30:T5dd,viewof zone_base = { let parent = document.createElement("div"); parent.value = "opt_1"; let svg = d3.select(DOM.svg(960, 240)); let cover = (await d3.xml(`https://da.introba.com/projects/pmx/data/nat_vent_zones_purple.svg?version=${Math.floor(Math.random() * 1000)}`).then(data => { svg.node().appendChild(data.documentElement.getElementById("root")); let style = document.createElement("style"); style.innerHTML = data.getElementsByTagName("style")[0].innerHTML; svg.attr("viewBox", data.getElementsByTagName("svg")[0].getAttribute("viewBox")); document.head.appendChild(style); svg.selectAll("#opt_x5F_1, #opt_x5F_2, #opt_x5F_3") .classed("hidden", true) .classed("clicked", false) .on("mouseover", function(d) { d3.select(this) .style("cursor", "pointer") .classed("hidden", false); }).on("mouseout", function(d) { if (!(d3.select(this).classed("clicked"))) d3.select(this).classed("hidden", true); }).on("click", function(d) { d3.selectAll("#opt_x5F_1, #opt_x5F_2, #opt_x5F_3").classed("clicked", false).classed("hidden", true); d3.select(this).classed("hidden", false).classed("clicked", true); parent.value = d3.select(this).attr("id").replace("_x5F", ""); parent.dispatchEvent(new CustomEvent("input")); }); svg.selectAll("#opt_x5F_1").classed("hidden", false).classed("clicked", true); parent.appendChild(svg.node()); })) return parent; }31:Tb00,viewof zone_tower = { let parent = document.createElement("div"); parent.value = "l6_z2"; let svg = d3.select(DOM.svg(960, 220)); let cover = (await d3.xml(`https://da.introba.com/projects/pmx/data/zones2.svg?version=${Math.floor(Math.random() * 1000)}`).then(data => { svg.node().appendChild(data.documentElement.getElementById("root")); let style = document.createElement("style"); style.innerHTML = data.getElementsByTagName("style")[0].innerHTML; svg.attr("viewBox", data.getElementsByTagName("svg")[0].getAttribute("viewBox")); document.head.appendChild(style); svg.selectAll("#l1_x5F_shadow, #l2_x5F_shadow, #l3_x5F_shadow, #l4_x5F_shadow, #l5_x5F_shadow, #l6_x5F_shadow, #l7_x5F_shadow, #l8_x5F_shadow, #z1_x5F_shadow, #z2_x5F_shadow, #z3_x5F_shadow, #z4_x5F_shadow, #z5_x5F_shadow").classed("hidden", true).classed("clicked", false); svg.selectAll("#l1, #l2, #l3, #l4, #l5, #l6, #l7, #l8") .classed("hidden", true) .classed("clicked", false) .on("mouseover", function(d) { d3.select(this) .style("cursor", "pointer") .classed("hidden", false); }).on("mouseout", function(d) { if (!(d3.select(this).classed("clicked"))) d3.select(this).classed("hidden", true); }).on("click", function(d) { d3.selectAll("#l1, #l2, #l3, #l4, #l5, #l6, #l7, #l8").classed("clicked", false).classed("hidden", true); d3.select(this).classed("hidden", false).classed("clicked", true); // let id = d3.select(this).attr("id"); // d3.select(`#${id}_x5F_shadow`).classed("hidden", false).classed("clicked", true); parent.value = d3.select(this).attr("id") + parent.value.slice(2, 5); parent.dispatchEvent(new CustomEvent("input")); }); svg.selectAll("#z1, #z2, #z3, #z4, #z5") .classed("hidden", true) .classed("clicked", false) .on("mouseover", function(d) { d3.select(this) .style("cursor", "pointer") .classed("hidden", false); }).on("mouseout", function(d) { if (!