utility grid and the homes' residents. The Colorado Residential Retrofit Energy District (CoRRED) project used building and grid co-simulation tools to model an advanced energy district demonstration in an existing residential neighborhood in Denver, Colorado, and explored how existing building and utility infrastructures can be enhanced with combinations of traditional energy-efficiency retrofit measures and integration of solar panels and other DER technologies to provide better affordability and reliability. We analyzed which packages of DERs most reliably enable demand flexibility in response to a time-of-use (TOU) rate. Our results clearly indicate that incorporating DERs into efficient electrification produces much bigger utility bill savings on an annual basis than efficient electrification without DERs. While electrifying a neighborhood will increase the maximum load, batteries and solar panels can reduce load during peak hours so that the community can be a net producer during peak periods. A remaining challenge is to overcome first cost barriers to implementing energy-efficiency and DER technologies, as modest utility bill savings require long payback periods that are not practical for most homeowners.