(d3.select(this).classed("clicked"))) d3.select(this).classed("hidden", true); }).on("click", function(d) { d3.selectAll("#z1, #z2, #z3, #z4, #z5").classed("clicked", false).classed("hidden", true); d3.select(this).classed("hidden", false).classed("clicked", true); // let id = d3.select(this).attr("id"); // d3.select(`#${id}_x5F_shadow`).classed("hidden", false).classed("clicked", true); parent.value = parent.value.slice(0, 3) + d3.select(this).attr("id"); parent.dispatchEvent(new CustomEvent("input")); }); svg.selectAll("#l6, #z2").classed("hidden", false).classed("clicked", true); parent.appendChild(svg.node()); })) return parent; }32:Tae2,viewof cp_zone = { let parent = document.createElement("div"); parent.value = {"Office": false, "Resi": false , "CEP": false}; let svg = d3.select(DOM.svg(960, 400)); const paths = ["base-02", "all-02", "off_rad-02", "resi_rad-02"]; svg.append("g") .attr("transform", "translate(480,0)") .selectAll(".rad") .data(paths) .join("image") .attr("class", "rad") .attr("href", d => `https://elementa.nyc/projects//dallas/data/${d}.jpg`) .attr("id", String) .attr("opacity", d => d === "base-02" ? 1: 0) .attr("width", 480) .attr("height", 400); const evalImg = d => { if (parent.value.Office && !parent.value.Resi) { return "off_rad-02"; } else if (parent.value.Resi && !parent.value.Office) { return "resi_rad-02"; } else if (parent.value.Resi && parent.value.Office) { return "all-02"; } else { return "base-02"; } } let cover = (await d3.xml(`https://elementa.nyc/projects//dallas/data/base_svg.svg?version=${Math.floor(Math.random() * 1000)}`).then(data => { svg.node().appendChild(data.documentElement.getElementById("root")); let style = document.createElement("style"); style.innerHTML = data.getElementsByTagName("style")[0].innerHTML; //svg.attr("viewBox", data.getElementsByTagName("svg")[0].getAttribute("viewBox")); document.head.appendChild(style); svg.selectAll("#Arrows").attr("opacity", 0); svg.selectAll("#Office, #Resi, #CEP") .attr("opacity", 0) .classed("clicked", function(d) { if (parent.value[d3.select(this).attr("id")]) { return true; } else { return false; } }) .on("mouseover", function(d) { d3.select(this) .style("cursor", "pointer") }).on("click", function(d) { if (!d3.select(this).classed("clicked")) { if (d3.select(this).attr("id") === "CEP") { d3.selectAll("#Arrows").classed("clicked", true); } d3.select(this).classed("clicked", true); parent.value[d3.select(this).attr("id")] = true; svg.selectAll(".rad").attr("opacity", 0); svg.select(`#${evalImg()}`).attr("opacity", 1); } else { if (d3.select(this).attr("id") === "CEP") { svg.selectAll("#Arrows").classed("clicked", false); } d3.select(this).classed("clicked", false); parent.value[d3.select(this).attr("id")] = false; svg.selectAll(".rad").attr("opacity", 0); svg.select(`#${evalImg()}`).attr("opacity", 1); } parent.dispatchEvent(new CustomEvent("input")); }); parent.appendChild(svg.node()); })) return parent; }33:T12a4,viewof tczone = { let parent = document.createElement("div"); parent.value = "tpl_SH1_TM1"; let svg = d3.select(DOM.svg(960, 440)); let cover = (await d3.xml(`https://da.introba.com/projects/pmx/data/zone6_revised.svg?version=${Math.floor(Math.random() * 1000)}`).then(data => { svg.node().appendChild(data.documentElement.getElementById("root")); let style = document.createElement("style"); style.innerHTML = data.getElementsByTagName("style")[0].innerHTML; svg.attr("viewBox", data.getElementsByTagName("svg")[0].getAttribute("viewBox")); document.head.appendChild(style); const els = ["#tpl", "#dpl", "#dpc", "#spc", "#SH1", "#SH0", "#TM1", "#TM0"]; const targets = els.map(d => d + "_target"); svg.selectAll(els.concat(targets).join(", ")).classed("sketchhidden", true).classed("clicked", false); ////////////////////////////////////////////////Glazing const glass = ["#tpl", "#dpl", "#dpc", "#spc"]; svg.selectAll(glass.join(", ")) .classed("sketchhidden", true) .classed("clicked", false) .on("mouseover", function(d) { d3.select(this) .style("cursor", "pointer") .classed("sketchhidden", false); }).on("mouseout", function(d) { if (!(d3.select(this).classed("clicked"))) d3.select(this).classed("sketchhidden", true); }).on("click", function(d) { d3.selectAll(glass.join(", ")).classed("clicked", false).classed("sketchhidden", true); d3.select(this).classed("sketchhidden", false).classed("clicked", true); d3.selectAll(glass.map(d => d + "_target").join(", ")).classed("clicked", false).classed("sketchhidden", true); const target = "#" + d3.select(this).attr("id") + "_target"; d3.select(target).classed("sketchhidden", false); let glazing = d3.select(this).attr("id"); let vals = parent.value.split("_"); vals[0] = glazing parent.value = vals.join("_"); parent.dispatchEvent(new CustomEvent("input")); }); ////////////////////////////////////////////////Shading const shades = ["#SH1", "#SH0"]; svg.selectAll(shades.join(", ")) .classed("sketchhidden", true) .classed("clicked", false) .on("mouseover", function(d) { d3.select(this) .style("cursor", "pointer") .classed("sketchhidden", false); }).on("mouseout", function(d) { if (!(d3.select(this).classed("clicked"))) d3.select(this).classed("sketchhidden", true); }).on("click", function(d) { d3.selectAll(shades.join(", ")).classed("clicked", false).classed("sketchhidden", true); d3.select(this).classed("sketchhidden", false).classed("clicked", true); d3.selectAll(shades.map(d => d + "_target").join(", ")).classed("clicked", false).classed("sketchhidden", true); const target = "#" + d3.select(this).attr("id") + "_target"; d3.select(target).classed("sketchhidden", false); let shade = d3.select(this).attr("id"); let vals = parent.value.split("_"); vals[1] = shade parent.value = vals.join("_"); parent.dispatchEvent(new CustomEvent("input")); }); ////////////////////////////////////////////////Thermal Mass const mass = ["#TM1", "#TM0"]; svg.selectAll(mass.join(", ")) .classed("sketchhidden", true) .classed("clicked", false) .on("mouseover", function(d) { d3.select(this) .style("cursor", "pointer") .classed("sketchhidden", false); }).on("mouseout", function(d) { if (!(d3.select(this).classed("clicked"))) d3.select(this).classed("sketchhidden", true); }).on("click", function(d) { d3.selectAll(mass.join(", ")).classed("clicked", false).classed("sketchhidden", true); d3.select(this).classed("sketchhidden", false).classed("clicked", true); d3.selectAll(mass.map(d => d + "_target").join(", ")).classed("clicked", false).classed("sketchhidden", true); const target = "#" + d3.select(this).attr("id") + "_target"; d3.select(target).classed("sketchhidden", false); let tMass = d3.select(this).attr("id"); let vals = parent.value.split("_"); vals[2] = tMass parent.value = vals.join("_"); parent.dispatchEvent(new CustomEvent("input")); }); const initialSelection = ["#tpl", "#SH1", "#TM1"]; svg.selectAll(initialSelection.join(", ")).classed("sketchhidden", false).classed("clicked", true); svg.selectAll(initialSelection.map(d => d + "_target").join(", ")).classed("sketchhidden", false) parent.appendChild(svg.node()); })) return parent; }34:T44c,html``35:[] 2e:{"title":"","cells":[{"id":0,"value":"html`

Natural Ventilation Analysis

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":761,"value":"md`To ensure that natural ventilation could provide adequate cooling to offset internal and external heat gains for an office in Toronto – particularly in the summer months - the Design Analytics team at Introba and the Performance Modeling team at Integral developed a bulk airflow model using IES and processed the results in Grasshopper. The chart below shows the number of potential discomfort hours (hours when the operative space temperature is above a comfortable level) throughout the year for each natural ventilation strategy. The diagrams represent three unique natural ventilation strategies.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":410,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":421,"value":"plot","pinned":false,"hidden":false,"mode":"ojs"},{"id":428,"value":"viewof timeRange","pinned":false,"hidden":false,"mode":"ojs"},{"id":417,"value":"import {plot} \nwith {zone_base as zone, timeRange as timeRange}\nfrom \"@jwperakis/pmx\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":425,"value":"import {viewof timeRange as timeRange} from \"@jwperakis/pmx\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":441,"value":"html`

Thermal Comfort Analysis

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":763,"value":"md`For a high-rise residential project in Toronto, the architectural design team wanted a systematic and data-driven approach to designing amenity spaces in the building. Design Analytics work helped create a framework for understanding the thermal comfort implications of various envelope options and conditioning strategies. The chart below shows the number of cold discomfort hours (hours when the operative space temperature is below a comfortable level) for each orientation, zone, and envelope strategy. The framework can then be used to make on-the-fly design changes early in the design process without re-running the energy simulations.
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":341,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":138,"value":"tower_series","pinned":false,"hidden":false,"mode":"ojs"},{"id":387,"value":"tower_legend","pinned":false,"hidden":false,"mode":"ojs"},{"id":135,"value":"import {series as tower_series, legend as tower_legend} \nwith {zone_tower as zone}\nfrom \"@elementa/pmx-amenity-space-analysis\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":380,"value":"tower_series.update(zone_tower)","pinned":false,"hidden":false,"mode":"ojs"},{"id":392,"value":"html`

Building Loads and Retrofit Analysis

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":765,"value":"md`What types of envelope improvements are needed if a building at Yale University uses low-temperature hot water for heating? Design Analytics demonstrated the impact of various envelope upgrades on the building heating load, and helped Yale University identify the conditions required for a transition away from steam to low temperature hot water. The graph below shows the range of heating loads corresponding to various outside air temperatures, given different envelope upgrade packages (selected in the dropdown). The areas in dark red represent loads that fall outside of the target range, while those in light orange indicate loads that are within the target range.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":130,"value":"viewof scenario_yale","pinned":false,"hidden":false,"mode":"ojs"},{"id":122,"value":"chart_yale","pinned":false,"hidden":false,"mode":"ojs"},{"id":118,"value":"import {chart as chart_yale, viewof scenario as scenario_yale} from \"@elementa/jameswperakis-yale-berkeley-update\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":451,"value":"chart_yale.update(scenario_yale.scenario)","pinned":false,"hidden":false,"mode":"ojs"},{"id":457,"value":"html`

Benchmarking

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":767,"value":"md`For a mixed-use development in Reston, Design Analytics worked with the architectural design team to evaluate performance using LEED. Compared to an ASHRAE 90.1 2010 Appendix G baseline, the project achieved 41% savings in site energy use and 30% savings in energy cost by optimizing the envelope and using a more efficient HVAC system. This level of savings would achieve 3 points for the LEED v4 Core & Shell Optimize Energy Performance credit.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":54,"value":"EPB_RestonPromenade","pinned":false,"hidden":false,"mode":"ojs"},{"id":49,"value":"import {stackedBar as EPB_RestonPromenade} from \"@elementa/reston-promenade-building-b\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":593,"value":"html`

Energy Use Characterization

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":772,"value":"md`Design Analytics recently helped renovate the ASHRAE headquarters in Atlanta, Georgia, demonstrating how an existing office building can achieve net-zero energy status. The graph below shows the breakdown in energy by end use for the proposed, all-electric design. Energy sources are aligned on the left side of the graphic, while energy sinks (conduction losses, equipment gains, etc.) are aligned on the right side.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":615,"value":"characterization","pinned":false,"hidden":false,"mode":"ojs"},{"id":613,"value":"import {characterization} from \"@elementa/ashrae-headquarters\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":497,"value":"html`

Perfomance Optimization

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":774,"value":"md`For a Post Office building in Canada, Design Analytics used a parametric approach to modeling in order to understand the sensitivity of different variables on building energy use. The charts below show the results of this analysis, with the horizontal axes showing incremental changes in each variable, and the vertical axes showing the impact of these changes on building energy use intensity (EUI) or thermal energy demand intensity (TEDI). The line represents the overall trend for each variable.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":511,"value":"viewof euiSwitch","pinned":false,"hidden":false,"mode":"ojs"},{"id":517,"value":"sensitivies","pinned":false,"hidden":false,"mode":"ojs"},{"id":514,"value":"import {sensitivies}\nwith {euiSwitch as euiSwitch}\nfrom \"@elementa/cpc-revision-2\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":507,"value":"import {viewof euiSwitch} from \"@elementa/cpc-revision-2\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":474,"value":"html`

Parametric Analysis

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":776,"value":"md`To better understand the effect of different performance characteristics on building EUI and TEDI, Design Analytics uses statistical models to generate results for thousands of variable combinations from a limited number of simulation runs. In the parallel coordinates plot below, each of the first six vertical axes represents a performance variable, and the last two axes show the EUI and TEDI resulting from the selected combinations. The dark lines in the chart are results for combinations simulated using the whole building energy model. The lighter lines represent results from all other combinations that are generated using statistical surrogate models.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":488,"value":"parcoords","pinned":false,"hidden":false,"mode":"ojs"},{"id":492,"value":"parLegend","pinned":false,"hidden":false,"mode":"ojs"},{"id":692,"value":"import {parcoords, parLegend} from \"@elementa/canada-post\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":732,"value":"html`

Psychrometrics

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":779,"value":"md`Each design project starts with a thorough understanding of the local climate. To aid this process, Design Analytics has developed custom tools to parse and visualize information from local weather files. The psychrometric chart is one example of a tool that is commonly used to look at EPW data to get a better sense of how temperature and humidity changes throughout the year.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":742,"value":"viewof psych_filters","pinned":false,"hidden":false,"mode":"ojs"},{"id":746,"value":"psychChart","pinned":false,"hidden":false,"mode":"ojs"},{"id":735,"value":"import {viewof filters as psych_filters} from \"@jwperakis/pyschrometrics\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":739,"value":"import {psychChart}\nwith {psych_filters as filters}\nfrom \"@jwperakis/pyschrometrics\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":523,"value":"html`

Masterplanning and Portfolio Analytics

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":781,"value":"md`As part of Integral's analysis of green building certification systems for Fannie Mae, Design Analytics developed a regionally-specific calculator for energy, carbon, and cost savings for different types of multifamily buildings across the country. This calculator will allow Fannie Mae to input basic data about any property—zip code, building type, building size (measured in number of units or gross floor area), and the Green Building Certification (GBC), and get estimates for the energy use intensity, total energy use, greenhouse gas emissions, and energy costs, along with savings for each relative to both a standard national baseline and the business-as-usual state energy code. The following graphic illustrates the estimates for baseline greenhouse gas for multifamily projects at the county level. The total savings for each project are calculated relative to this regionally-specific baseline.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":534,"value":"viewof type","pinned":false,"hidden":false,"mode":"ojs"},{"id":537,"value":"combined","pinned":false,"hidden":false,"mode":"ojs"},{"id":542,"value":"baseline_legend","pinned":false,"hidden":false,"mode":"ojs"},{"id":526,"value":"import {viewof type} from \"@jwperakis/gbc-impact-baseline-calculations\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":529,"value":"import {combined, baseline_legend}\nwith {type as type}\nfrom \"@jwperakis/gbc-impact-baseline-calculations\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":546,"value":"html`

Renewables and Battery Optimization

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":783,"value":"md`For the First National Technical Institute in Canada, Design Analytics developed an onsite renewable energy generation and battery storage model based on the project's electrical loads. The model optimizes battery charging and discharging given different system configurations. The starting state of the graph below depicts the hourly load profile for average days in the first two months (January and February). Use the context bar below the graph to select different times during the year or use the mouse to zoom and pan on the graph itself to see more information.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":568,"value":"viewof pv_scenario","pinned":false,"hidden":false,"mode":"ojs"},{"id":571,"value":"viewof pvSize","pinned":false,"hidden":false,"mode":"ojs"},{"id":574,"value":"viewof windSize","pinned":false,"hidden":false,"mode":"ojs"},{"id":577,"value":"viewof batterySize","pinned":false,"hidden":false,"mode":"ojs"},{"id":610,"value":"md`
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":585,"value":"chart","pinned":false,"hidden":false,"mode":"ojs"},{"id":590,"value":"pv_legend","pinned":false,"hidden":false,"mode":"ojs"},{"id":583,"value":"import {chart}\nwith {pv_scenario as scenario, pvSize as pvSize, windSize as windSize, batterySize as batterySize}\nfrom \"@elementa/first-nations-technical-institute\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":565,"value":"import {legend as pv_legend, output, viewof scenario as pv_scenario, viewof pvSize, viewof windSize, viewof batterySize}\nfrom \"@elementa/first-nations-technical-institute\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":710,"value":"html`

Central Plant Load Analysis

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":785,"value":"md`This analysis for a mixed use development in Dallas looked at the impact of centralized infrastructure on peak heating and cooling loads and overall energy use. By having a centralized infrastructure, the development can capture free heating from energy that would otherwise be wasted in a scenario with standalone, distributed systems.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":713,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":720,"value":"results","pinned":false,"hidden":false,"mode":"ojs"},{"id":723,"value":"cp_chart","pinned":false,"hidden":false,"mode":"ojs"},{"id":726,"value":"cp_chart.update(img.profile)","pinned":false,"hidden":false,"mode":"ojs"},{"id":718,"value":"import {results, img, chart2 as cp_chart}\nwith {cp_zone as zone}\nfrom \"@elementa/1100-mckinney-avenue-dallas\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":752,"value":"// html`

\n//

Hourly Thermal Comfort Metrics

\n//

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":787,"value":"//md`The Design Analytics team uses a variety of tools to understand the impacts of air temperature, mean radiant temperature, and direct solar exposure on occupant thermal comfort. For an atrium project with skylights, our team create a spatial map of thermal comfort, with hourly temperature profiles for each point in the atrium. The graph below shows the hourly results for operative temperature, at a point in the middle of the atrium, with single-pane glazing and no external shading.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":750,"value":"//op_plot","pinned":false,"hidden":false,"mode":"ojs"},{"id":748,"value":"//import {plot as op_plot} from \"@elementa/50-church-street-atrium\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":845,"value":"html`

Passive Comfort Analysis

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":847,"value":"md`Building designers, engineers, and owners often confront complex tradeoffs involving envelope performance, systems selection, and thermal comfort. The Design Analytics practices helps these stakeholders make more informed decisions by analyzing the impact of different design strategies on thermal comfort, and communicating – through sketches, diagrams, and text – how different building systems are related. The diagram and graph below show the effect of different combinations of design strategies on thermal comfort in an unconditioned atrium space.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":853,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":859,"value":"tcseries","pinned":false,"hidden":false,"mode":"ojs"},{"id":867,"value":"tclegend","pinned":false,"hidden":false,"mode":"ojs"},{"id":849,"value":"import {series as tcseries, legend as tclegend} from \"@elementa/amenity-space-passive-comfort-analysis\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":889,"value":"tcseries.update(tczone)","pinned":true,"hidden":false,"mode":"ojs"},{"id":882,"value":"html`\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":677,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":343,"value":"d3 = require(\"d3@v5\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":345,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W35","stdlib":"1","titling":"auto"}